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|---|---|---|---|
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@@ -1,24 +0,0 @@
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^.*\.Rproj$
|
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^\.gitlab-ci\.yml$
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^\.gitlab-ci\.R$
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^\.Renviron$
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^\.Rprofile$
|
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^\.Rproj\.user$
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^\.travis\.yml$
|
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^\.zenodo\.json$
|
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^_noinclude$
|
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^_pkgdown\.yml$
|
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^appveyor\.yml$
|
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^cran-comments\.md$
|
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^CRAN-RELEASE$
|
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^doc$
|
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^docs$
|
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^git_merge\.sh$
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^git_premaster\.sh$
|
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^git_siteonly\.sh$
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^index\.md$
|
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^installed_deps$
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^Meta$
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^pkgdown$
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^public$
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^reproduction.*R$
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-25
@@ -1,25 +0,0 @@
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Meta
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doc
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.Renviron
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.Rproj.user
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.Rhistory
|
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.RData
|
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.Ruserdata
|
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AMR.Rproj
|
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tests/testthat/Rplots.pdf
|
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inst/doc
|
||||
/src/*.o
|
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/src/*.o-*
|
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/src/*.d
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/src/*.so
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_noinclude
|
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*.dll
|
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vignettes/*.R
|
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.DS_Store
|
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.Rprofile
|
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^CRAN-RELEASE$
|
||||
git_premaster.sh
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git_merge.sh
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git_siteonly.sh
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packrat/lib*/
|
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packrat/src/
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@@ -1,49 +0,0 @@
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# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
install_if_needed <- function(pkg, repos, quiet) {
|
||||
package_path <- find.package(pkg, quiet = quiet)
|
||||
if (length(package_path) == 0) {
|
||||
message("NOTE: pkg ", pkg, " missing, installing...")
|
||||
install.packages(pkg, repos = repos, quiet = quiet)
|
||||
}
|
||||
}
|
||||
|
||||
gl_update_pkg_all <- function(repos = "https://cran.rstudio.com",
|
||||
quiet = TRUE,
|
||||
install_pkgdown = FALSE) {
|
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# update existing
|
||||
update.packages(ask = FALSE, repos = repos, quiet = quiet)
|
||||
|
||||
install_if_needed(pkg = "devtools", repos = repos, quiet = quiet)
|
||||
if (install_pkgdown == TRUE) {
|
||||
install_if_needed(pkg = "pkgdown", repos = repos, quiet = quiet)
|
||||
}
|
||||
|
||||
devtools::install_dev_deps(repos = repos, quiet = quiet, upgrade = TRUE)
|
||||
|
||||
cat("INSTALLED:\n")
|
||||
instld <- as.data.frame(installed.packages())
|
||||
rownames(instld) <- NULL
|
||||
print(instld[, c("Package", "Version")])
|
||||
|
||||
return(invisible(TRUE))
|
||||
}
|
||||
-100
@@ -1,100 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
stages:
|
||||
- build
|
||||
- test
|
||||
- deploy
|
||||
|
||||
# debian stretch only contains R 3.3...
|
||||
image: debian:buster-slim
|
||||
|
||||
before_script:
|
||||
- apt-get update -qq
|
||||
# install dependencies for packages
|
||||
- apt-get install -y wget locales libxml2-dev libssl-dev libcurl4-openssl-dev zlib1g-dev r-base > /dev/null
|
||||
# recent pandoc
|
||||
- wget --quiet https://github.com/jgm/pandoc/releases/download/2.6/pandoc-2.6-1-amd64.deb
|
||||
- dpkg -i pandoc*.deb
|
||||
- rm pandoc*.deb
|
||||
# set R system language
|
||||
- echo "LC_ALL=en_US.UTF-8" >> /etc/environment
|
||||
- echo "en_US.UTF-8 UTF-8" >> /etc/locale.gen
|
||||
- echo "LANG=en_US.UTF-8" > /etc/locale.conf
|
||||
- locale-gen
|
||||
# cache R packages
|
||||
- mkdir -p installed_deps
|
||||
- echo 'R_LIBS="installed_deps"' > .Renviron
|
||||
- echo 'R_LIBS_USER="installed_deps"' >> .Renviron
|
||||
- echo 'R_LIBS_SITE="installed_deps"' >> .Renviron
|
||||
# set language
|
||||
- echo 'LANGUAGE="en_US.utf8"' >> .Renviron
|
||||
- echo 'LANG="en_US.utf8"' >> .Renviron
|
||||
- echo 'LANGUAGE="en_US.utf8"' > ~/.Renviron
|
||||
- Rscript -e 'sessionInfo()'
|
||||
# install missing and outdated packages
|
||||
- Rscript -e 'source(".gitlab-ci.R"); gl_update_pkg_all(repos = "https://cran.rstudio.com", quiet = TRUE, install_pkgdown = TRUE)'
|
||||
|
||||
cache:
|
||||
key: "$CI_COMMIT_REF_SLUG"
|
||||
paths:
|
||||
- installed_deps/
|
||||
|
||||
R:
|
||||
stage: build
|
||||
allow_failure: false
|
||||
script:
|
||||
# remove vignettes folder and get VignetteBuilder field out of DESCRIPTION file
|
||||
- rm -rf vignettes
|
||||
- Rscript -e 'd <- read.dcf("DESCRIPTION"); d[, colnames(d) == "VignetteBuilder"] <- NA; write.dcf(d, "DESCRIPTION")'
|
||||
# build package
|
||||
- R CMD build . --no-build-vignettes --no-manual
|
||||
- PKG_FILE_NAME=$(ls -1t *.tar.gz | head -n 1)
|
||||
- R CMD check "${PKG_FILE_NAME}" --no-build-vignettes --no-manual --as-cran
|
||||
artifacts:
|
||||
paths:
|
||||
- '*.Rcheck/*'
|
||||
expire_in: '1 month'
|
||||
|
||||
coverage:
|
||||
stage: test
|
||||
allow_failure: true
|
||||
when: on_success
|
||||
only:
|
||||
- premaster
|
||||
- master
|
||||
script:
|
||||
- apt-get install --yes git
|
||||
# codecov token is set in https://gitlab.com/msberends/AMR/settings/ci_cd
|
||||
- Rscript -e "cc <- covr::package_coverage(); covr::codecov(coverage = cc, token = '$codecov'); cat('Code coverage:', covr::percent_coverage(cc))"
|
||||
coverage: '/Code coverage: \d+\.\d+/'
|
||||
|
||||
pages:
|
||||
stage: deploy
|
||||
when: always
|
||||
only:
|
||||
- master
|
||||
script:
|
||||
- Rscript -e "devtools::install(build = TRUE, upgrade = FALSE)"
|
||||
- R -e "pkgdown::build_site(examples = FALSE, lazy = TRUE, override = list(destination = 'public'))"
|
||||
artifacts:
|
||||
paths:
|
||||
- public
|
||||
@@ -0,0 +1,254 @@
|
||||
Metadata-Version: 2.4
|
||||
Name: AMR
|
||||
Version: 3.0.1.9091
|
||||
Summary: A Python wrapper for the AMR R package
|
||||
Home-page: https://github.com/msberends/AMR
|
||||
Author: Matthijs Berends
|
||||
Author-email: m.s.berends@umcg.nl
|
||||
License: GPL 2
|
||||
Project-URL: Bug Tracker, https://github.com/msberends/AMR/issues
|
||||
Classifier: Programming Language :: Python :: 3
|
||||
Classifier: Operating System :: OS Independent
|
||||
Requires-Python: >=3.6
|
||||
Description-Content-Type: text/markdown
|
||||
Requires-Dist: rpy2
|
||||
Requires-Dist: numpy
|
||||
Requires-Dist: pandas
|
||||
Dynamic: author
|
||||
Dynamic: author-email
|
||||
Dynamic: classifier
|
||||
Dynamic: description
|
||||
Dynamic: description-content-type
|
||||
Dynamic: home-page
|
||||
Dynamic: license
|
||||
Dynamic: project-url
|
||||
Dynamic: requires-dist
|
||||
Dynamic: requires-python
|
||||
Dynamic: summary
|
||||
|
||||
|
||||
The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the [`AMR` Python package](https://pypi.org/project/AMR/).
|
||||
|
||||
This Python package is a wrapper around the `AMR` R package. It uses the `rpy2` package internally. Despite the need to have R installed, Python users can now easily work with AMR data directly through Python code.
|
||||
|
||||
# Prerequisites
|
||||
|
||||
This package was only tested with a [virtual environment (venv)](https://docs.python.org/3/library/venv.html). You can set up such an environment by running:
|
||||
|
||||
```python
|
||||
# linux and macOS:
|
||||
python -m venv /path/to/new/virtual/environment
|
||||
|
||||
# Windows:
|
||||
python -m venv C:\path\to\new\virtual\environment
|
||||
```
|
||||
|
||||
Then you can [activate the environment](https://docs.python.org/3/library/venv.html#how-venvs-work), after which the venv is ready to work with.
|
||||
|
||||
# Install AMR
|
||||
|
||||
1. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
|
||||
|
||||
```bash
|
||||
pip install AMR
|
||||
```
|
||||
|
||||
2. Make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
|
||||
|
||||
For Linux:
|
||||
|
||||
```bash
|
||||
# Ubuntu / Debian
|
||||
sudo apt install r-base
|
||||
# Fedora:
|
||||
sudo dnf install R
|
||||
# CentOS/RHEL
|
||||
sudo yum install R
|
||||
```
|
||||
|
||||
For macOS (using [Homebrew](https://brew.sh)):
|
||||
|
||||
```bash
|
||||
brew install r
|
||||
```
|
||||
|
||||
For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
|
||||
|
||||
# Examples of Usage
|
||||
|
||||
## Cleaning Taxonomy
|
||||
|
||||
Here’s an example that demonstrates how to clean microorganism and drug names using the `AMR` Python package:
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import AMR
|
||||
|
||||
# Sample data
|
||||
data = {
|
||||
"MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
|
||||
"Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
|
||||
}
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# Use AMR functions to clean microorganism and drug names
|
||||
df['MO_clean'] = AMR.mo_name(df['MOs'])
|
||||
df['Drug_clean'] = AMR.ab_name(df['Drug'])
|
||||
|
||||
# Display the results
|
||||
print(df)
|
||||
```
|
||||
|
||||
| MOs | Drug | MO_clean | Drug_clean |
|
||||
|-------------|-----------|--------------------|---------------|
|
||||
| E. coli | Cipro | Escherichia coli | Ciprofloxacin |
|
||||
| ESCCOL | CIP | Escherichia coli | Ciprofloxacin |
|
||||
| esco | J01MA02 | Escherichia coli | Ciprofloxacin |
|
||||
| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
|
||||
|
||||
### Explanation
|
||||
|
||||
* **mo_name:** This function standardises microorganism names. Here, different variations of *Escherichia coli* (such as "E. coli", "ESCCOL", "esco", and "Esche coli") are all converted into the correct, standardised form, "Escherichia coli".
|
||||
|
||||
* **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
|
||||
|
||||
## Calculating AMR
|
||||
|
||||
```python
|
||||
import AMR
|
||||
import pandas as pd
|
||||
|
||||
df = AMR.example_isolates
|
||||
result = AMR.resistance(df["AMX"])
|
||||
print(result)
|
||||
```
|
||||
|
||||
```
|
||||
[0.59555556]
|
||||
```
|
||||
|
||||
## Generating Antibiograms
|
||||
|
||||
One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:
|
||||
|
||||
```python
|
||||
result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
|
||||
print(result2a)
|
||||
```
|
||||
|
||||
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|
||||
|-----------------|-----------------|-----------------|--------------------------|
|
||||
| CoNS | 7% (10/142) | 73% (183/252) | 30% (10/33) |
|
||||
| E. coli | 50% (196/392) | 88% (399/456) | 94% (393/416) |
|
||||
| K. pneumoniae | 0% (0/58) | 96% (53/55) | 89% (47/53) |
|
||||
| P. aeruginosa | 0% (0/30) | 100% (30/30) | None |
|
||||
| P. mirabilis | None | 94% (34/36) | None |
|
||||
| S. aureus | 6% (8/131) | 90% (171/191) | None |
|
||||
| S. epidermidis | 1% (1/91) | 64% (87/136) | None |
|
||||
| S. hominis | None | 80% (56/70) | None |
|
||||
| S. pneumoniae | 100% (112/112) | None | 100% (112/112) |
|
||||
|
||||
|
||||
```python
|
||||
result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
|
||||
print(result2b)
|
||||
```
|
||||
|
||||
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|
||||
|----------------|-----------------|------------------|--------------------------|
|
||||
| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641) |
|
||||
| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345) |
|
||||
|
||||
|
||||
In this example, we generate an antibiogram by selecting various antibiotics.
|
||||
|
||||
## Taxonomic Data Sets Now in Python!
|
||||
|
||||
As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antimicrobials`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
|
||||
|
||||
```python
|
||||
AMR.microorganisms
|
||||
```
|
||||
|
||||
| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
|
||||
|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
|
||||
| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| ... | ... | ... | ... | ... | ... | ... | ... |
|
||||
| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
|
||||
| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
|
||||
| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
|
||||
| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
|
||||
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
|
||||
|
||||
```python
|
||||
AMR.antimicrobials
|
||||
```
|
||||
|
||||
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|
||||
|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
|
||||
| AMA | 4649.0 | 4-aminosalicylic acid| Antimycobacterials | 12.00 | g | NaN | None |
|
||||
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
|
||||
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
|
||||
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
|
||||
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
|
||||
| ... | ... | ... | ... | ... | ... | ... | ... |
|
||||
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
|
||||
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
|
||||
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
|
||||
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
|
||||
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
|
||||
|
||||
|
||||
# Installation Channels
|
||||
|
||||
## Stable Release (CRAN)
|
||||
|
||||
The default `AMR` Python package uses the latest stable version of the `AMR` R package, published on CRAN. After running `pip install AMR`, import it as usual:
|
||||
|
||||
```python
|
||||
import AMR
|
||||
|
||||
AMR.example_isolates
|
||||
```
|
||||
|
||||
## Development Version (GitHub)
|
||||
|
||||
To use the latest development version of the `AMR` R package (sourced directly from GitHub), import the `beta` sub-package and alias it as `AMR`:
|
||||
|
||||
```python
|
||||
import AMR.beta as AMR
|
||||
|
||||
AMR.example_isolates
|
||||
```
|
||||
|
||||
Aliasing with `as AMR` keeps all downstream code identical to the stable import. Switching between the stable release and the development version requires changing only the import line — nothing else in your script needs to change.
|
||||
|
||||
# SIR Classification with `as_sir()`
|
||||
|
||||
## Using `enforce_method`
|
||||
|
||||
The `as_sir()` function in R uses S3 method dispatch to select the correct calculation method based on the input class: `<mic>` for MIC values and `<disk>` for disk diffusion values. Because Python objects do not carry R class attributes through the `rpy2` bridge, this automatic dispatch may not resolve correctly.
|
||||
|
||||
To explicitly specify the input type, use the `enforce_method` argument:
|
||||
|
||||
```python
|
||||
# Treat the column as MIC values — maps to R's as.sir.mic()
|
||||
AMR.as_sir(df["MIC_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="mic")
|
||||
|
||||
# Treat the column as disk diffusion values — maps to R's as.sir.disk()
|
||||
AMR.as_sir(df["disk_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="disk")
|
||||
```
|
||||
|
||||
Without `enforce_method`, R falls back to class-based dispatch on the raw Python input, which may fail or return unexpected results. Always supply `enforce_method` when calling `as_sir()` from Python.
|
||||
|
||||
# Conclusion
|
||||
|
||||
With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
|
||||
|
||||
By just running `import AMR`, users can seamlessly integrate the robust features of the R `AMR` package into Python workflows.
|
||||
|
||||
Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.
|
||||
@@ -0,0 +1,12 @@
|
||||
README.md
|
||||
setup.py
|
||||
AMR/__init__.py
|
||||
AMR/_engine.py
|
||||
AMR/beta.py
|
||||
AMR/datasets.py
|
||||
AMR/functions.py
|
||||
AMR.egg-info/PKG-INFO
|
||||
AMR.egg-info/SOURCES.txt
|
||||
AMR.egg-info/dependency_links.txt
|
||||
AMR.egg-info/requires.txt
|
||||
AMR.egg-info/top_level.txt
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
rpy2
|
||||
numpy
|
||||
pandas
|
||||
@@ -0,0 +1 @@
|
||||
AMR
|
||||
+249
@@ -0,0 +1,249 @@
|
||||
import sys
|
||||
|
||||
_DATASETS = frozenset({
|
||||
'example_isolates', 'microorganisms',
|
||||
'antimicrobials', 'clinical_breakpoints'
|
||||
})
|
||||
|
||||
class _AMRModule(type(sys.modules[__name__])):
|
||||
"""Lazy-loading module: nothing runs until an attribute is accessed."""
|
||||
|
||||
def __getattr__(self, name):
|
||||
if name in _DATASETS:
|
||||
from .datasets import get
|
||||
return get(name, source="cran")
|
||||
try:
|
||||
from . import functions
|
||||
return getattr(functions, name)
|
||||
except AttributeError:
|
||||
raise AttributeError(
|
||||
f"module 'AMR' has no attribute '{name}'")
|
||||
|
||||
sys.modules[__name__].__class__ = _AMRModule
|
||||
from .functions import custom_eucast_rules
|
||||
from .functions import ab_class
|
||||
from .functions import ab_selector
|
||||
from .functions import ab_from_text
|
||||
from .functions import ab_name
|
||||
from .functions import ab_cid
|
||||
from .functions import ab_synonyms
|
||||
from .functions import ab_tradenames
|
||||
from .functions import ab_group
|
||||
from .functions import ab_atc
|
||||
from .functions import ab_atc_group1
|
||||
from .functions import ab_atc_group2
|
||||
from .functions import ab_loinc
|
||||
from .functions import ab_ddd
|
||||
from .functions import ab_ddd_units
|
||||
from .functions import ab_info
|
||||
from .functions import ab_url
|
||||
from .functions import ab_property
|
||||
from .functions import add_custom_antimicrobials
|
||||
from .functions import clear_custom_antimicrobials
|
||||
from .functions import add_custom_microorganisms
|
||||
from .functions import clear_custom_microorganisms
|
||||
from .functions import age
|
||||
from .functions import age_groups
|
||||
from .functions import all_sir
|
||||
from .functions import all_sir_predictors
|
||||
from .functions import all_mic
|
||||
from .functions import all_mic_predictors
|
||||
from .functions import all_disk
|
||||
from .functions import all_disk_predictors
|
||||
from .functions import step_mic_log2
|
||||
from .functions import step_sir_numeric
|
||||
from .functions import amr_course
|
||||
from .functions import wisca
|
||||
from .functions import antibiogram
|
||||
from .functions import retrieve_wisca_parameters
|
||||
from .functions import wisca_plot
|
||||
from .functions import aminoglycosides
|
||||
from .functions import aminopenicillins
|
||||
from .functions import antifungals
|
||||
from .functions import antimycobacterials
|
||||
from .functions import betalactams
|
||||
from .functions import betalactams_with_inhibitor
|
||||
from .functions import carbapenems
|
||||
from .functions import cephalosporins
|
||||
from .functions import cephalosporins_1st
|
||||
from .functions import cephalosporins_2nd
|
||||
from .functions import cephalosporins_3rd
|
||||
from .functions import cephalosporins_4th
|
||||
from .functions import cephalosporins_5th
|
||||
from .functions import fluoroquinolones
|
||||
from .functions import glycopeptides
|
||||
from .functions import ionophores
|
||||
from .functions import isoxazolylpenicillins
|
||||
from .functions import lincosamides
|
||||
from .functions import lipoglycopeptides
|
||||
from .functions import macrolides
|
||||
from .functions import monobactams
|
||||
from .functions import nitrofurans
|
||||
from .functions import oxazolidinones
|
||||
from .functions import penicillins
|
||||
from .functions import peptides
|
||||
from .functions import phenicols
|
||||
from .functions import phosphonics
|
||||
from .functions import polymyxins
|
||||
from .functions import quinolones
|
||||
from .functions import rifamycins
|
||||
from .functions import spiropyrimidinetriones
|
||||
from .functions import streptogramins
|
||||
from .functions import sulfonamides
|
||||
from .functions import tetracyclines
|
||||
from .functions import trimethoprims
|
||||
from .functions import ureidopenicillins
|
||||
from .functions import amr_class
|
||||
from .functions import amr_selector
|
||||
from .functions import administrable_per_os
|
||||
from .functions import administrable_iv
|
||||
from .functions import not_intrinsic_resistant
|
||||
from .functions import as_ab
|
||||
from .functions import is_ab
|
||||
from .functions import ab_reset_session
|
||||
from .functions import as_av
|
||||
from .functions import is_av
|
||||
from .functions import as_disk
|
||||
from .functions import is_disk
|
||||
from .functions import as_mic
|
||||
from .functions import is_mic
|
||||
from .functions import rescale_mic
|
||||
from .functions import mic_p50
|
||||
from .functions import mic_p90
|
||||
from .functions import as_mo
|
||||
from .functions import is_mo
|
||||
from .functions import mo_uncertainties
|
||||
from .functions import mo_renamed
|
||||
from .functions import mo_failures
|
||||
from .functions import mo_reset_session
|
||||
from .functions import mo_cleaning_regex
|
||||
from .functions import as_sir
|
||||
from .functions import is_sir
|
||||
from .functions import is_sir_eligible
|
||||
from .functions import sir_interpretation_history
|
||||
from .functions import atc_online_property
|
||||
from .functions import atc_online_groups
|
||||
from .functions import atc_online_ddd
|
||||
from .functions import atc_online_ddd_units
|
||||
from .functions import av_from_text
|
||||
from .functions import av_name
|
||||
from .functions import av_cid
|
||||
from .functions import av_synonyms
|
||||
from .functions import av_tradenames
|
||||
from .functions import av_group
|
||||
from .functions import av_atc
|
||||
from .functions import av_loinc
|
||||
from .functions import av_ddd
|
||||
from .functions import av_ddd_units
|
||||
from .functions import av_info
|
||||
from .functions import av_url
|
||||
from .functions import av_property
|
||||
from .functions import availability
|
||||
from .functions import bug_drug_combinations
|
||||
from .functions import count_resistant
|
||||
from .functions import count_susceptible
|
||||
from .functions import count_S
|
||||
from .functions import count_SI
|
||||
from .functions import count_I
|
||||
from .functions import count_IR
|
||||
from .functions import count_R
|
||||
from .functions import count_all
|
||||
from .functions import n_sir
|
||||
from .functions import count_df
|
||||
from .functions import custom_interpretive_rules
|
||||
from .functions import custom_mdro_guideline
|
||||
from .functions import export_ncbi_biosample
|
||||
from .functions import first_isolate
|
||||
from .functions import filter_first_isolate
|
||||
from .functions import g_test
|
||||
from .functions import is_new_episode
|
||||
from .functions import ggplot_pca
|
||||
from .functions import ggplot_sir
|
||||
from .functions import geom_sir
|
||||
from .functions import guess_ab_col
|
||||
from .functions import interpretive_rules
|
||||
from .functions import eucast_rules
|
||||
from .functions import clsi_rules
|
||||
from .functions import eucast_dosage
|
||||
from .functions import italicise_taxonomy
|
||||
from .functions import italicize_taxonomy
|
||||
from .functions import inner_join_microorganisms
|
||||
from .functions import left_join_microorganisms
|
||||
from .functions import right_join_microorganisms
|
||||
from .functions import full_join_microorganisms
|
||||
from .functions import semi_join_microorganisms
|
||||
from .functions import anti_join_microorganisms
|
||||
from .functions import key_antimicrobials
|
||||
from .functions import all_antimicrobials
|
||||
from .functions import kurtosis
|
||||
from .functions import like
|
||||
from .functions import mdro
|
||||
from .functions import brmo
|
||||
from .functions import mrgn
|
||||
from .functions import mdr_tb
|
||||
from .functions import mdr_cmi2012
|
||||
from .functions import eucast_exceptional_phenotypes
|
||||
from .functions import mean_amr_distance
|
||||
from .functions import amr_distance_from_row
|
||||
from .functions import mo_matching_score
|
||||
from .functions import mo_name
|
||||
from .functions import mo_fullname
|
||||
from .functions import mo_shortname
|
||||
from .functions import mo_subspecies
|
||||
from .functions import mo_species
|
||||
from .functions import mo_genus
|
||||
from .functions import mo_family
|
||||
from .functions import mo_order
|
||||
from .functions import mo_class
|
||||
from .functions import mo_phylum
|
||||
from .functions import mo_kingdom
|
||||
from .functions import mo_domain
|
||||
from .functions import mo_type
|
||||
from .functions import mo_status
|
||||
from .functions import mo_pathogenicity
|
||||
from .functions import mo_gramstain
|
||||
from .functions import mo_is_gram_negative
|
||||
from .functions import mo_is_gram_positive
|
||||
from .functions import mo_is_yeast
|
||||
from .functions import mo_is_intrinsic_resistant
|
||||
from .functions import mo_oxygen_tolerance
|
||||
from .functions import mo_is_anaerobic
|
||||
from .functions import mo_morphology
|
||||
from .functions import mo_snomed
|
||||
from .functions import mo_ref
|
||||
from .functions import mo_authors
|
||||
from .functions import mo_year
|
||||
from .functions import mo_lpsn
|
||||
from .functions import mo_mycobank
|
||||
from .functions import mo_gbif
|
||||
from .functions import mo_rank
|
||||
from .functions import mo_taxonomy
|
||||
from .functions import mo_synonyms
|
||||
from .functions import mo_current
|
||||
from .functions import mo_group_members
|
||||
from .functions import mo_info
|
||||
from .functions import mo_url
|
||||
from .functions import mo_property
|
||||
from .functions import pca
|
||||
from .functions import theme_sir
|
||||
from .functions import labels_sir_count
|
||||
from .functions import resistance
|
||||
from .functions import susceptibility
|
||||
from .functions import sir_confidence_interval
|
||||
from .functions import proportion_R
|
||||
from .functions import proportion_IR
|
||||
from .functions import proportion_I
|
||||
from .functions import proportion_SI
|
||||
from .functions import proportion_S
|
||||
from .functions import proportion_df
|
||||
from .functions import sir_df
|
||||
from .functions import random_mic
|
||||
from .functions import random_disk
|
||||
from .functions import random_sir
|
||||
from .functions import resistance_predict
|
||||
from .functions import sir_predict
|
||||
from .functions import ggplot_sir_predict
|
||||
from .functions import skewness
|
||||
from .functions import top_n_microorganisms
|
||||
from .functions import reset_AMR_locale
|
||||
from .functions import translate_AMR
|
||||
@@ -0,0 +1,93 @@
|
||||
import os
|
||||
import sys
|
||||
import importlib.metadata as metadata
|
||||
|
||||
# Get the path to the virtual environment
|
||||
venv_path = sys.prefix
|
||||
r_lib_path = os.path.join(venv_path, "R_libs")
|
||||
os.makedirs(r_lib_path, exist_ok=True)
|
||||
|
||||
# Set environment variable before importing rpy2
|
||||
os.environ['R_LIBS_SITE'] = r_lib_path
|
||||
|
||||
from rpy2 import robjects
|
||||
from rpy2.robjects.vectors import StrVector
|
||||
from rpy2.robjects.packages import importr, isinstalled
|
||||
|
||||
# Import base and utils once
|
||||
base = importr('base')
|
||||
utils = importr('utils')
|
||||
|
||||
# Silence R console output entirely
|
||||
robjects.r('suppressMessages(suppressWarnings(sink(tempfile())))')
|
||||
base._libPaths(r_lib_path)
|
||||
|
||||
_installed_source = None
|
||||
|
||||
def _r_version():
|
||||
"""Return the currently installed AMR R package version, or None."""
|
||||
try:
|
||||
return str(robjects.r(
|
||||
f'as.character(packageVersion("AMR", lib.loc = "{r_lib_path}"))')[0])
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def _py_version():
|
||||
"""Return the Python AMR package version from metadata, or empty string."""
|
||||
try:
|
||||
return str(metadata.version('AMR'))
|
||||
except metadata.PackageNotFoundError:
|
||||
return ''
|
||||
|
||||
def _install_cran():
|
||||
"""Install AMR from CRAN into the isolated library."""
|
||||
print("AMR: Installing from CRAN...", flush=True)
|
||||
utils.install_packages(
|
||||
'AMR',
|
||||
repos='https://cloud.r-project.org',
|
||||
lib=r_lib_path,
|
||||
quiet=True
|
||||
)
|
||||
|
||||
def _install_github():
|
||||
"""Install AMR development version from GitHub into the isolated library."""
|
||||
print("AMR: Installing development version from GitHub...", flush=True)
|
||||
utils.install_packages(
|
||||
StrVector(['remotes', 'desc']),
|
||||
repos='https://cloud.r-project.org',
|
||||
lib=r_lib_path,
|
||||
quiet=True
|
||||
)
|
||||
remotes = importr('remotes', lib_loc=r_lib_path)
|
||||
remotes.install_github('msberends/AMR', lib=r_lib_path, quiet=True)
|
||||
|
||||
def ensure_amr(source="cran"):
|
||||
"""Ensure AMR is installed from the requested source. Idempotent per source."""
|
||||
global _installed_source
|
||||
|
||||
if _installed_source == source:
|
||||
return
|
||||
|
||||
install_fn = _install_github if source == "github" else _install_cran
|
||||
|
||||
if not isinstalled('AMR', lib_loc=r_lib_path):
|
||||
install_fn()
|
||||
else:
|
||||
# Check for version mismatch and update if needed
|
||||
r_ver = _r_version()
|
||||
py_ver = _py_version()
|
||||
if r_ver != py_ver:
|
||||
try:
|
||||
install_fn()
|
||||
except Exception as e:
|
||||
print(f"AMR: Could not update ({e})", flush=True)
|
||||
|
||||
print(f"AMR: R package version {_r_version()} ready.", flush=True)
|
||||
_installed_source = source
|
||||
|
||||
def restore_sink():
|
||||
"""Restore R console output after setup is complete."""
|
||||
try:
|
||||
robjects.r('sink()')
|
||||
except Exception:
|
||||
pass
|
||||
+22
@@ -0,0 +1,22 @@
|
||||
import sys
|
||||
|
||||
_DATASETS = frozenset({
|
||||
'example_isolates', 'microorganisms',
|
||||
'antimicrobials', 'clinical_breakpoints'
|
||||
})
|
||||
|
||||
class _BetaModule(type(sys.modules[__name__])):
|
||||
"""Lazy-loading module: installs AMR from GitHub on first access."""
|
||||
|
||||
def __getattr__(self, name):
|
||||
if name in _DATASETS:
|
||||
from .datasets import get
|
||||
return get(name, source="github")
|
||||
try:
|
||||
from . import functions
|
||||
return getattr(functions, name)
|
||||
except AttributeError:
|
||||
raise AttributeError(
|
||||
f"module 'AMR.beta' has no attribute '{name}'")
|
||||
|
||||
sys.modules[__name__].__class__ = _BetaModule
|
||||
@@ -0,0 +1,54 @@
|
||||
import pandas as pd
|
||||
from rpy2 import robjects
|
||||
from rpy2.robjects.conversion import localconverter
|
||||
from rpy2.robjects import default_converter, numpy2ri, pandas2ri
|
||||
|
||||
from ._engine import ensure_amr, restore_sink
|
||||
|
||||
_cache = {}
|
||||
_loaded_source = None
|
||||
|
||||
def _load_datasets(source="cran"):
|
||||
"""Load all AMR datasets into the module cache."""
|
||||
global _loaded_source
|
||||
|
||||
if _cache and _loaded_source == source:
|
||||
return
|
||||
|
||||
if _cache and _loaded_source != source:
|
||||
_cache.clear()
|
||||
|
||||
ensure_amr(source)
|
||||
|
||||
with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
|
||||
_cache['example_isolates'] = _load_example_isolates()
|
||||
_cache['microorganisms'] = robjects.r(
|
||||
'AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]')
|
||||
_cache['antimicrobials'] = robjects.r(
|
||||
'AMR::antimicrobials[, !sapply(AMR::antimicrobials, is.list)]')
|
||||
_cache['clinical_breakpoints'] = robjects.r(
|
||||
'AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]')
|
||||
|
||||
restore_sink()
|
||||
_loaded_source = source
|
||||
|
||||
def _load_example_isolates():
|
||||
df = robjects.r('''
|
||||
df <- AMR::example_isolates
|
||||
df[] <- lapply(df, function(x) {
|
||||
if (inherits(x, c("Date", "POSIXt", "factor"))) {
|
||||
as.character(x)
|
||||
} else {
|
||||
x
|
||||
}
|
||||
})
|
||||
df <- df[, !sapply(df, is.list)]
|
||||
df
|
||||
''')
|
||||
df['date'] = pd.to_datetime(df['date'])
|
||||
return df
|
||||
|
||||
def get(name, source="cran"):
|
||||
"""Retrieve a dataset by name, installing AMR if needed."""
|
||||
_load_datasets(source)
|
||||
return _cache[name]
|
||||
@@ -0,0 +1,984 @@
|
||||
import functools
|
||||
import rpy2.robjects as robjects
|
||||
from rpy2.robjects.packages import importr
|
||||
from rpy2.robjects.vectors import StrVector, FactorVector, IntVector, FloatVector, DataFrame
|
||||
from rpy2.robjects.conversion import localconverter
|
||||
from rpy2.robjects import default_converter, numpy2ri, pandas2ri
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
from ._engine import ensure_amr
|
||||
|
||||
# Ensure AMR is available before importing it in R
|
||||
ensure_amr("cran")
|
||||
amr_r = importr('AMR')
|
||||
|
||||
def convert_to_r(value):
|
||||
"""Convert Python lists/tuples to typed R vectors.
|
||||
|
||||
rpy2's default_converter passes Python lists to R as R lists, not as
|
||||
character/numeric vectors. This causes element-wise type-check functions
|
||||
such as is.mic(), is.sir(), and is.disk() to return a logical vector
|
||||
rather than a single logical, breaking R's scalar && operator.
|
||||
|
||||
This helper converts Python lists and tuples to the appropriate R vector
|
||||
type based on the element types, so R always receives a proper vector."""
|
||||
if isinstance(value, (list, tuple)):
|
||||
if len(value) == 0:
|
||||
return StrVector([])
|
||||
# bool must be checked before int because bool is a subclass of int
|
||||
if all(isinstance(v, bool) for v in value):
|
||||
return robjects.vectors.BoolVector(value)
|
||||
if all(isinstance(v, int) for v in value):
|
||||
return IntVector(value)
|
||||
if all(isinstance(v, float) for v in value):
|
||||
return FloatVector(value)
|
||||
if all(isinstance(v, str) for v in value):
|
||||
return StrVector(value)
|
||||
# Mixed types: coerce all to string
|
||||
return StrVector([str(v) for v in value])
|
||||
return value
|
||||
|
||||
def convert_to_python(r_output):
|
||||
# Check if it's a StrVector (R character vector)
|
||||
if isinstance(r_output, StrVector):
|
||||
return list(r_output) # Convert to a Python list of strings
|
||||
|
||||
# Check if it's a FactorVector (R factor)
|
||||
elif isinstance(r_output, FactorVector):
|
||||
return list(r_output) # Convert to a list of integers (factor levels)
|
||||
|
||||
# Check if it's an IntVector or FloatVector (numeric R vectors)
|
||||
elif isinstance(r_output, (IntVector, FloatVector)):
|
||||
return list(r_output) # Convert to a Python list of integers or floats
|
||||
|
||||
# Check if it's a pandas-compatible R data frame
|
||||
elif isinstance(r_output, (pd.DataFrame, DataFrame)):
|
||||
return r_output # Return as pandas DataFrame (already converted by pandas2ri)
|
||||
|
||||
# Check if the input is a NumPy array and has a string data type
|
||||
if isinstance(r_output, np.ndarray) and np.issubdtype(r_output.dtype, np.str_):
|
||||
return r_output.tolist() # Convert to a regular Python list
|
||||
|
||||
# Fall-back
|
||||
return r_output
|
||||
|
||||
def r_to_python(r_func):
|
||||
"""Decorator that converts Python list/tuple inputs to typed R vectors,
|
||||
runs the rpy2 function under a localconverter, and converts the output
|
||||
to a Python type."""
|
||||
@functools.wraps(r_func)
|
||||
def wrapper(*args, **kwargs):
|
||||
args = tuple(convert_to_r(a) for a in args)
|
||||
kwargs = {k: convert_to_r(v) for k, v in kwargs.items()}
|
||||
with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
|
||||
return convert_to_python(r_func(*args, **kwargs))
|
||||
return wrapper
|
||||
@r_to_python
|
||||
def custom_eucast_rules(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.custom_eucast_rules(*args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_class(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_class(*args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_selector(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_selector(*args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_from_text(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_from_text(*args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_name(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_name(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_cid(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_cid(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_synonyms(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_synonyms(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_tradenames(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_tradenames(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_group(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_group(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_atc(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_atc(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_atc_group1(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_atc_group1(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_atc_group2(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_atc_group2(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_loinc(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_loinc(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_ddd(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_ddd(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_ddd_units(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_ddd_units(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_info(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_info(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_url(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_url(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ab_property(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_property(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def add_custom_antimicrobials(x):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.add_custom_antimicrobials(x)
|
||||
@r_to_python
|
||||
def clear_custom_antimicrobials(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.clear_custom_antimicrobials(*args, **kwargs)
|
||||
@r_to_python
|
||||
def add_custom_microorganisms(x):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.add_custom_microorganisms(x)
|
||||
@r_to_python
|
||||
def clear_custom_microorganisms(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.clear_custom_microorganisms(*args, **kwargs)
|
||||
@r_to_python
|
||||
def age(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.age(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def age_groups(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.age_groups(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def all_sir(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.all_sir(*args, **kwargs)
|
||||
@r_to_python
|
||||
def all_sir_predictors(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.all_sir_predictors(*args, **kwargs)
|
||||
@r_to_python
|
||||
def all_mic(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.all_mic(*args, **kwargs)
|
||||
@r_to_python
|
||||
def all_mic_predictors(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.all_mic_predictors(*args, **kwargs)
|
||||
@r_to_python
|
||||
def all_disk(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.all_disk(*args, **kwargs)
|
||||
@r_to_python
|
||||
def all_disk_predictors(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.all_disk_predictors(*args, **kwargs)
|
||||
@r_to_python
|
||||
def step_mic_log2(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.step_mic_log2(*args, **kwargs)
|
||||
@r_to_python
|
||||
def step_sir_numeric(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.step_sir_numeric(*args, **kwargs)
|
||||
@r_to_python
|
||||
def amr_course(github_repo, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.amr_course(github_repo, *args, **kwargs)
|
||||
@r_to_python
|
||||
def wisca(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.wisca(*args, **kwargs)
|
||||
@r_to_python
|
||||
def antibiogram(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.antibiogram(*args, **kwargs)
|
||||
@r_to_python
|
||||
def retrieve_wisca_parameters(wisca_model, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.retrieve_wisca_parameters(wisca_model, *args, **kwargs)
|
||||
@r_to_python
|
||||
def wisca_plot(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.wisca_plot(*args, **kwargs)
|
||||
@r_to_python
|
||||
def aminoglycosides(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.aminoglycosides(*args, **kwargs)
|
||||
@r_to_python
|
||||
def aminopenicillins(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.aminopenicillins(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def antifungals(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.antifungals(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def antimycobacterials(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.antimycobacterials(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def betalactams(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.betalactams(*args, **kwargs)
|
||||
@r_to_python
|
||||
def betalactams_with_inhibitor(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.betalactams_with_inhibitor(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def carbapenems(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.carbapenems(*args, **kwargs)
|
||||
@r_to_python
|
||||
def cephalosporins(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.cephalosporins(*args, **kwargs)
|
||||
@r_to_python
|
||||
def cephalosporins_1st(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.cephalosporins_1st(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def cephalosporins_2nd(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.cephalosporins_2nd(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def cephalosporins_3rd(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.cephalosporins_3rd(*args, **kwargs)
|
||||
@r_to_python
|
||||
def cephalosporins_4th(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.cephalosporins_4th(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def cephalosporins_5th(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.cephalosporins_5th(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def fluoroquinolones(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.fluoroquinolones(*args, **kwargs)
|
||||
@r_to_python
|
||||
def glycopeptides(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.glycopeptides(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ionophores(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ionophores(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def isoxazolylpenicillins(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.isoxazolylpenicillins(*args, **kwargs)
|
||||
@r_to_python
|
||||
def lincosamides(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.lincosamides(*args, **kwargs)
|
||||
@r_to_python
|
||||
def lipoglycopeptides(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.lipoglycopeptides(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def macrolides(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.macrolides(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def monobactams(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.monobactams(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def nitrofurans(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.nitrofurans(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def oxazolidinones(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.oxazolidinones(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def penicillins(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.penicillins(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def peptides(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.peptides(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def phenicols(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.phenicols(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def phosphonics(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.phosphonics(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def polymyxins(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.polymyxins(*args, **kwargs)
|
||||
@r_to_python
|
||||
def quinolones(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.quinolones(*args, **kwargs)
|
||||
@r_to_python
|
||||
def rifamycins(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.rifamycins(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def spiropyrimidinetriones(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.spiropyrimidinetriones(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def streptogramins(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.streptogramins(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def sulfonamides(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.sulfonamides(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def tetracyclines(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.tetracyclines(*args, **kwargs)
|
||||
@r_to_python
|
||||
def trimethoprims(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.trimethoprims(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ureidopenicillins(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ureidopenicillins(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def amr_class(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.amr_class(*args, **kwargs)
|
||||
@r_to_python
|
||||
def amr_selector(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.amr_selector(*args, **kwargs)
|
||||
@r_to_python
|
||||
def administrable_per_os(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.administrable_per_os(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def administrable_iv(only_sir_columns = False, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.administrable_iv(only_sir_columns = False, *args, **kwargs)
|
||||
@r_to_python
|
||||
def not_intrinsic_resistant(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.not_intrinsic_resistant(*args, **kwargs)
|
||||
@r_to_python
|
||||
def as_ab(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.as_ab(*args, **kwargs)
|
||||
@r_to_python
|
||||
def is_ab(x):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.is_ab(x)
|
||||
@r_to_python
|
||||
def ab_reset_session(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ab_reset_session(*args, **kwargs)
|
||||
@r_to_python
|
||||
def as_av(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.as_av(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def is_av(x):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.is_av(x)
|
||||
@r_to_python
|
||||
def as_disk(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.as_disk(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def is_disk(x):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.is_disk(x)
|
||||
@r_to_python
|
||||
def as_mic(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.as_mic(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def is_mic(x):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.is_mic(x)
|
||||
@r_to_python
|
||||
def rescale_mic(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.rescale_mic(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mic_p50(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mic_p50(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def mic_p90(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mic_p90(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def as_mo(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.as_mo(*args, **kwargs)
|
||||
@r_to_python
|
||||
def is_mo(x):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.is_mo(x)
|
||||
@r_to_python
|
||||
def mo_uncertainties(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_uncertainties(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_renamed(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_renamed(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_failures(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_failures(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_reset_session(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_reset_session(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_cleaning_regex(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_cleaning_regex(*args, **kwargs)
|
||||
@r_to_python
|
||||
def as_sir(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.as_sir(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def is_sir(x):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.is_sir(x)
|
||||
@r_to_python
|
||||
def is_sir_eligible(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.is_sir_eligible(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def sir_interpretation_history(clean):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.sir_interpretation_history(clean)
|
||||
@r_to_python
|
||||
def atc_online_property(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.atc_online_property(*args, **kwargs)
|
||||
@r_to_python
|
||||
def atc_online_groups(atc_code, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.atc_online_groups(atc_code, *args, **kwargs)
|
||||
@r_to_python
|
||||
def atc_online_ddd(atc_code, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.atc_online_ddd(atc_code, *args, **kwargs)
|
||||
@r_to_python
|
||||
def atc_online_ddd_units(atc_code, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.atc_online_ddd_units(atc_code, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_from_text(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_from_text(*args, **kwargs)
|
||||
@r_to_python
|
||||
def av_name(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_name(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_cid(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_cid(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_synonyms(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_synonyms(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_tradenames(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_tradenames(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_group(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_group(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_atc(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_atc(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_loinc(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_loinc(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_ddd(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_ddd(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_ddd_units(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_ddd_units(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_info(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_info(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_url(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_url(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def av_property(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.av_property(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def availability(tbl, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.availability(tbl, *args, **kwargs)
|
||||
@r_to_python
|
||||
def bug_drug_combinations(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.bug_drug_combinations(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_resistant(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_resistant(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_susceptible(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_susceptible(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_S(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_S(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_SI(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_SI(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_I(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_I(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_IR(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_IR(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_R(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_R(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_all(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_all(*args, **kwargs)
|
||||
@r_to_python
|
||||
def n_sir(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.n_sir(*args, **kwargs)
|
||||
@r_to_python
|
||||
def count_df(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.count_df(*args, **kwargs)
|
||||
@r_to_python
|
||||
def custom_interpretive_rules(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.custom_interpretive_rules(*args, **kwargs)
|
||||
@r_to_python
|
||||
def custom_mdro_guideline(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.custom_mdro_guideline(*args, **kwargs)
|
||||
@r_to_python
|
||||
def export_ncbi_biosample(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.export_ncbi_biosample(*args, **kwargs)
|
||||
@r_to_python
|
||||
def first_isolate(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.first_isolate(*args, **kwargs)
|
||||
@r_to_python
|
||||
def filter_first_isolate(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.filter_first_isolate(*args, **kwargs)
|
||||
@r_to_python
|
||||
def g_test(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.g_test(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def is_new_episode(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.is_new_episode(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def ggplot_pca(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ggplot_pca(*args, **kwargs)
|
||||
@r_to_python
|
||||
def ggplot_sir(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ggplot_sir(*args, **kwargs)
|
||||
@r_to_python
|
||||
def geom_sir(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.geom_sir(*args, **kwargs)
|
||||
@r_to_python
|
||||
def guess_ab_col(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.guess_ab_col(*args, **kwargs)
|
||||
@r_to_python
|
||||
def interpretive_rules(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.interpretive_rules(*args, **kwargs)
|
||||
@r_to_python
|
||||
def eucast_rules(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.eucast_rules(*args, **kwargs)
|
||||
@r_to_python
|
||||
def clsi_rules(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.clsi_rules(*args, **kwargs)
|
||||
@r_to_python
|
||||
def eucast_dosage(ab, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.eucast_dosage(ab, *args, **kwargs)
|
||||
@r_to_python
|
||||
def italicise_taxonomy(string, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.italicise_taxonomy(string, *args, **kwargs)
|
||||
@r_to_python
|
||||
def italicize_taxonomy(string, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.italicize_taxonomy(string, *args, **kwargs)
|
||||
@r_to_python
|
||||
def inner_join_microorganisms(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.inner_join_microorganisms(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def left_join_microorganisms(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.left_join_microorganisms(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def right_join_microorganisms(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.right_join_microorganisms(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def full_join_microorganisms(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.full_join_microorganisms(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def semi_join_microorganisms(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.semi_join_microorganisms(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def anti_join_microorganisms(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.anti_join_microorganisms(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def key_antimicrobials(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.key_antimicrobials(*args, **kwargs)
|
||||
@r_to_python
|
||||
def all_antimicrobials(x = None, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.all_antimicrobials(x = None, *args, **kwargs)
|
||||
@r_to_python
|
||||
def kurtosis(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.kurtosis(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def like(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.like(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def mdro(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mdro(*args, **kwargs)
|
||||
@r_to_python
|
||||
def brmo(x = None, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.brmo(x = None, *args, **kwargs)
|
||||
@r_to_python
|
||||
def mrgn(x = None, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mrgn(x = None, *args, **kwargs)
|
||||
@r_to_python
|
||||
def mdr_tb(x = None, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mdr_tb(x = None, *args, **kwargs)
|
||||
@r_to_python
|
||||
def mdr_cmi2012(x = None, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mdr_cmi2012(x = None, *args, **kwargs)
|
||||
@r_to_python
|
||||
def eucast_exceptional_phenotypes(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.eucast_exceptional_phenotypes(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mean_amr_distance(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mean_amr_distance(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def amr_distance_from_row(amr_distance, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.amr_distance_from_row(amr_distance, *args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_matching_score(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_matching_score(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_name(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_name(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_fullname(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_fullname(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_shortname(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_shortname(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_subspecies(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_subspecies(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_species(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_species(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_genus(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_genus(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_family(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_family(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_order(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_order(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_class(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_class(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_phylum(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_phylum(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_kingdom(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_kingdom(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_domain(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_domain(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_type(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_type(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_status(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_status(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_pathogenicity(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_pathogenicity(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_gramstain(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_gramstain(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_is_gram_negative(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_is_gram_negative(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_is_gram_positive(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_is_gram_positive(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_is_yeast(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_is_yeast(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_is_intrinsic_resistant(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_is_intrinsic_resistant(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_oxygen_tolerance(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_oxygen_tolerance(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_is_anaerobic(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_is_anaerobic(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_morphology(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_morphology(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_snomed(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_snomed(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_ref(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_ref(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_authors(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_authors(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_year(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_year(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_lpsn(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_lpsn(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_mycobank(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_mycobank(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_gbif(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_gbif(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_rank(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_rank(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_taxonomy(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_taxonomy(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_synonyms(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_synonyms(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_current(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_current(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_group_members(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_group_members(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_info(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_info(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_url(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_url(*args, **kwargs)
|
||||
@r_to_python
|
||||
def mo_property(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.mo_property(*args, **kwargs)
|
||||
@r_to_python
|
||||
def pca(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.pca(*args, **kwargs)
|
||||
@r_to_python
|
||||
def theme_sir(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.theme_sir(*args, **kwargs)
|
||||
@r_to_python
|
||||
def labels_sir_count(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.labels_sir_count(*args, **kwargs)
|
||||
@r_to_python
|
||||
def resistance(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.resistance(*args, **kwargs)
|
||||
@r_to_python
|
||||
def susceptibility(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.susceptibility(*args, **kwargs)
|
||||
@r_to_python
|
||||
def sir_confidence_interval(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.sir_confidence_interval(*args, **kwargs)
|
||||
@r_to_python
|
||||
def proportion_R(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.proportion_R(*args, **kwargs)
|
||||
@r_to_python
|
||||
def proportion_IR(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.proportion_IR(*args, **kwargs)
|
||||
@r_to_python
|
||||
def proportion_I(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.proportion_I(*args, **kwargs)
|
||||
@r_to_python
|
||||
def proportion_SI(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.proportion_SI(*args, **kwargs)
|
||||
@r_to_python
|
||||
def proportion_S(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.proportion_S(*args, **kwargs)
|
||||
@r_to_python
|
||||
def proportion_df(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.proportion_df(*args, **kwargs)
|
||||
@r_to_python
|
||||
def sir_df(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.sir_df(*args, **kwargs)
|
||||
@r_to_python
|
||||
def random_mic(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.random_mic(*args, **kwargs)
|
||||
@r_to_python
|
||||
def random_disk(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.random_disk(*args, **kwargs)
|
||||
@r_to_python
|
||||
def random_sir(size = None, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.random_sir(size = None, *args, **kwargs)
|
||||
@r_to_python
|
||||
def resistance_predict(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.resistance_predict(*args, **kwargs)
|
||||
@r_to_python
|
||||
def sir_predict(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.sir_predict(*args, **kwargs)
|
||||
@r_to_python
|
||||
def ggplot_sir_predict(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.ggplot_sir_predict(*args, **kwargs)
|
||||
@r_to_python
|
||||
def skewness(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.skewness(x, *args, **kwargs)
|
||||
@r_to_python
|
||||
def top_n_microorganisms(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.top_n_microorganisms(*args, **kwargs)
|
||||
@r_to_python
|
||||
def reset_AMR_locale(*args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.reset_AMR_locale(*args, **kwargs)
|
||||
@r_to_python
|
||||
def translate_AMR(x, *args, **kwargs):
|
||||
"""Please see our website of the R package for the full manual: https://amr-for-r.org"""
|
||||
return amr_r.translate_AMR(x, *args, **kwargs)
|
||||
-72
@@ -1,72 +0,0 @@
|
||||
Package: AMR
|
||||
Version: 0.6.0
|
||||
Date: 2019-03-27
|
||||
Title: Antimicrobial Resistance Analysis
|
||||
Authors@R: c(
|
||||
person(
|
||||
given = c("Matthijs", "S."),
|
||||
family = "Berends",
|
||||
email = "m.s.berends@umcg.nl",
|
||||
role = c("aut", "cre"),
|
||||
comment = c(ORCID = "0000-0001-7620-1800")),
|
||||
person(
|
||||
given = c("Christian", "F."),
|
||||
family = "Luz",
|
||||
email = "c.f.luz@umcg.nl",
|
||||
role = "aut",
|
||||
comment = c(ORCID = "0000-0001-5809-5995")),
|
||||
person(
|
||||
given = c("Erwin", "E.", "A."),
|
||||
family = "Hassing",
|
||||
email = "e.hassing@certe.nl",
|
||||
role = "ctb"),
|
||||
person(
|
||||
given = "Corinna",
|
||||
family = "Glasner",
|
||||
email = "c.glasner@umcg.nl",
|
||||
role = c("aut", "ths"),
|
||||
comment = c(ORCID = "0000-0003-1241-1328")),
|
||||
person(
|
||||
given = c("Alex", "W."),
|
||||
family = "Friedrich",
|
||||
email = "alex.friedrich@umcg.nl",
|
||||
role = c("aut", "ths"),
|
||||
comment = c(ORCID = "0000-0003-4881-038X")),
|
||||
person(
|
||||
given = c("Bhanu", "N.", "M."),
|
||||
family = "Sinha",
|
||||
email = "b.sinha@umcg.nl",
|
||||
role = c("aut", "ths"),
|
||||
comment = c(ORCID = "0000-0003-1634-0010")))
|
||||
Description: Functions to simplify the analysis and prediction of Antimicrobial
|
||||
Resistance (AMR) and to work with microbial and antimicrobial properties by
|
||||
using evidence-based methods.
|
||||
Depends:
|
||||
R (>= 3.1.0)
|
||||
Imports:
|
||||
backports,
|
||||
crayon (>= 1.3.0),
|
||||
data.table (>= 1.9.0),
|
||||
dplyr (>= 0.7.0),
|
||||
ggplot2,
|
||||
hms,
|
||||
knitr (>= 1.0.0),
|
||||
microbenchmark,
|
||||
rlang (>= 0.3.1),
|
||||
tidyr (>= 0.7.0)
|
||||
Suggests:
|
||||
covr (>= 3.0.1),
|
||||
curl,
|
||||
readxl,
|
||||
rmarkdown,
|
||||
rstudioapi,
|
||||
rvest (>= 0.3.2),
|
||||
testthat (>= 1.0.2),
|
||||
xml2 (>= 1.0.0)
|
||||
VignetteBuilder: knitr
|
||||
URL: https://msberends.gitlab.io/AMR, https://gitlab.com/msberends/AMR
|
||||
BugReports: https://gitlab.com/msberends/AMR/issues
|
||||
License: GPL-2 | file LICENSE
|
||||
Encoding: UTF-8
|
||||
LazyData: true
|
||||
RoxygenNote: 6.1.1
|
||||
@@ -1,339 +0,0 @@
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 2, June 1991
|
||||
|
||||
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The licenses for most software are designed to take away your
|
||||
freedom to share and change it. By contrast, the GNU General Public
|
||||
License is intended to guarantee your freedom to share and change free
|
||||
software--to make sure the software is free for all its users. This
|
||||
General Public License applies to most of the Free Software
|
||||
Foundation's software and to any other program whose authors commit to
|
||||
using it. (Some other Free Software Foundation software is covered by
|
||||
the GNU Lesser General Public License instead.) You can apply it to
|
||||
your programs, too.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
this service if you wish), that you receive source code or can get it
|
||||
if you want it, that you can change the software or use pieces of it
|
||||
in new free programs; and that you know you can do these things.
|
||||
|
||||
To protect your rights, we need to make restrictions that forbid
|
||||
anyone to deny you these rights or to ask you to surrender the rights.
|
||||
These restrictions translate to certain responsibilities for you if you
|
||||
distribute copies of the software, or if you modify it.
|
||||
|
||||
For example, if you distribute copies of such a program, whether
|
||||
gratis or for a fee, you must give the recipients all the rights that
|
||||
you have. You must make sure that they, too, receive or can get the
|
||||
source code. And you must show them these terms so they know their
|
||||
rights.
|
||||
|
||||
We protect your rights with two steps: (1) copyright the software, and
|
||||
(2) offer you this license which gives you legal permission to copy,
|
||||
distribute and/or modify the software.
|
||||
|
||||
Also, for each author's protection and ours, we want to make certain
|
||||
that everyone understands that there is no warranty for this free
|
||||
software. If the software is modified by someone else and passed on, we
|
||||
want its recipients to know that what they have is not the original, so
|
||||
that any problems introduced by others will not reflect on the original
|
||||
authors' reputations.
|
||||
|
||||
Finally, any free program is threatened constantly by software
|
||||
patents. We wish to avoid the danger that redistributors of a free
|
||||
program will individually obtain patent licenses, in effect making the
|
||||
program proprietary. To prevent this, we have made it clear that any
|
||||
patent must be licensed for everyone's free use or not licensed at all.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
|
||||
|
||||
0. This License applies to any program or other work which contains
|
||||
a notice placed by the copyright holder saying it may be distributed
|
||||
under the terms of this General Public License. The "Program", below,
|
||||
refers to any such program or work, and a "work based on the Program"
|
||||
means either the Program or any derivative work under copyright law:
|
||||
that is to say, a work containing the Program or a portion of it,
|
||||
either verbatim or with modifications and/or translated into another
|
||||
language. (Hereinafter, translation is included without limitation in
|
||||
the term "modification".) Each licensee is addressed as "you".
|
||||
|
||||
Activities other than copying, distribution and modification are not
|
||||
covered by this License; they are outside its scope. The act of
|
||||
running the Program is not restricted, and the output from the Program
|
||||
is covered only if its contents constitute a work based on the
|
||||
Program (independent of having been made by running the Program).
|
||||
Whether that is true depends on what the Program does.
|
||||
|
||||
1. You may copy and distribute verbatim copies of the Program's
|
||||
source code as you receive it, in any medium, provided that you
|
||||
conspicuously and appropriately publish on each copy an appropriate
|
||||
copyright notice and disclaimer of warranty; keep intact all the
|
||||
notices that refer to this License and to the absence of any warranty;
|
||||
and give any other recipients of the Program a copy of this License
|
||||
along with the Program.
|
||||
|
||||
You may charge a fee for the physical act of transferring a copy, and
|
||||
you may at your option offer warranty protection in exchange for a fee.
|
||||
|
||||
2. You may modify your copy or copies of the Program or any portion
|
||||
of it, thus forming a work based on the Program, and copy and
|
||||
distribute such modifications or work under the terms of Section 1
|
||||
above, provided that you also meet all of these conditions:
|
||||
|
||||
a) You must cause the modified files to carry prominent notices
|
||||
stating that you changed the files and the date of any change.
|
||||
|
||||
b) You must cause any work that you distribute or publish, that in
|
||||
whole or in part contains or is derived from the Program or any
|
||||
part thereof, to be licensed as a whole at no charge to all third
|
||||
parties under the terms of this License.
|
||||
|
||||
c) If the modified program normally reads commands interactively
|
||||
when run, you must cause it, when started running for such
|
||||
interactive use in the most ordinary way, to print or display an
|
||||
announcement including an appropriate copyright notice and a
|
||||
notice that there is no warranty (or else, saying that you provide
|
||||
a warranty) and that users may redistribute the program under
|
||||
these conditions, and telling the user how to view a copy of this
|
||||
License. (Exception: if the Program itself is interactive but
|
||||
does not normally print such an announcement, your work based on
|
||||
the Program is not required to print an announcement.)
|
||||
|
||||
These requirements apply to the modified work as a whole. If
|
||||
identifiable sections of that work are not derived from the Program,
|
||||
and can be reasonably considered independent and separate works in
|
||||
themselves, then this License, and its terms, do not apply to those
|
||||
sections when you distribute them as separate works. But when you
|
||||
distribute the same sections as part of a whole which is a work based
|
||||
on the Program, the distribution of the whole must be on the terms of
|
||||
this License, whose permissions for other licensees extend to the
|
||||
entire whole, and thus to each and every part regardless of who wrote it.
|
||||
|
||||
Thus, it is not the intent of this section to claim rights or contest
|
||||
your rights to work written entirely by you; rather, the intent is to
|
||||
exercise the right to control the distribution of derivative or
|
||||
collective works based on the Program.
|
||||
|
||||
In addition, mere aggregation of another work not based on the Program
|
||||
with the Program (or with a work based on the Program) on a volume of
|
||||
a storage or distribution medium does not bring the other work under
|
||||
the scope of this License.
|
||||
|
||||
3. You may copy and distribute the Program (or a work based on it,
|
||||
under Section 2) in object code or executable form under the terms of
|
||||
Sections 1 and 2 above provided that you also do one of the following:
|
||||
|
||||
a) Accompany it with the complete corresponding machine-readable
|
||||
source code, which must be distributed under the terms of Sections
|
||||
1 and 2 above on a medium customarily used for software interchange; or,
|
||||
|
||||
b) Accompany it with a written offer, valid for at least three
|
||||
years, to give any third party, for a charge no more than your
|
||||
cost of physically performing source distribution, a complete
|
||||
machine-readable copy of the corresponding source code, to be
|
||||
distributed under the terms of Sections 1 and 2 above on a medium
|
||||
customarily used for software interchange; or,
|
||||
|
||||
c) Accompany it with the information you received as to the offer
|
||||
to distribute corresponding source code. (This alternative is
|
||||
allowed only for noncommercial distribution and only if you
|
||||
received the program in object code or executable form with such
|
||||
an offer, in accord with Subsection b above.)
|
||||
|
||||
The source code for a work means the preferred form of the work for
|
||||
making modifications to it. For an executable work, complete source
|
||||
code means all the source code for all modules it contains, plus any
|
||||
associated interface definition files, plus the scripts used to
|
||||
control compilation and installation of the executable. However, as a
|
||||
special exception, the source code distributed need not include
|
||||
anything that is normally distributed (in either source or binary
|
||||
form) with the major components (compiler, kernel, and so on) of the
|
||||
operating system on which the executable runs, unless that component
|
||||
itself accompanies the executable.
|
||||
|
||||
If distribution of executable or object code is made by offering
|
||||
access to copy from a designated place, then offering equivalent
|
||||
access to copy the source code from the same place counts as
|
||||
distribution of the source code, even though third parties are not
|
||||
compelled to copy the source along with the object code.
|
||||
|
||||
4. You may not copy, modify, sublicense, or distribute the Program
|
||||
except as expressly provided under this License. Any attempt
|
||||
otherwise to copy, modify, sublicense or distribute the Program is
|
||||
void, and will automatically terminate your rights under this License.
|
||||
However, parties who have received copies, or rights, from you under
|
||||
this License will not have their licenses terminated so long as such
|
||||
parties remain in full compliance.
|
||||
|
||||
5. You are not required to accept this License, since you have not
|
||||
signed it. However, nothing else grants you permission to modify or
|
||||
distribute the Program or its derivative works. These actions are
|
||||
prohibited by law if you do not accept this License. Therefore, by
|
||||
modifying or distributing the Program (or any work based on the
|
||||
Program), you indicate your acceptance of this License to do so, and
|
||||
all its terms and conditions for copying, distributing or modifying
|
||||
the Program or works based on it.
|
||||
|
||||
6. Each time you redistribute the Program (or any work based on the
|
||||
Program), the recipient automatically receives a license from the
|
||||
original licensor to copy, distribute or modify the Program subject to
|
||||
these terms and conditions. You may not impose any further
|
||||
restrictions on the recipients' exercise of the rights granted herein.
|
||||
You are not responsible for enforcing compliance by third parties to
|
||||
this License.
|
||||
|
||||
7. If, as a consequence of a court judgment or allegation of patent
|
||||
infringement or for any other reason (not limited to patent issues),
|
||||
conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot
|
||||
distribute so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you
|
||||
may not distribute the Program at all. For example, if a patent
|
||||
license would not permit royalty-free redistribution of the Program by
|
||||
all those who receive copies directly or indirectly through you, then
|
||||
the only way you could satisfy both it and this License would be to
|
||||
refrain entirely from distribution of the Program.
|
||||
|
||||
If any portion of this section is held invalid or unenforceable under
|
||||
any particular circumstance, the balance of the section is intended to
|
||||
apply and the section as a whole is intended to apply in other
|
||||
circumstances.
|
||||
|
||||
It is not the purpose of this section to induce you to infringe any
|
||||
patents or other property right claims or to contest validity of any
|
||||
such claims; this section has the sole purpose of protecting the
|
||||
integrity of the free software distribution system, which is
|
||||
implemented by public license practices. Many people have made
|
||||
generous contributions to the wide range of software distributed
|
||||
through that system in reliance on consistent application of that
|
||||
system; it is up to the author/donor to decide if he or she is willing
|
||||
to distribute software through any other system and a licensee cannot
|
||||
impose that choice.
|
||||
|
||||
This section is intended to make thoroughly clear what is believed to
|
||||
be a consequence of the rest of this License.
|
||||
|
||||
8. If the distribution and/or use of the Program is restricted in
|
||||
certain countries either by patents or by copyrighted interfaces, the
|
||||
original copyright holder who places the Program under this License
|
||||
may add an explicit geographical distribution limitation excluding
|
||||
those countries, so that distribution is permitted only in or among
|
||||
countries not thus excluded. In such case, this License incorporates
|
||||
the limitation as if written in the body of this License.
|
||||
|
||||
9. The Free Software Foundation may publish revised and/or new versions
|
||||
of the General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the Program
|
||||
specifies a version number of this License which applies to it and "any
|
||||
later version", you have the option of following the terms and conditions
|
||||
either of that version or of any later version published by the Free
|
||||
Software Foundation. If the Program does not specify a version number of
|
||||
this License, you may choose any version ever published by the Free Software
|
||||
Foundation.
|
||||
|
||||
10. If you wish to incorporate parts of the Program into other free
|
||||
programs whose distribution conditions are different, write to the author
|
||||
to ask for permission. For software which is copyrighted by the Free
|
||||
Software Foundation, write to the Free Software Foundation; we sometimes
|
||||
make exceptions for this. Our decision will be guided by the two goals
|
||||
of preserving the free status of all derivatives of our free software and
|
||||
of promoting the sharing and reuse of software generally.
|
||||
|
||||
NO WARRANTY
|
||||
|
||||
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
|
||||
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
|
||||
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
|
||||
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
|
||||
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
|
||||
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
|
||||
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
|
||||
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
|
||||
REPAIR OR CORRECTION.
|
||||
|
||||
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
|
||||
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
|
||||
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
|
||||
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
|
||||
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
|
||||
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
|
||||
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGES.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
convey the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
{description}
|
||||
Copyright (C) {year} {fullname}
|
||||
|
||||
This program is free software; you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation; either version 2 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License along
|
||||
with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program is interactive, make it output a short notice like this
|
||||
when it starts in an interactive mode:
|
||||
|
||||
Gnomovision version 69, Copyright (C) year name of author
|
||||
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it
|
||||
under certain conditions; type `show c' for details.
|
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||
parts of the General Public License. Of course, the commands you use may
|
||||
be called something other than `show w' and `show c'; they could even be
|
||||
mouse-clicks or menu items--whatever suits your program.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or your
|
||||
school, if any, to sign a "copyright disclaimer" for the program, if
|
||||
necessary. Here is a sample; alter the names:
|
||||
|
||||
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
|
||||
`Gnomovision' (which makes passes at compilers) written by James Hacker.
|
||||
|
||||
{signature of Ty Coon}, 1 April 1989
|
||||
Ty Coon, President of Vice
|
||||
|
||||
This General Public License does not permit incorporating your program into
|
||||
proprietary programs. If your program is a subroutine library, you may
|
||||
consider it more useful to permit linking proprietary applications with the
|
||||
library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License.
|
||||
@@ -1,298 +0,0 @@
|
||||
# Generated by roxygen2: do not edit by hand
|
||||
|
||||
S3method(as.data.frame,atc)
|
||||
S3method(as.data.frame,frequency_tbl)
|
||||
S3method(as.data.frame,mo)
|
||||
S3method(as.double,mic)
|
||||
S3method(as.integer,mic)
|
||||
S3method(as.numeric,mic)
|
||||
S3method(as.vector,frequency_tbl)
|
||||
S3method(as_tibble,frequency_tbl)
|
||||
S3method(barplot,mic)
|
||||
S3method(barplot,rsi)
|
||||
S3method(diff,frequency_tbl)
|
||||
S3method(droplevels,mic)
|
||||
S3method(droplevels,rsi)
|
||||
S3method(format,frequency_tbl)
|
||||
S3method(hist,frequency_tbl)
|
||||
S3method(kurtosis,data.frame)
|
||||
S3method(kurtosis,default)
|
||||
S3method(kurtosis,matrix)
|
||||
S3method(plot,frequency_tbl)
|
||||
S3method(plot,mic)
|
||||
S3method(plot,resistance_predict)
|
||||
S3method(plot,rsi)
|
||||
S3method(print,atc)
|
||||
S3method(print,catalogue_of_life_version)
|
||||
S3method(print,frequency_tbl)
|
||||
S3method(print,mic)
|
||||
S3method(print,mo)
|
||||
S3method(print,mo_renamed)
|
||||
S3method(print,mo_uncertainties)
|
||||
S3method(print,rsi)
|
||||
S3method(pull,atc)
|
||||
S3method(pull,mo)
|
||||
S3method(select,frequency_tbl)
|
||||
S3method(skewness,data.frame)
|
||||
S3method(skewness,default)
|
||||
S3method(skewness,matrix)
|
||||
S3method(summary,mic)
|
||||
S3method(summary,mo)
|
||||
S3method(summary,rsi)
|
||||
export("%like%")
|
||||
export(EUCAST_rules)
|
||||
export(ab_atc)
|
||||
export(ab_certe)
|
||||
export(ab_name)
|
||||
export(ab_official)
|
||||
export(ab_property)
|
||||
export(ab_tradenames)
|
||||
export(ab_trivial_nl)
|
||||
export(ab_umcg)
|
||||
export(abname)
|
||||
export(age)
|
||||
export(age_groups)
|
||||
export(anti_join_microorganisms)
|
||||
export(as.atc)
|
||||
export(as.mic)
|
||||
export(as.mo)
|
||||
export(as.rsi)
|
||||
export(atc_certe)
|
||||
export(atc_ddd)
|
||||
export(atc_groups)
|
||||
export(atc_name)
|
||||
export(atc_official)
|
||||
export(atc_online_ddd)
|
||||
export(atc_online_groups)
|
||||
export(atc_online_property)
|
||||
export(atc_property)
|
||||
export(atc_tradenames)
|
||||
export(atc_trivial_nl)
|
||||
export(atc_umcg)
|
||||
export(availability)
|
||||
export(brmo)
|
||||
export(catalogue_of_life_version)
|
||||
export(clean_mo_history)
|
||||
export(count_I)
|
||||
export(count_IR)
|
||||
export(count_R)
|
||||
export(count_S)
|
||||
export(count_SI)
|
||||
export(count_all)
|
||||
export(count_df)
|
||||
export(eucast_exceptional_phenotypes)
|
||||
export(eucast_rules)
|
||||
export(facet_rsi)
|
||||
export(filter_1st_cephalosporins)
|
||||
export(filter_2nd_cephalosporins)
|
||||
export(filter_3rd_cephalosporins)
|
||||
export(filter_4th_cephalosporins)
|
||||
export(filter_ab_class)
|
||||
export(filter_aminoglycosides)
|
||||
export(filter_carbapenems)
|
||||
export(filter_cephalosporins)
|
||||
export(filter_first_isolate)
|
||||
export(filter_first_weighted_isolate)
|
||||
export(filter_fluoroquinolones)
|
||||
export(filter_glycopeptides)
|
||||
export(filter_macrolides)
|
||||
export(filter_tetracyclines)
|
||||
export(first_isolate)
|
||||
export(freq)
|
||||
export(frequency_tbl)
|
||||
export(full_join_microorganisms)
|
||||
export(g.test)
|
||||
export(geom_rsi)
|
||||
export(get_locale)
|
||||
export(get_mo_source)
|
||||
export(ggplot_rsi)
|
||||
export(ggplot_rsi_predict)
|
||||
export(guess_ab_col)
|
||||
export(guess_atc)
|
||||
export(guess_mo)
|
||||
export(header)
|
||||
export(inner_join_microorganisms)
|
||||
export(interpretive_reading)
|
||||
export(is.atc)
|
||||
export(is.mic)
|
||||
export(is.mo)
|
||||
export(is.rsi)
|
||||
export(is.rsi.eligible)
|
||||
export(key_antibiotics)
|
||||
export(key_antibiotics_equal)
|
||||
export(kurtosis)
|
||||
export(labels_rsi_count)
|
||||
export(left_join_microorganisms)
|
||||
export(like)
|
||||
export(mdro)
|
||||
export(mo_authors)
|
||||
export(mo_class)
|
||||
export(mo_failures)
|
||||
export(mo_family)
|
||||
export(mo_fullname)
|
||||
export(mo_genus)
|
||||
export(mo_gramstain)
|
||||
export(mo_kingdom)
|
||||
export(mo_order)
|
||||
export(mo_phylum)
|
||||
export(mo_property)
|
||||
export(mo_rank)
|
||||
export(mo_ref)
|
||||
export(mo_renamed)
|
||||
export(mo_shortname)
|
||||
export(mo_species)
|
||||
export(mo_subspecies)
|
||||
export(mo_taxonomy)
|
||||
export(mo_type)
|
||||
export(mo_uncertainties)
|
||||
export(mo_url)
|
||||
export(mo_year)
|
||||
export(mrgn)
|
||||
export(n_rsi)
|
||||
export(p.symbol)
|
||||
export(portion_I)
|
||||
export(portion_IR)
|
||||
export(portion_R)
|
||||
export(portion_S)
|
||||
export(portion_SI)
|
||||
export(portion_df)
|
||||
export(ratio)
|
||||
export(read.4D)
|
||||
export(resistance_predict)
|
||||
export(right_join_microorganisms)
|
||||
export(rsi)
|
||||
export(rsi_predict)
|
||||
export(scale_rsi_colours)
|
||||
export(scale_y_percent)
|
||||
export(semi_join_microorganisms)
|
||||
export(set_mo_source)
|
||||
export(skewness)
|
||||
export(theme_rsi)
|
||||
export(top_freq)
|
||||
exportMethods(as.data.frame.atc)
|
||||
exportMethods(as.data.frame.frequency_tbl)
|
||||
exportMethods(as.data.frame.mo)
|
||||
exportMethods(as.double.mic)
|
||||
exportMethods(as.integer.mic)
|
||||
exportMethods(as.numeric.mic)
|
||||
exportMethods(as.vector.frequency_tbl)
|
||||
exportMethods(as_tibble.frequency_tbl)
|
||||
exportMethods(barplot.mic)
|
||||
exportMethods(barplot.rsi)
|
||||
exportMethods(diff.frequency_tbl)
|
||||
exportMethods(droplevels.mic)
|
||||
exportMethods(droplevels.rsi)
|
||||
exportMethods(format.frequency_tbl)
|
||||
exportMethods(hist.frequency_tbl)
|
||||
exportMethods(kurtosis)
|
||||
exportMethods(kurtosis.data.frame)
|
||||
exportMethods(kurtosis.default)
|
||||
exportMethods(kurtosis.matrix)
|
||||
exportMethods(plot.frequency_tbl)
|
||||
exportMethods(plot.mic)
|
||||
exportMethods(plot.rsi)
|
||||
exportMethods(print.atc)
|
||||
exportMethods(print.catalogue_of_life_version)
|
||||
exportMethods(print.frequency_tbl)
|
||||
exportMethods(print.mic)
|
||||
exportMethods(print.mo)
|
||||
exportMethods(print.mo_renamed)
|
||||
exportMethods(print.mo_uncertainties)
|
||||
exportMethods(print.rsi)
|
||||
exportMethods(pull.atc)
|
||||
exportMethods(pull.mo)
|
||||
exportMethods(select.frequency_tbl)
|
||||
exportMethods(skewness)
|
||||
exportMethods(skewness.data.frame)
|
||||
exportMethods(skewness.default)
|
||||
exportMethods(skewness.matrix)
|
||||
exportMethods(summary.mic)
|
||||
exportMethods(summary.mo)
|
||||
exportMethods(summary.rsi)
|
||||
importFrom(crayon,bgGreen)
|
||||
importFrom(crayon,bgRed)
|
||||
importFrom(crayon,bgYellow)
|
||||
importFrom(crayon,black)
|
||||
importFrom(crayon,blue)
|
||||
importFrom(crayon,bold)
|
||||
importFrom(crayon,green)
|
||||
importFrom(crayon,has_color)
|
||||
importFrom(crayon,italic)
|
||||
importFrom(crayon,magenta)
|
||||
importFrom(crayon,red)
|
||||
importFrom(crayon,silver)
|
||||
importFrom(crayon,strip_style)
|
||||
importFrom(crayon,underline)
|
||||
importFrom(crayon,white)
|
||||
importFrom(crayon,yellow)
|
||||
importFrom(data.table,as.data.table)
|
||||
importFrom(data.table,data.table)
|
||||
importFrom(data.table,setkey)
|
||||
importFrom(dplyr,"%>%")
|
||||
importFrom(dplyr,all_vars)
|
||||
importFrom(dplyr,any_vars)
|
||||
importFrom(dplyr,arrange)
|
||||
importFrom(dplyr,arrange_at)
|
||||
importFrom(dplyr,as_tibble)
|
||||
importFrom(dplyr,between)
|
||||
importFrom(dplyr,bind_cols)
|
||||
importFrom(dplyr,bind_rows)
|
||||
importFrom(dplyr,case_when)
|
||||
importFrom(dplyr,desc)
|
||||
importFrom(dplyr,distinct)
|
||||
importFrom(dplyr,everything)
|
||||
importFrom(dplyr,filter)
|
||||
importFrom(dplyr,filter_all)
|
||||
importFrom(dplyr,filter_at)
|
||||
importFrom(dplyr,full_join)
|
||||
importFrom(dplyr,funs)
|
||||
importFrom(dplyr,group_by)
|
||||
importFrom(dplyr,group_by_at)
|
||||
importFrom(dplyr,group_vars)
|
||||
importFrom(dplyr,if_else)
|
||||
importFrom(dplyr,lag)
|
||||
importFrom(dplyr,left_join)
|
||||
importFrom(dplyr,mutate)
|
||||
importFrom(dplyr,mutate_all)
|
||||
importFrom(dplyr,mutate_at)
|
||||
importFrom(dplyr,n)
|
||||
importFrom(dplyr,n_distinct)
|
||||
importFrom(dplyr,n_groups)
|
||||
importFrom(dplyr,progress_estimated)
|
||||
importFrom(dplyr,pull)
|
||||
importFrom(dplyr,row_number)
|
||||
importFrom(dplyr,select)
|
||||
importFrom(dplyr,select_if)
|
||||
importFrom(dplyr,slice)
|
||||
importFrom(dplyr,summarise)
|
||||
importFrom(dplyr,summarise_if)
|
||||
importFrom(dplyr,tibble)
|
||||
importFrom(dplyr,top_n)
|
||||
importFrom(dplyr,transmute)
|
||||
importFrom(dplyr,ungroup)
|
||||
importFrom(dplyr,vars)
|
||||
importFrom(grDevices,boxplot.stats)
|
||||
importFrom(graphics,arrows)
|
||||
importFrom(graphics,axis)
|
||||
importFrom(graphics,barplot)
|
||||
importFrom(graphics,hist)
|
||||
importFrom(graphics,plot)
|
||||
importFrom(graphics,points)
|
||||
importFrom(graphics,text)
|
||||
importFrom(hms,is.hms)
|
||||
importFrom(knitr,kable)
|
||||
importFrom(rlang,as_label)
|
||||
importFrom(rlang,enquos)
|
||||
importFrom(rlang,eval_tidy)
|
||||
importFrom(stats,complete.cases)
|
||||
importFrom(stats,fivenum)
|
||||
importFrom(stats,glm)
|
||||
importFrom(stats,lm)
|
||||
importFrom(stats,mad)
|
||||
importFrom(stats,pchisq)
|
||||
importFrom(stats,predict)
|
||||
importFrom(stats,sd)
|
||||
importFrom(utils,browseURL)
|
||||
importFrom(utils,browseVignettes)
|
||||
importFrom(utils,installed.packages)
|
||||
importFrom(utils,menu)
|
||||
@@ -1,503 +0,0 @@
|
||||
# AMR 0.6.0
|
||||
|
||||
**New website!**
|
||||
|
||||
We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.gitlab.io/AMR/) (built with the great [`pkgdown`](https://pkgdown.r-lib.org/))
|
||||
|
||||
* Contains the complete manual of this package and all of its functions with an explanation of their parameters
|
||||
* Contains a comprehensive tutorial about how to conduct antimicrobial resistance analysis, import data from WHONET or SPSS and many more.
|
||||
|
||||
#### New
|
||||
* **BREAKING**: removed deprecated functions, parameters and references to 'bactid'. Use `as.mo()` to identify an MO code.
|
||||
* Catalogue of Life as a new taxonomic source for data about microorganisms, which also contains all ITIS data we used previously. The `microorganisms` data set now contains:
|
||||
* All ~55,000 (sub)species from the kingdoms of Archaea, Bacteria and Protozoa
|
||||
* All ~3,000 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales and Schizosaccharomycetales (covering at least like all species of *Aspergillus*, *Candida*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*)
|
||||
* All ~2,000 (sub)species from ~100 other relevant genera, from the kingdoms of Animalia and Plantae (like *Strongyloides* and *Taenia*)
|
||||
* All ~15,000 previously accepted names of included (sub)species that have been taxonomically renamed
|
||||
* The responsible author(s) and year of scientific publication
|
||||
|
||||
This data is updated annually - check the included version with the new function `catalogue_of_life_version()`.
|
||||
* Due to this change, some `mo` codes changed (e.g. *Streptococcus* changed from `B_STRPTC` to `B_STRPT`). A translation table is used internally to support older microorganism IDs, so users will not notice this difference.
|
||||
* New function `mo_rank()` for the taxonomic rank (genus, species, infraspecies, etc.)
|
||||
* New function `mo_url()` to get the direct URL of a species from the Catalogue of Life
|
||||
* Support for data from [WHONET](https://whonet.org/) and [EARS-Net](https://ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/ears-net) (European Antimicrobial Resistance Surveillance Network):
|
||||
* Exported files from WHONET can be read and used in this package. For functions like `first_isolate()` and `eucast_rules()`, all parameters will be filled in automatically.
|
||||
* This package now knows all antibiotic abbrevations by EARS-Net (which are also being used by WHONET) - the `antibiotics` data set now contains a column `ears_net`.
|
||||
* The function `as.mo()` now knows all WHONET species abbreviations too, because almost 2,000 microbial abbreviations were added to the `microorganisms.codes` data set.
|
||||
* New filters for antimicrobial classes. Use these functions to filter isolates on results in one of more antibiotics from a specific class:
|
||||
```r
|
||||
filter_aminoglycosides()
|
||||
filter_carbapenems()
|
||||
filter_cephalosporins()
|
||||
filter_1st_cephalosporins()
|
||||
filter_2nd_cephalosporins()
|
||||
filter_3rd_cephalosporins()
|
||||
filter_4th_cephalosporins()
|
||||
filter_fluoroquinolones()
|
||||
filter_glycopeptides()
|
||||
filter_macrolides()
|
||||
filter_tetracyclines()
|
||||
```
|
||||
The `antibiotics` data set will be searched, after which the input data will be checked for column names with a value in any abbreviations, codes or official names found in the `antibiotics` data set.
|
||||
For example:
|
||||
```r
|
||||
septic_patients %>% filter_glycopeptides(result = "R")
|
||||
# Filtering on glycopeptide antibacterials: any of `vanc` or `teic` is R
|
||||
septic_patients %>% filter_glycopeptides(result = "R", scope = "all")
|
||||
# Filtering on glycopeptide antibacterials: all of `vanc` and `teic` is R
|
||||
```
|
||||
* All `ab_*` functions are deprecated and replaced by `atc_*` functions:
|
||||
```r
|
||||
ab_property -> atc_property()
|
||||
ab_name -> atc_name()
|
||||
ab_official -> atc_official()
|
||||
ab_trivial_nl -> atc_trivial_nl()
|
||||
ab_certe -> atc_certe()
|
||||
ab_umcg -> atc_umcg()
|
||||
ab_tradenames -> atc_tradenames()
|
||||
```
|
||||
These functions use `as.atc()` internally. The old `atc_property` has been renamed `atc_online_property()`. This is done for two reasons: firstly, not all ATC codes are of antibiotics (ab) but can also be of antivirals or antifungals. Secondly, the input must have class `atc` or must be coerable to this class. Properties of these classes should start with the same class name, analogous to `as.mo()` and e.g. `mo_genus`.
|
||||
* New functions `set_mo_source()` and `get_mo_source()` to use your own predefined MO codes as input for `as.mo()` and consequently all `mo_*` functions
|
||||
* Support for the upcoming [`dplyr`](https://dplyr.tidyverse.org) version 0.8.0
|
||||
* New function `guess_ab_col()` to find an antibiotic column in a table
|
||||
* New function `mo_failures()` to review values that could not be coerced to a valid MO code, using `as.mo()`. This latter function will now only show a maximum of 10 uncoerced values and will refer to `mo_failures()`.
|
||||
* New function `mo_uncertainties()` to review values that could be coerced to a valid MO code using `as.mo()`, but with uncertainty.
|
||||
* New function `mo_renamed()` to get a list of all returned values from `as.mo()` that have had taxonomic renaming
|
||||
* New function `age()` to calculate the (patients) age in years
|
||||
* New function `age_groups()` to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
|
||||
* New function `ggplot_rsi_predict()` as well as the base R `plot()` function can now be used for resistance prediction calculated with `resistance_predict()`:
|
||||
```r
|
||||
x <- resistance_predict(septic_patients, col_ab = "amox")
|
||||
plot(x)
|
||||
ggplot_rsi_predict(x)
|
||||
```
|
||||
* Functions `filter_first_isolate()` and `filter_first_weighted_isolate()` to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
|
||||
```r
|
||||
septic_patients %>% filter_first_isolate(...)
|
||||
# or
|
||||
filter_first_isolate(septic_patients, ...)
|
||||
```
|
||||
is equal to:
|
||||
```r
|
||||
septic_patients %>%
|
||||
mutate(only_firsts = first_isolate(septic_patients, ...)) %>%
|
||||
filter(only_firsts == TRUE) %>%
|
||||
select(-only_firsts)
|
||||
```
|
||||
* New function `availability()` to check the number of available (non-empty) results in a `data.frame`
|
||||
* New vignettes about how to conduct AMR analysis, predict antimicrobial resistance, use the *G*-test and more. These are also available (and even easier readable) on our website: https://msberends.gitlab.io/AMR.
|
||||
|
||||
#### Changed
|
||||
* Function `eucast_rules()`:
|
||||
* Updated EUCAST Clinical breakpoints to [version 9.0 of 1 January 2019](http://www.eucast.org/clinical_breakpoints/), the data set `septic_patients` now reflects these changes
|
||||
* Fixed a critical bug where some rules that depend on previous applied rules would not be applied adequately
|
||||
* Emphasised in manual that penicillin is meant as benzylpenicillin (ATC [J01CE01](https://www.whocc.no/atc_ddd_index/?code=J01CE01))
|
||||
* New info is returned when running this function, stating exactly what has been changed or added. Use `eucast_rules(..., verbose = TRUE)` to get a data set with all changed per bug and drug combination.
|
||||
* Removed data sets `microorganisms.oldDT`, `microorganisms.prevDT`, `microorganisms.unprevDT` and `microorganismsDT` since they were no longer needed and only contained info already available in the `microorganisms` data set
|
||||
* Added 65 antibiotics to the `antibiotics` data set, from the [Pharmaceuticals Community Register](http://ec.europa.eu/health/documents/community-register/html/atc.htm) of the European Commission
|
||||
* Removed columns `atc_group1_nl` and `atc_group2_nl` from the `antibiotics` data set
|
||||
* Functions `atc_ddd()` and `atc_groups()` have been renamed `atc_online_ddd()` and `atc_online_groups()`. The old functions are deprecated and will be removed in a future version.
|
||||
* Function `guess_mo()` is now deprecated in favour of `as.mo()` and will be removed in future versions
|
||||
* Function `guess_atc()` is now deprecated in favour of `as.atc()` and will be removed in future versions
|
||||
* Improvements for `as.mo()`:
|
||||
* Now handles incorrect spelling, like `i` instead of `y` and `f` instead of `ph`:
|
||||
```r
|
||||
# mo_fullname() uses as.mo() internally
|
||||
|
||||
mo_fullname("Sthafilokockus aaureuz")
|
||||
#> [1] "Staphylococcus aureus"
|
||||
|
||||
mo_fullname("S. klossi")
|
||||
#> [1] "Staphylococcus kloosii"
|
||||
```
|
||||
* Uncertainty of the algorithm is now divided into four levels, 0 to 3, where the default `allow_uncertain = TRUE` is equal to uncertainty level 2. Run `?as.mo` for more info about these levels.
|
||||
```r
|
||||
# equal:
|
||||
as.mo(..., allow_uncertain = TRUE)
|
||||
as.mo(..., allow_uncertain = 2)
|
||||
|
||||
# also equal:
|
||||
as.mo(..., allow_uncertain = FALSE)
|
||||
as.mo(..., allow_uncertain = 0)
|
||||
```
|
||||
Using `as.mo(..., allow_uncertain = 3)` could lead to very unreliable results.
|
||||
* Implemented the latest publication of Becker *et al.* (2019), for categorising coagulase-negative *Staphylococci*
|
||||
* All microbial IDs that found are now saved to a local file `~/.Rhistory_mo`. Use the new function `clean_mo_history()` to delete this file, which resets the algorithms.
|
||||
* Incoercible results will now be considered 'unknown', MO code `UNKNOWN`. On foreign systems, properties of these will be translated to all languages already previously supported: German, Dutch, French, Italian, Spanish and Portuguese:
|
||||
```r
|
||||
mo_genus("qwerty", language = "es")
|
||||
# Warning:
|
||||
# one unique value (^= 100.0%) could not be coerced and is considered 'unknown': "qwerty". Use mo_failures() to review it.
|
||||
#> [1] "(género desconocido)"
|
||||
```
|
||||
* Fix for vector containing only empty values
|
||||
* Finds better results when input is in other languages
|
||||
* Better handling for subspecies
|
||||
* Better handling for *Salmonellae*, especially the 'city like' serovars like *Salmonella London*
|
||||
* Understanding of highly virulent *E. coli* strains like EIEC, EPEC and STEC
|
||||
* There will be looked for uncertain results at default - these results will be returned with an informative warning
|
||||
* Manual (help page) now contains more info about the algorithms
|
||||
* Progress bar will be shown when it takes more than 3 seconds to get results
|
||||
* Support for formatted console text
|
||||
* Console will return the percentage of uncoercable input
|
||||
* Function `first_isolate()`:
|
||||
* Fixed a bug where distances between dates would not be calculated right - in the `septic_patients` data set this yielded a difference of 0.15% more isolates
|
||||
* Will now use a column named like "patid" for the patient ID (parameter `col_patientid`), when this parameter was left blank
|
||||
* Will now use a column named like "key(...)ab" or "key(...)antibiotics" for the key antibiotics (parameter `col_keyantibiotics()`), when this parameter was left blank
|
||||
* Removed parameter `output_logical`, the function will now always return a logical value
|
||||
* Renamed parameter `filter_specimen` to `specimen_group`, although using `filter_specimen` will still work
|
||||
* A note to the manual pages of the `portion` functions, that low counts can influence the outcome and that the `portion` functions may camouflage this, since they only return the portion (albeit being dependent on the `minimum` parameter)
|
||||
* Merged data sets `microorganisms.certe` and `microorganisms.umcg` into `microorganisms.codes`
|
||||
* Function `mo_taxonomy()` now contains the kingdom too
|
||||
* Reduce false positives for `is.rsi.eligible()` using the new `threshold` parameter
|
||||
* New colours for `scale_rsi_colours()`
|
||||
* Summaries of class `mo` will now return the top 3 and the unique count, e.g. using `summary(mo)`
|
||||
* Small text updates to summaries of class `rsi` and `mic`
|
||||
* Function `as.rsi()`:
|
||||
* Now gives a warning when inputting MIC values
|
||||
* Now accepts high and low resistance: `"HIGH S"` will return `S`
|
||||
* Frequency tables (`freq()` function):
|
||||
* Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:
|
||||
```r
|
||||
# Determine genus of microorganisms (mo) in `septic_patients` data set:
|
||||
# OLD WAY
|
||||
septic_patients %>%
|
||||
mutate(genus = mo_genus(mo)) %>%
|
||||
freq(genus)
|
||||
# NEW WAY
|
||||
septic_patients %>%
|
||||
freq(mo_genus(mo))
|
||||
|
||||
# Even supports grouping variables:
|
||||
septic_patients %>%
|
||||
group_by(gender) %>%
|
||||
freq(mo_genus(mo))
|
||||
```
|
||||
* Header info is now available as a list, with the `header` function
|
||||
* The parameter `header` is now set to `TRUE` at default, even for markdown
|
||||
* Added header info for class `mo` to show unique count of families, genera and species
|
||||
* Now honours the `decimal.mark` setting, which just like `format` defaults to `getOption("OutDec")`
|
||||
* The new `big.mark` parameter will at default be `","` when `decimal.mark = "."` and `"."` otherwise
|
||||
* Fix for header text where all observations are `NA`
|
||||
* New parameter `droplevels` to exclude empty factor levels when input is a factor
|
||||
* Factor levels will be in header when present in input data (maximum of 5)
|
||||
* Fix for using `select()` on frequency tables
|
||||
* Function `scale_y_percent()` now contains the `limits` parameter
|
||||
* Automatic parameter filling for `mdro()`, `key_antibiotics()` and `eucast_rules()`
|
||||
* Updated examples for resistance prediction (`resistance_predict()` function)
|
||||
* Fix for `as.mic()` to support more values ending in (several) zeroes
|
||||
* if using different lengths of pattern and x in `%like%`, it will now return the call
|
||||
|
||||
#### Other
|
||||
* Updated licence text to emphasise GPL 2.0 and that this is an R package.
|
||||
|
||||
# AMR 0.5.0
|
||||
|
||||
#### New
|
||||
* Repository moved to GitLab: https://gitlab.com/msberends/AMR
|
||||
* Function `count_all` to get all available isolates (that like all `portion_*` and `count_*` functions also supports `summarise` and `group_by`), the old `n_rsi` is now an alias of `count_all`
|
||||
* Function `get_locale` to determine language for language-dependent output for some `mo_*` functions. This is now the default value for their `language` parameter, by which the system language will be used at default.
|
||||
* Data sets `microorganismsDT`, `microorganisms.prevDT`, `microorganisms.unprevDT` and `microorganisms.oldDT` to improve the speed of `as.mo`. They are for reference only, since they are primarily for internal use of `as.mo`.
|
||||
* Function `read.4D` to read from the 4D database of the MMB department of the UMCG
|
||||
* Functions `mo_authors` and `mo_year` to get specific values about the scientific reference of a taxonomic entry
|
||||
|
||||
#### Changed
|
||||
* Functions `MDRO`, `BRMO`, `MRGN` and `EUCAST_exceptional_phenotypes` were renamed to `mdro`, `brmo`, `mrgn` and `eucast_exceptional_phenotypes`
|
||||
* `EUCAST_rules` was renamed to `eucast_rules`, the old function still exists as a deprecated function
|
||||
* Big changes to the `eucast_rules` function:
|
||||
* Now also applies rules from the EUCAST 'Breakpoint tables for bacteria', version 8.1, 2018, http://www.eucast.org/clinical_breakpoints/ (see Source of the function)
|
||||
* New parameter `rules` to specify which rules should be applied (expert rules, breakpoints, others or all)
|
||||
* New parameter `verbose` which can be set to `TRUE` to get very specific messages about which columns and rows were affected
|
||||
* Better error handling when rules cannot be applied (i.e. new values could not be inserted)
|
||||
* The number of affected values will now only be measured once per row/column combination
|
||||
* Data set `septic_patients` now reflects these changes
|
||||
* Added parameter `pipe` for piperacillin (J01CA12), also to the `mdro` function
|
||||
* Small fixes to EUCAST clinical breakpoint rules
|
||||
* Added column `kingdom` to the microorganisms data set, and function `mo_kingdom` to look up values
|
||||
* Tremendous speed improvement for `as.mo` (and subsequently all `mo_*` functions), as empty values wil be ignored *a priori*
|
||||
* Fewer than 3 characters as input for `as.mo` will return NA
|
||||
* Function `as.mo` (and all `mo_*` wrappers) now supports genus abbreviations with "species" attached
|
||||
```r
|
||||
as.mo("E. species") # B_ESCHR
|
||||
mo_fullname("E. spp.") # "Escherichia species"
|
||||
as.mo("S. spp") # B_STPHY
|
||||
mo_fullname("S. species") # "Staphylococcus species"
|
||||
```
|
||||
* Added parameter `combine_IR` (TRUE/FALSE) to functions `portion_df` and `count_df`, to indicate that all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
|
||||
* Fix for `portion_*(..., as_percent = TRUE)` when minimal number of isolates would not be met
|
||||
* Added parameter `also_single_tested` for `portion_*` and `count_*` functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see `?portion`
|
||||
* Using `portion_*` functions now throws a warning when total available isolate is below parameter `minimum`
|
||||
* Functions `as.mo`, `as.rsi`, `as.mic`, `as.atc` and `freq` will not set package name as attribute anymore
|
||||
* Frequency tables - `freq()`:
|
||||
* Support for grouping variables, test with:
|
||||
```r
|
||||
septic_patients %>%
|
||||
group_by(hospital_id) %>%
|
||||
freq(gender)
|
||||
```
|
||||
* Support for (un)selecting columns:
|
||||
```r
|
||||
septic_patients %>%
|
||||
freq(hospital_id) %>%
|
||||
select(-count, -cum_count) # only get item, percent, cum_percent
|
||||
```
|
||||
* Check for `hms::is.hms`
|
||||
* Now prints in markdown at default in non-interactive sessions
|
||||
* No longer adds the factor level column and sorts factors on count again
|
||||
* Support for class `difftime`
|
||||
* New parameter `na`, to choose which character to print for empty values
|
||||
* New parameter `header` to turn the header info off (default when `markdown = TRUE`)
|
||||
* New parameter `title` to manually setbthe title of the frequency table
|
||||
* `first_isolate` now tries to find columns to use as input when parameters are left blank
|
||||
* Improvements for MDRO algorithm (function `mdro`)
|
||||
* Data set `septic_patients` is now a `data.frame`, not a tibble anymore
|
||||
* Removed diacritics from all authors (columns `microorganisms$ref` and `microorganisms.old$ref`) to comply with CRAN policy to only allow ASCII characters
|
||||
* Fix for `mo_property` not working properly
|
||||
* Fix for `eucast_rules` where some Streptococci would become ceftazidime R in EUCAST rule 4.5
|
||||
* Support for named vectors of class `mo`, useful for `top_freq()`
|
||||
* `ggplot_rsi` and `scale_y_percent` have `breaks` parameter
|
||||
* AI improvements for `as.mo`:
|
||||
* `"CRS"` -> *Stenotrophomonas maltophilia*
|
||||
* `"CRSM"` -> *Stenotrophomonas maltophilia*
|
||||
* `"MSSA"` -> *Staphylococcus aureus*
|
||||
* `"MSSE"` -> *Staphylococcus epidermidis*
|
||||
* Fix for `join` functions
|
||||
* Speed improvement for `is.rsi.eligible`, now 15-20 times faster
|
||||
* In `g.test`, when `sum(x)` is below 1000 or any of the expected values is below 5, Fisher's Exact Test will be suggested
|
||||
* `ab_name` will try to fall back on `as.atc` when no results are found
|
||||
* Removed the addin to view data sets
|
||||
* Percentages will now will rounded more logically (e.g. in `freq` function)
|
||||
|
||||
#### Other
|
||||
* New dependency on package `crayon`, to support formatted text in the console
|
||||
* Dependency `tidyr` is now mandatory (went to `Import` field) since `portion_df` and `count_df` rely on it
|
||||
* Updated vignettes to comply with README
|
||||
|
||||
|
||||
# AMR 0.4.0
|
||||
|
||||
#### New
|
||||
* The data set `microorganisms` now contains **all microbial taxonomic data from ITIS** (kingdoms Bacteria, Fungi and Protozoa), the Integrated Taxonomy Information System, available via https://itis.gov. The data set now contains more than 18,000 microorganisms with all known bacteria, fungi and protozoa according ITIS with genus, species, subspecies, family, order, class, phylum and subkingdom. The new data set `microorganisms.old` contains all previously known taxonomic names from those kingdoms.
|
||||
* New functions based on the existing function `mo_property`:
|
||||
* Taxonomic names: `mo_phylum`, `mo_class`, `mo_order`, `mo_family`, `mo_genus`, `mo_species`, `mo_subspecies`
|
||||
* Semantic names: `mo_fullname`, `mo_shortname`
|
||||
* Microbial properties: `mo_type`, `mo_gramstain`
|
||||
* Author and year: `mo_ref`
|
||||
|
||||
They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:
|
||||
```r
|
||||
mo_gramstain("E. coli")
|
||||
# [1] "Gram negative"
|
||||
mo_gramstain("E. coli", language = "de") # German
|
||||
# [1] "Gramnegativ"
|
||||
mo_gramstain("E. coli", language = "es") # Spanish
|
||||
# [1] "Gram negativo"
|
||||
mo_fullname("S. group A", language = "pt") # Portuguese
|
||||
# [1] "Streptococcus grupo A"
|
||||
```
|
||||
|
||||
Furthermore, former taxonomic names will give a note about the current taxonomic name:
|
||||
```r
|
||||
mo_gramstain("Esc blattae")
|
||||
# Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)
|
||||
# [1] "Gram negative"
|
||||
```
|
||||
* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptible isolates
|
||||
* Extra function `count_df` (which works like `portion_df`) to get all counts of S, I and R of a data set with antibiotic columns, with support for grouped variables
|
||||
* Function `is.rsi.eligible` to check for columns that have valid antimicrobial results, but do not have the `rsi` class yet. Transform the columns of your raw data with: `data %>% mutate_if(is.rsi.eligible, as.rsi)`
|
||||
* Functions `as.mo` and `is.mo` as replacements for `as.bactid` and `is.bactid` (since the `microoganisms` data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The `as.mo` function determines microbial IDs using intelligent rules:
|
||||
```r
|
||||
as.mo("E. coli")
|
||||
# [1] B_ESCHR_COL
|
||||
as.mo("MRSA")
|
||||
# [1] B_STPHY_AUR
|
||||
as.mo("S group A")
|
||||
# [1] B_STRPTC_GRA
|
||||
```
|
||||
And with great speed too - on a quite regular Linux server from 2007 it takes us less than 0.02 seconds to transform 25,000 items:
|
||||
```r
|
||||
thousands_of_E_colis <- rep("E. coli", 25000)
|
||||
microbenchmark::microbenchmark(as.mo(thousands_of_E_colis), unit = "s")
|
||||
# Unit: seconds
|
||||
# min median max neval
|
||||
# 0.01817717 0.01843957 0.03878077 100
|
||||
```
|
||||
* Added parameter `reference_df` for `as.mo`, so users can supply their own microbial IDs, name or codes as a reference table
|
||||
* Renamed all previous references to `bactid` to `mo`, like:
|
||||
* Column names inputs of `EUCAST_rules`, `first_isolate` and `key_antibiotics`
|
||||
* Column names of datasets `microorganisms` and `septic_patients`
|
||||
* All old syntaxes will still work with this version, but will throw warnings
|
||||
* Function `labels_rsi_count` to print datalabels on a RSI `ggplot2` model
|
||||
* Functions `as.atc` and `is.atc` to transform/look up antibiotic ATC codes as defined by the WHO. The existing function `guess_atc` is now an alias of `as.atc`.
|
||||
|
||||
* Function `ab_property` and its aliases: `ab_name`, `ab_tradenames`, `ab_certe`, `ab_umcg` and `ab_trivial_nl`
|
||||
* Introduction to AMR as a vignette
|
||||
* Removed clipboard functions as it violated the CRAN policy
|
||||
* Renamed `septic_patients$sex` to `septic_patients$gender`
|
||||
|
||||
#### Changed
|
||||
* Added three antimicrobial agents to the `antibiotics` data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)
|
||||
* Added 163 trade names to the `antibiotics` data set, it now contains 298 different trade names in total, e.g.:
|
||||
```r
|
||||
ab_official("Bactroban")
|
||||
# [1] "Mupirocin"
|
||||
ab_name(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))
|
||||
# [1] "Mupirocin" "Amoxicillin" "Azithromycin" "Flucloxacillin"
|
||||
ab_atc(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))
|
||||
# [1] "R01AX06" "J01CA04" "J01FA10" "J01CF05"
|
||||
```
|
||||
* For `first_isolate`, rows will be ignored when there's no species available
|
||||
* Function `ratio` is now deprecated and will be removed in a future release, as it is not really the scope of this package
|
||||
* Fix for `as.mic` for values ending in zeroes after a real number
|
||||
* Small fix where *B. fragilis* would not be found in the `microorganisms.umcg` data set
|
||||
* Added `prevalence` column to the `microorganisms` data set
|
||||
* Added parameters `minimum` and `as_percent` to `portion_df`
|
||||
* Support for quasiquotation in the functions series `count_*` and `portions_*`, and `n_rsi`. This allows to check for more than 2 vectors or columns.
|
||||
```r
|
||||
septic_patients %>% select(amox, cipr) %>% count_IR()
|
||||
# which is the same as:
|
||||
septic_patients %>% count_IR(amox, cipr)
|
||||
|
||||
septic_patients %>% portion_S(amcl)
|
||||
septic_patients %>% portion_S(amcl, gent)
|
||||
septic_patients %>% portion_S(amcl, gent, pita)
|
||||
```
|
||||
* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` parameter has value `portion_df` at default, but can be set to `count_df`.
|
||||
* Fix for `ggplot_rsi` when the `ggplot2` package was not loaded
|
||||
* Added datalabels function `labels_rsi_count` to `ggplot_rsi`
|
||||
* Added possibility to set any parameter to `geom_rsi` (and `ggplot_rsi`) so you can set your own preferences
|
||||
* Fix for joins, where predefined suffices would not be honoured
|
||||
* Added parameter `quote` to the `freq` function
|
||||
* Added generic function `diff` for frequency tables
|
||||
* Added longest en shortest character length in the frequency table (`freq`) header of class `character`
|
||||
* Support for types (classes) list and matrix for `freq`
|
||||
```r
|
||||
my_matrix = with(septic_patients, matrix(c(age, gender), ncol = 2))
|
||||
freq(my_matrix)
|
||||
```
|
||||
For lists, subsetting is possible:
|
||||
```r
|
||||
my_list = list(age = septic_patients$age, gender = septic_patients$gender)
|
||||
my_list %>% freq(age)
|
||||
my_list %>% freq(gender)
|
||||
```
|
||||
|
||||
#### Other
|
||||
* More unit tests to ensure better integrity of functions
|
||||
|
||||
# AMR 0.3.0
|
||||
|
||||
#### New
|
||||
* **BREAKING**: `rsi_df` was removed in favour of new functions `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` to selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old `rsi` function. The old function still works, but is deprecated.
|
||||
* New function `portion_df` to get all portions of S, I and R of a data set with antibiotic columns, with support for grouped variables
|
||||
* **BREAKING**: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call *key antibiotics*) to include more first isolates (afterwards called first *weighted* isolates) are now as follows:
|
||||
* Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole
|
||||
* Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
|
||||
* Gram-negative: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
|
||||
* Support for `ggplot2`
|
||||
* New functions `geom_rsi`, `facet_rsi`, `scale_y_percent`, `scale_rsi_colours` and `theme_rsi`
|
||||
* New wrapper function `ggplot_rsi` to apply all above functions on a data set:
|
||||
* `septic_patients %>% select(tobr, gent) %>% ggplot_rsi` will show portions of S, I and R immediately in a pretty plot
|
||||
* Support for grouped variables, see `?ggplot_rsi`
|
||||
* Determining bacterial ID:
|
||||
* New functions `as.bactid` and `is.bactid` to transform/ look up microbial ID's.
|
||||
* The existing function `guess_bactid` is now an alias of `as.bactid`
|
||||
* New Becker classification for *Staphylococcus* to categorise them into Coagulase Negative *Staphylococci* (CoNS) and Coagulase Positve *Staphylococci* (CoPS)
|
||||
* New Lancefield classification for *Streptococcus* to categorise them into Lancefield groups
|
||||
* For convience, new descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
|
||||
* Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test), which use is the same as `chisq.test`
|
||||
* ~~Function `ratio` to transform a vector of values to a preset ratio~~
|
||||
* ~~For example: `ratio(c(10, 500, 10), ratio = "1:2:1")` would return `130, 260, 130`~~
|
||||
* Support for Addins menu in RStudio to quickly insert `%in%` or `%like%` (and give them keyboard shortcuts), or to view the datasets that come with this package
|
||||
* Function `p.symbol` to transform p values to their related symbols: `0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1`
|
||||
* Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS. These functions use the `clipr` package, but are a little altered to also support headless Linux servers (so you can use it in RStudio Server)
|
||||
* New for frequency tables (function `freq`):
|
||||
* A vignette to explain its usage
|
||||
* Support for `rsi` (antimicrobial resistance) to use as input
|
||||
* Support for `table` to use as input: `freq(table(x, y))`
|
||||
* Support for existing functions `hist` and `plot` to use a frequency table as input: `hist(freq(df$age))`
|
||||
* Support for `as.vector`, `as.data.frame`, `as_tibble` and `format`
|
||||
* Support for quasiquotation: `freq(mydata, mycolumn)` is the same as `mydata %>% freq(mycolumn)`
|
||||
* Function `top_freq` function to return the top/below *n* items as vector
|
||||
* Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR)
|
||||
* Possibility to globally set the default for the amount of items to print, with `options(max.print.freq = n)` where *n* is your preset value
|
||||
|
||||
#### Changed
|
||||
* Improvements for forecasting with `resistance_predict` and added more examples
|
||||
* More antibiotics added as parameters for EUCAST rules
|
||||
* Updated version of the `septic_patients` data set to better reflect the reality
|
||||
* Pretty printing for tibbles removed as it is not really the scope of this package
|
||||
* Printing of `mic` and `rsi` classes now returns all values - use `freq` to check distributions
|
||||
* Improved speed of key antibiotics comparison for determining first isolates
|
||||
* Column names for the `key_antibiotics` function are now generic: 6 for broadspectrum ABs, 6 for Gram-positive specific and 6 for Gram-negative specific ABs
|
||||
* Speed improvement for the `abname` function
|
||||
* `%like%` now supports multiple patterns
|
||||
* Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the parameter `toConsole` is not longer needed.
|
||||
* Fix for `freq` where the class of an item would be lost
|
||||
* Small translational improvements to the `septic_patients` dataset and the column `bactid` now has the new class `"bactid"`
|
||||
* Small improvements to the `microorganisms` dataset (especially for *Salmonella*) and the column `bactid` now has the new class `"bactid"`
|
||||
* Combined MIC/RSI values will now be coerced by the `rsi` and `mic` functions:
|
||||
* `as.rsi("<=0.002; S")` will return `S`
|
||||
* `as.mic("<=0.002; S")` will return `<=0.002`
|
||||
* Now possible to coerce MIC values with a space between operator and value, i.e. `as.mic("<= 0.002")` now works
|
||||
* Classes `rsi` and `mic` do not add the attribute `package.version` anymore
|
||||
* Added `"groups"` option for `atc_property(..., property)`. It will return a vector of the ATC hierarchy as defined by the [WHO](https://www.whocc.no/atc/structure_and_principles/). The new function `atc_groups` is a convenient wrapper around this.
|
||||
* Build-in host check for `atc_property` as it requires the host set by `url` to be responsive
|
||||
* Improved `first_isolate` algorithm to exclude isolates where bacteria ID or genus is unavailable
|
||||
* Fix for warning *hybrid evaluation forced for row_number* ([`924b62`](https://github.com/tidyverse/dplyr/commit/924b62)) from the `dplyr` package v0.7.5 and above
|
||||
* Support for empty values and for 1 or 2 columns as input for `guess_bactid` (now called `as.bactid`)
|
||||
* So `yourdata %>% select(genus, species) %>% as.bactid()` now also works
|
||||
* Other small fixes
|
||||
|
||||
#### Other
|
||||
* Added integration tests (check if everything works as expected) for all releases of R 3.1 and higher
|
||||
* Linux and macOS: https://travis-ci.org/msberends/AMR
|
||||
* Windows: https://ci.appveyor.com/project/msberends/amr
|
||||
* Added thesis advisors to DESCRIPTION file
|
||||
|
||||
# AMR 0.2.0
|
||||
|
||||
#### New
|
||||
* Full support for Windows, Linux and macOS
|
||||
* Full support for old R versions, only R-3.0.0 (April 2013) or later is needed (needed packages may have other dependencies)
|
||||
* Function `n_rsi` to count cases where antibiotic test results were available, to be used in conjunction with `dplyr::summarise`, see ?rsi
|
||||
* Function `guess_bactid` to **determine the ID** of a microorganism based on genus/species or known abbreviations like MRSA
|
||||
* Function `guess_atc` to **determine the ATC** of an antibiotic based on name, trade name, or known abbreviations
|
||||
* Function `freq` to create **frequency tables**, with additional info in a header
|
||||
* Function `MDRO` to **determine Multi Drug Resistant Organisms (MDRO)** with support for country-specific guidelines.
|
||||
* [Exceptional resistances defined by EUCAST](http://www.eucast.org/expert_rules_and_intrinsic_resistance) are also supported instead of countries alone
|
||||
* Functions `BRMO` and `MRGN` are wrappers for Dutch and German guidelines, respectively
|
||||
* New algorithm to determine weighted isolates, can now be `"points"` or `"keyantibiotics"`, see `?first_isolate`
|
||||
* New print format for `tibble`s and `data.table`s
|
||||
|
||||
#### Changed
|
||||
* Fixed `rsi` class for vectors that contain only invalid antimicrobial interpretations
|
||||
* Renamed dataset `ablist` to `antibiotics`
|
||||
* Renamed dataset `bactlist` to `microorganisms`
|
||||
* Added common abbreviations and trade names to the `antibiotics` dataset
|
||||
* Added more microorganisms to the `microorganisms` dataset
|
||||
* Added analysis examples on help page of dataset `septic_patients`
|
||||
* Added support for character vector in `join` functions
|
||||
* Added warnings when a join results in more rows after than before the join
|
||||
* Altered `%like%` to make it case insensitive
|
||||
* For parameters of functions `first_isolate` and `EUCAST_rules` column names are now case-insensitive
|
||||
* Functions `as.rsi` and `as.mic` now add the package name and version as attributes
|
||||
|
||||
#### Other
|
||||
* Expanded `README.md` with more examples
|
||||
* Added [ORCID](https://orcid.org) of authors to DESCRIPTION file
|
||||
* Added unit testing with the `testthat` package
|
||||
* Added build tests for Linux and macOS using Travis CI (https://travis-ci.org/msberends/AMR)
|
||||
* Added line coverage checking using CodeCov (https://codecov.io/gh/msberends/AMR/tree/master/R)
|
||||
|
||||
# AMR 0.1.1
|
||||
|
||||
* `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
|
||||
* Edited column names to comply with GLIMS, the laboratory information system
|
||||
* Added more valid MIC values
|
||||
* Renamed 'Daily Defined Dose' to 'Defined Daily Dose'
|
||||
* Added barplots for `rsi` and `mic` classes
|
||||
|
||||
# AMR 0.1.0
|
||||
|
||||
* First submission to CRAN.
|
||||
-162
@@ -1,162 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Name of an antibiotic
|
||||
#'
|
||||
#' Convert antibiotic codes to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{antibiotics}}.
|
||||
#' @param abcode a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}
|
||||
#' @param from,to type to transform from and to. See \code{\link{antibiotics}} for its column names. WIth \code{from = "guess"} the from will be guessed from \code{"atc"}, \code{"certe"} and \code{"umcg"}. When using \code{to = "atc"}, the ATC code will be searched using \code{\link{as.atc}}.
|
||||
#' @param textbetween text to put between multiple returned texts
|
||||
#' @param tolower return output as lower case with function \code{\link{tolower}}.
|
||||
#' @details \strong{The \code{\link{ab_property}} functions are faster and more concise}, but do not support concatenated strings, like \code{abname("AMCL+GENT"}.
|
||||
#' @keywords ab antibiotics
|
||||
#' @source \code{\link{antibiotics}}
|
||||
#' @inheritSection WHOCC WHOCC
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% pull
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' abname("AMCL")
|
||||
#' # "Amoxicillin and beta-lactamase inhibitor"
|
||||
#'
|
||||
#' # It is quite flexible at default (having `from = "guess"`)
|
||||
#' abname(c("amox", "J01CA04", "Trimox", "dispermox", "Amoxil"))
|
||||
#' # "Amoxicillin" "Amoxicillin" "Amoxicillin" "Amoxicillin" "Amoxicillin"
|
||||
#'
|
||||
#' # Multiple antibiotics can be combined with "+".
|
||||
#' # The second antibiotic will be set to lower case when `tolower` was not set:
|
||||
#' abname("AMCL+GENT", textbetween = "/")
|
||||
#' # "amoxicillin and enzyme inhibitor/gentamicin"
|
||||
#'
|
||||
#' abname(c("AMCL", "GENT"))
|
||||
#' # "Amoxicillin and beta-lactamase inhibitor" "Gentamicin"
|
||||
#'
|
||||
#' abname("AMCL", to = "trivial_nl")
|
||||
#' # "Amoxicilline/clavulaanzuur"
|
||||
#'
|
||||
#' abname("AMCL", to = "atc")
|
||||
#' # "J01CR02"
|
||||
#'
|
||||
#' # specific codes for University Medical Center Groningen (UMCG):
|
||||
#' abname("J01CR02", from = "atc", to = "umcg")
|
||||
#' # "AMCL"
|
||||
#'
|
||||
#' # specific codes for Certe:
|
||||
#' abname("J01CR02", from = "atc", to = "certe")
|
||||
#' # "amcl"
|
||||
abname <- function(abcode,
|
||||
from = c("guess", "atc", "certe", "umcg"),
|
||||
to = 'official',
|
||||
textbetween = ' + ',
|
||||
tolower = FALSE) {
|
||||
|
||||
if (length(to) != 1L) {
|
||||
stop('`to` must be of length 1', call. = FALSE)
|
||||
}
|
||||
|
||||
if (to == "atc") {
|
||||
return(as.character(as.atc(abcode)))
|
||||
}
|
||||
|
||||
abx <- AMR::antibiotics
|
||||
|
||||
from <- from[1]
|
||||
colnames(abx) <- colnames(abx) %>% tolower()
|
||||
from <- from %>% tolower()
|
||||
to <- to %>% tolower()
|
||||
|
||||
if (!(from %in% colnames(abx) | from == "guess") |
|
||||
!to %in% colnames(abx)) {
|
||||
stop(paste0('Invalid `from` or `to`. Choose one of ',
|
||||
colnames(abx) %>% paste(collapse = ", "), '.'), call. = FALSE)
|
||||
}
|
||||
|
||||
abcode <- as.character(abcode)
|
||||
abcode.bak <- abcode
|
||||
|
||||
for (i in 1:length(abcode)) {
|
||||
if (abcode[i] %like% "[+]") {
|
||||
# support for multiple ab's with +
|
||||
parts <- trimws(strsplit(abcode[i], split = "+", fixed = TRUE)[[1]])
|
||||
ab1 <- abname(parts[1], from = from, to = to)
|
||||
ab2 <- abname(parts[2], from = from, to = to)
|
||||
if (missing(tolower)) {
|
||||
ab2 <- tolower(ab2)
|
||||
}
|
||||
abcode[i] <- paste0(ab1, textbetween, ab2)
|
||||
next
|
||||
}
|
||||
if (from %in% c("atc", "guess")) {
|
||||
if (abcode[i] %in% abx$atc) {
|
||||
abcode[i] <- abx[which(abx$atc == abcode[i]),] %>% pull(to) %>% .[1]
|
||||
next
|
||||
}
|
||||
}
|
||||
if (from %in% c("certe", "guess")) {
|
||||
if (abcode[i] %in% abx$certe) {
|
||||
abcode[i] <- abx[which(abx$certe == abcode[i]),] %>% pull(to) %>% .[1]
|
||||
next
|
||||
}
|
||||
}
|
||||
if (from %in% c("umcg", "guess")) {
|
||||
if (abcode[i] %in% abx$umcg) {
|
||||
abcode[i] <- abx[which(abx$umcg == abcode[i]),] %>% pull(to) %>% .[1]
|
||||
next
|
||||
}
|
||||
}
|
||||
if (from %in% c("trade_name", "guess")) {
|
||||
if (abcode[i] %in% abx$trade_name) {
|
||||
abcode[i] <- abx[which(abx$trade_name == abcode[i]),] %>% pull(to) %>% .[1]
|
||||
next
|
||||
}
|
||||
if (sum(abx$trade_name %like% abcode[i]) > 0) {
|
||||
abcode[i] <- abx[which(abx$trade_name %like% abcode[i]),] %>% pull(to) %>% .[1]
|
||||
next
|
||||
}
|
||||
}
|
||||
|
||||
if (from != "guess") {
|
||||
# when not found, try any `from`
|
||||
abcode[i] <- abx[which(abx[,from] == abcode[i]),] %>% pull(to) %>% .[1]
|
||||
}
|
||||
|
||||
if (is.na(abcode[i]) | length(abcode[i] == 0)) {
|
||||
# try as.atc
|
||||
try(suppressWarnings(
|
||||
abcode[i] <- as.atc(abcode[i])
|
||||
), silent = TRUE)
|
||||
if (is.na(abcode[i])) {
|
||||
# still not found
|
||||
abcode[i] <- abcode.bak[i]
|
||||
warning('Code "', abcode.bak[i], '" not found in antibiotics list.', call. = FALSE)
|
||||
} else {
|
||||
# fill in the found ATC code
|
||||
abcode[i] <- abname(abcode[i], from = "atc", to = to)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (tolower == TRUE) {
|
||||
abcode <- abcode %>% tolower()
|
||||
}
|
||||
|
||||
abcode
|
||||
}
|
||||
@@ -1,156 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Age in years of individuals
|
||||
#'
|
||||
#' Calculates age in years based on a reference date, which is the sytem date at default.
|
||||
#' @param x date(s), will be coerced with \code{\link{as.POSIXlt}}
|
||||
#' @param reference reference date(s) (defaults to today), will be coerced with \code{\link{as.POSIXlt}} and cannot be lower than \code{x}
|
||||
#' @return Integer (no decimals)
|
||||
#' @seealso \code{\link{age_groups}} to split age into age groups
|
||||
#' @importFrom dplyr if_else
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
#' @examples
|
||||
#' df <- data.frame(birth_date = Sys.Date() - runif(100) * 25000)
|
||||
#' df$age <- age(df$birth_date)
|
||||
age <- function(x, reference = Sys.Date()) {
|
||||
if (length(x) != length(reference)) {
|
||||
if (length(reference) == 1) {
|
||||
reference <- rep(reference, length(x))
|
||||
} else {
|
||||
stop("`x` and `reference` must be of same length, or `reference` must be of length 1.")
|
||||
}
|
||||
}
|
||||
x <- base::as.POSIXlt(x)
|
||||
reference <- base::as.POSIXlt(reference)
|
||||
|
||||
# from https://stackoverflow.com/a/25450756/4575331
|
||||
years_gap <- reference$year - x$year
|
||||
ages <- if_else(reference$mon < x$mon | (reference$mon == x$mon & reference$mday < x$mday),
|
||||
as.integer(years_gap - 1),
|
||||
as.integer(years_gap))
|
||||
|
||||
if (any(ages < 0, na.rm = TRUE)) {
|
||||
warning("NAs introduced for ages below 0.")
|
||||
ages[ages < 0] <- NA_integer_
|
||||
}
|
||||
if (any(ages > 120, na.rm = TRUE)) {
|
||||
warning("Some ages are > 120.")
|
||||
}
|
||||
|
||||
ages
|
||||
}
|
||||
|
||||
#' Split ages into age groups
|
||||
#'
|
||||
#' Split ages into age groups defined by the \code{split} parameter. This allows for easier demographic (antimicrobial resistance) analysis.
|
||||
#' @param x age, e.g. calculated with \code{\link{age}}
|
||||
#' @param split_at values to split \code{x} at, defaults to age groups 0-11, 12-24, 26-54, 55-74 and 75+. See Details.
|
||||
#' @details To split ages, the input can be:
|
||||
#' \itemize{
|
||||
#' \item{A numeric vector. A vector of e.g. \code{c(10, 20)} will split on 0-9, 10-19 and 20+. A value of only \code{50} will split on 0-49 and 50+.
|
||||
#' The default is to split on young children (0-11), youth (12-24), young adults (26-54), middle-aged adults (55-74) and elderly (75+).}
|
||||
#' \item{A character:}
|
||||
#' \itemize{
|
||||
#' \item{\code{"children"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.}
|
||||
#' \item{\code{"elderly"} or \code{"seniors"}, equivalent of: \code{c(65, 75, 85, 95)}. This will split on 0-64, 65-74, 75-84, 85-94 and 95+.}
|
||||
#' \item{\code{"fives"}, equivalent of: \code{1:24 * 5}. This will split on 0-4, 5-9, 10-14, 15-19 and so forth, until 120.}
|
||||
#' \item{\code{"tens"}, equivalent of: \code{1:12 * 10}. This will split on 0-9, 10-19, 20-29 and so forth, until 120.}
|
||||
#' }
|
||||
#' }
|
||||
#' @keywords age_group age
|
||||
#' @return Ordered \code{\link{factor}}
|
||||
#' @seealso \code{\link{age}} to determine ages based on one or more reference dates
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)
|
||||
#'
|
||||
#' # split into 0-49 and 50+
|
||||
#' age_groups(ages, 50)
|
||||
#'
|
||||
#' # split into 0-19, 20-49 and 50+
|
||||
#' age_groups(ages, c(20, 50))
|
||||
#'
|
||||
#' # split into groups of ten years
|
||||
#' age_groups(ages, 1:12 * 10)
|
||||
#' age_groups(ages, split_at = "tens")
|
||||
#'
|
||||
#' # split into groups of five years
|
||||
#' age_groups(ages, 1:24 * 5)
|
||||
#' age_groups(ages, split_at = "fives")
|
||||
#'
|
||||
#' # split specifically for children
|
||||
#' age_groups(ages, "children")
|
||||
#' # same:
|
||||
#' age_groups(ages, c(1, 2, 4, 6, 13, 17))
|
||||
#'
|
||||
#' # resistance of ciprofloxacine per age group
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' mutate(first_isolate = first_isolate(.)) %>%
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' mo == as.mo("E. coli")) %>%
|
||||
#' group_by(age_group = age_groups(age)) %>%
|
||||
#' select(age_group,
|
||||
#' cipr) %>%
|
||||
#' ggplot_rsi(x = "age_group")
|
||||
age_groups <- function(x, split_at = c(12, 25, 55, 75)) {
|
||||
if (is.character(split_at)) {
|
||||
split_at <- split_at[1L]
|
||||
if (split_at %like% "^child") {
|
||||
split_at <- c(0, 1, 2, 4, 6, 13, 18)
|
||||
} else if (split_at %like% "^(elder|senior)") {
|
||||
split_at <- c(65, 75, 85, 95)
|
||||
} else if (split_at %like% "^five") {
|
||||
split_at <- 1:24 * 5
|
||||
} else if (split_at %like% "^ten") {
|
||||
split_at <- 1:12 * 10
|
||||
}
|
||||
}
|
||||
split_at <- as.integer(split_at)
|
||||
if (!is.numeric(x) | !is.numeric(split_at)) {
|
||||
stop("`x` and `split_at` must both be numeric.")
|
||||
}
|
||||
split_at <- sort(unique(split_at))
|
||||
if (!split_at[1] == 0) {
|
||||
split_at <- c(0, split_at)
|
||||
}
|
||||
if (length(split_at) == 1) {
|
||||
# only 0 available
|
||||
stop("invalid value for `split_at`.")
|
||||
}
|
||||
|
||||
# turn input values to 'split_at' indices
|
||||
y <- x
|
||||
labs <- split_at
|
||||
for (i in 1:length(split_at)) {
|
||||
y[x >= split_at[i]] <- i
|
||||
# create labels
|
||||
labs[i - 1] <- paste0(unique(c(split_at[i - 1], split_at[i] - 1)), collapse = "-")
|
||||
}
|
||||
|
||||
# last category
|
||||
labs[length(labs)] <- paste0(split_at[length(split_at)], "+")
|
||||
|
||||
factor(labs[y], levels = labs, ordered = TRUE)
|
||||
}
|
||||
@@ -1,67 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' The \code{AMR} Package
|
||||
#'
|
||||
#' Welcome to the \code{AMR} package.
|
||||
#' @details
|
||||
#' \code{AMR} is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any table format, including WHONET/EARS-Net data.
|
||||
#'
|
||||
#' We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
|
||||
#'
|
||||
#' This package can be used for:
|
||||
#' \itemize{
|
||||
#' \item{Reference for microorganisms, since it contains almost all 60,000 microbial (sub)species from the Catalogue of Life}
|
||||
#' \item{Calculating antimicrobial resistance}
|
||||
#' \item{Calculating empirical susceptibility of both mono therapy and combination therapy}
|
||||
#' \item{Predicting future antimicrobial resistance using regression models}
|
||||
#' \item{Getting properties for any microorganism (like Gram stain, species, genus or family)}
|
||||
#' \item{Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name)}
|
||||
#' \item{Plotting antimicrobial resistance}
|
||||
#' \item{Determining first isolates to be used for AMR analysis}
|
||||
#' \item{Applying EUCAST expert rules (not the translation from MIC to RSI values)}
|
||||
#' \item{Determining multi-drug resistant organisms (MDRO)}
|
||||
#' \item{Descriptive statistics: frequency tables, kurtosis and skewness}
|
||||
#' }
|
||||
#' @section Authors:
|
||||
#' Matthijs S. Berends[1,2] Christian F. Luz[1], Erwin E.A. Hassing[2], Corinna Glasner[1], Alex W. Friedrich[1], Bhanu N.M. Sinha[1] \cr
|
||||
#'
|
||||
#' [1] Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands - \url{rug.nl} \url{umcg.nl} \cr
|
||||
#' [2] Certe Medical Diagnostics & Advice, Groningen, the Netherlands - \url{certe.nl}
|
||||
|
||||
#' @section Read more on our website!:
|
||||
#' On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
|
||||
|
||||
#' @section Contact us:
|
||||
#' For suggestions, comments or questions, please contact us at:
|
||||
#'
|
||||
#' Matthijs S. Berends \cr
|
||||
#' m.s.berends [at] umcg [dot] nl \cr
|
||||
#' Department of Medical Microbiology, University of Groningen \cr
|
||||
#' University Medical Center Groningen \cr
|
||||
#' Post Office Box 30001 \cr
|
||||
#' 9700 RB Groningen
|
||||
#'
|
||||
#' If you have found a bug, please file a new issue at: \cr
|
||||
#' \url{https://gitlab.com/msberends/AMR/issues}
|
||||
#' @name AMR
|
||||
#' @rdname AMR
|
||||
NULL
|
||||
@@ -1,195 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Transform to ATC code
|
||||
#'
|
||||
#' Use this function to determine the ATC code of one or more antibiotics. The data set \code{\link{antibiotics}} will be searched for abbreviations, official names and trade names.
|
||||
#' @param x character vector to determine \code{ATC} code
|
||||
#' @rdname as.atc
|
||||
#' @aliases atc
|
||||
#' @keywords atc
|
||||
#' @inheritSection WHOCC WHOCC
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% filter slice pull
|
||||
#' @details Use the \code{\link{ab_property}} functions to get properties based on the returned ATC code, see Examples.
|
||||
#'
|
||||
#' In the ATC classification system, the active substances are classified in a hierarchy with five different levels. The system has fourteen main anatomical/pharmacological groups or 1st levels. Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups. The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance. The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
|
||||
#' Source: \url{https://www.whocc.no/atc/structure_and_principles/}
|
||||
#' @return Character (vector) with class \code{"act"}. Unknown values will return \code{NA}.
|
||||
#' @seealso \code{\link{antibiotics}} for the dataframe that is being used to determine ATCs.
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # These examples all return "J01FA01", the ATC code of Erythromycin:
|
||||
#' as.atc("J01FA01")
|
||||
#' as.atc("Erythromycin")
|
||||
#' as.atc("eryt")
|
||||
#' as.atc(" eryt 123")
|
||||
#' as.atc("ERYT")
|
||||
#' as.atc("ERY")
|
||||
#' as.atc("Erythrocin") # Trade name
|
||||
#' as.atc("Eryzole") # Trade name
|
||||
#' as.atc("Pediamycin") # Trade name
|
||||
#'
|
||||
#' # Use ab_* functions to get a specific property based on an ATC code
|
||||
#' Cipro <- as.atc("cipro") # returns `J01MA02`
|
||||
#' atc_official(Cipro) # returns "Ciprofloxacin"
|
||||
#' atc_umcg(Cipro) # returns "CIPR", the code used in the UMCG
|
||||
as.atc <- function(x) {
|
||||
|
||||
x.new <- rep(NA_character_, length(x))
|
||||
x <- trimws(x, which = "both")
|
||||
# keep only a-z when it's not an ATC code
|
||||
x[!x %like% "[A-Z][0-9]{2}[A-Z]{2}[0-9]{2}"] <- gsub("[^a-zA-Z]+", "", x[!x %like% "[A-Z][0-9]{2}[A-Z]{2}[0-9]{2}"])
|
||||
|
||||
x.bak <- x
|
||||
x <- unique(x)
|
||||
failures <- character(0)
|
||||
|
||||
for (i in 1:length(x)) {
|
||||
if (is.na(x[i]) | is.null(x[i]) | identical(x[i], "")) {
|
||||
x.new[i] <- x[i]
|
||||
next
|
||||
}
|
||||
|
||||
fail <- TRUE
|
||||
|
||||
# first try atc
|
||||
found <- AMR::antibiotics[which(AMR::antibiotics$atc == x[i]),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
|
||||
# try ATC in ATC code form, even if it does not exist in the antibiotics data set YET
|
||||
if (length(found) == 0 & x[i] %like% '[A-Z][0-9][0-9][A-Z][A-Z][0-9][0-9]') {
|
||||
warning("ATC code ", x[i], " is not yet in the `antibiotics` data set.")
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- x[i]
|
||||
}
|
||||
|
||||
# try abbreviation of EARS-Net/WHONET
|
||||
found <- AMR::antibiotics[which(tolower(AMR::antibiotics$ears_net) == tolower(x[i])),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
|
||||
# try abbreviation of certe and glims
|
||||
found <- AMR::antibiotics[which(tolower(AMR::antibiotics$certe) == tolower(x[i])),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
found <- AMR::antibiotics[which(tolower(AMR::antibiotics$umcg) == tolower(x[i])),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
|
||||
# try exact official name
|
||||
found <- AMR::antibiotics[which(tolower(AMR::antibiotics$official) == tolower(x[i])),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
|
||||
# try exact official Dutch
|
||||
found <- AMR::antibiotics[which(tolower(AMR::antibiotics$official_nl) == tolower(x[i])),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
|
||||
# try trade name
|
||||
found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$trade_name, ")") %like% x[i]),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
|
||||
# try abbreviation
|
||||
found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$abbr, ")") %like% x[i]),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
|
||||
# nothing helped, try first chars of official name, but only if nchar > 4 (cipro, nitro, fosfo)
|
||||
if (nchar(x[i]) > 4) {
|
||||
found <- AMR::antibiotics[which(AMR::antibiotics$official %like% paste0("^", substr(x[i], 1, 5))),]$atc
|
||||
if (length(found) > 0) {
|
||||
fail <- FALSE
|
||||
x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
|
||||
}
|
||||
}
|
||||
|
||||
# not found
|
||||
if (fail == TRUE) {
|
||||
failures <- c(failures, x[i])
|
||||
}
|
||||
}
|
||||
|
||||
failures <- failures[!failures %in% c(NA, NULL, NaN)]
|
||||
if (length(failures) > 0) {
|
||||
warning("These values could not be coerced to a valid atc: ",
|
||||
paste('"', unique(failures), '"', sep = "", collapse = ', '),
|
||||
".",
|
||||
call. = FALSE)
|
||||
}
|
||||
class(x.new) <- "atc"
|
||||
x.new
|
||||
}
|
||||
|
||||
#' @rdname as.atc
|
||||
#' @export
|
||||
is.atc <- function(x) {
|
||||
identical(class(x), "atc")
|
||||
}
|
||||
|
||||
#' @exportMethod print.atc
|
||||
#' @export
|
||||
#' @noRd
|
||||
print.atc <- function(x, ...) {
|
||||
cat("Class 'atc'\n")
|
||||
print.default(as.character(x), quote = FALSE)
|
||||
}
|
||||
|
||||
#' @exportMethod as.data.frame.atc
|
||||
#' @export
|
||||
#' @noRd
|
||||
as.data.frame.atc <- function (x, ...) {
|
||||
# same as as.data.frame.character but with removed stringsAsFactors
|
||||
nm <- paste(deparse(substitute(x), width.cutoff = 500L),
|
||||
collapse = " ")
|
||||
if (!"nm" %in% names(list(...))) {
|
||||
as.data.frame.vector(x, ..., nm = nm)
|
||||
} else {
|
||||
as.data.frame.vector(x, ...)
|
||||
}
|
||||
}
|
||||
|
||||
#' @exportMethod pull.atc
|
||||
#' @export
|
||||
#' @importFrom dplyr pull
|
||||
#' @noRd
|
||||
pull.atc <- function(.data, ...) {
|
||||
pull(as.data.frame(.data), ...)
|
||||
}
|
||||
-198
@@ -1,198 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Get ATC properties from WHOCC website
|
||||
#'
|
||||
#' Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \cr \strong{This function requires an internet connection.}
|
||||
#' @param atc_code a character or character vector with ATC code(s) of antibiotic(s)
|
||||
#' @param property property of an ATC code. Valid values are \code{"ATC"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"}, \code{"Note"} and \code{groups}. For this last option, all hierarchical groups of an ATC code will be returned, see Examples.
|
||||
#' @param administration type of administration when using \code{property = "Adm.R"}, see Details
|
||||
#' @param url url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.
|
||||
#' @param ... parameters to pass on to \code{atc_property}
|
||||
#' @details
|
||||
#' Options for parameter \code{administration}:
|
||||
#' \itemize{
|
||||
#' \item{\code{"Implant"}}{ = Implant}
|
||||
#' \item{\code{"Inhal"}}{ = Inhalation}
|
||||
#' \item{\code{"Instill"}}{ = Instillation}
|
||||
#' \item{\code{"N"}}{ = nasal}
|
||||
#' \item{\code{"O"}}{ = oral}
|
||||
#' \item{\code{"P"}}{ = parenteral}
|
||||
#' \item{\code{"R"}}{ = rectal}
|
||||
#' \item{\code{"SL"}}{ = sublingual/buccal}
|
||||
#' \item{\code{"TD"}}{ = transdermal}
|
||||
#' \item{\code{"V"}}{ = vaginal}
|
||||
#' }
|
||||
#'
|
||||
#' Abbreviations of return values when using \code{property = "U"} (unit):
|
||||
#' \itemize{
|
||||
#' \item{\code{"g"}}{ = gram}
|
||||
#' \item{\code{"mg"}}{ = milligram}
|
||||
#' \item{\code{"mcg"}}{ = microgram}
|
||||
#' \item{\code{"U"}}{ = unit}
|
||||
#' \item{\code{"TU"}}{ = thousand units}
|
||||
#' \item{\code{"MU"}}{ = million units}
|
||||
#' \item{\code{"mmol"}}{ = millimole}
|
||||
#' \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
|
||||
#' }
|
||||
#' @export
|
||||
#' @rdname atc_online
|
||||
#' @importFrom dplyr %>% progress_estimated
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
|
||||
#' @examples
|
||||
#' \donttest{
|
||||
#' # oral DDD (Defined Daily Dose) of amoxicillin
|
||||
#' atc_online_property("J01CA04", "DDD", "O")
|
||||
#' # parenteral DDD (Defined Daily Dose) of amoxicillin
|
||||
#' atc_online_property("J01CA04", "DDD", "P")
|
||||
#'
|
||||
#' atc_online_property("J01CA04", property = "groups") # search hierarchical groups of amoxicillin
|
||||
#' # [1] "ANTIINFECTIVES FOR SYSTEMIC USE"
|
||||
#' # [2] "ANTIBACTERIALS FOR SYSTEMIC USE"
|
||||
#' # [3] "BETA-LACTAM ANTIBACTERIALS, PENICILLINS"
|
||||
#' # [4] "Penicillins with extended spectrum"
|
||||
#' }
|
||||
atc_online_property <- function(atc_code,
|
||||
property,
|
||||
administration = 'O',
|
||||
url = 'https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no') {
|
||||
|
||||
if (!all(c("curl", "rvest", "xml2") %in% rownames(installed.packages()))) {
|
||||
stop("Packages 'xml2', 'rvest' and 'curl' are required for this function")
|
||||
}
|
||||
|
||||
if (!all(atc_code %in% AMR::antibiotics)) {
|
||||
atc_code <- as.character(as.atc(atc_code))
|
||||
}
|
||||
|
||||
if (!curl::has_internet()) {
|
||||
message("There appears to be no internet connection.")
|
||||
return(rep(NA, length(atc_code)))
|
||||
}
|
||||
|
||||
if (length(property) != 1L) {
|
||||
stop('`property` must be of length 1', call. = FALSE)
|
||||
}
|
||||
if (length(administration) != 1L) {
|
||||
stop('`administration` must be of length 1', call. = FALSE)
|
||||
}
|
||||
|
||||
# also allow unit as property
|
||||
if (property %like% 'unit') {
|
||||
property <- 'U'
|
||||
}
|
||||
|
||||
# validation of properties
|
||||
valid_properties <- c("ATC", "Name", "DDD", "U", "Adm.R", "Note", "groups")
|
||||
valid_properties.bak <- valid_properties
|
||||
|
||||
property <- tolower(property)
|
||||
valid_properties <- tolower(valid_properties)
|
||||
|
||||
if (!property %in% valid_properties) {
|
||||
stop('Invalid `property`, use one of ', paste(valid_properties.bak, collapse = ", "), '.')
|
||||
}
|
||||
|
||||
if (property == 'ddd') {
|
||||
returnvalue <- rep(NA_real_, length(atc_code))
|
||||
} else if (property == 'groups') {
|
||||
returnvalue <- list()
|
||||
} else {
|
||||
returnvalue <- rep(NA_character_, length(atc_code))
|
||||
}
|
||||
|
||||
progress <- progress_estimated(n = length(atc_code))
|
||||
|
||||
for (i in 1:length(atc_code)) {
|
||||
|
||||
progress$tick()$print()
|
||||
|
||||
atc_url <- sub('%s', atc_code[i], url, fixed = TRUE)
|
||||
|
||||
if (property == "groups") {
|
||||
tbl <- xml2::read_html(atc_url) %>%
|
||||
rvest::html_node("#content") %>%
|
||||
rvest::html_children() %>%
|
||||
rvest::html_node("a")
|
||||
|
||||
# get URLS of items
|
||||
hrefs <- tbl %>% rvest::html_attr("href")
|
||||
# get text of items
|
||||
texts <- tbl %>% rvest::html_text()
|
||||
# select only text items where URL like "code="
|
||||
texts <- texts[grepl("?code=", tolower(hrefs), fixed = TRUE)]
|
||||
# last one is antibiotics, skip it
|
||||
texts <- texts[1:length(texts) - 1]
|
||||
returnvalue <- c(list(texts), returnvalue)
|
||||
|
||||
} else {
|
||||
tbl <- xml2::read_html(atc_url) %>%
|
||||
rvest::html_nodes('table') %>%
|
||||
rvest::html_table(header = TRUE) %>%
|
||||
as.data.frame(stringsAsFactors = FALSE)
|
||||
|
||||
# case insensitive column names
|
||||
colnames(tbl) <- tolower(colnames(tbl)) %>% gsub('^atc.*', 'atc', .)
|
||||
|
||||
if (length(tbl) == 0) {
|
||||
warning('ATC not found: ', atc_code[i], '. Please check ', atc_url, '.', call. = FALSE)
|
||||
returnvalue[i] <- NA
|
||||
next
|
||||
}
|
||||
|
||||
if (property %in% c('atc', 'name')) {
|
||||
# ATC and name are only in first row
|
||||
returnvalue[i] <- tbl[1, property]
|
||||
} else {
|
||||
if (!'adm.r' %in% colnames(tbl) | is.na(tbl[1, 'adm.r'])) {
|
||||
returnvalue[i] <- NA
|
||||
next
|
||||
} else {
|
||||
for (j in 1:nrow(tbl)) {
|
||||
if (tbl[j, 'adm.r'] == administration) {
|
||||
returnvalue[i] <- tbl[j, property]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (property == "groups" & length(returnvalue) == 1) {
|
||||
returnvalue <- returnvalue[[1]]
|
||||
}
|
||||
|
||||
returnvalue
|
||||
}
|
||||
|
||||
#' @rdname atc_online
|
||||
#' @export
|
||||
atc_online_groups <- function(atc_code, ...) {
|
||||
atc_online_property(atc_code = atc_code, property = "groups", ...)
|
||||
}
|
||||
|
||||
#' @rdname atc_online
|
||||
#' @export
|
||||
atc_online_ddd <- function(atc_code, ...) {
|
||||
atc_online_property(atc_code = atc_code, property = "ddd", ...)
|
||||
}
|
||||
|
||||
@@ -1,107 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Property of an antibiotic
|
||||
#'
|
||||
#' Use these functions to return a specific property of an antibiotic from the \code{\link{antibiotics}} data set, based on their ATC code. Get such a code with \code{\link{as.atc}}.
|
||||
#' @param x a (vector of a) valid \code{\link{atc}} code or any text that can be coerced to a valid atc with \code{\link{as.atc}}
|
||||
#' @param property one of the column names of one of the \code{\link{antibiotics}} data set, like \code{"atc"} and \code{"official"}
|
||||
#' @param language language of the returned text, defaults to English (\code{"en"}) and can be set with \code{\link{getOption}("AMR_locale")}. Either one of \code{"en"} (English) or \code{"nl"} (Dutch).
|
||||
#' @rdname atc_property
|
||||
#' @return A vector of values. In case of \code{atc_tradenames}, if \code{x} is of length one, a vector will be returned. Otherwise a \code{\link{list}}, with \code{x} as names.
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% left_join pull
|
||||
#' @seealso \code{\link{antibiotics}}
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' as.atc("amcl") # J01CR02
|
||||
#' atc_name("amcl") # Amoxicillin and beta-lactamase inhibitor
|
||||
#' atc_name("amcl", "nl") # Amoxicilline met enzymremmer
|
||||
#' atc_trivial_nl("amcl") # Amoxicilline/clavulaanzuur
|
||||
#' atc_certe("amcl") # amcl
|
||||
#' atc_umcg("amcl") # AMCL
|
||||
atc_property <- function(x, property = 'official') {
|
||||
property <- property[1]
|
||||
if (!property %in% colnames(AMR::antibiotics)) {
|
||||
stop("invalid property: ", property, " - use a column name of the `antibiotics` data set")
|
||||
}
|
||||
if (!is.atc(x)) {
|
||||
x <- as.atc(x) # this will give a warning if x cannot be coerced
|
||||
}
|
||||
suppressWarnings(
|
||||
data.frame(atc = x, stringsAsFactors = FALSE) %>%
|
||||
left_join(AMR::antibiotics, by = "atc") %>%
|
||||
pull(property)
|
||||
)
|
||||
}
|
||||
|
||||
#' @rdname atc_property
|
||||
#' @export
|
||||
atc_official <- function(x, language = NULL) {
|
||||
|
||||
if (is.null(language)) {
|
||||
language <- getOption("AMR_locale", default = "en")[1L]
|
||||
} else {
|
||||
language <- tolower(language[1])
|
||||
}
|
||||
if (language %in% c("en", "")) {
|
||||
atc_property(x, "official")
|
||||
} else if (language == "nl") {
|
||||
atc_property(x, "official_nl")
|
||||
} else {
|
||||
stop("Unsupported language: '", language, "' - use one of: 'en', 'nl'", call. = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
#' @rdname atc_property
|
||||
#' @export
|
||||
atc_name <- atc_official
|
||||
|
||||
#' @rdname atc_property
|
||||
#' @export
|
||||
atc_trivial_nl <- function(x) {
|
||||
atc_property(x, "trivial_nl")
|
||||
}
|
||||
|
||||
#' @rdname atc_property
|
||||
#' @export
|
||||
atc_certe <- function(x) {
|
||||
atc_property(x, "certe")
|
||||
}
|
||||
|
||||
#' @rdname atc_property
|
||||
#' @export
|
||||
atc_umcg <- function(x) {
|
||||
atc_property(x, "umcg")
|
||||
}
|
||||
|
||||
#' @rdname atc_property
|
||||
#' @export
|
||||
atc_tradenames <- function(x) {
|
||||
res <- atc_property(x, "trade_name")
|
||||
res <- strsplit(res, "|", fixed = TRUE)
|
||||
if (length(x) == 1) {
|
||||
res <- unlist(res)
|
||||
} else {
|
||||
names(res) <- x
|
||||
}
|
||||
res
|
||||
}
|
||||
@@ -1,88 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Check availability of columns
|
||||
#'
|
||||
#' Easy check for availability of columns in a data set. This makes it easy to get an idea of which antibiotic combination can be used for calculation with e.g. \code{\link{portion_IR}}.
|
||||
#' @param tbl a \code{data.frame} or \code{list}
|
||||
#' @param width number of characters to present the visual availability, defaults to filling the width of the console
|
||||
#' @return \code{data.frame} with column names of \code{tbl} as row names
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
#' @examples
|
||||
#' availability(septic_patients)
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>% availability()
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' select_if(is.rsi) %>%
|
||||
#' availability()
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' filter(mo == as.mo("E. coli")) %>%
|
||||
#' select_if(is.rsi) %>%
|
||||
#' availability()
|
||||
availability <- function(tbl, width = NULL) {
|
||||
x <- base::sapply(tbl, function(x) { 1 - base::sum(base::is.na(x)) / base::length(x) })
|
||||
n <- base::sapply(tbl, function(x) base::length(x[!base::is.na(x)]))
|
||||
IR <- base::sapply(tbl, function(x) base::ifelse(is.rsi(x), portion_IR(x, minimum = 0), NA))
|
||||
IR_print <- character(length(IR))
|
||||
IR_print[!is.na(IR)] <- percent(IR[!is.na(IR)], round = 1, force_zero = TRUE)
|
||||
IR_print[is.na(IR)] <- ""
|
||||
|
||||
if (is.null(width)) {
|
||||
width <- options()$width -
|
||||
(max(nchar(colnames(tbl))) +
|
||||
# count col
|
||||
8 +
|
||||
# available % column
|
||||
10 +
|
||||
# resistant % column
|
||||
10 +
|
||||
# extra margin
|
||||
5)
|
||||
width <- width / 2
|
||||
}
|
||||
|
||||
if (length(IR[is.na(IR)]) == ncol(tbl)) {
|
||||
width <- width * 2 + 10
|
||||
}
|
||||
|
||||
x_chars_IR <- strrep("#", round(width * IR, digits = 2))
|
||||
x_chars_S <- strrep("-", width - nchar(x_chars_IR))
|
||||
vis_resistance <- paste0("|", x_chars_IR, x_chars_S, "|")
|
||||
vis_resistance[is.na(IR)] <- ""
|
||||
|
||||
x_chars <- strrep("#", round(x, digits = 2) / (1 / width))
|
||||
x_chars_empty <- strrep("-", width - nchar(x_chars))
|
||||
|
||||
df <- data.frame(count = n,
|
||||
available = percent(x, round = 1, force_zero = TRUE),
|
||||
visual_availabilty = paste0("|", x_chars, x_chars_empty, "|"),
|
||||
resistant = IR_print,
|
||||
visual_resistance = vis_resistance)
|
||||
if (length(IR[is.na(IR)]) == ncol(tbl)) {
|
||||
df[,1:3]
|
||||
} else {
|
||||
df
|
||||
}
|
||||
}
|
||||
@@ -1,136 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' The Catalogue of Life
|
||||
#'
|
||||
#' This package contains the complete taxonomic tree of almost all microorganisms from the authoritative and comprehensive Catalogue of Life.
|
||||
#' @section Catalogue of Life:
|
||||
#' \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
|
||||
#' This package contains the complete taxonomic tree of almost all microorganisms (~60,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
|
||||
#'
|
||||
#' \link[=catalogue_of_life]{Click here} for more information about the included taxa. The Catalogue of Life releases updates annually; check which version was included in this package with \code{\link{catalogue_of_life_version}()}.
|
||||
#' @section Included taxa:
|
||||
#' Included are:
|
||||
#' \itemize{
|
||||
#' \item{All ~55,000 (sub)species from the kingdoms of Archaea, Bacteria and Protozoa}
|
||||
#' \item{All ~3,500 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (like all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).}
|
||||
#' \item{All ~2,000 (sub)species from ~100 other relevant genera, from the kingdoms of Animalia and Plantae (like \emph{Strongyloides} and \emph{Taenia})}
|
||||
#' \item{All ~15,000 previously accepted names of included (sub)species that have been taxonomically renamed}
|
||||
#' \item{The complete taxonomic tree of all included (sub)species: from kingdom to subspecies}
|
||||
#' \item{The responsible author(s) and year of scientific publication}
|
||||
#' }
|
||||
#'
|
||||
#' The Catalogue of Life (\url{http://www.catalogueoflife.org}) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.6 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation.
|
||||
#'
|
||||
#' The syntax used to transform the original data to a cleansed R format, can be found here: \url{https://gitlab.com/msberends/AMR/blob/master/reproduction_of_microorganisms.R}.
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @name catalogue_of_life
|
||||
#' @rdname catalogue_of_life
|
||||
#' @seealso Data set \code{\link{microorganisms}} for the actual data. \cr
|
||||
#' Function \code{\link{as.mo}()} to use the data for intelligent determination of microorganisms.
|
||||
#' @examples
|
||||
#' # Get version info of included data set
|
||||
#' catalogue_of_life_version()
|
||||
#'
|
||||
#'
|
||||
#' # Get a note when a species was renamed
|
||||
#' mo_shortname("Chlamydia psittaci")
|
||||
#' # Note: 'Chlamydia psittaci' (Page, 1968) was renamed
|
||||
#' # 'Chlamydophila psittaci' (Everett et al., 1999)
|
||||
#' # [1] "C. psittaci"
|
||||
#'
|
||||
#' # Get any property from the entire taxonomic tree for all included species
|
||||
#' mo_class("E. coli")
|
||||
#' # [1] "Gammaproteobacteria"
|
||||
#'
|
||||
#' mo_family("E. coli")
|
||||
#' # [1] "Enterobacteriaceae"
|
||||
#'
|
||||
#' mo_gramstain("E. coli") # based on kingdom and phylum, see ?mo_gramstain
|
||||
#' # [1] "Gram negative"
|
||||
#'
|
||||
#' mo_ref("E. coli")
|
||||
#' # [1] "Castellani et al., 1919"
|
||||
#'
|
||||
#' # Do not get mistaken - the package only includes microorganisms
|
||||
#' mo_phylum("C. elegans")
|
||||
#' # [1] "Cyanobacteria" # Bacteria?!
|
||||
#' mo_fullname("C. elegans")
|
||||
#' # [1] "Chroococcus limneticus elegans" # Because a microorganism was found
|
||||
NULL
|
||||
|
||||
#' Version info of included Catalogue of Life
|
||||
#'
|
||||
#' This function returns information about the included data from the Catalogue of Life. It also shows if the included version is their latest annual release. The Catalogue of Life releases their annual release in March each year.
|
||||
#' @seealso \code{\link{microorganisms}}
|
||||
#' @details The list item \code{...$catalogue_of_life$is_latest_annual_release} is based on the system date.
|
||||
#'
|
||||
#' For DSMZ, see \code{?microorganisms}.
|
||||
#' @return a \code{list}, which prints in pretty format
|
||||
#' @inheritSection catalogue_of_life Catalogue of Life
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @importFrom crayon bold underline
|
||||
#' @importFrom dplyr filter
|
||||
#' @export
|
||||
#' @examples
|
||||
#' library(dplyr)
|
||||
#' microorganisms %>% freq(kingdom)
|
||||
#' microorganisms %>% group_by(kingdom) %>% freq(phylum, nmax = NULL)
|
||||
catalogue_of_life_version <- function() {
|
||||
# see the `catalogue_of_life` list in R/data.R
|
||||
lst <- list(catalogue_of_life =
|
||||
list(version = gsub("{year}", catalogue_of_life$year, catalogue_of_life$version, fixed = TRUE),
|
||||
url = gsub("{year}", catalogue_of_life$year, catalogue_of_life$url_CoL, fixed = TRUE),
|
||||
# annual release always somewhere in May, so before June is TRUE, FALSE otherwise
|
||||
is_latest_annual_release = Sys.Date() < as.Date(paste0(catalogue_of_life$year + 1, "-06-01")),
|
||||
n = nrow(filter(AMR::microorganisms, source == "CoL"))),
|
||||
deutsche_sammlung_von_mikroorganismen_und_zellkulturen =
|
||||
list(version = "Prokaryotic Nomenclature Up-to-Date from DSMZ",
|
||||
url = catalogue_of_life$url_DSMZ,
|
||||
yearmonth = catalogue_of_life$yearmonth_DSMZ,
|
||||
n = nrow(filter(AMR::microorganisms, source == "DSMZ"))),
|
||||
total_included =
|
||||
list(
|
||||
n_total_species = nrow(AMR::microorganisms),
|
||||
n_total_synonyms = nrow(AMR::microorganisms.old)))
|
||||
|
||||
structure(.Data = lst,
|
||||
class = c("catalogue_of_life_version", "list"))
|
||||
}
|
||||
|
||||
#' @exportMethod print.catalogue_of_life_version
|
||||
#' @export
|
||||
#' @noRd
|
||||
print.catalogue_of_life_version <- function(x, ...) {
|
||||
lst <- x
|
||||
cat(paste0(bold("Included in this AMR package are:\n\n"),
|
||||
underline(lst$catalogue_of_life$version), "\n",
|
||||
" Available at: ", lst$catalogue_of_life$url, "\n",
|
||||
" Number of included species: ", format(lst$catalogue_of_life$n, big.mark = ","), "\n",
|
||||
" (based on your system time, this is most likely ", ifelse(lst$catalogue_of_life$is_latest_annual_release, "", "not "), "the latest annual release)\n\n",
|
||||
underline(paste0(lst$deutsche_sammlung_von_mikroorganismen_und_zellkulturen$version, " (",
|
||||
lst$deutsche_sammlung_von_mikroorganismen_und_zellkulturen$yearmonth, ")")), "\n",
|
||||
" Available at: ", lst$deutsche_sammlung_von_mikroorganismen_und_zellkulturen$url, "\n",
|
||||
" Number of included species: ", format(lst$deutsche_sammlung_von_mikroorganismen_und_zellkulturen$n, big.mark = ","), "\n\n",
|
||||
"=> Total number of species included: ", format(lst$total_included$n_total_species, big.mark = ","), "\n",
|
||||
"=> Total number of synonyms included: ", format(lst$total_included$n_total_synonyms, big.mark = ","), "\n\n",
|
||||
"See for more info ?microorganisms and ?catalogue_of_life.\n"))
|
||||
}
|
||||
@@ -1,237 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Count isolates
|
||||
#'
|
||||
#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
|
||||
#'
|
||||
#' \code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
|
||||
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.
|
||||
#' @inheritParams portion
|
||||
#' @details These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
|
||||
#'
|
||||
#' \code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_S(...) + count_IR(...)}.
|
||||
#'
|
||||
#' \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
|
||||
#' @source Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
|
||||
#' @seealso \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
|
||||
#' @keywords resistance susceptibility rsi antibiotics isolate isolates
|
||||
#' @return Integer
|
||||
#' @rdname count
|
||||
#' @name count
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
|
||||
#' ?septic_patients
|
||||
#'
|
||||
#' # Count resistant isolates
|
||||
#' count_R(septic_patients$amox)
|
||||
#' count_IR(septic_patients$amox)
|
||||
#'
|
||||
#' # Or susceptible isolates
|
||||
#' count_S(septic_patients$amox)
|
||||
#' count_SI(septic_patients$amox)
|
||||
#'
|
||||
#' # Count all available isolates
|
||||
#' count_all(septic_patients$amox)
|
||||
#' n_rsi(septic_patients$amox)
|
||||
#'
|
||||
#' # Since n_rsi counts available isolates, you can
|
||||
#' # calculate back to count e.g. non-susceptible isolates.
|
||||
#' # This results in the same:
|
||||
#' count_IR(septic_patients$amox)
|
||||
#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(R = count_R(cipr),
|
||||
#' I = count_I(cipr),
|
||||
#' S = count_S(cipr),
|
||||
#' n1 = count_all(cipr), # the actual total; sum of all three
|
||||
#' n2 = n_rsi(cipr), # same - analogous to n_distinct
|
||||
#' total = n()) # NOT the number of tested isolates!
|
||||
#'
|
||||
#' # Count co-resistance between amoxicillin/clav acid and gentamicin,
|
||||
#' # so we can see that combination therapy does a lot more than mono therapy.
|
||||
#' # Please mind that `portion_S` calculates percentages right away instead.
|
||||
#' count_S(septic_patients$amcl) # S = 1057 (67.1%)
|
||||
#' count_all(septic_patients$amcl) # n = 1576
|
||||
#'
|
||||
#' count_S(septic_patients$gent) # S = 1372 (74.0%)
|
||||
#' count_all(septic_patients$gent) # n = 1855
|
||||
#'
|
||||
#' with(septic_patients,
|
||||
#' count_S(amcl, gent)) # S = 1396 (92.0%)
|
||||
#' with(septic_patients, # n = 1517
|
||||
#' n_rsi(amcl, gent))
|
||||
#'
|
||||
#' # Get portions S/I/R immediately of all rsi columns
|
||||
#' septic_patients %>%
|
||||
#' select(amox, cipr) %>%
|
||||
#' count_df(translate = FALSE)
|
||||
#'
|
||||
#' # It also supports grouping variables
|
||||
#' septic_patients %>%
|
||||
#' select(hospital_id, amox, cipr) %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' count_df(translate = FALSE)
|
||||
#'
|
||||
count_R <- function(..., also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "R",
|
||||
include_I = FALSE,
|
||||
minimum = 0,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE,
|
||||
only_count = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname count
|
||||
#' @export
|
||||
count_IR <- function(..., also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "R",
|
||||
include_I = TRUE,
|
||||
minimum = 0,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE,
|
||||
only_count = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname count
|
||||
#' @export
|
||||
count_I <- function(..., also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "I",
|
||||
include_I = FALSE,
|
||||
minimum = 0,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE,
|
||||
only_count = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname count
|
||||
#' @export
|
||||
count_SI <- function(..., also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "S",
|
||||
include_I = TRUE,
|
||||
minimum = 0,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE,
|
||||
only_count = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname count
|
||||
#' @export
|
||||
count_S <- function(..., also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "S",
|
||||
include_I = FALSE,
|
||||
minimum = 0,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE,
|
||||
only_count = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname count
|
||||
#' @export
|
||||
count_all <- function(...) {
|
||||
# only print warnings once, if needed
|
||||
count_S(...) + suppressWarnings(count_IR(...))
|
||||
}
|
||||
|
||||
#' @rdname count
|
||||
#' @export
|
||||
n_rsi <- function(...) {
|
||||
# only print warnings once, if needed
|
||||
count_S(...) + suppressWarnings(count_IR(...))
|
||||
}
|
||||
|
||||
#' @rdname count
|
||||
#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
|
||||
#' @export
|
||||
count_df <- function(data,
|
||||
translate_ab = getOption("get_antibiotic_names", "official"),
|
||||
combine_IR = FALSE) {
|
||||
|
||||
if (!"data.frame" %in% class(data)) {
|
||||
stop("`count_df` must be called on a data.frame")
|
||||
}
|
||||
|
||||
if (data %>% select_if(is.rsi) %>% ncol() == 0) {
|
||||
stop("No columns with class 'rsi' found. See ?as.rsi.")
|
||||
}
|
||||
|
||||
if (as.character(translate_ab) == "TRUE") {
|
||||
translate_ab <- "official"
|
||||
}
|
||||
options(get_antibiotic_names = translate_ab)
|
||||
|
||||
resS <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = count_S) %>%
|
||||
mutate(Interpretation = "S") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
if (combine_IR == FALSE) {
|
||||
resI <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = count_I) %>%
|
||||
mutate(Interpretation = "I") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
resR <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = count_R) %>%
|
||||
mutate(Interpretation = "R") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
data.groups <- group_vars(data)
|
||||
|
||||
res <- bind_rows(resS, resI, resR) %>%
|
||||
mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
|
||||
tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
|
||||
} else {
|
||||
resIR <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = count_IR) %>%
|
||||
mutate(Interpretation = "IR") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
data.groups <- group_vars(data)
|
||||
|
||||
res <- bind_rows(resS, resIR) %>%
|
||||
mutate(Interpretation = factor(Interpretation, levels = c("IR", "S"), ordered = TRUE)) %>%
|
||||
tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
|
||||
}
|
||||
|
||||
if (!translate_ab == FALSE) {
|
||||
if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
|
||||
stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
|
||||
}
|
||||
res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
|
||||
}
|
||||
|
||||
res
|
||||
}
|
||||
@@ -1,263 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Data set with ~500 antibiotics
|
||||
#'
|
||||
#' A data set containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDDs. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
|
||||
#' @format A \code{\link{data.frame}} with 488 observations and 17 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical), like \code{J01CR02}}
|
||||
#' \item{\code{ears_net}}{EARS-Net code (European Antimicrobial Resistance Surveillance Network), like \code{AMC}}
|
||||
#' \item{\code{certe}}{Certe code, like \code{amcl}}
|
||||
#' \item{\code{umcg}}{UMCG code, like \code{AMCL}}
|
||||
#' \item{\code{abbr}}{Abbreviation as used by many countries, used internally by \code{\link{as.atc}}}
|
||||
#' \item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and beta-lactamase inhibitor"}}
|
||||
#' \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
|
||||
#' \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
|
||||
#' \item{\code{trade_name}}{Trade name as used by many countries (a total of 294), used internally by \code{\link{as.atc}}}
|
||||
#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
|
||||
#' \item{\code{oral_units}}{Units of \code{ddd_units}}
|
||||
#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
|
||||
#' \item{\code{iv_units}}{Units of \code{iv_ddd}}
|
||||
#' \item{\code{atc_group1}}{ATC group, like \code{"Macrolides, lincosamides and streptogramins"}}
|
||||
#' \item{\code{atc_group2}}{Subgroup of \code{atc_group1}, like \code{"Macrolides"}}
|
||||
#' \item{\code{useful_gramnegative}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
|
||||
#' \item{\code{useful_grampositive}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
|
||||
#' }
|
||||
#' @source World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
|
||||
#'
|
||||
#' Table antibiotic coding EARSS (from WHONET 5.3): \url{http://www.madsonline.dk/Tutorials/landskoder_antibiotika_WM.pdf}
|
||||
#'
|
||||
#' EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016: \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}
|
||||
#'
|
||||
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
|
||||
#' @inheritSection WHOCC WHOCC
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @seealso \code{\link{microorganisms}}
|
||||
# use this later to further fill AMR::antibiotics
|
||||
# drug <- "Ciprofloxacin"
|
||||
# url <- xml2::read_html(paste0("https://www.ncbi.nlm.nih.gov/pccompound?term=", drug)) %>%
|
||||
# html_nodes(".rslt") %>%
|
||||
# .[[1]] %>%
|
||||
# html_nodes(".title a") %>%
|
||||
# html_attr("href") %>%
|
||||
# gsub("/compound/", "/rest/pug_view/data/compound/", ., fixed = TRUE) %>%
|
||||
# paste0("/XML/?response_type=display")
|
||||
# synonyms <- url %>%
|
||||
# read_xml() %>%
|
||||
# xml_contents() %>% .[[6]] %>%
|
||||
# xml_contents() %>% .[[8]] %>%
|
||||
# xml_contents() %>% .[[3]] %>%
|
||||
# xml_contents() %>% .[[3]] %>%
|
||||
# xml_contents() %>%
|
||||
# paste() %>%
|
||||
# .[. %like% "StringValueList"] %>%
|
||||
# gsub("[</]+StringValueList[>]", "", .)
|
||||
|
||||
# last two columns created with:
|
||||
# antibiotics %>%
|
||||
# mutate(useful_gramnegative =
|
||||
# if_else(
|
||||
# atc_group1 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
|
||||
# atc_group2 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
|
||||
# official %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)',
|
||||
# FALSE,
|
||||
# NA
|
||||
# ),
|
||||
# useful_grampositive =
|
||||
# if_else(
|
||||
# atc_group1 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
|
||||
# atc_group2 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
|
||||
# official %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)',
|
||||
# FALSE,
|
||||
# NA
|
||||
# )
|
||||
# )
|
||||
#
|
||||
# ADD NEW TRADE NAMES FROM OTHER DATAFRAME
|
||||
# antibiotics_add_to_property <- function(ab_df, atc, property, value) {
|
||||
# if (length(atc) > 1L) {
|
||||
# stop("only one atc at a time")
|
||||
# }
|
||||
# if (!property %in% c("abbr", "trade_name")) {
|
||||
# stop("only possible for abbr and trade_name")
|
||||
# }
|
||||
#
|
||||
# value <- gsub(ab_df[which(ab_df$atc == atc),] %>% pull("official"), "", value, fixed = TRUE)
|
||||
# value <- gsub("||", "|", value, fixed = TRUE)
|
||||
# value <- gsub("[äáàâ]", "a", value)
|
||||
# value <- gsub("[ëéèê]", "e", value)
|
||||
# value <- gsub("[ïíìî]", "i", value)
|
||||
# value <- gsub("[öóòô]", "o", value)
|
||||
# value <- gsub("[üúùû]", "u", value)
|
||||
# if (!atc %in% ab_df$atc) {
|
||||
# message("SKIPPING - UNKNOWN ATC: ", atc)
|
||||
# }
|
||||
# if (is.na(value)) {
|
||||
# message("SKIPPING - VALUE MISSES: ", atc)
|
||||
# }
|
||||
# if (atc %in% ab_df$atc & !is.na(value)) {
|
||||
# current <- ab_df[which(ab_df$atc == atc),] %>% pull(property)
|
||||
# if (!is.na(current)) {
|
||||
# value <- paste(current, value, sep = "|")
|
||||
# }
|
||||
# value <- strsplit(value, "|", fixed = TRUE) %>% unlist() %>% unique() %>% paste(collapse = "|")
|
||||
# value <- gsub("||", "|", value, fixed = TRUE)
|
||||
# # print(value)
|
||||
# ab_df[which(ab_df$atc == atc), property] <- value
|
||||
# message("Added ", value, " to ", ab_official(atc), " (", atc, ", ", ab_certe(atc), ")")
|
||||
# }
|
||||
# ab_df
|
||||
# }
|
||||
#
|
||||
"antibiotics"
|
||||
|
||||
#' Data set with ~65,000 microorganisms
|
||||
#'
|
||||
#' A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using \code{\link{as.mo}}.
|
||||
#' @inheritSection catalogue_of_life Catalogue of Life
|
||||
#' @format A \code{\link{data.frame}} with 65,629 observations and 16 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{mo}}{ID of microorganism as used by this package}
|
||||
#' \item{\code{col_id}}{Catalogue of Life ID}
|
||||
#' \item{\code{fullname}}{Full name, like \code{"Echerichia coli"}}
|
||||
#' \item{\code{kingdom}}{Taxonomic kingdom of the microorganism}
|
||||
#' \item{\code{phylum}}{Taxonomic phylum of the microorganism}
|
||||
#' \item{\code{class}}{Taxonomic class of the microorganism}
|
||||
#' \item{\code{order}}{Taxonomic order of the microorganism}
|
||||
#' \item{\code{family}}{Taxonomic family of the microorganism}
|
||||
#' \item{\code{genus}}{Taxonomic genus of the microorganism}
|
||||
#' \item{\code{species}}{Taxonomic species of the microorganism}
|
||||
#' \item{\code{subspecies}}{Taxonomic subspecies of the microorganism}
|
||||
#' \item{\code{rank}}{Taxonomic rank of the microorganism, like \code{"species"} or \code{"genus"}}
|
||||
#' \item{\code{ref}}{Author(s) and year of concerning scientific publication}
|
||||
#' \item{\code{species_id}}{ID of the species as used by the Catalogue of Life}
|
||||
#' \item{\code{source}}{Either \code{"CoL"}, \code{"DSMZ"} (see source) or "manually added"}
|
||||
#' \item{\code{prevalence}}{Prevalence of the microorganism, see \code{?as.mo}}
|
||||
#' }
|
||||
#' @details Manually added were:
|
||||
#' \itemize{
|
||||
#' \item{9 species of \emph{Streptococcus} (beta haemolytic groups A, B, C, D, F, G, H, K and unspecified)}
|
||||
#' \item{2 species of \emph{Staphylococcus} (coagulase-negative [CoNS] and coagulase-positive [CoPS])}
|
||||
#' \item{3 other undefined (unknown, unknown Gram negatives and unknown Gram positives)}
|
||||
#' \item{8,830 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) that are not in the Catalogue of Life}
|
||||
#' }
|
||||
#' @section About the records from DSMZ (see source):
|
||||
#' Names of prokaryotes are defined as being validly published by the International Code of Nomenclature of Bacteria. Validly published are all names which are included in the Approved Lists of Bacterial Names and the names subsequently published in the International Journal of Systematic Bacteriology (IJSB) and, from January 2000, in the International Journal of Systematic and Evolutionary Microbiology (IJSEM) as original articles or in the validation lists.
|
||||
#'
|
||||
#' From: \url{https://www.dsmz.de/support/bacterial-nomenclature-up-to-date-downloads/readme.html}
|
||||
#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), \url{www.catalogueoflife.org} (check included annual version with \code{\link{catalogue_of_life_version}()}).
|
||||
#'
|
||||
#' Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, \url{http://www.dsmz.de/bacterial-diversity/prokaryotic-nomenclature-up-to-date} (check included version with \code{\link{catalogue_of_life_version}()}).
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @seealso \code{\link{as.mo}}, \code{\link{mo_property}}, \code{\link{microorganisms.codes}}
|
||||
"microorganisms"
|
||||
|
||||
catalogue_of_life <- list(
|
||||
year = 2018,
|
||||
version = "Catalogue of Life: {year} Annual Checklist",
|
||||
url_CoL = "http://www.catalogueoflife.org/annual-checklist/{year}/",
|
||||
url_DSMZ = "https://www.dsmz.de/microorganisms/pnu/bacterial_nomenclature_info_mm.php",
|
||||
yearmonth_DSMZ = "February 2019"
|
||||
)
|
||||
|
||||
#' Data set with previously accepted taxonomic names
|
||||
#'
|
||||
#' A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by \code{\link{as.mo}}.
|
||||
#' @inheritSection catalogue_of_life Catalogue of Life
|
||||
#' @format A \code{\link{data.frame}} with 16,911 observations and 4 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{col_id}}{Catalogue of Life ID}
|
||||
#' \item{\code{tsn_new}}{New Catalogue of Life ID}
|
||||
#' \item{\code{fullname}}{Old taxonomic name of the microorganism}
|
||||
#' \item{\code{ref}}{Author(s) and year of concerning scientific publication}
|
||||
#' }
|
||||
#' @source [3] Catalogue of Life: Annual Checklist (public online database), \url{www.catalogueoflife.org}.
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @seealso \code{\link{as.mo}} \code{\link{mo_property}} \code{\link{microorganisms}}
|
||||
"microorganisms.old"
|
||||
|
||||
#' Translation table for microorganism codes
|
||||
#'
|
||||
#' A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with \code{\link{set_mo_source}}.
|
||||
#' @format A \code{\link{data.frame}} with 5,171 observations and 2 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{certe}}{Commonly used code of a microorganism}
|
||||
#' \item{\code{mo}}{ID of the microorganism in the \code{\link{microorganisms}} data set}
|
||||
#' }
|
||||
#' @inheritSection catalogue_of_life Catalogue of Life
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @seealso \code{\link{as.mo}} \code{\link{microorganisms}}
|
||||
"microorganisms.codes"
|
||||
|
||||
#' Data set with 2,000 blood culture isolates from septic patients
|
||||
#'
|
||||
#' An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This \code{data.frame} can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
|
||||
#' @format A \code{\link{data.frame}} with 2,000 observations and 49 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{date}}{date of receipt at the laboratory}
|
||||
#' \item{\code{hospital_id}}{ID of the hospital, from A to D}
|
||||
#' \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
|
||||
#' \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
|
||||
#' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
|
||||
#' \item{\code{age}}{age of the patient}
|
||||
#' \item{\code{gender}}{gender of the patient}
|
||||
#' \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
|
||||
#' \item{\code{mo}}{ID of microorganism created with \code{\link{as.mo}}, see also \code{\link{microorganisms}}}
|
||||
#' \item{\code{peni:rifa}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{abname}}}
|
||||
#' }
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
"septic_patients"
|
||||
|
||||
#' Data set with 500 isolates - WHONET example
|
||||
#'
|
||||
#' This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The data itself was based on our \code{\link{septic_patients}} data set.
|
||||
#' @format A \code{\link{data.frame}} with 500 observations and 53 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{Identification number}}{ID of the sample}
|
||||
#' \item{\code{Specimen number}}{ID of the specimen}
|
||||
#' \item{\code{Organism}}{Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using \code{\link{as.mo}}.}
|
||||
#' \item{\code{Country}}{Country of origin}
|
||||
#' \item{\code{Laboratory}}{Name of laboratory}
|
||||
#' \item{\code{Last name}}{Last name of patient}
|
||||
#' \item{\code{First name}}{Initial of patient}
|
||||
#' \item{\code{Sex}}{Gender of patient}
|
||||
#' \item{\code{Age}}{Age of patient}
|
||||
#' \item{\code{Age category}}{Age group, can also be looked up using \code{\link{age_groups}}}
|
||||
#' \item{\code{Date of admission}}{Date of hospital admission}
|
||||
#' \item{\code{Specimen date}}{Date when specimen was received at laboratory}
|
||||
#' \item{\code{Specimen type}}{Specimen type or group}
|
||||
#' \item{\code{Specimen type (Numeric)}}{Translation of \code{"Specimen type"}}
|
||||
#' \item{\code{Reason}}{Reason of request with Differential Diagnosis}
|
||||
#' \item{\code{Isolate number}}{ID of isolate}
|
||||
#' \item{\code{Organism type}}{Type of microorganism, can also be looked up using \code{\link{mo_type}}}
|
||||
#' \item{\code{Serotype}}{Serotype of microorganism}
|
||||
#' \item{\code{Beta-lactamase}}{Microorganism produces beta-lactamase?}
|
||||
#' \item{\code{ESBL}}{Microorganism produces extended spectrum beta-lactamase?}
|
||||
#' \item{\code{Carbapenemase}}{Microorganism produces carbapenemase?}
|
||||
#' \item{\code{MRSA screening test}}{Microorganism is possible MRSA?}
|
||||
#' \item{\code{Inducible clindamycin resistance}}{Clindamycin can be induced?}
|
||||
#' \item{\code{Comment}}{Other comments}
|
||||
#' \item{\code{Date of data entry}}{Date this data was entered in WHONET}
|
||||
#' \item{\code{AMP_ND10:CIP_EE}}{27 different antibiotics. You can lookup the abbreviatons in the \code{\link{antibiotics}} data set, or use e.g. \code{\link{atc_name}("AMP")} to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using \code{\link{as.rsi}}.}
|
||||
#' }
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
"WHONET"
|
||||
-133
@@ -1,133 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Deprecated functions
|
||||
#'
|
||||
#' These functions are so-called '\link{Deprecated}'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
#' @keywords internal
|
||||
#' @name AMR-deprecated
|
||||
#' @rdname AMR-deprecated
|
||||
ratio <- function(x, ratio) {
|
||||
.Deprecated(package = "AMR")
|
||||
|
||||
if (!all(is.numeric(x))) {
|
||||
stop('`x` must be a vector of numeric values.')
|
||||
}
|
||||
if (length(ratio) == 1) {
|
||||
if (ratio %like% '^([0-9]+([.][0-9]+)?[-,:])+[0-9]+([.][0-9]+)?$') {
|
||||
# support for "1:2:1", "1-2-1", "1,2,1" and even "1.75:2:1.5"
|
||||
ratio <- ratio %>% strsplit("[-,:]") %>% unlist() %>% as.double()
|
||||
} else {
|
||||
stop('Invalid `ratio`: ', ratio, '.')
|
||||
}
|
||||
}
|
||||
if (length(x) != 1 & length(x) != length(ratio)) {
|
||||
stop('`x` and `ratio` must be of same size.')
|
||||
}
|
||||
sum(x, na.rm = TRUE) * (ratio / sum(ratio, na.rm = TRUE))
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
guess_mo <- function(...) {
|
||||
.Deprecated(new = "as.mo", package = "AMR")
|
||||
as.mo(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
guess_atc <- function(...) {
|
||||
.Deprecated(new = "as.atc", package = "AMR")
|
||||
as.atc(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_property <- function(...) {
|
||||
.Deprecated(new = "atc_property", package = "AMR")
|
||||
atc_property(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_atc <- function(...) {
|
||||
.Deprecated(new = "as.atc", package = "AMR")
|
||||
as.atc(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_official <- function(...) {
|
||||
.Deprecated(new = "atc_official", package = "AMR")
|
||||
atc_official(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_name <- function(...) {
|
||||
.Deprecated(new = "atc_name", package = "AMR")
|
||||
atc_name(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_trivial_nl <- function(...) {
|
||||
.Deprecated(new = "atc_trivial_nl", package = "AMR")
|
||||
atc_trivial_nl(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_certe <- function(...) {
|
||||
.Deprecated(new = "atc_certe", package = "AMR")
|
||||
atc_certe(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_umcg <- function(...) {
|
||||
.Deprecated(new = "atc_umcg", package = "AMR")
|
||||
atc_umcg(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_tradenames <- function(...) {
|
||||
.Deprecated(new = "atc_tradenames", package = "AMR")
|
||||
atc_tradenames(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
atc_ddd <- function(...) {
|
||||
.Deprecated(new = "atc_online_ddd", package = "AMR")
|
||||
atc_online_ddd(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
atc_groups <- function(...) {
|
||||
.Deprecated(new = "atc_online_groups", package = "AMR")
|
||||
atc_online_groups(...)
|
||||
}
|
||||
|
||||
-1934
File diff suppressed because it is too large.
Load diff
@@ -1,298 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Filter isolates on result in antibiotic class
|
||||
#'
|
||||
#' Filter isolates on results in specific antibiotic variables based on their class (ATC groups). This makes it easy to get a list of isolates that were tested for e.g. any aminoglycoside.
|
||||
#' @param tbl a data set
|
||||
#' @param ab_class an antimicrobial class, like \code{"carbapenems"}. More specifically, this should be a text that can be found in a 4th level ATC group (chemical subgroup) or a 5th level ATC group (chemical substance), please see \href{https://www.whocc.no/atc/structure_and_principles/}{this explanation on the WHOCC website}.
|
||||
#' @param result an antibiotic result: S, I or R (or a combination of more of them)
|
||||
#' @param scope the scope to check which variables to check, can be \code{"any"} (default) or \code{"all"}
|
||||
#' @param ... parameters passed on to \code{\link[dplyr]{filter_at}}
|
||||
#' @details The \code{\link{antibiotics}} data set will be searched for \code{ab_class} in the columns \code{atc_group1} and \code{atc_group2} (case-insensitive). Next, \code{tbl} will be checked for column names with a value in any abbreviations, codes or official names found in the \code{antibiotics} data set.
|
||||
#' @rdname filter_ab_class
|
||||
#' @keywords filter fillter_class
|
||||
#' @importFrom dplyr filter_at %>% select vars any_vars all_vars
|
||||
#' @importFrom crayon bold blue
|
||||
#' @export
|
||||
#' @examples
|
||||
#' library(dplyr)
|
||||
#'
|
||||
#' # filter on isolates that have any result for any aminoglycoside
|
||||
#' septic_patients %>% filter_aminoglycosides()
|
||||
#'
|
||||
#' # this is essentially the same as:
|
||||
#' septic_patients %>%
|
||||
#' filter_at(.vars = vars(c("gent", "tobr", "amik", "kana")),
|
||||
#' .vars_predicate = any_vars(. %in% c("S", "I", "R")))
|
||||
#'
|
||||
#'
|
||||
#' # filter on isolates that show resistance to ANY aminoglycoside
|
||||
#' septic_patients %>% filter_aminoglycosides("R")
|
||||
#'
|
||||
#' # filter on isolates that show resistance to ALL aminoglycosides
|
||||
#' septic_patients %>% filter_aminoglycosides("R", "all")
|
||||
#'
|
||||
#' # filter on isolates that show resistance to
|
||||
#' # any aminoglycoside and any fluoroquinolone
|
||||
#' septic_patients %>%
|
||||
#' filter_aminoglycosides("R") %>%
|
||||
#' filter_fluoroquinolones("R")
|
||||
#'
|
||||
#' # filter on isolates that show resistance to
|
||||
#' # all aminoglycosides and all fluoroquinolones
|
||||
#' septic_patients %>%
|
||||
#' filter_aminoglycosides("R", "all") %>%
|
||||
#' filter_fluoroquinolones("R", "all")
|
||||
filter_ab_class <- function(tbl,
|
||||
ab_class,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
scope <- scope[1L]
|
||||
if (is.null(result)) {
|
||||
result <- c("S", "I", "R")
|
||||
}
|
||||
# make result = "IR" work too:
|
||||
result <- unlist(strsplit(result, ""))
|
||||
|
||||
if (!all(result %in% c("S", "I", "R"))) {
|
||||
stop("`result` must be one or more of: S, I, R", call. = FALSE)
|
||||
}
|
||||
if (!all(scope %in% c("any", "all"))) {
|
||||
stop("`scope` must be one of: any, all", call. = FALSE)
|
||||
}
|
||||
|
||||
vars_df <- colnames(tbl)[tolower(colnames(tbl)) %in% tolower(ab_class_vars(ab_class))]
|
||||
atc_groups <- ab_class_atcgroups(ab_class)
|
||||
|
||||
if (length(vars_df) > 0) {
|
||||
if (length(result) == 1) {
|
||||
operator <- " is "
|
||||
} else {
|
||||
operator <- " is one of "
|
||||
}
|
||||
if (scope == "any") {
|
||||
scope_txt <- " or "
|
||||
scope_fn <- any_vars
|
||||
} else {
|
||||
scope_txt <- " and "
|
||||
scope_fn <- all_vars
|
||||
if (length(vars_df) > 1) {
|
||||
operator <- gsub("is", "are", operator)
|
||||
}
|
||||
}
|
||||
if (length(vars_df) > 1) {
|
||||
scope <- paste(scope, "of ")
|
||||
} else {
|
||||
scope <- ""
|
||||
}
|
||||
message(blue(paste0("Filtering on ", atc_groups, ": ", scope,
|
||||
paste(bold(paste0("`", vars_df, "`")), collapse = scope_txt), operator, toString(result))))
|
||||
tbl %>%
|
||||
filter_at(vars(vars_df),
|
||||
scope_fn(. %in% result),
|
||||
...)
|
||||
} else {
|
||||
warning(paste0("no antibiotics of class ", atc_groups, " found, leaving data unchanged"), call. = FALSE)
|
||||
tbl
|
||||
}
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_aminoglycosides <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "aminoglycoside",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_carbapenems <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "carbapenem",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_cephalosporins <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "cephalosporin",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_1st_cephalosporins <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "first-generation cephalosporin",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_2nd_cephalosporins <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "second-generation cephalosporin",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_3rd_cephalosporins <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "third-generation cephalosporin",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_4th_cephalosporins <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "fourth-generation cephalosporin",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_fluoroquinolones <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "fluoroquinolone",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_glycopeptides <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "glycopeptide",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_macrolides <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "macrolide",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @rdname filter_ab_class
|
||||
#' @export
|
||||
filter_tetracyclines <- function(tbl,
|
||||
result = NULL,
|
||||
scope = "any",
|
||||
...) {
|
||||
filter_ab_class(tbl = tbl,
|
||||
ab_class = "tetracycline",
|
||||
result = result,
|
||||
scope = scope,
|
||||
...)
|
||||
}
|
||||
|
||||
#' @importFrom dplyr %>% filter_at vars any_vars select
|
||||
ab_class_vars <- function(ab_class) {
|
||||
ab_vars <- AMR::antibiotics %>%
|
||||
filter_at(vars(c("atc_group1", "atc_group2")), any_vars(. %like% ab_class)) %>%
|
||||
select(atc:trade_name) %>%
|
||||
as.matrix() %>%
|
||||
as.character() %>%
|
||||
paste(collapse = "|") %>%
|
||||
strsplit("|", fixed = TRUE) %>%
|
||||
unlist() %>%
|
||||
unique()
|
||||
ab_vars[!is.na(ab_vars)]
|
||||
}
|
||||
|
||||
#' @importFrom dplyr %>% filter pull
|
||||
ab_class_atcgroups <- function(ab_class) {
|
||||
ifelse(ab_class %in% c("aminoglycoside",
|
||||
"carbapenem",
|
||||
"cephalosporin",
|
||||
"first-generation cephalosporin",
|
||||
"second-generation cephalosporin",
|
||||
"third-generation cephalosporin",
|
||||
"fourth-generation cephalosporin",
|
||||
"fluoroquinolone",
|
||||
"glycopeptide",
|
||||
"macrolide",
|
||||
"tetracycline"),
|
||||
paste0(ab_class, "s"),
|
||||
AMR::antibiotics %>%
|
||||
filter(atc %in% ab_class_vars(ab_class)) %>%
|
||||
pull("atc_group2") %>%
|
||||
unique() %>%
|
||||
tolower() %>%
|
||||
paste(collapse = "/")
|
||||
)
|
||||
}
|
||||
@@ -1,571 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Determine first (weighted) isolates
|
||||
#'
|
||||
#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
|
||||
#' @param tbl a \code{data.frame} containing isolates.
|
||||
#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class
|
||||
#' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
|
||||
#' @param col_mo column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.
|
||||
#' @param col_testcode column name of the test codes. Use \code{col_testcode = NULL} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.
|
||||
#' @param col_specimen column name of the specimen type or group
|
||||
#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
|
||||
#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use \code{col_keyantibiotics = FALSE} to prevent this.
|
||||
#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
|
||||
#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
|
||||
#' @param icu_exclude logical whether ICU isolates should be excluded (rows with value \code{TRUE} in column \code{col_icu})
|
||||
#' @param specimen_group value in column \code{col_specimen} to filter on
|
||||
#' @param type type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details
|
||||
#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details
|
||||
#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details
|
||||
#' @param info print progress
|
||||
#' @param ... parameters passed on to the \code{first_isolate} function
|
||||
#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
|
||||
#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
|
||||
#'
|
||||
#' The function \code{filter_first_isolate} is essentially equal to:
|
||||
#' \preformatted{
|
||||
#' tbl \%>\%
|
||||
#' mutate(only_firsts = first_isolate(tbl, ...)) \%>\%
|
||||
#' filter(only_firsts == TRUE) \%>\%
|
||||
#' select(-only_firsts)
|
||||
#' }
|
||||
#' The function \code{filter_first_weighted_isolate} is essentially equal to:
|
||||
#' \preformatted{
|
||||
#' tbl \%>\%
|
||||
#' mutate(keyab = key_antibiotics(.)) \%>\%
|
||||
#' mutate(only_weighted_firsts = first_isolate(tbl,
|
||||
#' col_keyantibiotics = "keyab", ...)) \%>\%
|
||||
#' filter(only_weighted_firsts == TRUE) \%>\%
|
||||
#' select(-only_weighted_firsts)
|
||||
#' }
|
||||
#' @section Key antibiotics:
|
||||
#' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
|
||||
#'
|
||||
#' \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
|
||||
#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
|
||||
#'
|
||||
#' \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
|
||||
#' A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
|
||||
#' @rdname first_isolate
|
||||
#' @keywords isolate isolates first
|
||||
#' @seealso \code{\link{key_antibiotics}}
|
||||
#' @export
|
||||
#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange pull
|
||||
#' @importFrom crayon blue bold silver
|
||||
#' @return Logical vector
|
||||
#' @source Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # septic_patients is a dataset available in the AMR package. It is true, genuine data.
|
||||
#' ?septic_patients
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' # Filter on first isolates:
|
||||
#' septic_patients %>%
|
||||
#' mutate(first_isolate = first_isolate(.,
|
||||
#' col_date = "date",
|
||||
#' col_patient_id = "patient_id",
|
||||
#' col_mo = "mo")) %>%
|
||||
#' filter(first_isolate == TRUE)
|
||||
#'
|
||||
#' # Which can be shortened to:
|
||||
#' septic_patients %>%
|
||||
#' filter_first_isolate()
|
||||
#' # or for first weighted isolates:
|
||||
#' septic_patients %>%
|
||||
#' filter_first_weighted_isolate()
|
||||
#'
|
||||
#' # Now let's see if first isolates matter:
|
||||
#' A <- septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(count = n_rsi(gent), # gentamicin availability
|
||||
#' resistance = portion_IR(gent)) # gentamicin resistance
|
||||
#'
|
||||
#' B <- septic_patients %>%
|
||||
#' filter_first_weighted_isolate() %>% # the 1st isolate filter
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(count = n_rsi(gent), # gentamicin availability
|
||||
#' resistance = portion_IR(gent)) # gentamicin resistance
|
||||
#'
|
||||
#' # Have a look at A and B.
|
||||
#' # B is more reliable because every isolate is only counted once.
|
||||
#' # Gentamicin resitance in hospital D appears to be 5.4% higher than
|
||||
#' # when you (erroneously) would have used all isolates!
|
||||
#'
|
||||
#'
|
||||
#' ## OTHER EXAMPLES:
|
||||
#'
|
||||
#' \dontrun{
|
||||
#'
|
||||
#' # set key antibiotics to a new variable
|
||||
#' tbl$keyab <- key_antibiotics(tbl)
|
||||
#'
|
||||
#' tbl$first_isolate <-
|
||||
#' first_isolate(tbl)
|
||||
#'
|
||||
#' tbl$first_isolate_weighed <-
|
||||
#' first_isolate(tbl,
|
||||
#' col_keyantibiotics = 'keyab')
|
||||
#'
|
||||
#' tbl$first_blood_isolate <-
|
||||
#' first_isolate(tbl,
|
||||
#' specimen_group = 'Blood')
|
||||
#'
|
||||
#' tbl$first_blood_isolate_weighed <-
|
||||
#' first_isolate(tbl,
|
||||
#' specimen_group = 'Blood',
|
||||
#' col_keyantibiotics = 'keyab')
|
||||
#'
|
||||
#' tbl$first_urine_isolate <-
|
||||
#' first_isolate(tbl,
|
||||
#' specimen_group = 'Urine')
|
||||
#'
|
||||
#' tbl$first_urine_isolate_weighed <-
|
||||
#' first_isolate(tbl,
|
||||
#' specimen_group = 'Urine',
|
||||
#' col_keyantibiotics = 'keyab')
|
||||
#'
|
||||
#' tbl$first_resp_isolate <-
|
||||
#' first_isolate(tbl,
|
||||
#' specimen_group = 'Respiratory')
|
||||
#'
|
||||
#' tbl$first_resp_isolate_weighed <-
|
||||
#' first_isolate(tbl,
|
||||
#' specimen_group = 'Respiratory',
|
||||
#' col_keyantibiotics = 'keyab')
|
||||
#' }
|
||||
first_isolate <- function(tbl,
|
||||
col_date = NULL,
|
||||
col_patient_id = NULL,
|
||||
col_mo = NULL,
|
||||
col_testcode = NULL,
|
||||
col_specimen = NULL,
|
||||
col_icu = NULL,
|
||||
col_keyantibiotics = NULL,
|
||||
episode_days = 365,
|
||||
testcodes_exclude = NULL,
|
||||
icu_exclude = FALSE,
|
||||
specimen_group = NULL,
|
||||
type = "keyantibiotics",
|
||||
ignore_I = TRUE,
|
||||
points_threshold = 2,
|
||||
info = TRUE,
|
||||
...) {
|
||||
|
||||
if (!is.data.frame(tbl)) {
|
||||
stop("`tbl` must be a data.frame.", call. = FALSE)
|
||||
}
|
||||
|
||||
dots <- unlist(list(...))
|
||||
if (length(dots) != 0) {
|
||||
# backwards compatibility with old parameters
|
||||
dots.names <- dots %>% names()
|
||||
if ('filter_specimen' %in% dots.names) {
|
||||
specimen_group <- dots[which(dots.names == 'filter_specimen')]
|
||||
}
|
||||
}
|
||||
|
||||
# try to find columns based on type
|
||||
# -- mo
|
||||
if (is.null(col_mo)) {
|
||||
col_mo <- search_type_in_df(tbl = tbl, type = "mo")
|
||||
}
|
||||
if (is.null(col_mo)) {
|
||||
stop("`col_mo` must be set.", call. = FALSE)
|
||||
}
|
||||
|
||||
# -- date
|
||||
if (is.null(col_date)) {
|
||||
col_date <- search_type_in_df(tbl = tbl, type = "date")
|
||||
}
|
||||
if (is.null(col_date)) {
|
||||
stop("`col_date` must be set.", call. = FALSE)
|
||||
}
|
||||
# convert to Date (pipes/pull for supporting tibbles too)
|
||||
tbl[, col_date] <- tbl %>% pull(col_date) %>% as.Date()
|
||||
|
||||
# -- patient id
|
||||
if (is.null(col_patient_id)) {
|
||||
if (all(c("First name", "Last name", "Sex", "Identification number") %in% colnames(tbl))) {
|
||||
# WHONET support
|
||||
tbl <- tbl %>% mutate(patient_id = paste(`First name`, `Last name`, Sex))
|
||||
col_patient_id <- "patient_id"
|
||||
message(blue(paste0("NOTE: Using combined columns ", bold("`First name`, `Last name` and `Sex`"), " as input for `col_patient_id`.")))
|
||||
} else {
|
||||
col_patient_id <- search_type_in_df(tbl = tbl, type = "patient_id")
|
||||
}
|
||||
}
|
||||
if (is.null(col_patient_id)) {
|
||||
stop("`col_patient_id` must be set.", call. = FALSE)
|
||||
}
|
||||
|
||||
# -- key antibiotics
|
||||
if (is.null(col_keyantibiotics)) {
|
||||
col_keyantibiotics <- search_type_in_df(tbl = tbl, type = "keyantibiotics")
|
||||
}
|
||||
if (isFALSE(col_keyantibiotics)) {
|
||||
col_keyantibiotics <- NULL
|
||||
}
|
||||
|
||||
# -- specimen
|
||||
if (is.null(col_specimen)) {
|
||||
col_specimen <- search_type_in_df(tbl = tbl, type = "specimen")
|
||||
}
|
||||
if (isFALSE(col_specimen)) {
|
||||
col_specimen <- NULL
|
||||
}
|
||||
|
||||
# check if columns exist
|
||||
check_columns_existance <- function(column, tblname = tbl) {
|
||||
if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
|
||||
stop('Please check tbl for existance.')
|
||||
}
|
||||
|
||||
if (!is.null(column)) {
|
||||
if (!(column %in% colnames(tblname))) {
|
||||
stop('Column `', column, '` not found.')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
check_columns_existance(col_date)
|
||||
check_columns_existance(col_patient_id)
|
||||
check_columns_existance(col_mo)
|
||||
check_columns_existance(col_testcode)
|
||||
check_columns_existance(col_icu)
|
||||
check_columns_existance(col_keyantibiotics)
|
||||
|
||||
# join to microorganisms data set
|
||||
tbl <- tbl %>%
|
||||
mutate_at(vars(col_mo), as.mo) %>%
|
||||
left_join_microorganisms(by = col_mo)
|
||||
col_genus <- "genus"
|
||||
col_species <- "species"
|
||||
|
||||
if (is.null(col_testcode)) {
|
||||
testcodes_exclude <- NULL
|
||||
}
|
||||
# remove testcodes
|
||||
if (!is.null(testcodes_exclude) & info == TRUE) {
|
||||
cat('[Criterion] Excluded test codes:\n', toString(testcodes_exclude), '\n')
|
||||
}
|
||||
|
||||
if (is.null(col_icu)) {
|
||||
icu_exclude <- FALSE
|
||||
} else {
|
||||
tbl <- tbl %>%
|
||||
mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
|
||||
}
|
||||
|
||||
if (is.null(col_specimen)) {
|
||||
specimen_group <- NULL
|
||||
}
|
||||
|
||||
# filter on specimen group and keyantibiotics when they are filled in
|
||||
if (!is.null(specimen_group)) {
|
||||
check_columns_existance(col_specimen, tbl)
|
||||
if (info == TRUE) {
|
||||
cat('[Criterion] Excluded other than specimen group \'', specimen_group, '\'\n', sep = '')
|
||||
}
|
||||
}
|
||||
if (!is.null(col_keyantibiotics)) {
|
||||
tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics))
|
||||
}
|
||||
|
||||
if (is.null(testcodes_exclude)) {
|
||||
testcodes_exclude <- ''
|
||||
}
|
||||
|
||||
# create new dataframe with original row index and right sorting
|
||||
tbl <- tbl %>%
|
||||
mutate(first_isolate_row_index = 1:nrow(tbl),
|
||||
date_lab = tbl %>% pull(col_date),
|
||||
patient_id = tbl %>% pull(col_patient_id),
|
||||
species = tbl %>% pull(col_species),
|
||||
genus = tbl %>% pull(col_genus)) %>%
|
||||
mutate(species = if_else(is.na(species) | species == "(no MO)", "", species),
|
||||
genus = if_else(is.na(genus) | genus == "(no MO)", "", genus))
|
||||
|
||||
if (is.null(specimen_group)) {
|
||||
# not filtering on specimen
|
||||
if (icu_exclude == FALSE) {
|
||||
if (info == TRUE & !is.null(col_icu)) {
|
||||
cat('[Criterion] Included isolates from ICU.\n')
|
||||
}
|
||||
tbl <- tbl %>%
|
||||
arrange_at(c(col_patient_id,
|
||||
col_genus,
|
||||
col_species,
|
||||
col_date))
|
||||
row.start <- 1
|
||||
row.end <- nrow(tbl)
|
||||
} else {
|
||||
if (info == TRUE) {
|
||||
cat('[Criterion] Excluded isolates from ICU.\n')
|
||||
}
|
||||
tbl <- tbl %>%
|
||||
arrange_at(c(col_icu,
|
||||
col_patient_id,
|
||||
col_genus,
|
||||
col_species,
|
||||
col_date))
|
||||
|
||||
suppressWarnings(
|
||||
row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
|
||||
)
|
||||
suppressWarnings(
|
||||
row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
|
||||
)
|
||||
}
|
||||
|
||||
} else {
|
||||
# filtering on specimen and only analyse these row to save time
|
||||
if (icu_exclude == FALSE) {
|
||||
if (info == TRUE & !is.null(col_icu)) {
|
||||
cat('[Criterion] Included isolates from ICU.\n')
|
||||
}
|
||||
tbl <- tbl %>%
|
||||
arrange_at(c(col_specimen,
|
||||
col_patient_id,
|
||||
col_genus,
|
||||
col_species,
|
||||
col_date))
|
||||
suppressWarnings(
|
||||
row.start <- which(tbl %>% pull(col_specimen) == specimen_group) %>% min(na.rm = TRUE)
|
||||
)
|
||||
suppressWarnings(
|
||||
row.end <- which(tbl %>% pull(col_specimen) == specimen_group) %>% max(na.rm = TRUE)
|
||||
)
|
||||
} else {
|
||||
if (info == TRUE) {
|
||||
cat('[Criterion] Excluded isolates from ICU.\n')
|
||||
}
|
||||
tbl <- tbl %>%
|
||||
arrange_at(c(col_icu,
|
||||
col_specimen,
|
||||
col_patient_id,
|
||||
col_genus,
|
||||
col_species,
|
||||
col_date))
|
||||
suppressWarnings(
|
||||
row.start <- which(tbl %>% pull(col_specimen) == specimen_group
|
||||
& tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
|
||||
)
|
||||
suppressWarnings(
|
||||
row.end <- which(tbl %>% pull(col_specimen) == specimen_group
|
||||
& tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
|
||||
)
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
if (abs(row.start) == Inf | abs(row.end) == Inf) {
|
||||
if (info == TRUE) {
|
||||
message(paste("=> Found", bold("no isolates")))
|
||||
}
|
||||
# NAs where genus is unavailable
|
||||
return(tbl %>%
|
||||
mutate(real_first_isolate = if_else(genus == '', NA, FALSE)) %>%
|
||||
pull(real_first_isolate)
|
||||
)
|
||||
}
|
||||
|
||||
# suppress warnings because dplyr wants us to use library(dplyr) when using filter(row_number())
|
||||
suppressWarnings(
|
||||
scope.size <- tbl %>%
|
||||
filter(
|
||||
row_number() %>% between(row.start,
|
||||
row.end),
|
||||
genus != "",
|
||||
species != "") %>%
|
||||
nrow()
|
||||
)
|
||||
|
||||
identify_new_year = function(x, episode_days) {
|
||||
# I asked on StackOverflow:
|
||||
# https://stackoverflow.com/questions/42122245/filter-one-row-every-year
|
||||
if (length(x) == 1) {
|
||||
return(TRUE)
|
||||
}
|
||||
indices = integer(0)
|
||||
start = x[1]
|
||||
ind = 1
|
||||
indices[ind] = ind
|
||||
for (i in 2:length(x)) {
|
||||
if (as.numeric(x[i] - start >= episode_days)) {
|
||||
ind = ind + 1
|
||||
indices[ind] = i
|
||||
start = x[i]
|
||||
}
|
||||
}
|
||||
result <- rep(FALSE, length(x))
|
||||
result[indices] <- TRUE
|
||||
return(result)
|
||||
}
|
||||
|
||||
# Analysis of first isolate ----
|
||||
all_first <- tbl %>%
|
||||
mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
|
||||
& genus == lag(genus)
|
||||
& species == lag(species),
|
||||
FALSE,
|
||||
TRUE)) %>%
|
||||
group_by_at(vars(patient_id,
|
||||
genus,
|
||||
species)) %>%
|
||||
mutate(more_than_episode_ago = identify_new_year(x = date_lab,
|
||||
episode_days = episode_days)) %>%
|
||||
ungroup()
|
||||
|
||||
weighted.notice <- ''
|
||||
if (!is.null(col_keyantibiotics)) {
|
||||
weighted.notice <- 'weighted '
|
||||
if (info == TRUE) {
|
||||
if (type == 'keyantibiotics') {
|
||||
cat('[Criterion] Inclusion based on key antibiotics, ')
|
||||
if (ignore_I == FALSE) {
|
||||
cat('not ')
|
||||
}
|
||||
cat('ignoring I.\n')
|
||||
}
|
||||
if (type == 'points') {
|
||||
cat(paste0('[Criterion] Inclusion based on key antibiotics, using points threshold of '
|
||||
, points_threshold, '.\n'))
|
||||
}
|
||||
}
|
||||
type_param <- type
|
||||
# suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
|
||||
suppressWarnings(
|
||||
all_first <- all_first %>%
|
||||
mutate(key_ab_lag = lag(key_ab)) %>%
|
||||
mutate(key_ab_other = !key_antibiotics_equal(x = key_ab,
|
||||
y = key_ab_lag,
|
||||
type = type_param,
|
||||
ignore_I = ignore_I,
|
||||
points_threshold = points_threshold,
|
||||
info = info)) %>%
|
||||
mutate(
|
||||
real_first_isolate =
|
||||
if_else(
|
||||
between(row_number(), row.start, row.end)
|
||||
& genus != ""
|
||||
& species != ""
|
||||
& (other_pat_or_mo | more_than_episode_ago | key_ab_other),
|
||||
TRUE,
|
||||
FALSE))
|
||||
)
|
||||
} else {
|
||||
# no key antibiotics
|
||||
# suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
|
||||
suppressWarnings(
|
||||
all_first <- all_first %>%
|
||||
mutate(
|
||||
real_first_isolate =
|
||||
if_else(
|
||||
between(row_number(), row.start, row.end)
|
||||
& genus != ""
|
||||
& species != ""
|
||||
& (other_pat_or_mo | more_than_episode_ago),
|
||||
TRUE,
|
||||
FALSE))
|
||||
)
|
||||
}
|
||||
|
||||
# first one as TRUE
|
||||
all_first[row.start, 'real_first_isolate'] <- TRUE
|
||||
# no tests that should be included, or ICU
|
||||
if (!is.null(col_testcode)) {
|
||||
all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE
|
||||
}
|
||||
if (icu_exclude == TRUE) {
|
||||
all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
|
||||
}
|
||||
|
||||
# NAs where genus is unavailable
|
||||
all_first <- all_first %>%
|
||||
mutate(real_first_isolate = if_else(genus %in% c('', '(no MO)', NA), NA, real_first_isolate))
|
||||
|
||||
all_first <- all_first %>%
|
||||
arrange(first_isolate_row_index) %>%
|
||||
pull(real_first_isolate)
|
||||
|
||||
if (info == TRUE) {
|
||||
decimal.mark <- getOption("OutDec")
|
||||
big.mark <- ifelse(decimal.mark != ",", ",", ".")
|
||||
n_found <- base::sum(all_first, na.rm = TRUE)
|
||||
p_found_total <- percent(n_found / nrow(tbl), force_zero = TRUE)
|
||||
p_found_scope <- percent(n_found / scope.size, force_zero = TRUE)
|
||||
# mark up number of found
|
||||
n_found <- base::format(n_found, big.mark = big.mark, decimal.mark = decimal.mark)
|
||||
if (p_found_total != p_found_scope) {
|
||||
msg_txt <- paste0("=> Found ",
|
||||
bold(paste0(n_found, " first ", weighted.notice, "isolates")),
|
||||
" (", p_found_scope, " within scope and ", p_found_total, " of total)")
|
||||
} else {
|
||||
msg_txt <- paste0("=> Found ",
|
||||
bold(paste0(n_found, " first ", weighted.notice, "isolates")),
|
||||
" (", p_found_total, " of total)")
|
||||
}
|
||||
base::message(msg_txt)
|
||||
}
|
||||
|
||||
all_first
|
||||
|
||||
}
|
||||
|
||||
#' @rdname first_isolate
|
||||
#' @importFrom dplyr filter
|
||||
#' @export
|
||||
filter_first_isolate <- function(tbl,
|
||||
col_date = NULL,
|
||||
col_patient_id = NULL,
|
||||
col_mo = NULL,
|
||||
...) {
|
||||
filter(tbl, first_isolate(tbl = tbl,
|
||||
col_date = col_date,
|
||||
col_patient_id = col_patient_id,
|
||||
col_mo = col_mo,
|
||||
...))
|
||||
}
|
||||
|
||||
#' @rdname first_isolate
|
||||
#' @importFrom dplyr %>% mutate filter
|
||||
#' @export
|
||||
filter_first_weighted_isolate <- function(tbl,
|
||||
col_date = NULL,
|
||||
col_patient_id = NULL,
|
||||
col_mo = NULL,
|
||||
col_keyantibiotics = NULL,
|
||||
...) {
|
||||
tbl_keyab <- tbl %>%
|
||||
mutate(keyab = suppressMessages(key_antibiotics(.,
|
||||
col_mo = col_mo,
|
||||
...))) %>%
|
||||
mutate(firsts = first_isolate(.,
|
||||
col_date = col_date,
|
||||
col_patient_id = col_patient_id,
|
||||
col_mo = col_mo,
|
||||
col_keyantibiotics = "keyab",
|
||||
...))
|
||||
tbl[which(tbl_keyab$firsts == TRUE),]
|
||||
}
|
||||
-234
@@ -1,234 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' \emph{G}-test for Count Data
|
||||
#'
|
||||
#' \code{g.test} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link{chisq.test}} but is more reliable [1]. A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).
|
||||
#' @inherit stats::chisq.test params return
|
||||
#' @details If \code{x} is a matrix with one row or column, or if \code{x} is a vector and \code{y} is not given, then a \emph{goodness-of-fit test} is performed (\code{x} is treated as a one-dimensional contingency table). The entries of \code{x} must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in \code{p}, or are all equal if \code{p} is not given.
|
||||
#'
|
||||
#' If \code{x} is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of \code{x} must be non-negative integers. Otherwise, \code{x} and \code{y} must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these. Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals.
|
||||
#'
|
||||
#' The p-value is computed from the asymptotic chi-squared distribution of the test statistic.
|
||||
#'
|
||||
#' In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the \emph{G}-test) but rather that for Fisher's exact test.
|
||||
#'
|
||||
#' In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by \code{p}, each sample being of size \code{n = sum(x)}. This simulation is done in \R and may be slow.
|
||||
#' @section \emph{G}-test of goodness-of-fit (likelihood ratio test):
|
||||
#' Use the \emph{G}-test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross).
|
||||
#'
|
||||
#' If the expected number of observations in any category is too small, the \emph{G}-test may give inaccurate results, and you should use an exact test instead (\code{\link{fisher.test}}).
|
||||
#'
|
||||
#' The \emph{G}-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (\code{\link{chisq.test}}); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar.
|
||||
#'
|
||||
#' @section \emph{G}-test of independence:
|
||||
#' Use the \emph{G}-test of independence when you have two nominal variables, each with two or more possible values. You want to know whether the proportions for one variable are different among values of the other variable.
|
||||
#'
|
||||
#' It is also possible to do a \emph{G}-test of independence with more than two nominal variables. For example, Jackson et al. (2013) also had data for children under 3, so you could do an analysis of old vs. young, thigh vs. arm, and reaction vs. no reaction, all analyzed together.
|
||||
#'
|
||||
#' Fisher's exact test (\code{\link{fisher.test}}) is an \strong{exact} test, where the \emph{G}-test is still only an \strong{approximation}. For any 2x2 table, Fisher's Exact test may be slower but will still run in seconds, even if the sum of your observations is multiple millions.
|
||||
#'
|
||||
#' The \emph{G}-test of independence is an alternative to the chi-square test of independence (\code{\link{chisq.test}}), and they will give approximately the same results.
|
||||
#' @section How the test works:
|
||||
#' Unlike the exact test of goodness-of-fit (\code{\link{fisher.test}}), the \emph{G}-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the \emph{G}-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.
|
||||
#'
|
||||
#' The \emph{G}-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate a \emph{G}-statistic is:
|
||||
#'
|
||||
#' \code{G <- 2 * sum(x * log(x / E))}
|
||||
#'
|
||||
#' where \code{E} are the expected values. Since this is chi-square distributed, the p value can be calculated with:
|
||||
#'
|
||||
#' \code{p <- stats::pchisq(G, df, lower.tail = FALSE)}
|
||||
#'
|
||||
#' where \code{df} are the degrees of freedom.
|
||||
#'
|
||||
#' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use \emph{G}-tests for each category, of course.
|
||||
#' @keywords chi
|
||||
#' @seealso \code{\link{chisq.test}}
|
||||
#' @references [1] McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
|
||||
#' @source This code is almost identical to \code{\link{chisq.test}}, except that:
|
||||
#' \itemize{
|
||||
#' \item{The calculation of the statistic was changed to \code{2 * sum(x * log(x / E))}}
|
||||
#' \item{Yates' continuity correction was removed as it does not apply to a \emph{G}-test}
|
||||
#' \item{The possibility to simulate p values with \code{simulate.p.value} was removed}
|
||||
#' }
|
||||
#' @export
|
||||
#' @importFrom stats pchisq complete.cases
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # = EXAMPLE 1 =
|
||||
#' # Shivrain et al. (2006) crossed clearfield rice (which are resistant
|
||||
#' # to the herbicide imazethapyr) with red rice (which are susceptible to
|
||||
#' # imazethapyr). They then crossed the hybrid offspring and examined the
|
||||
#' # F2 generation, where they found 772 resistant plants, 1611 moderately
|
||||
#' # resistant plants, and 737 susceptible plants. If resistance is controlled
|
||||
#' # by a single gene with two co-dominant alleles, you would expect a 1:2:1
|
||||
#' # ratio.
|
||||
#'
|
||||
#' x <- c(772, 1611, 737)
|
||||
#' G <- g.test(x, p = c(1, 2, 1) / 4)
|
||||
#' # G$p.value = 0.12574.
|
||||
#'
|
||||
#' # There is no significant difference from a 1:2:1 ratio.
|
||||
#' # Meaning: resistance controlled by a single gene with two co-dominant
|
||||
#' # alleles, is plausible.
|
||||
#'
|
||||
#'
|
||||
#' # = EXAMPLE 2 =
|
||||
#' # Red crossbills (Loxia curvirostra) have the tip of the upper bill either
|
||||
#' # right or left of the lower bill, which helps them extract seeds from pine
|
||||
#' # cones. Some have hypothesized that frequency-dependent selection would
|
||||
#' # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992)
|
||||
#' # observed 1752 right-billed and 1895 left-billed crossbills.
|
||||
#'
|
||||
#' x <- c(1752, 1895)
|
||||
#' g.test(x)
|
||||
#' # p = 0.01787343
|
||||
#'
|
||||
#' # There is a significant difference from a 1:1 ratio.
|
||||
#' # Meaning: there are significantly more left-billed birds.
|
||||
#'
|
||||
g.test <- function(x,
|
||||
y = NULL,
|
||||
# correct = TRUE,
|
||||
p = rep(1/length(x), length(x)),
|
||||
rescale.p = FALSE) {
|
||||
DNAME <- deparse(substitute(x))
|
||||
if (is.data.frame(x))
|
||||
x <- as.matrix(x)
|
||||
if (is.matrix(x)) {
|
||||
if (min(dim(x)) == 1L)
|
||||
x <- as.vector(x)
|
||||
}
|
||||
if (!is.matrix(x) && !is.null(y)) {
|
||||
if (length(x) != length(y))
|
||||
stop("'x' and 'y' must have the same length")
|
||||
DNAME2 <- deparse(substitute(y))
|
||||
xname <- if (length(DNAME) > 1L || nchar(DNAME, "w") >
|
||||
30)
|
||||
""
|
||||
else DNAME
|
||||
yname <- if (length(DNAME2) > 1L || nchar(DNAME2, "w") >
|
||||
30)
|
||||
""
|
||||
else DNAME2
|
||||
OK <- complete.cases(x, y)
|
||||
x <- factor(x[OK])
|
||||
y <- factor(y[OK])
|
||||
if ((nlevels(x) < 2L) || (nlevels(y) < 2L))
|
||||
stop("'x' and 'y' must have at least 2 levels")
|
||||
x <- table(x, y)
|
||||
names(dimnames(x)) <- c(xname, yname)
|
||||
DNAME <- paste(paste(DNAME, collapse = "\n"), "and",
|
||||
paste(DNAME2, collapse = "\n"))
|
||||
}
|
||||
if (any(x < 0) || anyNA(x))
|
||||
stop("all entries of 'x' must be nonnegative and finite")
|
||||
if ((n <- sum(x)) == 0)
|
||||
stop("at least one entry of 'x' must be positive")
|
||||
# if (simulate.p.value) {
|
||||
# setMETH <- function() METHOD <<- paste(METHOD, "with simulated p-value\n\t (based on",
|
||||
# B, "replicates)")
|
||||
# almost.1 <- 1 - 64 * .Machine$double.eps
|
||||
# }
|
||||
if (is.matrix(x)) {
|
||||
METHOD <- "G-test of independence"
|
||||
nr <- as.integer(nrow(x))
|
||||
nc <- as.integer(ncol(x))
|
||||
if (is.na(nr) || is.na(nc) || is.na(nr * nc))
|
||||
stop("invalid nrow(x) or ncol(x)", domain = NA)
|
||||
# add fisher.test suggestion
|
||||
if (nr == 2 && nc == 2)
|
||||
warning("`fisher.test()` is always more reliable for 2x2 tables and although must slower, often only takes seconds.")
|
||||
sr <- rowSums(x)
|
||||
sc <- colSums(x)
|
||||
E <- outer(sr, sc, "*")/n
|
||||
v <- function(r, c, n) c * r * (n - r) * (n - c)/n^3
|
||||
V <- outer(sr, sc, v, n)
|
||||
dimnames(E) <- dimnames(x)
|
||||
# if (simulate.p.value && all(sr > 0) && all(sc > 0)) {
|
||||
# setMETH()
|
||||
# tmp <- .Call(chisq_sim, sr, sc, B, E, PACKAGE = "stats")
|
||||
# STATISTIC <- 2 * sum(x * log(x / E)) # sum(sort((x - E)^2/E, decreasing = TRUE)) for chisq.test
|
||||
# PARAMETER <- NA
|
||||
# PVAL <- (1 + sum(tmp >= almost.1 * STATISTIC))/(B +
|
||||
# 1)
|
||||
# }
|
||||
# else {
|
||||
# if (simulate.p.value)
|
||||
# warning("cannot compute simulated p-value with zero marginals")
|
||||
# if (correct && nrow(x) == 2L && ncol(x) == 2L) {
|
||||
# YATES <- min(0.5, abs(x - E))
|
||||
# if (YATES > 0)
|
||||
# METHOD <- paste(METHOD, "with Yates' continuity correction")
|
||||
# }
|
||||
# else YATES <- 0
|
||||
STATISTIC <- 2 * sum(x * log(x / E)) # sum((abs(x - E) - YATES)^2/E) for chisq.test
|
||||
PARAMETER <- (nr - 1L) * (nc - 1L)
|
||||
PVAL <- pchisq(STATISTIC, PARAMETER, lower.tail = FALSE)
|
||||
# }
|
||||
}
|
||||
else {
|
||||
if (length(dim(x)) > 2L)
|
||||
stop("invalid 'x'")
|
||||
if (length(x) == 1L)
|
||||
stop("'x' must at least have 2 elements")
|
||||
if (length(x) != length(p))
|
||||
stop("'x' and 'p' must have the same number of elements")
|
||||
if (any(p < 0))
|
||||
stop("probabilities must be non-negative.")
|
||||
if (abs(sum(p) - 1) > sqrt(.Machine$double.eps)) {
|
||||
if (rescale.p)
|
||||
p <- p/sum(p)
|
||||
else stop("probabilities must sum to 1.")
|
||||
}
|
||||
METHOD <- "G-test of goodness-of-fit (likelihood ratio test)"
|
||||
E <- n * p
|
||||
V <- n * p * (1 - p)
|
||||
STATISTIC <- 2 * sum(x * log(x / E)) # sum((x - E)^2/E) for chisq.test
|
||||
names(E) <- names(x)
|
||||
# if (simulate.p.value) {
|
||||
# setMETH()
|
||||
# nx <- length(x)
|
||||
# sm <- matrix(sample.int(nx, B * n, TRUE, prob = p),
|
||||
# nrow = n)
|
||||
# ss <- apply(sm, 2L, function(x, E, k) {
|
||||
# sum((table(factor(x, levels = 1L:k)) - E)^2/E)
|
||||
# }, E = E, k = nx)
|
||||
# PARAMETER <- NA
|
||||
# PVAL <- (1 + sum(ss >= almost.1 * STATISTIC))/(B +
|
||||
# 1)
|
||||
# }
|
||||
# else {
|
||||
PARAMETER <- length(x) - 1
|
||||
PVAL <- pchisq(STATISTIC, PARAMETER, lower.tail = FALSE)
|
||||
# }
|
||||
}
|
||||
names(STATISTIC) <- "X-squared"
|
||||
names(PARAMETER) <- "df"
|
||||
if (any(E < 5) && is.finite(PARAMETER))
|
||||
warning("G-statistic approximation may be incorrect due to E < 5")
|
||||
|
||||
structure(list(statistic = STATISTIC, parameter = PARAMETER,
|
||||
p.value = PVAL, method = METHOD, data.name = DNAME,
|
||||
observed = x, expected = E, residuals = (x - E)/sqrt(E),
|
||||
stdres = (x - E)/sqrt(V)), class = "htest")
|
||||
}
|
||||
@@ -1,57 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Get language for AMR
|
||||
#'
|
||||
#' Determines the system language to be used for language-dependent output of AMR functions, like \code{\link{mo_gramstain}} and \code{\link{mo_type}}.
|
||||
#' @details The system language can be overwritten with \code{\link{getOption}("AMR_locale")}.
|
||||
#' @section Supported languages:
|
||||
#' Supported languages are \code{"en"} (English), \code{"de"} (German), \code{"nl"} (Dutch), \code{"es"} (Spanish), \code{"it"} (Italian), \code{"fr"} (French), and \code{"pt"} (Portuguese).
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
get_locale <- function() {
|
||||
if (!is.null(getOption("AMR_locale"))) {
|
||||
if (getOption("AMR_locale") %in% c("en", "de", "nl", "es", "it", "fr", "pt")) {
|
||||
return(getOption("AMR_locale"))
|
||||
}
|
||||
}
|
||||
lang <- Sys.getlocale("LC_COLLATE")
|
||||
# grepl with case = FALSE is faster than like
|
||||
if (grepl("^(English|en_|EN_)", lang, ignore.case = FALSE)) {
|
||||
# as first option to optimise speed
|
||||
"en"
|
||||
} else if (grepl("^(German|Deutsch|de_|DE_)", lang, ignore.case = FALSE)) {
|
||||
"de"
|
||||
} else if (grepl("^(Dutch|Nederlands|nl_|NL_)", lang, ignore.case = FALSE)) {
|
||||
"nl"
|
||||
} else if (grepl("^(Spanish|Espa.ol|es_|ES_)", lang, ignore.case = FALSE)) {
|
||||
"es"
|
||||
} else if (grepl("^(Italian|Italiano|it_|IT_)", lang, ignore.case = FALSE)) {
|
||||
"it"
|
||||
} else if (grepl("^(French|Fran.ais|fr_|FR_)", lang, ignore.case = FALSE)) {
|
||||
"fr"
|
||||
} else if (grepl("^(Portuguese|Portugu.s|pt_|PT_)", lang, ignore.case = FALSE)) {
|
||||
"pt"
|
||||
} else {
|
||||
# other language -> set to English
|
||||
"en"
|
||||
}
|
||||
}
|
||||
-367
@@ -1,367 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' AMR plots with \code{ggplot2}
|
||||
#'
|
||||
#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
|
||||
#' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})
|
||||
#' @param position position adjustment of bars, either \code{"fill"} (default when \code{fun} is \code{\link{count_df}}), \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) or \code{"dodge"}
|
||||
#' @param x variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
|
||||
#' @param fill variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
|
||||
#' @param breaks numeric vector of positions
|
||||
#' @param limits numeric vector of length two providing limits of the scale, use \code{NA} to refer to the existing minimum or maximum
|
||||
#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable
|
||||
#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.
|
||||
#' @param fun function to transform \code{data}, either \code{\link{count_df}} (default) or \code{\link{portion_df}}
|
||||
#' @param nrow (when using \code{facet}) number of rows
|
||||
#' @param datalabels show datalabels using \code{labels_rsi_count}, will at default only be shown when \code{fun = count_df}
|
||||
#' @param datalabels.size size of the datalabels
|
||||
#' @param datalabels.colour colour of the datalabels
|
||||
#' @param ... other parameters passed on to \code{geom_rsi}
|
||||
#' @details At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
|
||||
#'
|
||||
#' \strong{The functions}\cr
|
||||
#' \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{fun} (\code{\link{count_df}} at default, can also be \code{\link{portion_df}}) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
|
||||
#'
|
||||
#' \code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
|
||||
#'
|
||||
#' \code{scale_y_percent} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2]{scale_continuous}}.
|
||||
#'
|
||||
#' \code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R, using \code{\link[ggplot2]{scale_brewer}}.
|
||||
#'
|
||||
#' \code{theme_rsi} is a \code{ggplot \link[ggplot2]{theme}} with minimal distraction.
|
||||
#'
|
||||
#' \code{labels_rsi_count} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2]{geom_text}}
|
||||
#'
|
||||
#' \code{ggplot_rsi} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\code{\%>\%}). See Examples.
|
||||
#' @rdname ggplot_rsi
|
||||
#' @importFrom utils installed.packages
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' library(dplyr)
|
||||
#' library(ggplot2)
|
||||
#'
|
||||
#' # get antimicrobial results for drugs against a UTI:
|
||||
#' ggplot(septic_patients %>% select(amox, nitr, fosf, trim, cipr)) +
|
||||
#' geom_rsi()
|
||||
#'
|
||||
#' # prettify the plot using some additional functions:
|
||||
#' df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
|
||||
#' ggplot(df) +
|
||||
#' geom_rsi() +
|
||||
#' scale_y_percent() +
|
||||
#' scale_rsi_colours() +
|
||||
#' labels_rsi_count() +
|
||||
#' theme_rsi()
|
||||
#'
|
||||
#' # or better yet, simplify this using the wrapper function - a single command:
|
||||
#' septic_patients %>%
|
||||
#' select(amox, nitr, fosf, trim, cipr) %>%
|
||||
#' ggplot_rsi()
|
||||
#'
|
||||
#' # get only portions and no counts:
|
||||
#' septic_patients %>%
|
||||
#' select(amox, nitr, fosf, trim, cipr) %>%
|
||||
#' ggplot_rsi(fun = portion_df)
|
||||
#'
|
||||
#' # add other ggplot2 parameters as you like:
|
||||
#' septic_patients %>%
|
||||
#' select(amox, nitr, fosf, trim, cipr) %>%
|
||||
#' ggplot_rsi(width = 0.5,
|
||||
#' colour = "black",
|
||||
#' size = 1,
|
||||
#' linetype = 2,
|
||||
#' alpha = 0.25)
|
||||
#'
|
||||
#' # resistance of ciprofloxacine per age group
|
||||
#' septic_patients %>%
|
||||
#' mutate(first_isolate = first_isolate(.)) %>%
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' mo == as.mo("E. coli")) %>%
|
||||
#' # `age_group` is also a function of this package:
|
||||
#' group_by(age_group = age_groups(age)) %>%
|
||||
#' select(age_group,
|
||||
#' cipr) %>%
|
||||
#' ggplot_rsi(x = "age_group")
|
||||
#' \donttest{
|
||||
#'
|
||||
#' # for colourblind mode, use divergent colours from the viridis package:
|
||||
#' septic_patients %>%
|
||||
#' select(amox, nitr, fosf, trim, cipr) %>%
|
||||
#' ggplot_rsi() + scale_fill_viridis_d()
|
||||
#'
|
||||
#'
|
||||
#' # it also supports groups (don't forget to use the group var on `x` or `facet`):
|
||||
#' septic_patients %>%
|
||||
#' select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' ggplot_rsi(x = hospital_id,
|
||||
#' facet = Antibiotic,
|
||||
#' nrow = 1) +
|
||||
#' labs(title = "AMR of Anti-UTI Drugs Per Hospital",
|
||||
#' x = "Hospital")
|
||||
#'
|
||||
#' # genuine analysis: check 2 most prevalent microorganisms
|
||||
#' septic_patients %>%
|
||||
#' # create new bacterial ID's, with all CoNS under the same group (Becker et al.)
|
||||
#' mutate(mo = as.mo(mo, Becker = TRUE)) %>%
|
||||
#' # filter on top three bacterial ID's
|
||||
#' filter(mo %in% top_freq(freq(.$mo), 3)) %>%
|
||||
#' # determine first isolates
|
||||
#' mutate(first_isolate = first_isolate(.,
|
||||
#' col_date = "date",
|
||||
#' col_patient_id = "patient_id",
|
||||
#' col_mo = "mo")) %>%
|
||||
#' # filter on first isolates
|
||||
#' filter(first_isolate == TRUE) %>%
|
||||
#' # get short MO names (like "E. coli")
|
||||
#' mutate(mo = mo_shortname(mo, Becker = TRUE)) %>%
|
||||
#' # select this short name and some antiseptic drugs
|
||||
#' select(mo, cfur, gent, cipr) %>%
|
||||
#' # group by MO
|
||||
#' group_by(mo) %>%
|
||||
#' # plot the thing, putting MOs on the facet
|
||||
#' ggplot_rsi(x = Antibiotic,
|
||||
#' facet = mo,
|
||||
#' translate_ab = FALSE,
|
||||
#' nrow = 1) +
|
||||
#' labs(title = "AMR of Top Three Microorganisms In Blood Culture Isolates",
|
||||
#' subtitle = "Only First Isolates, CoNS grouped according to Becker et al. (2014)",
|
||||
#' x = "Microorganisms")
|
||||
#' }
|
||||
ggplot_rsi <- function(data,
|
||||
position = NULL,
|
||||
x = "Antibiotic",
|
||||
fill = "Interpretation",
|
||||
# params = list(),
|
||||
facet = NULL,
|
||||
breaks = seq(0, 1, 0.1),
|
||||
limits = NULL,
|
||||
translate_ab = "official",
|
||||
fun = count_df,
|
||||
nrow = NULL,
|
||||
datalabels = TRUE,
|
||||
datalabels.size = 3,
|
||||
datalabels.colour = "grey15",
|
||||
...) {
|
||||
|
||||
stopifnot_installed_package("ggplot2")
|
||||
|
||||
fun_name <- deparse(substitute(fun))
|
||||
if (!fun_name %in% c("portion_df", "count_df")) {
|
||||
stop("`fun` must be portion_df or count_df")
|
||||
}
|
||||
|
||||
x <- x[1]
|
||||
facet <- facet[1]
|
||||
|
||||
# we work with aes_string later on
|
||||
x_deparse <- deparse(substitute(x))
|
||||
if (x_deparse != "x") {
|
||||
x <- x_deparse
|
||||
}
|
||||
if (x %like% '".*"') {
|
||||
x <- substr(x, 2, nchar(x) - 1)
|
||||
}
|
||||
facet_deparse <- deparse(substitute(facet))
|
||||
if (facet_deparse != "facet") {
|
||||
facet <- facet_deparse
|
||||
}
|
||||
if (facet %like% '".*"') {
|
||||
facet <- substr(facet, 2, nchar(facet) - 1)
|
||||
}
|
||||
if (facet %in% c("NULL", "")) {
|
||||
facet <- NULL
|
||||
}
|
||||
|
||||
p <- ggplot2::ggplot(data = data) +
|
||||
geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, fun = fun, ...) +
|
||||
theme_rsi()
|
||||
|
||||
if (fill == "Interpretation") {
|
||||
# set RSI colours
|
||||
p <- p + scale_rsi_colours()
|
||||
}
|
||||
if (is.null(position)) {
|
||||
position <- "fill"
|
||||
}
|
||||
if (fun_name == "portion_df"
|
||||
| (fun_name == "count_df" & position == "fill")) {
|
||||
# portions, so use y scale with percentage
|
||||
p <- p + scale_y_percent(breaks = breaks, limits = limits)
|
||||
}
|
||||
|
||||
if (fun_name == "count_df" & datalabels == TRUE) {
|
||||
p <- p + labels_rsi_count(position = position,
|
||||
x = x,
|
||||
datalabels.size = datalabels.size,
|
||||
datalabels.colour = datalabels.colour)
|
||||
}
|
||||
|
||||
if (!is.null(facet)) {
|
||||
p <- p + facet_rsi(facet = facet, nrow = nrow)
|
||||
}
|
||||
|
||||
p
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
geom_rsi <- function(position = NULL,
|
||||
x = c("Antibiotic", "Interpretation"),
|
||||
fill = "Interpretation",
|
||||
translate_ab = "official",
|
||||
fun = count_df,
|
||||
...) {
|
||||
|
||||
stopifnot_installed_package("ggplot2")
|
||||
|
||||
fun_name <- deparse(substitute(fun))
|
||||
if (!fun_name %in% c("portion_df", "count_df", "fun")) {
|
||||
stop("`fun` must be portion_df or count_df")
|
||||
}
|
||||
y <- "Value"
|
||||
if (identical(fun, count_df)) {
|
||||
if (missing(position) | is.null(position)) {
|
||||
position <- "fill"
|
||||
}
|
||||
} else {
|
||||
if (missing(position) | is.null(position)) {
|
||||
position <- "stack"
|
||||
}
|
||||
}
|
||||
|
||||
x <- x[1]
|
||||
|
||||
# we work with aes_string later on
|
||||
x_deparse <- deparse(substitute(x))
|
||||
if (x_deparse != "x") {
|
||||
x <- x_deparse
|
||||
}
|
||||
if (x %like% '".*"') {
|
||||
x <- substr(x, 2, nchar(x) - 1)
|
||||
}
|
||||
|
||||
if (tolower(x) %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
|
||||
x <- "Antibiotic"
|
||||
} else if (tolower(x) %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
|
||||
x <- "Interpretation"
|
||||
}
|
||||
|
||||
options(get_antibiotic_names = translate_ab)
|
||||
|
||||
ggplot2::layer(geom = "bar", stat = "identity", position = position,
|
||||
mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
|
||||
data = fun, params = list(...))
|
||||
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
facet_rsi <- function(facet = c("Interpretation", "Antibiotic"), nrow = NULL) {
|
||||
|
||||
stopifnot_installed_package("ggplot2")
|
||||
|
||||
facet <- facet[1]
|
||||
|
||||
# we work with aes_string later on
|
||||
facet_deparse <- deparse(substitute(facet))
|
||||
if (facet_deparse != "facet") {
|
||||
facet <- facet_deparse
|
||||
}
|
||||
if (facet %like% '".*"') {
|
||||
facet <- substr(facet, 2, nchar(facet) - 1)
|
||||
}
|
||||
|
||||
if (tolower(facet) %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
|
||||
facet <- "Interpretation"
|
||||
} else if (tolower(facet) %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
|
||||
facet <- "Antibiotic"
|
||||
}
|
||||
|
||||
ggplot2::facet_wrap(facets = facet, scales = "free_x", nrow = nrow)
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
scale_y_percent <- function(breaks = seq(0, 1, 0.1), limits = NULL) {
|
||||
stopifnot_installed_package("ggplot2")
|
||||
|
||||
if (all(breaks[breaks != 0] > 1)) {
|
||||
breaks <- breaks / 100
|
||||
}
|
||||
ggplot2::scale_y_continuous(breaks = breaks,
|
||||
labels = percent(breaks),
|
||||
limits = limits)
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
scale_rsi_colours <- function() {
|
||||
stopifnot_installed_package("ggplot2")
|
||||
#ggplot2::scale_fill_brewer(palette = "RdYlGn")
|
||||
ggplot2::scale_fill_manual(values = c("#b22222", "#ae9c20", "#7cfc00"))
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
theme_rsi <- function() {
|
||||
stopifnot_installed_package("ggplot2")
|
||||
ggplot2::theme_minimal() +
|
||||
ggplot2::theme(panel.grid.major.x = ggplot2::element_blank(),
|
||||
panel.grid.minor = ggplot2::element_blank(),
|
||||
panel.grid.major.y = ggplot2::element_line(colour = "grey75"))
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
labels_rsi_count <- function(position = NULL,
|
||||
x = "Antibiotic",
|
||||
datalabels.size = 3,
|
||||
datalabels.colour = "grey15") {
|
||||
stopifnot_installed_package("ggplot2")
|
||||
if (is.null(position)) {
|
||||
position <- "fill"
|
||||
}
|
||||
if (position == "fill") {
|
||||
position <- ggplot2::position_fill(vjust = 0.5)
|
||||
}
|
||||
ggplot2::geom_text(mapping = ggplot2::aes_string(label = "lbl",
|
||||
x = x,
|
||||
y = "Value"),
|
||||
position = position,
|
||||
data = getlbls,
|
||||
inherit.aes = FALSE,
|
||||
size = datalabels.size,
|
||||
colour = datalabels.colour)
|
||||
}
|
||||
|
||||
#' @importFrom dplyr %>% group_by mutate
|
||||
getlbls <- function(data) {
|
||||
data %>%
|
||||
count_df() %>%
|
||||
group_by(Antibiotic) %>%
|
||||
mutate(lbl = paste0(percent(Value / sum(Value, na.rm = TRUE), force_zero = TRUE),
|
||||
" (n=", Value, ")")) %>%
|
||||
mutate(lbl = ifelse(lbl == "0.0% (n=0)", "", lbl))
|
||||
}
|
||||
|
||||
-106
@@ -1,106 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
globalVariables(c(".",
|
||||
"..property",
|
||||
"antibiotic",
|
||||
"Antibiotic",
|
||||
"antibiotics",
|
||||
"atc",
|
||||
"authors",
|
||||
"Becker",
|
||||
"certe",
|
||||
"cnt",
|
||||
"col_id",
|
||||
"count",
|
||||
"count.x",
|
||||
"count.y",
|
||||
"cum_count",
|
||||
"cum_percent",
|
||||
"date_lab",
|
||||
"diff.percent",
|
||||
"fctlvl",
|
||||
"First name",
|
||||
"first_isolate_row_index",
|
||||
"Freq",
|
||||
"fullname",
|
||||
"fullname_lower",
|
||||
"genus",
|
||||
"gramstain",
|
||||
"index",
|
||||
"Interpretation",
|
||||
"input",
|
||||
"item",
|
||||
"key_ab",
|
||||
"key_ab_lag",
|
||||
"key_ab_other",
|
||||
"kingdom",
|
||||
"labs",
|
||||
"Lancefield",
|
||||
"Last name",
|
||||
"lbl",
|
||||
"median",
|
||||
"mic",
|
||||
"microorganisms",
|
||||
"microorganisms.codes",
|
||||
"microorganisms.old",
|
||||
"microorganisms.oldDT",
|
||||
"microorganisms.prevDT",
|
||||
"microorganisms.unprevDT",
|
||||
"microorganismsDT",
|
||||
"mo",
|
||||
"mo.old",
|
||||
"more_than_episode_ago",
|
||||
"n",
|
||||
"name",
|
||||
"observations",
|
||||
"observed",
|
||||
"official",
|
||||
"other_pat_or_mo",
|
||||
"Pasted",
|
||||
"patient_id",
|
||||
"phylum",
|
||||
"prevalence",
|
||||
"prevalent",
|
||||
"property",
|
||||
"psae",
|
||||
"R",
|
||||
"real_first_isolate",
|
||||
"ref",
|
||||
"S",
|
||||
"se_max",
|
||||
"se_min",
|
||||
"septic_patients",
|
||||
"Sex",
|
||||
"shortname",
|
||||
"species",
|
||||
"species_id",
|
||||
"subspecies",
|
||||
"trade_name",
|
||||
"transmute",
|
||||
"tsn",
|
||||
"tsn_new",
|
||||
"umcg",
|
||||
"value",
|
||||
"Value",
|
||||
"x",
|
||||
"y",
|
||||
"year"))
|
||||
@@ -1,136 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Guess antibiotic column
|
||||
#'
|
||||
#' This tries to find a column name in a data set based on information from the \code{\link{antibiotics}} data set. Also supports WHONET abbreviations. You can look for an antibiotic (trade) name or abbreviation and it will search the \code{data.frame} for any column containing a name or ATC code of that antibiotic.
|
||||
#' @param tbl a \code{data.frame}
|
||||
#' @param col a character to look for
|
||||
#' @param verbose a logical to indicate whether additional info should be printed
|
||||
#' @importFrom dplyr %>% select filter_all any_vars
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' df <- data.frame(amox = "S",
|
||||
#' tetr = "R")
|
||||
#'
|
||||
#' guess_ab_col(df, "amoxicillin")
|
||||
#' # [1] "amox"
|
||||
#' guess_ab_col(df, "J01AA07") # ATC code of Tetracycline
|
||||
#' # [1] "tetr"
|
||||
#'
|
||||
#' guess_ab_col(df, "J01AA07", verbose = TRUE)
|
||||
#' # using column `tetr` for col "J01AA07"
|
||||
#' # [1] "tetr"
|
||||
#'
|
||||
#' # WHONET codes
|
||||
#' df <- data.frame(AMP_ND10 = "R",
|
||||
#' AMC_ED20 = "S")
|
||||
#' guess_ab_col(df, "ampicillin")
|
||||
#' # [1] "AMP_ND10"
|
||||
#' guess_ab_col(df, "J01CR02")
|
||||
#' # [1] "AMC_ED20"
|
||||
#' guess_ab_col(df, as.atc("augmentin"))
|
||||
#' # [1] "AMC_ED20"
|
||||
guess_ab_col <- function(tbl = NULL, col = NULL, verbose = FALSE) {
|
||||
if (is.null(tbl) & is.null(col)) {
|
||||
return(as.name("guess_ab_col"))
|
||||
}
|
||||
#stop("This function should not be called directly.")
|
||||
if (length(col) > 1) {
|
||||
warning("argument 'col' has length > 1 and only the first element will be used")
|
||||
col <- col[1]
|
||||
}
|
||||
if (!is.data.frame(tbl)) {
|
||||
stop("`tbl` must be a data.frame")
|
||||
}
|
||||
|
||||
tbl_names <- colnames(tbl)
|
||||
tbl_names_stripped <- colnames(tbl) %>%
|
||||
strsplit("_") %>%
|
||||
lapply(function(x) {x[1]}) %>%
|
||||
unlist()
|
||||
|
||||
if (col %in% tbl_names) {
|
||||
return(col)
|
||||
}
|
||||
ab_result <- antibiotics %>%
|
||||
select(atc:trade_name) %>%
|
||||
filter_all(any_vars(tolower(.) == tolower(col))) %>%
|
||||
filter_all(any_vars(. %in% tbl_names))
|
||||
|
||||
if (nrow(ab_result) == 0 & nchar(col) > 4) {
|
||||
# use like when col >= 5 characters
|
||||
ab_result <- antibiotics %>%
|
||||
select(atc:trade_name) %>%
|
||||
filter_all(any_vars(tolower(.) %like% tolower(col))) %>%
|
||||
filter_all(any_vars(. %in% tbl_names))
|
||||
}
|
||||
|
||||
# WHONET
|
||||
if (nrow(ab_result) == 0) {
|
||||
# use like when col >= 5 characters
|
||||
ab_result <- antibiotics %>%
|
||||
select(atc:trade_name) %>%
|
||||
filter_all(any_vars(tolower(.) == tolower(col))) %>%
|
||||
filter_all(any_vars(. %in% tbl_names_stripped))
|
||||
}
|
||||
|
||||
if (nrow(ab_result) > 1) {
|
||||
# looking more and more for reliable hit
|
||||
ab_result_1 <- ab_result %>% filter(tolower(atc) == tolower(col))
|
||||
if (nrow(ab_result_1) == 0) {
|
||||
ab_result_1 <- ab_result %>% filter(tolower(certe) == tolower(col))
|
||||
}
|
||||
if (nrow(ab_result_1) == 0) {
|
||||
ab_result_1 <- ab_result %>% filter(tolower(umcg) == tolower(col))
|
||||
}
|
||||
if (nrow(ab_result_1) == 0) {
|
||||
ab_result_1 <- ab_result %>% filter(tolower(official) == tolower(col))
|
||||
}
|
||||
if (nrow(ab_result_1) == 0) {
|
||||
ab_result_1 <- ab_result[1, ]
|
||||
}
|
||||
ab_result <- ab_result_1
|
||||
}
|
||||
|
||||
if (length(ab_result) == 0) {
|
||||
if (verbose == TRUE) {
|
||||
message('no result found for col "', col, '"')
|
||||
}
|
||||
return(NULL)
|
||||
} else {
|
||||
result <- tbl_names[tbl_names %in% ab_result]
|
||||
if (length(result) == 0) {
|
||||
result <- tbl_names[tbl_names_stripped %in% ab_result]
|
||||
}
|
||||
if (length(result) == 0) {
|
||||
if (verbose == TRUE) {
|
||||
message('no result found for col "', col, '"')
|
||||
}
|
||||
return(NULL)
|
||||
}
|
||||
if (verbose == TRUE) {
|
||||
message('using column `', result, '` for col "', col, '"')
|
||||
}
|
||||
return(result)
|
||||
}
|
||||
}
|
||||
@@ -1,158 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Join a table with \code{microorganisms}
|
||||
#'
|
||||
#' Join the dataset \code{\link{microorganisms}} easily to an existing table or character vector.
|
||||
#' @rdname join
|
||||
#' @name join
|
||||
#' @aliases join inner_join
|
||||
#' @param x existing table to join, or character vector
|
||||
#' @param by a variable to join by - if left empty will search for a column with class \code{mo} (created with \code{\link{as.mo}}) or will be \code{"mo"} if that column name exists in \code{x}, could otherwise be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
|
||||
#' @param suffix if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
|
||||
#' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.
|
||||
#' @details \strong{Note:} As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
#' @examples
|
||||
#' left_join_microorganisms(as.mo("K. pneumoniae"))
|
||||
#' left_join_microorganisms("B_KLBSL_PNE")
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>% left_join_microorganisms()
|
||||
#'
|
||||
#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
#' to = as.Date("2018-01-07"),
|
||||
#' by = 1),
|
||||
#' bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
|
||||
#' "E. coli", "E. coli", "E. coli")),
|
||||
#' stringsAsFactors = FALSE)
|
||||
#' colnames(df)
|
||||
#' df_joined <- left_join_microorganisms(df, "bacteria")
|
||||
#' colnames(df_joined)
|
||||
inner_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
|
||||
checked <- joins_check_df(x, by)
|
||||
x <- checked$x
|
||||
by <- checked$by
|
||||
join <- suppressWarnings(
|
||||
dplyr::inner_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
|
||||
)
|
||||
if (nrow(join) > nrow(x)) {
|
||||
warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
|
||||
}
|
||||
join
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
left_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
|
||||
checked <- joins_check_df(x, by)
|
||||
x <- checked$x
|
||||
by <- checked$by
|
||||
join <- suppressWarnings(
|
||||
dplyr::left_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
|
||||
)
|
||||
if (nrow(join) > nrow(x)) {
|
||||
warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
|
||||
}
|
||||
join
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
right_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
|
||||
checked <- joins_check_df(x, by)
|
||||
x <- checked$x
|
||||
by <- checked$by
|
||||
join <- suppressWarnings(
|
||||
dplyr::right_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
|
||||
)
|
||||
if (nrow(join) > nrow(x)) {
|
||||
warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
|
||||
}
|
||||
join
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
full_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
|
||||
checked <- joins_check_df(x, by)
|
||||
x <- checked$x
|
||||
by <- checked$by
|
||||
join <- suppressWarnings(
|
||||
dplyr::full_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
|
||||
)
|
||||
if (nrow(join) > nrow(x)) {
|
||||
warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
|
||||
}
|
||||
join
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
semi_join_microorganisms <- function(x, by = NULL, ...) {
|
||||
checked <- joins_check_df(x, by)
|
||||
x <- checked$x
|
||||
by <- checked$by
|
||||
suppressWarnings(
|
||||
dplyr::semi_join(x = x, y = AMR::microorganisms, by = by, ...)
|
||||
)
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
anti_join_microorganisms <- function(x, by = NULL, ...) {
|
||||
checked <- joins_check_df(x, by)
|
||||
x <- checked$x
|
||||
by <- checked$by
|
||||
suppressWarnings(
|
||||
dplyr::anti_join(x = x, y = AMR::microorganisms, by = by, ...)
|
||||
)
|
||||
}
|
||||
|
||||
joins_check_df <- function(x, by) {
|
||||
if (!any(class(x) %in% c("data.frame", "matrix"))) {
|
||||
x <- data.frame(mo = as.character(x), stringsAsFactors = FALSE)
|
||||
if (is.null(by)) {
|
||||
by <- "mo"
|
||||
}
|
||||
}
|
||||
if (is.null(by)) {
|
||||
# search for column with class `mo` and return first one found
|
||||
by <- colnames(x)[lapply(x, is.mo) == TRUE][1]
|
||||
if (is.na(by)) {
|
||||
if ("mo" %in% colnames(x)) {
|
||||
by <- "mo"
|
||||
} else {
|
||||
stop("Cannot join - no column found with name or class `mo`.", call. = FALSE)
|
||||
}
|
||||
}
|
||||
message('Joining, by = "', by, '"') # message same as dplyr::join functions
|
||||
}
|
||||
if (is.null(names(by))) {
|
||||
joinby <- colnames(AMR::microorganisms)[1]
|
||||
names(joinby) <- by
|
||||
} else {
|
||||
joinby <- by
|
||||
}
|
||||
list(x = x,
|
||||
by = joinby)
|
||||
}
|
||||
@@ -1,267 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Key antibiotics for first \emph{weighted} isolates
|
||||
#'
|
||||
#' These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
|
||||
#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
|
||||
#' @param x,y characters to compare
|
||||
#' @inheritParams first_isolate
|
||||
#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of \strong{broad-spectrum} antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
|
||||
#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram positives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
|
||||
#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram negatives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
|
||||
#' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
|
||||
#' @param ... other parameters passed on to function
|
||||
#' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it would not.
|
||||
#'
|
||||
#' At default, the antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
|
||||
#' \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"vanc"}, \code{"teic"}, \code{"tetr"}, \code{"eryt"}, \code{"oxac"}, \code{"rifa"}.
|
||||
#'
|
||||
#' At default, the antibiotics that are used for \strong{Gram negative bacteria} are (colum names): \cr
|
||||
#' \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"gent"}, \code{"tobr"}, \code{"coli"}, \code{"cfot"}, \code{"cfta"}, \code{"mero"}.
|
||||
#'
|
||||
#'
|
||||
#' The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical vector.
|
||||
#' @inheritSection first_isolate Key antibiotics
|
||||
#' @rdname key_antibiotics
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% mutate if_else pull
|
||||
#' @importFrom crayon blue bold
|
||||
#' @seealso \code{\link{first_isolate}}
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # septic_patients is a dataset available in the AMR package
|
||||
#' ?septic_patients
|
||||
|
||||
#' library(dplyr)
|
||||
#' # set key antibiotics to a new variable
|
||||
#' my_patients <- septic_patients %>%
|
||||
#' mutate(keyab = key_antibiotics(.)) %>%
|
||||
#' mutate(
|
||||
#' # now calculate first isolates
|
||||
#' first_regular = first_isolate(., col_keyantibiotics = FALSE),
|
||||
#' # and first WEIGHTED isolates
|
||||
#' first_weighted = first_isolate(., col_keyantibiotics = "keyab")
|
||||
#' )
|
||||
#'
|
||||
#' # Check the difference, in this data set it results in 7% more isolates:
|
||||
#' sum(my_patients$first_regular, na.rm = TRUE)
|
||||
#' sum(my_patients$first_weighted, na.rm = TRUE)
|
||||
#'
|
||||
#'
|
||||
#' # output of the `key_antibiotics` function could be like this:
|
||||
#' strainA <- "SSSRR.S.R..S"
|
||||
#' strainB <- "SSSIRSSSRSSS"
|
||||
#'
|
||||
#' key_antibiotics_equal(strainA, strainB)
|
||||
#' # TRUE, because I is ignored (as well as missing values)
|
||||
#'
|
||||
#' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
|
||||
#' # FALSE, because I is not ignored and so the 4th value differs
|
||||
key_antibiotics <- function(tbl,
|
||||
col_mo = NULL,
|
||||
universal_1 = guess_ab_col(tbl, "amox"),
|
||||
universal_2 = guess_ab_col(tbl, "amcl"),
|
||||
universal_3 = guess_ab_col(tbl, "cfur"),
|
||||
universal_4 = guess_ab_col(tbl, "pita"),
|
||||
universal_5 = guess_ab_col(tbl, "cipr"),
|
||||
universal_6 = guess_ab_col(tbl, "trsu"),
|
||||
GramPos_1 = guess_ab_col(tbl, "vanc"),
|
||||
GramPos_2 = guess_ab_col(tbl, "teic"),
|
||||
GramPos_3 = guess_ab_col(tbl, "tetr"),
|
||||
GramPos_4 = guess_ab_col(tbl, "eryt"),
|
||||
GramPos_5 = guess_ab_col(tbl, "oxac"),
|
||||
GramPos_6 = guess_ab_col(tbl, "rifa"),
|
||||
GramNeg_1 = guess_ab_col(tbl, "gent"),
|
||||
GramNeg_2 = guess_ab_col(tbl, "tobr"),
|
||||
GramNeg_3 = guess_ab_col(tbl, "coli"),
|
||||
GramNeg_4 = guess_ab_col(tbl, "cfot"),
|
||||
GramNeg_5 = guess_ab_col(tbl, "cfta"),
|
||||
GramNeg_6 = guess_ab_col(tbl, "mero"),
|
||||
warnings = TRUE,
|
||||
...) {
|
||||
|
||||
# try to find columns based on type
|
||||
# -- mo
|
||||
if (is.null(col_mo)) {
|
||||
col_mo <- search_type_in_df(tbl = tbl, type = "mo")
|
||||
}
|
||||
if (is.null(col_mo)) {
|
||||
stop("`col_mo` must be set.", call. = FALSE)
|
||||
}
|
||||
|
||||
# check columns
|
||||
col.list <- c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6,
|
||||
GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6,
|
||||
GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6)
|
||||
col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = warnings)
|
||||
universal_1 <- col.list[universal_1]
|
||||
universal_2 <- col.list[universal_2]
|
||||
universal_3 <- col.list[universal_3]
|
||||
universal_4 <- col.list[universal_4]
|
||||
universal_5 <- col.list[universal_5]
|
||||
universal_6 <- col.list[universal_6]
|
||||
GramPos_1 <- col.list[GramPos_1]
|
||||
GramPos_2 <- col.list[GramPos_2]
|
||||
GramPos_3 <- col.list[GramPos_3]
|
||||
GramPos_4 <- col.list[GramPos_4]
|
||||
GramPos_5 <- col.list[GramPos_5]
|
||||
GramPos_6 <- col.list[GramPos_6]
|
||||
GramNeg_1 <- col.list[GramNeg_1]
|
||||
GramNeg_2 <- col.list[GramNeg_2]
|
||||
GramNeg_3 <- col.list[GramNeg_3]
|
||||
GramNeg_4 <- col.list[GramNeg_4]
|
||||
GramNeg_5 <- col.list[GramNeg_5]
|
||||
GramNeg_6 <- col.list[GramNeg_6]
|
||||
|
||||
universal <- c(universal_1, universal_2, universal_3,
|
||||
universal_4, universal_5, universal_6)
|
||||
|
||||
gram_positive = c(universal,
|
||||
GramPos_1, GramPos_2, GramPos_3,
|
||||
GramPos_4, GramPos_5, GramPos_6)
|
||||
gram_positive <- gram_positive[!is.null(gram_positive)]
|
||||
|
||||
gram_negative = c(universal,
|
||||
GramNeg_1, GramNeg_2, GramNeg_3,
|
||||
GramNeg_4, GramNeg_5, GramNeg_6)
|
||||
gram_negative <- gram_negative[!is.null(gram_negative)]
|
||||
|
||||
# join to microorganisms data set
|
||||
tbl <- tbl %>%
|
||||
mutate_at(vars(col_mo), as.mo) %>%
|
||||
left_join_microorganisms(by = col_mo) %>%
|
||||
mutate(key_ab = NA_character_,
|
||||
gramstain = mo_gramstain(pull(., col_mo)))
|
||||
|
||||
# Gram +
|
||||
tbl <- tbl %>% mutate(key_ab =
|
||||
if_else(gramstain == "Gram positive",
|
||||
apply(X = tbl[, gram_positive],
|
||||
MARGIN = 1,
|
||||
FUN = function(x) paste(x, collapse = "")),
|
||||
key_ab))
|
||||
|
||||
# Gram -
|
||||
tbl <- tbl %>% mutate(key_ab =
|
||||
if_else(gramstain == "Gram negative",
|
||||
apply(X = tbl[, gram_negative],
|
||||
MARGIN = 1,
|
||||
FUN = function(x) paste(x, collapse = "")),
|
||||
key_ab))
|
||||
|
||||
# format
|
||||
key_abs <- tbl %>%
|
||||
pull(key_ab) %>%
|
||||
gsub('(NA|NULL)', '.', .) %>%
|
||||
gsub('[^SIR]', '.', ., ignore.case = TRUE)
|
||||
|
||||
key_abs
|
||||
|
||||
}
|
||||
|
||||
#' @importFrom dplyr progress_estimated %>%
|
||||
#' @rdname key_antibiotics
|
||||
#' @export
|
||||
key_antibiotics_equal <- function(x,
|
||||
y,
|
||||
type = c("keyantibiotics", "points"),
|
||||
ignore_I = TRUE,
|
||||
points_threshold = 2,
|
||||
info = FALSE) {
|
||||
# x is active row, y is lag
|
||||
|
||||
type <- type[1]
|
||||
|
||||
if (length(x) != length(y)) {
|
||||
stop('Length of `x` and `y` must be equal.')
|
||||
}
|
||||
|
||||
# only show progress bar on points or when at least 5000 isolates
|
||||
info_needed <- info == TRUE & (type == "points" | length(x) > 5000)
|
||||
|
||||
result <- logical(length(x))
|
||||
|
||||
if (info_needed == TRUE) {
|
||||
p <- dplyr::progress_estimated(length(x))
|
||||
}
|
||||
|
||||
for (i in 1:length(x)) {
|
||||
|
||||
if (info_needed == TRUE) {
|
||||
p$tick()$print()
|
||||
}
|
||||
|
||||
if (is.na(x[i])) {
|
||||
x[i] <- ''
|
||||
}
|
||||
if (is.na(y[i])) {
|
||||
y[i] <- ''
|
||||
}
|
||||
|
||||
if (x[i] == y[i]) {
|
||||
|
||||
result[i] <- TRUE
|
||||
|
||||
} else if (nchar(x[i]) != nchar(y[i])) {
|
||||
|
||||
result[i] <- FALSE
|
||||
|
||||
} else {
|
||||
|
||||
x_split <- strsplit(x[i], "")[[1]]
|
||||
y_split <- strsplit(y[i], "")[[1]]
|
||||
|
||||
if (type == 'keyantibiotics') {
|
||||
|
||||
if (ignore_I == TRUE) {
|
||||
x_split[x_split == "I"] <- "."
|
||||
y_split[y_split == "I"] <- "."
|
||||
}
|
||||
|
||||
y_split[x_split == "."] <- "."
|
||||
x_split[y_split == "."] <- "."
|
||||
|
||||
result[i] <- all(x_split == y_split)
|
||||
|
||||
} else if (type == 'points') {
|
||||
# count points for every single character:
|
||||
# - no change is 0 points
|
||||
# - I <-> S|R is 0.5 point
|
||||
# - S|R <-> R|S is 1 point
|
||||
# use the levels of as.rsi (S = 1, I = 2, R = 3)
|
||||
|
||||
suppressWarnings(x_split <- x_split %>% as.rsi() %>% as.double())
|
||||
suppressWarnings(y_split <- y_split %>% as.rsi() %>% as.double())
|
||||
|
||||
points <- (x_split - y_split) %>% abs() %>% sum(na.rm = TRUE) / 2
|
||||
result[i] <- points >= points_threshold
|
||||
|
||||
} else {
|
||||
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
|
||||
}
|
||||
}
|
||||
}
|
||||
if (info_needed == TRUE) {
|
||||
cat('\n')
|
||||
}
|
||||
result
|
||||
}
|
||||
@@ -1,62 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Kurtosis of the sample
|
||||
#'
|
||||
#' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable.
|
||||
#'
|
||||
#' @param x a vector of values, a \code{matrix} or a \code{data frame}
|
||||
#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
|
||||
#' @exportMethod kurtosis
|
||||
#' @seealso \code{\link{skewness}}
|
||||
#' @rdname kurtosis
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
kurtosis <- function(x, na.rm = FALSE) {
|
||||
UseMethod("kurtosis")
|
||||
}
|
||||
|
||||
#' @exportMethod kurtosis.default
|
||||
#' @rdname kurtosis
|
||||
#' @export
|
||||
kurtosis.default <- function (x, na.rm = FALSE) {
|
||||
x <- as.vector(x)
|
||||
if (na.rm == TRUE) {
|
||||
x <- x[!is.na(x)]
|
||||
}
|
||||
n <- length(x)
|
||||
n * base::sum((x - base::mean(x, na.rm = na.rm))^4, na.rm = na.rm) /
|
||||
(base::sum((x - base::mean(x, na.rm = na.rm))^2, na.rm = na.rm)^2)
|
||||
}
|
||||
|
||||
#' @exportMethod kurtosis.matrix
|
||||
#' @rdname kurtosis
|
||||
#' @export
|
||||
kurtosis.matrix <- function (x, na.rm = FALSE) {
|
||||
base::apply(x, 2, kurtosis.default, na.rm = na.rm)
|
||||
}
|
||||
|
||||
#' @exportMethod kurtosis.data.frame
|
||||
#' @rdname kurtosis
|
||||
#' @export
|
||||
kurtosis.data.frame <- function (x, na.rm = FALSE) {
|
||||
base::sapply(x, kurtosis.default, na.rm = na.rm)
|
||||
}
|
||||
@@ -1,84 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Pattern Matching
|
||||
#'
|
||||
#' Convenient wrapper around \code{\link[base]{grep}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive. Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors.
|
||||
#' @inheritParams base::grepl
|
||||
#' @return A \code{logical} vector
|
||||
#' @name like
|
||||
#' @rdname like
|
||||
#' @export
|
||||
#' @details Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...).
|
||||
#' @source Idea from the \href{https://github.com/Rdatatable/data.table/blob/master/R/like.R}{\code{like} function from the \code{data.table} package}, but made it case insensitive at default and let it support multiple patterns.
|
||||
#' @seealso \code{\link[base]{grep}}
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # simple test
|
||||
#' a <- "This is a test"
|
||||
#' b <- "TEST"
|
||||
#' a %like% b
|
||||
#' #> TRUE
|
||||
#' b %like% a
|
||||
#' #> FALSE
|
||||
#'
|
||||
#' # also supports multiple patterns, length must be equal to x
|
||||
#' a <- c("Test case", "Something different", "Yet another thing")
|
||||
#' b <- c("case", "diff", "yet")
|
||||
#' a %like% b
|
||||
#' #> TRUE TRUE TRUE
|
||||
#'
|
||||
#' # get frequencies of bacteria whose name start with 'Ent' or 'ent'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' left_join_microorganisms() %>%
|
||||
#' filter(genus %like% '^ent') %>%
|
||||
#' freq(genus, species)
|
||||
like <- function(x, pattern) {
|
||||
if (length(pattern) > 1) {
|
||||
if (length(x) != length(pattern)) {
|
||||
pattern <- pattern[1]
|
||||
warning('only the first element of argument `pattern` used for `%like%`', call. = TRUE)
|
||||
} else {
|
||||
# x and pattern are of same length, so items with each other
|
||||
res <- vector(length = length(pattern))
|
||||
for (i in 1:length(res)) {
|
||||
if (is.factor(x[i])) {
|
||||
res[i] <- as.integer(x[i]) %in% base::grep(pattern[i], levels(x[i]), ignore.case = TRUE)
|
||||
} else {
|
||||
res[i] <- base::grepl(pattern[i], x[i], ignore.case = TRUE)
|
||||
}
|
||||
}
|
||||
return(res)
|
||||
}
|
||||
}
|
||||
|
||||
# the regular way how grepl works; just one pattern against one or more x
|
||||
if (is.factor(x)) {
|
||||
as.integer(x) %in% base::grep(pattern, levels(x), ignore.case = TRUE)
|
||||
} else {
|
||||
base::grepl(pattern, x, ignore.case = TRUE)
|
||||
}
|
||||
}
|
||||
|
||||
#' @rdname like
|
||||
#' @export
|
||||
"%like%" <- like
|
||||
@@ -1,496 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Determine multidrug-resistant organisms (MDRO)
|
||||
#'
|
||||
#' Determine which isolates are multidrug-resistant organisms (MDRO) according to country-specific guidelines.
|
||||
#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
|
||||
#' @param country country code to determine guidelines. EUCAST rules will be used when left empty, see Details. Should be or a code from the \href{https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2#Officially_assigned_code_elements}{list of ISO 3166-1 alpha-2 country codes}. Case-insensitive. Currently supported are \code{de} (Germany) and \code{nl} (the Netherlands).
|
||||
#' @param info print progress
|
||||
#' @inheritParams eucast_rules
|
||||
#' @param metr column name of an antibiotic, see Antibiotics
|
||||
#' @param ... parameters that are passed on to methods
|
||||
#' @inheritSection eucast_rules Antibiotics
|
||||
#' @details When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}).
|
||||
#' @return Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}.
|
||||
#' @rdname mdro
|
||||
#' @importFrom dplyr %>%
|
||||
#' @importFrom crayon red blue bold
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' library(dplyr)
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' mutate(EUCAST = mdro(.),
|
||||
#' BRMO = brmo(.))
|
||||
mdro <- function(tbl,
|
||||
country = NULL,
|
||||
col_mo = NULL,
|
||||
info = TRUE,
|
||||
amcl = guess_ab_col(),
|
||||
amik = guess_ab_col(),
|
||||
amox = guess_ab_col(),
|
||||
ampi = guess_ab_col(),
|
||||
azit = guess_ab_col(),
|
||||
aztr = guess_ab_col(),
|
||||
cefa = guess_ab_col(),
|
||||
cfra = guess_ab_col(),
|
||||
cfep = guess_ab_col(),
|
||||
cfot = guess_ab_col(),
|
||||
cfox = guess_ab_col(),
|
||||
cfta = guess_ab_col(),
|
||||
cftr = guess_ab_col(),
|
||||
cfur = guess_ab_col(),
|
||||
chlo = guess_ab_col(),
|
||||
cipr = guess_ab_col(),
|
||||
clar = guess_ab_col(),
|
||||
clin = guess_ab_col(),
|
||||
clox = guess_ab_col(),
|
||||
coli = guess_ab_col(),
|
||||
czol = guess_ab_col(),
|
||||
dapt = guess_ab_col(),
|
||||
doxy = guess_ab_col(),
|
||||
erta = guess_ab_col(),
|
||||
eryt = guess_ab_col(),
|
||||
fosf = guess_ab_col(),
|
||||
fusi = guess_ab_col(),
|
||||
gent = guess_ab_col(),
|
||||
imip = guess_ab_col(),
|
||||
kana = guess_ab_col(),
|
||||
levo = guess_ab_col(),
|
||||
linc = guess_ab_col(),
|
||||
line = guess_ab_col(),
|
||||
mero = guess_ab_col(),
|
||||
metr = guess_ab_col(),
|
||||
mino = guess_ab_col(),
|
||||
moxi = guess_ab_col(),
|
||||
nali = guess_ab_col(),
|
||||
neom = guess_ab_col(),
|
||||
neti = guess_ab_col(),
|
||||
nitr = guess_ab_col(),
|
||||
novo = guess_ab_col(),
|
||||
norf = guess_ab_col(),
|
||||
oflo = guess_ab_col(),
|
||||
peni = guess_ab_col(),
|
||||
pipe = guess_ab_col(),
|
||||
pita = guess_ab_col(),
|
||||
poly = guess_ab_col(),
|
||||
qida = guess_ab_col(),
|
||||
rifa = guess_ab_col(),
|
||||
roxi = guess_ab_col(),
|
||||
siso = guess_ab_col(),
|
||||
teic = guess_ab_col(),
|
||||
tetr = guess_ab_col(),
|
||||
tica = guess_ab_col(),
|
||||
tige = guess_ab_col(),
|
||||
tobr = guess_ab_col(),
|
||||
trim = guess_ab_col(),
|
||||
trsu = guess_ab_col(),
|
||||
vanc = guess_ab_col()) {
|
||||
|
||||
if (!is.data.frame(tbl)) {
|
||||
stop("`tbl` must be a data frame.", call. = FALSE)
|
||||
}
|
||||
|
||||
# try to find columns based on type
|
||||
# -- mo
|
||||
if (is.null(col_mo)) {
|
||||
col_mo <- search_type_in_df(tbl = tbl, type = "mo")
|
||||
}
|
||||
if (is.null(col_mo)) {
|
||||
stop("`col_mo` must be set.", call. = FALSE)
|
||||
}
|
||||
|
||||
# strip whitespaces
|
||||
if (length(country) > 1) {
|
||||
stop('`country` must be a length one character string.', call. = FALSE)
|
||||
}
|
||||
|
||||
if (is.null(country)) {
|
||||
country <- 'EUCAST'
|
||||
}
|
||||
country <- trimws(country)
|
||||
if (tolower(country) != 'eucast' & !country %like% '^[a-z]{2}$') {
|
||||
stop('This is not a valid ISO 3166-1 alpha-2 country code: "', country, '". Please see ?mdro.', call. = FALSE)
|
||||
}
|
||||
|
||||
# create list and make country code case-independent
|
||||
guideline <- list(country = list(code = tolower(country)))
|
||||
|
||||
if (guideline$country$code == 'eucast') {
|
||||
guideline$country$name <- '(European guidelines)'
|
||||
guideline$name <- 'EUCAST Expert Rules, "Intrinsic Resistance and Exceptional Phenotypes Tables"'
|
||||
guideline$version <- 'Version 3.1'
|
||||
guideline$source <- 'http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf'
|
||||
# support per country:
|
||||
} else if (guideline$country$code == 'de') {
|
||||
guideline$country$name <- 'Germany'
|
||||
guideline$name <- ''
|
||||
guideline$version <- ''
|
||||
guideline$source <- ''
|
||||
} else if (guideline$country$code == 'nl') {
|
||||
guideline$country$name <- 'The Netherlands'
|
||||
guideline$name <- 'WIP-Richtlijn BRMO'
|
||||
guideline$version <- 'Revision as of December 2017'
|
||||
guideline$source <- 'https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH'
|
||||
# add here more countries like this:
|
||||
# } else if (country$code == 'xx') {
|
||||
# country$name <- 'country name'
|
||||
} else {
|
||||
stop('This country code is currently unsupported: ', guideline$country$code, call. = FALSE)
|
||||
}
|
||||
|
||||
if (info == TRUE) {
|
||||
cat("Determining multidrug-resistant organisms (MDRO), according to:\n",
|
||||
"Guideline: ", red(paste0(guideline$name, ", ", guideline$version, "\n")),
|
||||
"Country : ", red(paste0(guideline$country$name, "\n")),
|
||||
"Source : ", blue(paste0(guideline$source, "\n")),
|
||||
"\n", sep = "")
|
||||
}
|
||||
|
||||
# check columns
|
||||
if (identical(amcl, as.name("guess_ab_col"))) { amcl <- guess_ab_col(tbl, "amcl", verbose = info) }
|
||||
if (identical(amik, as.name("guess_ab_col"))) { amik <- guess_ab_col(tbl, "amik", verbose = info) }
|
||||
if (identical(amox, as.name("guess_ab_col"))) { amox <- guess_ab_col(tbl, "amox", verbose = info) }
|
||||
if (identical(ampi, as.name("guess_ab_col"))) { ampi <- guess_ab_col(tbl, "ampi", verbose = info) }
|
||||
if (identical(azit, as.name("guess_ab_col"))) { azit <- guess_ab_col(tbl, "azit", verbose = info) }
|
||||
if (identical(aztr, as.name("guess_ab_col"))) { aztr <- guess_ab_col(tbl, "aztr", verbose = info) }
|
||||
if (identical(cefa, as.name("guess_ab_col"))) { cefa <- guess_ab_col(tbl, "cefa", verbose = info) }
|
||||
if (identical(cfra, as.name("guess_ab_col"))) { cfra <- guess_ab_col(tbl, "cfra", verbose = info) }
|
||||
if (identical(cfep, as.name("guess_ab_col"))) { cfep <- guess_ab_col(tbl, "cfep", verbose = info) }
|
||||
if (identical(cfot, as.name("guess_ab_col"))) { cfot <- guess_ab_col(tbl, "cfot", verbose = info) }
|
||||
if (identical(cfox, as.name("guess_ab_col"))) { cfox <- guess_ab_col(tbl, "cfox", verbose = info) }
|
||||
if (identical(cfta, as.name("guess_ab_col"))) { cfta <- guess_ab_col(tbl, "cfta", verbose = info) }
|
||||
if (identical(cftr, as.name("guess_ab_col"))) { cftr <- guess_ab_col(tbl, "cftr", verbose = info) }
|
||||
if (identical(cfur, as.name("guess_ab_col"))) { cfur <- guess_ab_col(tbl, "cfur", verbose = info) }
|
||||
if (identical(chlo, as.name("guess_ab_col"))) { chlo <- guess_ab_col(tbl, "chlo", verbose = info) }
|
||||
if (identical(cipr, as.name("guess_ab_col"))) { cipr <- guess_ab_col(tbl, "cipr", verbose = info) }
|
||||
if (identical(clar, as.name("guess_ab_col"))) { clar <- guess_ab_col(tbl, "clar", verbose = info) }
|
||||
if (identical(clin, as.name("guess_ab_col"))) { clin <- guess_ab_col(tbl, "clin", verbose = info) }
|
||||
if (identical(clox, as.name("guess_ab_col"))) { clox <- guess_ab_col(tbl, "clox", verbose = info) }
|
||||
if (identical(coli, as.name("guess_ab_col"))) { coli <- guess_ab_col(tbl, "coli", verbose = info) }
|
||||
if (identical(czol, as.name("guess_ab_col"))) { czol <- guess_ab_col(tbl, "czol", verbose = info) }
|
||||
if (identical(dapt, as.name("guess_ab_col"))) { dapt <- guess_ab_col(tbl, "dapt", verbose = info) }
|
||||
if (identical(doxy, as.name("guess_ab_col"))) { doxy <- guess_ab_col(tbl, "doxy", verbose = info) }
|
||||
if (identical(erta, as.name("guess_ab_col"))) { erta <- guess_ab_col(tbl, "erta", verbose = info) }
|
||||
if (identical(eryt, as.name("guess_ab_col"))) { eryt <- guess_ab_col(tbl, "eryt", verbose = info) }
|
||||
if (identical(fosf, as.name("guess_ab_col"))) { fosf <- guess_ab_col(tbl, "fosf", verbose = info) }
|
||||
if (identical(fusi, as.name("guess_ab_col"))) { fusi <- guess_ab_col(tbl, "fusi", verbose = info) }
|
||||
if (identical(gent, as.name("guess_ab_col"))) { gent <- guess_ab_col(tbl, "gent", verbose = info) }
|
||||
if (identical(imip, as.name("guess_ab_col"))) { imip <- guess_ab_col(tbl, "imip", verbose = info) }
|
||||
if (identical(kana, as.name("guess_ab_col"))) { kana <- guess_ab_col(tbl, "kana", verbose = info) }
|
||||
if (identical(levo, as.name("guess_ab_col"))) { levo <- guess_ab_col(tbl, "levo", verbose = info) }
|
||||
if (identical(linc, as.name("guess_ab_col"))) { linc <- guess_ab_col(tbl, "linc", verbose = info) }
|
||||
if (identical(line, as.name("guess_ab_col"))) { line <- guess_ab_col(tbl, "line", verbose = info) }
|
||||
if (identical(mero, as.name("guess_ab_col"))) { mero <- guess_ab_col(tbl, "mero", verbose = info) }
|
||||
if (identical(metr, as.name("guess_ab_col"))) { metr <- guess_ab_col(tbl, "metr", verbose = info) }
|
||||
if (identical(mino, as.name("guess_ab_col"))) { mino <- guess_ab_col(tbl, "mino", verbose = info) }
|
||||
if (identical(moxi, as.name("guess_ab_col"))) { moxi <- guess_ab_col(tbl, "moxi", verbose = info) }
|
||||
if (identical(nali, as.name("guess_ab_col"))) { nali <- guess_ab_col(tbl, "nali", verbose = info) }
|
||||
if (identical(neom, as.name("guess_ab_col"))) { neom <- guess_ab_col(tbl, "neom", verbose = info) }
|
||||
if (identical(neti, as.name("guess_ab_col"))) { neti <- guess_ab_col(tbl, "neti", verbose = info) }
|
||||
if (identical(nitr, as.name("guess_ab_col"))) { nitr <- guess_ab_col(tbl, "nitr", verbose = info) }
|
||||
if (identical(novo, as.name("guess_ab_col"))) { novo <- guess_ab_col(tbl, "novo", verbose = info) }
|
||||
if (identical(norf, as.name("guess_ab_col"))) { norf <- guess_ab_col(tbl, "norf", verbose = info) }
|
||||
if (identical(oflo, as.name("guess_ab_col"))) { oflo <- guess_ab_col(tbl, "oflo", verbose = info) }
|
||||
if (identical(peni, as.name("guess_ab_col"))) { peni <- guess_ab_col(tbl, "peni", verbose = info) }
|
||||
if (identical(pipe, as.name("guess_ab_col"))) { pipe <- guess_ab_col(tbl, "pipe", verbose = info) }
|
||||
if (identical(pita, as.name("guess_ab_col"))) { pita <- guess_ab_col(tbl, "pita", verbose = info) }
|
||||
if (identical(poly, as.name("guess_ab_col"))) { poly <- guess_ab_col(tbl, "poly", verbose = info) }
|
||||
if (identical(qida, as.name("guess_ab_col"))) { qida <- guess_ab_col(tbl, "qida", verbose = info) }
|
||||
if (identical(rifa, as.name("guess_ab_col"))) { rifa <- guess_ab_col(tbl, "rifa", verbose = info) }
|
||||
if (identical(roxi, as.name("guess_ab_col"))) { roxi <- guess_ab_col(tbl, "roxi", verbose = info) }
|
||||
if (identical(siso, as.name("guess_ab_col"))) { siso <- guess_ab_col(tbl, "siso", verbose = info) }
|
||||
if (identical(teic, as.name("guess_ab_col"))) { teic <- guess_ab_col(tbl, "teic", verbose = info) }
|
||||
if (identical(tetr, as.name("guess_ab_col"))) { tetr <- guess_ab_col(tbl, "tetr", verbose = info) }
|
||||
if (identical(tica, as.name("guess_ab_col"))) { tica <- guess_ab_col(tbl, "tica", verbose = info) }
|
||||
if (identical(tige, as.name("guess_ab_col"))) { tige <- guess_ab_col(tbl, "tige", verbose = info) }
|
||||
if (identical(tobr, as.name("guess_ab_col"))) { tobr <- guess_ab_col(tbl, "tobr", verbose = info) }
|
||||
if (identical(trim, as.name("guess_ab_col"))) { trim <- guess_ab_col(tbl, "trim", verbose = info) }
|
||||
if (identical(trsu, as.name("guess_ab_col"))) { trsu <- guess_ab_col(tbl, "trsu", verbose = info) }
|
||||
if (identical(vanc, as.name("guess_ab_col"))) { vanc <- guess_ab_col(tbl, "vanc", verbose = info) }
|
||||
col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot,
|
||||
cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
|
||||
czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana,
|
||||
levo, linc, line, mero, metr, mino, moxi, nali, neom, neti,
|
||||
nitr, novo, norf, oflo, peni, pipe, pita, poly, qida, rifa,
|
||||
roxi, siso, teic, tetr, tica, tige, tobr, trim, trsu, vanc)
|
||||
if (length(col.list) < 60) {
|
||||
warning('Some columns do not exist -- THIS MAY STRONGLY INFLUENCE THE OUTCOME.',
|
||||
immediate. = TRUE,
|
||||
call. = FALSE)
|
||||
}
|
||||
col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
|
||||
amcl <- col.list[amcl]
|
||||
amik <- col.list[amik]
|
||||
amox <- col.list[amox]
|
||||
ampi <- col.list[ampi]
|
||||
azit <- col.list[azit]
|
||||
aztr <- col.list[aztr]
|
||||
cefa <- col.list[cefa]
|
||||
cfra <- col.list[cfra]
|
||||
cfep <- col.list[cfep]
|
||||
cfot <- col.list[cfot]
|
||||
cfox <- col.list[cfox]
|
||||
cfta <- col.list[cfta]
|
||||
cftr <- col.list[cftr]
|
||||
cfur <- col.list[cfur]
|
||||
chlo <- col.list[chlo]
|
||||
cipr <- col.list[cipr]
|
||||
clar <- col.list[clar]
|
||||
clin <- col.list[clin]
|
||||
clox <- col.list[clox]
|
||||
coli <- col.list[coli]
|
||||
czol <- col.list[czol]
|
||||
dapt <- col.list[dapt]
|
||||
doxy <- col.list[doxy]
|
||||
erta <- col.list[erta]
|
||||
eryt <- col.list[eryt]
|
||||
fosf <- col.list[fosf]
|
||||
fusi <- col.list[fusi]
|
||||
gent <- col.list[gent]
|
||||
imip <- col.list[imip]
|
||||
kana <- col.list[kana]
|
||||
levo <- col.list[levo]
|
||||
linc <- col.list[linc]
|
||||
line <- col.list[line]
|
||||
mero <- col.list[mero]
|
||||
metr <- col.list[metr]
|
||||
mino <- col.list[mino]
|
||||
moxi <- col.list[moxi]
|
||||
nali <- col.list[nali]
|
||||
neom <- col.list[neom]
|
||||
neti <- col.list[neti]
|
||||
nitr <- col.list[nitr]
|
||||
novo <- col.list[novo]
|
||||
norf <- col.list[norf]
|
||||
oflo <- col.list[oflo]
|
||||
peni <- col.list[peni]
|
||||
pipe <- col.list[pipe]
|
||||
pita <- col.list[pita]
|
||||
poly <- col.list[poly]
|
||||
qida <- col.list[qida]
|
||||
rifa <- col.list[rifa]
|
||||
roxi <- col.list[roxi]
|
||||
siso <- col.list[siso]
|
||||
teic <- col.list[teic]
|
||||
tetr <- col.list[tetr]
|
||||
tica <- col.list[tica]
|
||||
tige <- col.list[tige]
|
||||
tobr <- col.list[tobr]
|
||||
trim <- col.list[trim]
|
||||
trsu <- col.list[trsu]
|
||||
vanc <- col.list[vanc]
|
||||
|
||||
# antibiotic classes
|
||||
aminoglycosides <- c(tobr, gent) # can also be kana but that one is often intrinsic R
|
||||
cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
|
||||
cephalosporins_3rd <- c(cfot, cftr, cfta)
|
||||
carbapenems <- c(erta, imip, mero)
|
||||
fluoroquinolones <- c(oflo, cipr, levo, moxi)
|
||||
|
||||
# helper function for editing the table
|
||||
trans_tbl <- function(to, rows, cols, any_all) {
|
||||
cols <- cols[!is.na(cols)]
|
||||
if (length(rows) > 0 & length(cols) > 0) {
|
||||
if (any_all == "any") {
|
||||
col_filter <- which(tbl[, cols] == 'R')
|
||||
} else if (any_all == "all") {
|
||||
col_filter <- tbl %>%
|
||||
mutate(index = 1:nrow(.)) %>%
|
||||
filter_at(vars(cols), all_vars(. == "R")) %>%
|
||||
pull((index))
|
||||
}
|
||||
rows <- rows[rows %in% col_filter]
|
||||
tbl[rows, 'MDRO'] <<- to
|
||||
}
|
||||
}
|
||||
|
||||
tbl <- tbl %>%
|
||||
mutate_at(vars(col_mo), as.mo) %>%
|
||||
# join to microorganisms data set
|
||||
left_join_microorganisms(by = col_mo) %>%
|
||||
# add unconfirmed to where genus is available
|
||||
mutate(MDRO = ifelse(!is.na(genus), 1, NA_integer_))
|
||||
|
||||
if (guideline$country$code == 'eucast') {
|
||||
# EUCAST ------------------------------------------------------------------
|
||||
# Table 5
|
||||
trans_tbl(3,
|
||||
which(tbl$family == 'Enterobacteriaceae'
|
||||
| tbl$fullname %like% '^Pseudomonas aeruginosa'
|
||||
| tbl$genus == 'Acinetobacter'),
|
||||
coli,
|
||||
"all")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Salmonella Typhi'),
|
||||
c(carbapenems, fluoroquinolones),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Haemophilus influenzae'),
|
||||
c(cephalosporins_3rd, carbapenems, fluoroquinolones),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Moraxella catarrhalis'),
|
||||
c(cephalosporins_3rd, fluoroquinolones),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Neisseria meningitidis'),
|
||||
c(cephalosporins_3rd, fluoroquinolones),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Neisseria gonorrhoeae'),
|
||||
azit,
|
||||
"any")
|
||||
# Table 6
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
|
||||
c(vanc, teic, dapt, line, qida, tige),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$genus == 'Corynebacterium'),
|
||||
c(vanc, teic, dapt, line, qida, tige),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Streptococcus pneumoniae'),
|
||||
c(carbapenems, vanc, teic, dapt, line, qida, tige, rifa),
|
||||
"any")
|
||||
trans_tbl(3, # Sr. groups A/B/C/G
|
||||
which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|equisimilis|equi|zooepidemicus|dysgalactiae|anginosus)'),
|
||||
c(peni, cephalosporins, vanc, teic, dapt, line, qida, tige),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$genus == 'Enterococcus'),
|
||||
c(dapt, line, tige, teic),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Enterococcus faecalis'),
|
||||
c(ampi, amox),
|
||||
"any")
|
||||
# Table 7
|
||||
trans_tbl(3,
|
||||
which(tbl$genus == 'Bacteroides'),
|
||||
metr,
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Clostridium difficile'),
|
||||
c(metr, vanc),
|
||||
"any")
|
||||
}
|
||||
|
||||
if (guideline$country$code == 'de') {
|
||||
# Germany -----------------------------------------------------------------
|
||||
stop("We are still working on German guidelines in this beta version.", call. = FALSE)
|
||||
}
|
||||
|
||||
if (guideline$country$code == 'nl') {
|
||||
# Netherlands -------------------------------------------------------------
|
||||
aminoglycosides <- aminoglycosides[!is.na(aminoglycosides)]
|
||||
fluoroquinolones <- fluoroquinolones[!is.na(fluoroquinolones)]
|
||||
carbapenems <- carbapenems[!is.na(carbapenems)]
|
||||
|
||||
# Table 1
|
||||
trans_tbl(3,
|
||||
which(tbl$family == 'Enterobacteriaceae'),
|
||||
c(aminoglycosides, fluoroquinolones),
|
||||
"all")
|
||||
|
||||
trans_tbl(2,
|
||||
which(tbl$family == 'Enterobacteriaceae'),
|
||||
c(carbapenems),
|
||||
"any")
|
||||
|
||||
# Table 2
|
||||
trans_tbl(2,
|
||||
which(tbl$genus == 'Acinetobacter'),
|
||||
c(carbapenems),
|
||||
"any")
|
||||
trans_tbl(3,
|
||||
which(tbl$genus == 'Acinetobacter'),
|
||||
c(aminoglycosides, fluoroquinolones),
|
||||
"all")
|
||||
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% '^Stenotrophomonas maltophilia'),
|
||||
trsu,
|
||||
"all")
|
||||
|
||||
if (!is.na(mero) & !is.na(imip)
|
||||
& !is.na(gent) & !is.na(tobr)
|
||||
& !is.na(cipr)
|
||||
& !is.na(cfta)
|
||||
& !is.na(pita) ) {
|
||||
tbl <- tbl %>% mutate(
|
||||
psae = 0,
|
||||
psae = ifelse(mero == "R" | imip == "R", psae + 1, psae),
|
||||
psae = ifelse(gent == "R" & tobr == "R", psae + 1, psae),
|
||||
psae = ifelse(cipr == "R", psae + 1, psae),
|
||||
psae = ifelse(cfta == "R", psae + 1, psae),
|
||||
psae = ifelse(pita == "R", psae + 1, psae),
|
||||
psae = ifelse(is.na(psae), 0, psae)
|
||||
)
|
||||
} else {
|
||||
tbl$psae <- 0
|
||||
}
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Pseudomonas aeruginosa'
|
||||
& tbl$psae >= 3
|
||||
), 'MDRO'] <- 3
|
||||
|
||||
# Table 3
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% 'Streptococcus pneumoniae'),
|
||||
peni,
|
||||
"all")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% 'Streptococcus pneumoniae'),
|
||||
vanc,
|
||||
"all")
|
||||
trans_tbl(3,
|
||||
which(tbl$fullname %like% 'Enterococcus faecium'),
|
||||
c(peni, vanc),
|
||||
"all")
|
||||
}
|
||||
|
||||
factor(x = tbl$MDRO,
|
||||
levels = 1:3,
|
||||
labels = c('Negative', 'Positive, unconfirmed', 'Positive'),
|
||||
ordered = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname mdro
|
||||
#' @export
|
||||
brmo <- function(..., country = "nl") {
|
||||
mdro(..., country = "nl")
|
||||
}
|
||||
|
||||
#' @rdname mdro
|
||||
#' @export
|
||||
mrgn <- function(tbl, country = "de", ...) {
|
||||
mdro(tbl = tbl, country = "de", ...)
|
||||
}
|
||||
|
||||
#' @rdname mdro
|
||||
#' @export
|
||||
eucast_exceptional_phenotypes <- function(tbl, country = "EUCAST", ...) {
|
||||
mdro(tbl = tbl, country = "EUCAST", ...)
|
||||
}
|
||||
@@ -1,259 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Class 'mic'
|
||||
#'
|
||||
#' This transforms a vector to a new class \code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
|
||||
#' @rdname as.mic
|
||||
#' @param x vector
|
||||
#' @param na.rm a logical indicating whether missing values should be removed
|
||||
#' @return Ordered factor with new class \code{mic}
|
||||
#' @keywords mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
#' @seealso \code{\link{as.rsi}}
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
|
||||
#' is.mic(mic_data)
|
||||
#'
|
||||
#' # this can also coerce combined MIC/RSI values:
|
||||
#' as.mic("<=0.002; S") # will return <=0.002
|
||||
#'
|
||||
#' plot(mic_data)
|
||||
#' barplot(mic_data)
|
||||
#' freq(mic_data)
|
||||
as.mic <- function(x, na.rm = FALSE) {
|
||||
if (is.mic(x)) {
|
||||
x
|
||||
} else {
|
||||
x <- x %>% unlist()
|
||||
if (na.rm == TRUE) {
|
||||
x <- x[!is.na(x)]
|
||||
}
|
||||
x.bak <- x
|
||||
|
||||
# comma to period
|
||||
x <- gsub(',', '.', x, fixed = TRUE)
|
||||
# remove space between operator and number ("<= 0.002" -> "<=0.002")
|
||||
x <- gsub('(<|=|>) +', '\\1', x)
|
||||
# starting dots must start with 0
|
||||
x <- gsub('^[.]+', '0.', x)
|
||||
# <=0.2560.512 should be 0.512
|
||||
x <- gsub('.*[.].*[.]', '0.', x)
|
||||
# remove ending .0
|
||||
x <- gsub('[.]+0$', '', x)
|
||||
# remove all after last digit
|
||||
x <- gsub('[^0-9]+$', '', x)
|
||||
# remove last zeroes
|
||||
x <- gsub('([.].?)0+$', '\\1', x)
|
||||
x <- gsub('(.*[.])0+$', '\\10', x)
|
||||
# remove ending .0 again
|
||||
x <- gsub('[.]+0$', '', x)
|
||||
# force to be character
|
||||
x <- as.character(x)
|
||||
|
||||
# previously unempty values now empty - should return a warning later on
|
||||
x[x.bak != "" & x == ""] <- "invalid"
|
||||
|
||||
# these are alllowed MIC values and will become factor levels
|
||||
lvls <- c("<0.002", "<=0.002", "0.002", ">=0.002", ">0.002",
|
||||
"<0.003", "<=0.003", "0.003", ">=0.003", ">0.003",
|
||||
"<0.004", "<=0.004", "0.004", ">=0.004", ">0.004",
|
||||
"<0.006", "<=0.006", "0.006", ">=0.006", ">0.006",
|
||||
"<0.008", "<=0.008", "0.008", ">=0.008", ">0.008",
|
||||
"<0.012", "<=0.012", "0.012", ">=0.012", ">0.012",
|
||||
"<0.0125", "<=0.0125", "0.0125", ">=0.0125", ">0.0125",
|
||||
"<0.016", "<=0.016", "0.016", ">=0.016", ">0.016",
|
||||
"<0.023", "<=0.023", "0.023", ">=0.023", ">0.023",
|
||||
"<0.025", "<=0.025", "0.025", ">=0.025", ">0.025",
|
||||
"<0.03", "<=0.03", "0.03", ">=0.03", ">0.03",
|
||||
"<0.032", "<=0.032", "0.032", ">=0.032", ">0.032",
|
||||
"<0.047", "<=0.047", "0.047", ">=0.047", ">0.047",
|
||||
"<0.05", "<=0.05", "0.05", ">=0.05", ">0.05",
|
||||
"<0.054", "<=0.054", "0.054", ">=0.054", ">0.054",
|
||||
"<0.06", "<=0.06", "0.06", ">=0.06", ">0.06",
|
||||
"<0.0625", "<=0.0625", "0.0625", ">=0.0625", ">0.0625",
|
||||
"<0.063", "<=0.063", "0.063", ">=0.063", ">0.063",
|
||||
"<0.064", "<=0.064", "0.064", ">=0.064", ">0.064",
|
||||
"<0.09", "<=0.09", "0.09", ">=0.09", ">0.09",
|
||||
"<0.094", "<=0.094", "0.094", ">=0.094", ">0.094",
|
||||
"<0.12", "<=0.12", "0.12", ">=0.12", ">0.12",
|
||||
"<0.125", "<=0.125", "0.125", ">=0.125", ">0.125",
|
||||
"<0.128", "<=0.128", "0.128", ">=0.128", ">0.128",
|
||||
"<0.16", "<=0.16", "0.16", ">=0.16", ">0.16",
|
||||
"<0.19", "<=0.19", "0.19", ">=0.19", ">0.19",
|
||||
"<0.23", "<=0.23", "0.23", ">=0.23", ">0.23",
|
||||
"<0.25", "<=0.25", "0.25", ">=0.25", ">0.25",
|
||||
"<0.256", "<=0.256", "0.256", ">=0.256", ">0.256",
|
||||
"<0.28", "<=0.28", "0.28", ">=0.28", ">0.28",
|
||||
"<0.3", "<=0.3", "0.3", ">=0.3", ">0.3",
|
||||
"<0.32", "<=0.32", "0.32", ">=0.32", ">0.32",
|
||||
"<0.36", "<=0.36", "0.36", ">=0.36", ">0.36",
|
||||
"<0.38", "<=0.38", "0.38", ">=0.38", ">0.38",
|
||||
"<0.5", "<=0.5", "0.5", ">=0.5", ">0.5",
|
||||
"<0.512", "<=0.512", "0.512", ">=0.512", ">0.512",
|
||||
"<0.64", "<=0.64", "0.64", ">=0.64", ">0.64",
|
||||
"<0.75", "<=0.75", "0.75", ">=0.75", ">0.75",
|
||||
"<1", "<=1", "1", ">=1", ">1",
|
||||
"<1.5", "<=1.5", "1.5", ">=1.5", ">1.5",
|
||||
"<2", "<=2", "2", ">=2", ">2",
|
||||
"<3", "<=3", "3", ">=3", ">3",
|
||||
"<4", "<=4", "4", ">=4", ">4",
|
||||
"<5", "<=5", "5", ">=5", ">5",
|
||||
"<6", "<=6", "6", ">=6", ">6",
|
||||
"<7", "<=7", "7", ">=7", ">7",
|
||||
"<8", "<=8", "8", ">=8", ">8",
|
||||
"<10", "<=10", "10", ">=10", ">10",
|
||||
"<12", "<=12", "12", ">=12", ">12",
|
||||
"<16", "<=16", "16", ">=16", ">16",
|
||||
"<20", "<=20", "20", ">=20", ">20",
|
||||
"<24", "<=24", "24", ">=24", ">24",
|
||||
"<32", "<=32", "32", ">=32", ">32",
|
||||
"<40", "<=40", "40", ">=40", ">40",
|
||||
"<48", "<=48", "48", ">=48", ">48",
|
||||
"<64", "<=64", "64", ">=64", ">64",
|
||||
"<80", "<=80", "80", ">=80", ">80",
|
||||
"<96", "<=96", "96", ">=96", ">96",
|
||||
"<128", "<=128", "128", ">=128", ">128",
|
||||
"<160", "<=160", "160", ">=160", ">160",
|
||||
"<256", "<=256", "256", ">=256", ">256",
|
||||
"<320", "<=320", "320", ">=320", ">320",
|
||||
"<512", "<=512", "512", ">=512", ">512",
|
||||
"<1024", "<=1024", "1024", ">=1024", ">1024")
|
||||
|
||||
na_before <- x[is.na(x) | x == ''] %>% length()
|
||||
x[!x %in% lvls] <- NA
|
||||
na_after <- x[is.na(x) | x == ''] %>% length()
|
||||
|
||||
if (na_before != na_after) {
|
||||
list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
|
||||
unique() %>%
|
||||
sort()
|
||||
list_missing <- paste0('"', list_missing , '"', collapse = ", ")
|
||||
warning(na_after - na_before, ' results truncated (',
|
||||
round(((na_after - na_before) / length(x)) * 100),
|
||||
'%) that were invalid MICs: ',
|
||||
list_missing, call. = FALSE)
|
||||
}
|
||||
|
||||
x <- factor(x, levels = lvls, ordered = TRUE)
|
||||
class(x) <- c('mic', 'ordered', 'factor')
|
||||
x
|
||||
}
|
||||
}
|
||||
|
||||
#' @rdname as.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
is.mic <- function(x) {
|
||||
class(x) %>% identical(c('mic', 'ordered', 'factor'))
|
||||
}
|
||||
|
||||
#' @exportMethod as.double.mic
|
||||
#' @export
|
||||
#' @noRd
|
||||
as.double.mic <- function(x, ...) {
|
||||
as.double(gsub('(<|=|>)+', '', as.character(x)))
|
||||
}
|
||||
|
||||
#' @exportMethod as.integer.mic
|
||||
#' @export
|
||||
#' @noRd
|
||||
as.integer.mic <- function(x, ...) {
|
||||
as.integer(gsub('(<|=|>)+', '', as.character(x)))
|
||||
}
|
||||
|
||||
#' @exportMethod as.numeric.mic
|
||||
#' @export
|
||||
#' @noRd
|
||||
as.numeric.mic <- function(x, ...) {
|
||||
as.numeric(gsub('(<|=|>)+', '', as.character(x)))
|
||||
}
|
||||
|
||||
#' @exportMethod droplevels.mic
|
||||
#' @export
|
||||
#' @noRd
|
||||
droplevels.mic <- function(x, exclude = if(anyNA(levels(x))) NULL else NA, ...) {
|
||||
x <- droplevels.factor(x, exclude = exclude, ...)
|
||||
class(x) <- c('mic', 'ordered', 'factor')
|
||||
x
|
||||
}
|
||||
|
||||
#' @exportMethod print.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% tibble group_by summarise pull
|
||||
#' @noRd
|
||||
print.mic <- function(x, ...) {
|
||||
cat("Class 'mic'\n")
|
||||
print(as.character(x), quote = FALSE)
|
||||
}
|
||||
|
||||
#' @exportMethod summary.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
#' @noRd
|
||||
summary.mic <- function(object, ...) {
|
||||
x <- object
|
||||
n_total <- x %>% length()
|
||||
x <- x[!is.na(x)]
|
||||
n <- x %>% length()
|
||||
c(
|
||||
"Class" = 'mic',
|
||||
"<NA>" = n_total - n,
|
||||
"Min." = sort(x)[1] %>% as.character(),
|
||||
"Max." = sort(x)[n] %>% as.character()
|
||||
)
|
||||
}
|
||||
|
||||
#' @exportMethod plot.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% group_by summarise
|
||||
#' @importFrom graphics plot text
|
||||
#' @noRd
|
||||
plot.mic <- function(x, ...) {
|
||||
x_name <- deparse(substitute(x))
|
||||
create_barplot_mic(x, x_name, ...)
|
||||
}
|
||||
|
||||
#' @exportMethod barplot.mic
|
||||
#' @export
|
||||
#' @importFrom graphics barplot axis
|
||||
#' @noRd
|
||||
barplot.mic <- function(height, ...) {
|
||||
x_name <- deparse(substitute(height))
|
||||
create_barplot_mic(height, x_name, ...)
|
||||
}
|
||||
|
||||
#' @importFrom graphics barplot axis
|
||||
#' @importFrom dplyr %>% group_by summarise
|
||||
create_barplot_mic <- function(x, x_name, ...) {
|
||||
data <- data.frame(mic = droplevels(x), cnt = 1) %>%
|
||||
group_by(mic) %>%
|
||||
summarise(cnt = sum(cnt))
|
||||
barplot(table(droplevels.factor(x)),
|
||||
ylab = 'Frequency',
|
||||
xlab = 'MIC value',
|
||||
main = paste('MIC values of', x_name),
|
||||
axes = FALSE,
|
||||
...)
|
||||
axis(2, seq(0, max(data$cnt)))
|
||||
}
|
||||
@@ -1,202 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
# No export, no Rd
|
||||
addin_insert_in <- function() {
|
||||
rstudioapi::insertText(" %in% ")
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
addin_insert_like <- function() {
|
||||
rstudioapi::insertText(" %like% ")
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
# works exactly like round(), but rounds `round(44.55, 1)` as 44.6 instead of 44.5 and adds decimal zeroes until `digits` is reached
|
||||
round2 <- function(x, digits = 0, force_zero = TRUE) {
|
||||
# https://stackoverflow.com/a/12688836/4575331
|
||||
val <- (trunc((abs(x) * 10 ^ digits) + 0.5) / 10 ^ digits) * sign(x)
|
||||
if (digits > 0 & force_zero == TRUE) {
|
||||
val[val != as.integer(val)] <- paste0(val[val != as.integer(val)],
|
||||
strrep("0", max(0, digits - nchar(gsub(".*[.](.*)$", "\\1", val[val != as.integer(val)])))))
|
||||
}
|
||||
val
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
percent <- function(x, round = 1, force_zero = FALSE, decimal.mark = getOption("OutDec"), ...) {
|
||||
|
||||
decimal.mark.options <- getOption("OutDec")
|
||||
options(OutDec = ".")
|
||||
|
||||
val <- round2(x, round + 2, force_zero = FALSE) # round up 0.5
|
||||
val <- round(x = val * 100, digits = round) # remove floating point error
|
||||
|
||||
if (force_zero == TRUE) {
|
||||
if (any(val == as.integer(val) & !is.na(val))) {
|
||||
# add zeroes to all integers
|
||||
val[val == as.integer(as.character(val))] <- paste0(val[val == as.integer(val)], ".", strrep(0, round))
|
||||
}
|
||||
# add extra zeroes if needed
|
||||
val_decimals <- nchar(gsub(".*[.](.*)", "\\1", as.character(val)))
|
||||
val[val_decimals < round] <- paste0(val[val_decimals < round], strrep(0, max(0, round - val_decimals)))
|
||||
}
|
||||
pct <- base::paste0(val, "%")
|
||||
pct[pct %in% c("NA%", "NaN%")] <- NA_character_
|
||||
if (decimal.mark != ".") {
|
||||
pct <- gsub(".", decimal.mark, pct, fixed = TRUE)
|
||||
}
|
||||
options(OutDec = decimal.mark.options)
|
||||
pct
|
||||
}
|
||||
|
||||
check_available_columns <- function(tbl, col.list, info = TRUE) {
|
||||
# check columns
|
||||
col.list <- col.list[!is.na(col.list) & !is.null(col.list)]
|
||||
names(col.list) <- col.list
|
||||
col.list.bak <- col.list
|
||||
# are they available as upper case or lower case then?
|
||||
for (i in 1:length(col.list)) {
|
||||
if (is.null(col.list[i]) | isTRUE(is.na(col.list[i]))) {
|
||||
col.list[i] <- NULL
|
||||
} else if (toupper(col.list[i]) %in% colnames(tbl)) {
|
||||
col.list[i] <- toupper(col.list[i])
|
||||
} else if (tolower(col.list[i]) %in% colnames(tbl)) {
|
||||
col.list[i] <- tolower(col.list[i])
|
||||
} else if (!col.list[i] %in% colnames(tbl)) {
|
||||
col.list[i] <- NULL
|
||||
}
|
||||
}
|
||||
if (!all(col.list %in% colnames(tbl))) {
|
||||
if (info == TRUE) {
|
||||
warning('These columns do not exist and will be ignored: ',
|
||||
col.list.bak[!(col.list %in% colnames(tbl))] %>% toString(),
|
||||
'.\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.',
|
||||
immediate. = TRUE,
|
||||
call. = FALSE)
|
||||
}
|
||||
}
|
||||
col.list
|
||||
}
|
||||
|
||||
# Coefficient of variation (CV)
|
||||
cv <- function(x, na.rm = TRUE) {
|
||||
stats::sd(x, na.rm = na.rm) / base::abs(base::mean(x, na.rm = na.rm))
|
||||
}
|
||||
|
||||
# Coefficient of dispersion, or coefficient of quartile variation (CQV).
|
||||
# (Bonett et al., 2006: Confidence interval for a coefficient of quartile variation).
|
||||
cqv <- function(x, na.rm = TRUE) {
|
||||
fives <- stats::fivenum(x, na.rm = na.rm)
|
||||
(fives[4] - fives[2]) / (fives[4] + fives[2])
|
||||
}
|
||||
|
||||
# show bytes as kB/MB/GB
|
||||
# size_humanreadable(123456) # 121 kB
|
||||
# size_humanreadable(12345678) # 11.8 MB
|
||||
size_humanreadable <- function(bytes, decimals = 1) {
|
||||
bytes <- bytes %>% as.double()
|
||||
# Adapted from:
|
||||
# http://jeffreysambells.com/2012/10/25/human-readable-filesize-php
|
||||
size <- c('B','kB','MB','GB','TB','PB','EB','ZB','YB')
|
||||
factor <- floor((nchar(bytes) - 1) / 3)
|
||||
# added slight improvement; no decimals for B and kB:
|
||||
decimals <- rep(decimals, length(bytes))
|
||||
decimals[size[factor + 1] %in% c('B', 'kB')] <- 0
|
||||
|
||||
out <- paste(sprintf(paste0("%.", decimals, "f"), bytes / (1024 ^ factor)), size[factor + 1])
|
||||
out
|
||||
}
|
||||
|
||||
#' @importFrom crayon blue bold red
|
||||
#' @importFrom dplyr %>% pull
|
||||
search_type_in_df <- function(tbl, type) {
|
||||
# try to find columns based on type
|
||||
found <- NULL
|
||||
|
||||
colnames(tbl) <- trimws(colnames(tbl))
|
||||
|
||||
# -- mo
|
||||
if (type == "mo") {
|
||||
if ("mo" %in% lapply(tbl, class)) {
|
||||
found <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
|
||||
} else if (any(colnames(tbl) %like% "^(mo|microorganism|organism|bacteria)")) {
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "^(mo|microorganism|organism|bacteria)"][1]
|
||||
} else if (any(colnames(tbl) %like% "species")) {
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "species"][1]
|
||||
}
|
||||
|
||||
}
|
||||
# -- key antibiotics
|
||||
if (type == "keyantibiotics") {
|
||||
if (any(colnames(tbl) %like% "^key.*(ab|antibiotics)")) {
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "^key.*(ab|antibiotics)"][1]
|
||||
}
|
||||
}
|
||||
# -- date
|
||||
if (type == "date") {
|
||||
if (any(colnames(tbl) %like% "^(specimen date|specimen_date|spec_date)")) {
|
||||
# WHONET support
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "^(specimen date|specimen_date|spec_date)"][1]
|
||||
if (!any(class(tbl %>% pull(found)) %in% c("Date", "POSIXct"))) {
|
||||
stop(red(paste0("ERROR: Found column `", bold(found), "` to be used as input for `col_", type,
|
||||
"`, but this column contains no valid dates. Transform its values to valid dates first.")),
|
||||
call. = FALSE)
|
||||
}
|
||||
} else {
|
||||
for (i in 1:ncol(tbl)) {
|
||||
if (any(class(tbl %>% pull(i)) %in% c("Date", "POSIXct"))) {
|
||||
found <- colnames(tbl)[i]
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
# -- patient id
|
||||
if (type == "patient_id") {
|
||||
if (any(colnames(tbl) %like% "^(identification |patient|patid)")) {
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "^(identification |patient|patid)"][1]
|
||||
}
|
||||
}
|
||||
# -- specimen
|
||||
if (type == "specimen") {
|
||||
if (any(colnames(tbl) %like% "(specimen type|spec_type)")) {
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "(specimen type|spec_type)"][1]
|
||||
} else if (any(colnames(tbl) %like% "^(specimen)")) {
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "^(specimen)"][1]
|
||||
}
|
||||
}
|
||||
|
||||
if (!is.null(found)) {
|
||||
msg <- paste0("NOTE: Using column `", bold(found), "` as input for `col_", type, "`.")
|
||||
if (type %in% c("keyantibiotics", "specimen")) {
|
||||
msg <- paste(msg, "Use", bold(paste0("col_", type), " = FALSE"), "to prevent this.")
|
||||
}
|
||||
message(blue(msg))
|
||||
}
|
||||
found
|
||||
}
|
||||
|
||||
stopifnot_installed_package <- function(package) {
|
||||
if (!package %in% base::rownames(utils::installed.packages())) {
|
||||
stop("this function requires the ", package, " package.", call. = FALSE)
|
||||
}
|
||||
}
|
||||
-117
@@ -1,117 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
# print successful as.mo coercions to file, not uncertain ones
|
||||
#' @importFrom dplyr %>% distinct filter
|
||||
set_mo_history <- function(x, mo, uncertainty_level, force = FALSE) {
|
||||
file_location <- base::path.expand('~/.Rhistory_mo')
|
||||
if (base::interactive() | force == TRUE) {
|
||||
mo_hist <- read_mo_history(uncertainty_level = uncertainty_level, force = force)
|
||||
df <- data.frame(x, mo, stringsAsFactors = FALSE) %>%
|
||||
distinct(x, .keep_all = TRUE) %>%
|
||||
filter(!is.na(x) & !is.na(mo))
|
||||
if (nrow(df) == 0) {
|
||||
return(base::invisible())
|
||||
}
|
||||
x <- toupper(df$x)
|
||||
mo <- df$mo
|
||||
for (i in 1:length(x)) {
|
||||
# save package version too, as both the as.mo() algorithm and the reference data set may change
|
||||
if (NROW(mo_hist[base::which(mo_hist$x == x[i] &
|
||||
mo_hist$uncertainty_level >= uncertainty_level &
|
||||
mo_hist$package_version == utils::packageVersion("AMR")),]) == 0) {
|
||||
base::write(x = c(x[i], mo[i], uncertainty_level, base::as.character(utils::packageVersion("AMR"))),
|
||||
file = file_location,
|
||||
ncolumns = 4,
|
||||
append = TRUE,
|
||||
sep = "\t")
|
||||
}
|
||||
}
|
||||
}
|
||||
return(base::invisible())
|
||||
}
|
||||
|
||||
get_mo_history <- function(x, uncertainty_level, force = FALSE) {
|
||||
file_read <- read_mo_history(uncertainty_level = uncertainty_level, force = force)
|
||||
if (base::is.null(file_read)) {
|
||||
NA
|
||||
} else {
|
||||
data.frame(x = toupper(x), stringsAsFactors = FALSE) %>%
|
||||
left_join(file_read, by = "x") %>%
|
||||
pull(mo)
|
||||
}
|
||||
}
|
||||
|
||||
#' @importFrom dplyr %>% filter distinct
|
||||
read_mo_history <- function(uncertainty_level = 2, force = FALSE, unfiltered = FALSE) {
|
||||
file_location <- base::path.expand('~/.Rhistory_mo')
|
||||
if (!base::file.exists(file_location) | (!base::interactive() & force == FALSE)) {
|
||||
return(NULL)
|
||||
}
|
||||
uncertainty_level_param <- uncertainty_level
|
||||
file_read <- utils::read.table(file = file_location,
|
||||
header = FALSE,
|
||||
sep = "\t",
|
||||
col.names = c("x", "mo", "uncertainty_level", "package_version"),
|
||||
stringsAsFactors = FALSE)
|
||||
# Below: filter on current package version.
|
||||
# Even current fullnames may be replaced by new taxonomic names, so new versions of
|
||||
# the Catalogue of Life must not lead to data corruption.
|
||||
|
||||
if (unfiltered == FALSE) {
|
||||
file_read <- file_read %>%
|
||||
filter(package_version == utils::packageVersion("AMR"),
|
||||
# only take unknowns if uncertainty_level_param is higher
|
||||
((mo == "UNKNOWN" & uncertainty_level_param == uncertainty_level) |
|
||||
(mo != "UNKNOWN" & uncertainty_level_param >= uncertainty_level))) %>%
|
||||
arrange(desc(uncertainty_level)) %>%
|
||||
distinct(x, mo, .keep_all = TRUE)
|
||||
}
|
||||
|
||||
if (nrow(file_read) == 0) {
|
||||
NULL
|
||||
} else {
|
||||
file_read
|
||||
}
|
||||
}
|
||||
|
||||
#' @rdname as.mo
|
||||
#' @importFrom crayon red
|
||||
#' @importFrom utils menu
|
||||
#' @export
|
||||
clean_mo_history <- function(...) {
|
||||
file_location <- base::path.expand('~/.Rhistory_mo')
|
||||
if (file.exists(file_location)) {
|
||||
if (interactive() & !isTRUE(list(...)$force)) {
|
||||
q <- menu(title = paste("This will remove all",
|
||||
format(nrow(read_mo_history(999, unfiltered = TRUE)), big.mark = ","),
|
||||
"previously determined microbial IDs. Are you sure?"),
|
||||
choices = c("Yes", "No"),
|
||||
graphics = FALSE)
|
||||
if (q != 1) {
|
||||
return(invisible())
|
||||
}
|
||||
}
|
||||
unlink(file_location)
|
||||
cat(red("File", file_location, "removed."))
|
||||
}
|
||||
}
|
||||
|
||||
-602
@@ -1,602 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Property of a microorganism
|
||||
#'
|
||||
#' Use these functions to return a specific property of a microorganism from the \code{\link{microorganisms}} data set. All input values will be evaluated internally with \code{\link{as.mo}}.
|
||||
#' @param x any (vector of) text that can be coerced to a valid microorganism code with \code{\link{as.mo}}
|
||||
#' @param property one of the column names of one of the \code{\link{microorganisms}} data set or \code{"shortname"}
|
||||
#' @param language language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.
|
||||
#' @param ... other parameters passed on to \code{\link{as.mo}}
|
||||
#' @param open browse the URL using \code{\link[utils]{browseURL}()}
|
||||
#' @details All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for \code{mo_ref}, \code{mo_authors} and \code{mo_year}. This leads to the following results:
|
||||
#' \itemize{
|
||||
#' \item{\code{mo_fullname("Chlamydia psittaci")} will return \code{"Chlamydophila psittaci"} (with a warning about the renaming)}
|
||||
#' \item{\code{mo_ref("Chlamydia psittaci")} will return \code{"Page, 1968"} (with a warning about the renaming)}
|
||||
#' \item{\code{mo_ref("Chlamydophila psittaci")} will return \code{"Everett et al., 1999"} (without a warning)}
|
||||
#' }
|
||||
#'
|
||||
#' The Gram stain - \code{mo_gramstain()} - will be determined on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002) who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram positive - all other bacteria are considered Gram negative. Species outside the kingdom of Bacteria will return a value \code{NA}.
|
||||
#'
|
||||
#' The function \code{mo_url()} will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
|
||||
#' @inheritSection get_locale Supported languages
|
||||
#' @inheritSection catalogue_of_life Catalogue of Life
|
||||
#' @inheritSection as.mo Source
|
||||
#' @rdname mo_property
|
||||
#' @name mo_property
|
||||
#' @return \itemize{
|
||||
#' \item{An \code{integer} in case of \code{mo_year}}
|
||||
#' \item{A \code{list} in case of \code{mo_taxonomy}}
|
||||
#' \item{A named \code{character} in case of \code{mo_url}}
|
||||
#' \item{A \code{character} in all other cases}
|
||||
#' }
|
||||
#' @export
|
||||
#' @seealso \code{\link{microorganisms}}
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' ## taxonomic tree
|
||||
#' mo_kingdom("E. coli") # "Bacteria"
|
||||
#' mo_phylum("E. coli") # "Proteobacteria"
|
||||
#' mo_class("E. coli") # "Gammaproteobacteria"
|
||||
#' mo_order("E. coli") # "Enterobacteriales"
|
||||
#' mo_family("E. coli") # "Enterobacteriaceae"
|
||||
#' mo_genus("E. coli") # "Escherichia"
|
||||
#' mo_species("E. coli") # "coli"
|
||||
#' mo_subspecies("E. coli") # ""
|
||||
#'
|
||||
#' ## colloquial properties
|
||||
#' mo_fullname("E. coli") # "Escherichia coli"
|
||||
#' mo_shortname("E. coli") # "E. coli"
|
||||
#'
|
||||
#' ## other properties
|
||||
#' mo_gramstain("E. coli") # "Gram negative"
|
||||
#' mo_type("E. coli") # "Bacteria" (equal to kingdom)
|
||||
#' mo_rank("E. coli") # "species"
|
||||
#' mo_url("E. coli") # get the direct url to the Catalogue of Life
|
||||
#'
|
||||
#' ## scientific reference
|
||||
#' mo_ref("E. coli") # "Castellani et al., 1919"
|
||||
#' mo_authors("E. coli") # "Castellani et al."
|
||||
#' mo_year("E. coli") # 1919
|
||||
#'
|
||||
#'
|
||||
#' # Abbreviations known in the field
|
||||
#' mo_genus("MRSA") # "Staphylococcus"
|
||||
#' mo_species("MRSA") # "aureus"
|
||||
#' mo_shortname("MRSA") # "S. aureus"
|
||||
#' mo_gramstain("MRSA") # "Gram positive"
|
||||
#'
|
||||
#' mo_genus("VISA") # "Staphylococcus"
|
||||
#' mo_species("VISA") # "aureus"
|
||||
#'
|
||||
#'
|
||||
#' # Known subspecies
|
||||
#' mo_genus("doylei") # "Campylobacter"
|
||||
#' mo_species("doylei") # "jejuni"
|
||||
#' mo_fullname("doylei") # "Campylobacter jejuni doylei"
|
||||
#'
|
||||
#' mo_fullname("K. pneu rh") # "Klebsiella pneumoniae rhinoscleromatis"
|
||||
#' mo_shortname("K. pneu rh") # "K. pneumoniae"
|
||||
#'
|
||||
#'
|
||||
#' # Becker classification, see ?as.mo
|
||||
#' mo_fullname("S. epi") # "Staphylococcus epidermidis"
|
||||
#' mo_fullname("S. epi", Becker = TRUE) # "Coagulase-negative Staphylococcus (CoNS)"
|
||||
#' mo_shortname("S. epi") # "S. epidermidis"
|
||||
#' mo_shortname("S. epi", Becker = TRUE) # "CoNS"
|
||||
#'
|
||||
#' # Lancefield classification, see ?as.mo
|
||||
#' mo_fullname("S. pyo") # "Streptococcus pyogenes"
|
||||
#' mo_fullname("S. pyo", Lancefield = TRUE) # "Streptococcus group A"
|
||||
#' mo_shortname("S. pyo") # "S. pyogenes"
|
||||
#' mo_shortname("S. pyo", Lancefield = TRUE) # "GAS" ('Group A streptococci')
|
||||
#'
|
||||
#'
|
||||
#' # language support for German, Dutch, Spanish, Portuguese, Italian and French
|
||||
#' mo_gramstain("E. coli", language = "de") # "Gramnegativ"
|
||||
#' mo_gramstain("E. coli", language = "nl") # "Gram-negatief"
|
||||
#' mo_gramstain("E. coli", language = "es") # "Gram negativo"
|
||||
#'
|
||||
#' # mo_type is equal to mo_kingdom, but mo_kingdom will remain official
|
||||
#' mo_kingdom("E. coli") # "Bacteria" on a German system
|
||||
#' mo_type("E. coli") # "Bakterien" on a German system
|
||||
#' mo_type("E. coli") # "Bacteria" on an English system
|
||||
#'
|
||||
#' mo_fullname("S. pyogenes",
|
||||
#' Lancefield = TRUE,
|
||||
#' language = "de") # "Streptococcus Gruppe A"
|
||||
#' mo_fullname("S. pyogenes",
|
||||
#' Lancefield = TRUE,
|
||||
#' language = "nl") # "Streptococcus groep A"
|
||||
#'
|
||||
#'
|
||||
#' # get a list with the complete taxonomy (kingdom to subspecies)
|
||||
#' mo_taxonomy("E. coli")
|
||||
mo_fullname <- function(x, language = get_locale(), ...) {
|
||||
x <- mo_validate(x = x, property = "fullname", ...)
|
||||
mo_translate(x, language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @importFrom dplyr %>% left_join mutate pull
|
||||
#' @export
|
||||
mo_shortname <- function(x, language = get_locale(), ...) {
|
||||
dots <- list(...)
|
||||
Becker <- dots$Becker
|
||||
if (is.null(Becker)) {
|
||||
Becker <- FALSE
|
||||
}
|
||||
Lancefield <- dots$Lancefield
|
||||
if (is.null(Lancefield)) {
|
||||
Lancefield <- FALSE
|
||||
}
|
||||
|
||||
shorten <- function(x) {
|
||||
# easiest: no transformations needed
|
||||
x <- mo_fullname(x, language = "en")
|
||||
# shorten for the ones that have a space: shorten first word and write out second word
|
||||
shorten_these <- x %like% " " & !x %like% "Streptococcus group "
|
||||
x[shorten_these] <- paste0(substr(x[shorten_these], 1, 1),
|
||||
". ",
|
||||
x[shorten_these] %>%
|
||||
strsplit(" ", fixed = TRUE) %>%
|
||||
unlist() %>%
|
||||
.[2])
|
||||
x
|
||||
}
|
||||
|
||||
if (isFALSE(Becker) & isFALSE(Lancefield)) {
|
||||
result <- shorten(x)
|
||||
|
||||
} else {
|
||||
# get result without transformations
|
||||
res1 <- AMR::as.mo(x, Becker = FALSE, Lancefield = FALSE, reference_df = dots$reference_df)
|
||||
# and result with transformations
|
||||
res2 <- suppressWarnings(AMR::as.mo(res1, ...))
|
||||
if (res1 == res2
|
||||
& !res1 %like% "^B_STRPT_GR") {
|
||||
result <- shorten(x)
|
||||
} else {
|
||||
res2_fullname <- mo_fullname(res2, language = language)
|
||||
res2_fullname[res2_fullname %like% " \\(CoNS\\)"] <- "CoNS"
|
||||
res2_fullname[res2_fullname %like% " \\(CoPS\\)"] <- "CoPS"
|
||||
res2_fullname[res2_fullname %like% " \\(KNS\\)"] <- "KNS"
|
||||
res2_fullname[res2_fullname %like% " \\(KPS\\)"] <- "KPS"
|
||||
res2_fullname[res2_fullname %like% " \\(CNS\\)"] <- "CNS"
|
||||
res2_fullname[res2_fullname %like% " \\(CPS\\)"] <- "CPS"
|
||||
res2_fullname <- gsub("Streptococcus (group|Gruppe|gruppe|groep|grupo|gruppo|groupe) (.)",
|
||||
"G\\2S",
|
||||
res2_fullname) # turn "Streptococcus group A" and "Streptococcus grupo A" to "GAS"
|
||||
res2_fullname_vector <- res2_fullname[res2_fullname == mo_fullname(res1)]
|
||||
res2_fullname[res2_fullname == mo_fullname(res1)] <- paste0(substr(mo_genus(res2_fullname_vector), 1, 1),
|
||||
". ",
|
||||
suppressWarnings(mo_species(res2_fullname_vector)))
|
||||
if (sum(res1 == res2, na.rm = TRUE) > 0) {
|
||||
res1[res1 == res2] <- paste0(substr(mo_genus(res1[res1 == res2]), 1, 1),
|
||||
". ",
|
||||
suppressWarnings(mo_species(res1[res1 == res2])))
|
||||
}
|
||||
res1[res1 != res2] <- res2_fullname
|
||||
result <- as.character(res1)
|
||||
}
|
||||
}
|
||||
|
||||
mo_translate(result, language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_subspecies <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "subspecies", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_species <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "species", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_genus <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "genus", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_family <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "family", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_order <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "order", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_class <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "class", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_phylum <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "phylum", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_kingdom <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "kingdom", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_type <- function(x, language = get_locale(), ...) {
|
||||
mo_translate(mo_validate(x = x, property = "kingdom", ...), language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_gramstain <- function(x, language = get_locale(), ...) {
|
||||
x.bak <- x
|
||||
x.mo <- as.mo(x, ...)
|
||||
x.phylum <- mo_phylum(x.mo)
|
||||
x[x.phylum %in% c("Actinobacteria",
|
||||
"Chloroflexi",
|
||||
"Firmicutes",
|
||||
"Tenericutes")] <- "Gram positive"
|
||||
x[x != "Gram positive"] <- "Gram negative"
|
||||
x[mo_kingdom(x.mo) != "Bacteria"] <- NA_character_
|
||||
x[x.mo == "B_GRAMP"] <- "Gram positive"
|
||||
x[x.mo == "B_GRAMN"] <- "Gram negative"
|
||||
|
||||
mo_translate(x, language = language)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_ref <- function(x, ...) {
|
||||
mo_validate(x = x, property = "ref", ...)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_authors <- function(x, ...) {
|
||||
x <- mo_validate(x = x, property = "ref", ...)
|
||||
# remove last 4 digits and presumably the comma and space that preceeds them
|
||||
x[!is.na(x)] <- gsub(",? ?[0-9]{4}", "", x[!is.na(x)])
|
||||
suppressWarnings(x)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_year <- function(x, ...) {
|
||||
x <- mo_validate(x = x, property = "ref", ...)
|
||||
# get last 4 digits
|
||||
x[!is.na(x)] <- gsub(".*([0-9]{4})$", "\\1", x[!is.na(x)])
|
||||
suppressWarnings(as.integer(x))
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_rank <- function(x, ...) {
|
||||
mo_validate(x = x, property = "rank", ...)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_taxonomy <- function(x, language = get_locale(), ...) {
|
||||
x <- AMR::as.mo(x, ...)
|
||||
base::list(kingdom = mo_kingdom(x, language = language),
|
||||
phylum = mo_phylum(x, language = language),
|
||||
class = mo_class(x, language = language),
|
||||
order = mo_order(x, language = language),
|
||||
family = mo_family(x, language = language),
|
||||
genus = mo_genus(x, language = language),
|
||||
species = mo_species(x, language = language),
|
||||
subspecies = mo_subspecies(x, language = language))
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @importFrom utils browseURL
|
||||
#' @importFrom dplyr %>% left_join select mutate case_when
|
||||
#' @export
|
||||
mo_url <- function(x, open = FALSE, ...) {
|
||||
mo <- AMR::as.mo(x = x, ... = ...)
|
||||
df <- data.frame(mo, stringsAsFactors = FALSE) %>%
|
||||
left_join(select(AMR::microorganisms, mo, source, species_id), by = "mo") %>%
|
||||
mutate(url = case_when(source == "CoL" ~
|
||||
paste0(gsub("{year}", catalogue_of_life$year, catalogue_of_life$url_CoL, fixed = TRUE), "details/species/id/", species_id),
|
||||
source == "DSMZ" ~
|
||||
paste0(catalogue_of_life$url_DSMZ, "?bnu_no=", species_id, "#", species_id),
|
||||
TRUE ~
|
||||
NA_character_))
|
||||
|
||||
u <- df$url
|
||||
names(u) <- mo_fullname(mo)
|
||||
if (open == TRUE) {
|
||||
if (length(u) > 1) {
|
||||
warning("only the first URL will be opened, as `browseURL()` only suports one string.")
|
||||
}
|
||||
browseURL(u[1L])
|
||||
}
|
||||
u
|
||||
}
|
||||
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @importFrom data.table data.table as.data.table setkey
|
||||
#' @export
|
||||
mo_property <- function(x, property = 'fullname', language = get_locale(), ...) {
|
||||
if (length(property) != 1L) {
|
||||
stop("'property' must be of length 1.")
|
||||
}
|
||||
if (!property %in% colnames(AMR::microorganisms)) {
|
||||
stop("invalid property: '", property, "' - use a column name of the `microorganisms` data set")
|
||||
}
|
||||
|
||||
mo_translate(mo_validate(x = x, property = property, ...), language = language)
|
||||
}
|
||||
|
||||
#' @importFrom dplyr %>% case_when
|
||||
mo_translate <- function(x, language) {
|
||||
if (is.null(language)) {
|
||||
return(x)
|
||||
}
|
||||
if (language %in% c("en", "")) {
|
||||
return(x)
|
||||
}
|
||||
|
||||
supported <- c("en", "de", "nl", "es", "pt", "it", "fr")
|
||||
if (!language %in% supported) {
|
||||
stop("Unsupported language: '", language, "' - use one of: ", paste0("'", sort(supported), "'", collapse = ", "), call. = FALSE)
|
||||
}
|
||||
|
||||
x_tobetranslated <- grepl(x = x,
|
||||
pattern = "(Coagulase-negative Staphylococcus|Coagulase-positive Staphylococcus|Beta-haemolytic Streptococcus|unknown Gram negatives|unknown Gram positives|unknown name|unknown kingdom|unknown phylum|unknown class|unknown order|unknown family|unknown genus|unknown species|unknown subspecies|unknown rank|CoNS|CoPS|Gram negative|Gram positive|Bacteria|Fungi|Protozoa|biogroup|biotype|vegetative|group|Group)")
|
||||
|
||||
if (sum(x_tobetranslated, na.rm = TRUE) == 0) {
|
||||
return(x)
|
||||
}
|
||||
|
||||
# only translate the ones that need translation
|
||||
x[x_tobetranslated] <- case_when(
|
||||
# German
|
||||
language == "de" ~ x[x_tobetranslated] %>%
|
||||
gsub("Coagulase-negative Staphylococcus","Koagulase-negative Staphylococcus", ., fixed = TRUE) %>%
|
||||
gsub("Coagulase-positive Staphylococcus","Koagulase-positive Staphylococcus", ., fixed = TRUE) %>%
|
||||
gsub("Beta-haemolytic Streptococcus", "Beta-h\u00e4molytischer Streptococcus", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram negatives", "unbekannte Gramnegativen", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram positives", "unbekannte Grampositiven", ., fixed = TRUE) %>%
|
||||
gsub("unknown name", "unbekannte Name", ., fixed = TRUE) %>%
|
||||
gsub("unknown kingdom", "unbekanntes Reich", ., fixed = TRUE) %>%
|
||||
gsub("unknown phylum", "unbekannter Stamm", ., fixed = TRUE) %>%
|
||||
gsub("unknown class", "unbekannte Klasse", ., fixed = TRUE) %>%
|
||||
gsub("unknown order", "unbekannte Ordnung", ., fixed = TRUE) %>%
|
||||
gsub("unknown family", "unbekannte Familie", ., fixed = TRUE) %>%
|
||||
gsub("unknown genus", "unbekannte Gattung", ., fixed = TRUE) %>%
|
||||
gsub("unknown species", "unbekannte Art", ., fixed = TRUE) %>%
|
||||
gsub("unknown subspecies", "unbekannte Unterart", ., fixed = TRUE) %>%
|
||||
gsub("unknown rank", "unbekannter Rang", ., fixed = TRUE) %>%
|
||||
gsub("(CoNS)", "(KNS)", ., fixed = TRUE) %>%
|
||||
gsub("(CoPS)", "(KPS)", ., fixed = TRUE) %>%
|
||||
gsub("Gram negative", "Gramnegativ", ., fixed = TRUE) %>%
|
||||
gsub("Gram positive", "Grampositiv", ., fixed = TRUE) %>%
|
||||
gsub("Bacteria", "Bakterien", ., fixed = TRUE) %>%
|
||||
gsub("Fungi", "Hefen/Pilze", ., fixed = TRUE) %>%
|
||||
gsub("Protozoa", "Protozoen", ., fixed = TRUE) %>%
|
||||
gsub("biogroup", "Biogruppe", ., fixed = TRUE) %>%
|
||||
gsub("biotype", "Biotyp", ., fixed = TRUE) %>%
|
||||
gsub("vegetative", "vegetativ", ., fixed = TRUE) %>%
|
||||
gsub("([([ ]*?)group", "\\1Gruppe", .) %>%
|
||||
gsub("([([ ]*?)Group", "\\1Gruppe", .) %>%
|
||||
iconv(to = "UTF-8"),
|
||||
|
||||
# Dutch
|
||||
language == "nl" ~ x[x_tobetranslated] %>%
|
||||
gsub("Coagulase-negative Staphylococcus","Coagulase-negatieve Staphylococcus", ., fixed = TRUE) %>%
|
||||
gsub("Coagulase-positive Staphylococcus","Coagulase-positieve Staphylococcus", ., fixed = TRUE) %>%
|
||||
gsub("Beta-haemolytic Streptococcus", "Beta-hemolytische Streptococcus", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram negatives", "onbekende Gram-negatieven", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram positives", "onbekende Gram-positieven", ., fixed = TRUE) %>%
|
||||
gsub("unknown name", "onbekende naam", ., fixed = TRUE) %>%
|
||||
gsub("unknown kingdom", "onbekend koninkrijk", ., fixed = TRUE) %>%
|
||||
gsub("unknown phylum", "onbekende fylum", ., fixed = TRUE) %>%
|
||||
gsub("unknown class", "onbekende klasse", ., fixed = TRUE) %>%
|
||||
gsub("unknown order", "onbekende orde", ., fixed = TRUE) %>%
|
||||
gsub("unknown family", "onbekende familie", ., fixed = TRUE) %>%
|
||||
gsub("unknown genus", "onbekend geslacht", ., fixed = TRUE) %>%
|
||||
gsub("unknown species", "onbekende soort", ., fixed = TRUE) %>%
|
||||
gsub("unknown subspecies", "onbekende ondersoort", ., fixed = TRUE) %>%
|
||||
gsub("unknown rank", "onbekende rang", ., fixed = TRUE) %>%
|
||||
gsub("(CoNS)", "(CNS)", ., fixed = TRUE) %>%
|
||||
gsub("(CoPS)", "(CPS)", ., fixed = TRUE) %>%
|
||||
gsub("Gram negative", "Gram-negatief", ., fixed = TRUE) %>%
|
||||
gsub("Gram positive", "Gram-positief", ., fixed = TRUE) %>%
|
||||
gsub("Bacteria", "Bacteri\u00ebn", ., fixed = TRUE) %>%
|
||||
gsub("Fungi", "Schimmels/gisten", ., fixed = TRUE) %>%
|
||||
gsub("Protozoa", "protozo\u00ebn", ., fixed = TRUE) %>%
|
||||
gsub("biogroup", "biogroep", ., fixed = TRUE) %>%
|
||||
# gsub("biotype", "biotype", ., fixed = TRUE) %>%
|
||||
gsub("vegetative", "vegetatief", ., fixed = TRUE) %>%
|
||||
gsub("([([ ]*?)group", "\\1groep", .) %>%
|
||||
gsub("([([ ]*?)Group", "\\1Groep", .) %>%
|
||||
iconv(to = "UTF-8"),
|
||||
|
||||
# Spanish
|
||||
language == "es" ~ x[x_tobetranslated] %>%
|
||||
# not 'negativa'
|
||||
# https://www.sciencedirect.com/science/article/pii/S0123939215000739
|
||||
gsub("Coagulase-negative Staphylococcus","Staphylococcus coagulasa negativo", ., fixed = TRUE) %>%
|
||||
gsub("Coagulase-positive Staphylococcus","Staphylococcus coagulasa positivo", ., fixed = TRUE) %>%
|
||||
gsub("Beta-haemolytic Streptococcus", "Streptococcus Beta-hemol\u00edtico", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram negatives", "Gram negativos desconocidos", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram positives", "Gram positivos desconocidos", ., fixed = TRUE) %>%
|
||||
gsub("unknown name", "nombre desconocido", ., fixed = TRUE) %>%
|
||||
gsub("unknown kingdom", "reino desconocido", ., fixed = TRUE) %>%
|
||||
gsub("unknown phylum", "filo desconocido", ., fixed = TRUE) %>%
|
||||
gsub("unknown class", "clase desconocida", ., fixed = TRUE) %>%
|
||||
gsub("unknown order", "orden desconocido", ., fixed = TRUE) %>%
|
||||
gsub("unknown family", "familia desconocida", ., fixed = TRUE) %>%
|
||||
gsub("unknown genus", "g\u00e9nero desconocido", ., fixed = TRUE) %>%
|
||||
gsub("unknown species", "especie desconocida", ., fixed = TRUE) %>%
|
||||
gsub("unknown subspecies", "subespecie desconocida", ., fixed = TRUE) %>%
|
||||
gsub("unknown rank", "rango desconocido", ., fixed = TRUE) %>%
|
||||
gsub("(CoNS)", "(SCN)", ., fixed = TRUE) %>%
|
||||
gsub("(CoPS)", "(SCP)", ., fixed = TRUE) %>%
|
||||
gsub("Gram negative", "Gram negativo", ., fixed = TRUE) %>%
|
||||
gsub("Gram positive", "Gram positivo", ., fixed = TRUE) %>%
|
||||
gsub("Bacteria", "Bacterias", ., fixed = TRUE) %>%
|
||||
gsub("Fungi", "Hongos", ., fixed = TRUE) %>%
|
||||
gsub("Protozoa", "Protozoarios", ., fixed = TRUE) %>%
|
||||
gsub("biogroup", "biogrupo", ., fixed = TRUE) %>%
|
||||
gsub("biotype", "biotipo", ., fixed = TRUE) %>%
|
||||
gsub("vegetative", "vegetativo", ., fixed = TRUE) %>%
|
||||
gsub("([([ ]*?)group", "\\1grupo", .) %>%
|
||||
gsub("([([ ]*?)Group", "\\1Grupo", .) %>%
|
||||
iconv(to = "UTF-8"),
|
||||
|
||||
# Italian
|
||||
language == "it" ~ x[x_tobetranslated] %>%
|
||||
gsub("Coagulase-negative Staphylococcus","Staphylococcus negativo coagulasi", ., fixed = TRUE) %>%
|
||||
gsub("Coagulase-positive Staphylococcus","Staphylococcus positivo coagulasi", ., fixed = TRUE) %>%
|
||||
gsub("Beta-haemolytic Streptococcus", "Streptococcus Beta-emolitico", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram negatives", "Gram negativi sconosciuti", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram positives", "Gram positivi sconosciuti", ., fixed = TRUE) %>%
|
||||
gsub("unknown name", "nome sconosciuto", ., fixed = TRUE) %>%
|
||||
gsub("unknown kingdom", "regno sconosciuto", ., fixed = TRUE) %>%
|
||||
gsub("unknown phylum", "phylum sconosciuto", ., fixed = TRUE) %>%
|
||||
gsub("unknown class", "classe sconosciuta", ., fixed = TRUE) %>%
|
||||
gsub("unknown order", "ordine sconosciuto", ., fixed = TRUE) %>%
|
||||
gsub("unknown family", "famiglia sconosciuta", ., fixed = TRUE) %>%
|
||||
gsub("unknown genus", "genere sconosciuto", ., fixed = TRUE) %>%
|
||||
gsub("unknown species", "specie sconosciute", ., fixed = TRUE) %>%
|
||||
gsub("unknown subspecies", "sottospecie sconosciute", ., fixed = TRUE) %>%
|
||||
gsub("unknown rank", "grado sconosciuto", ., fixed = TRUE) %>%
|
||||
gsub("Gram negative", "Gram negativo", ., fixed = TRUE) %>%
|
||||
gsub("Gram positive", "Gram positivo", ., fixed = TRUE) %>%
|
||||
gsub("Bacteria", "Batteri", ., fixed = TRUE) %>%
|
||||
gsub("Fungi", "Fungo", ., fixed = TRUE) %>%
|
||||
gsub("Protozoa", "Protozoi", ., fixed = TRUE) %>%
|
||||
gsub("biogroup", "biogruppo", ., fixed = TRUE) %>%
|
||||
gsub("biotype", "biotipo", ., fixed = TRUE) %>%
|
||||
gsub("vegetative", "vegetativo", ., fixed = TRUE) %>%
|
||||
gsub("([([ ]*?)group", "\\1gruppo", .) %>%
|
||||
gsub("([([ ]*?)Group", "\\1Gruppo", .),
|
||||
|
||||
# French
|
||||
language == "fr" ~ x[x_tobetranslated] %>%
|
||||
gsub("Coagulase-negative Staphylococcus","Staphylococcus \u00e0 coagulase n\u00e9gative", ., fixed = TRUE) %>%
|
||||
gsub("Coagulase-positive Staphylococcus","Staphylococcus \u00e0 coagulase positif", ., fixed = TRUE) %>%
|
||||
gsub("Beta-haemolytic Streptococcus", "Streptococcus B\u00eata-h\u00e9molytique", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram negatives", "Gram n\u00e9gatifs inconnus", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram positives", "Gram positifs inconnus", ., fixed = TRUE) %>%
|
||||
gsub("unknown name", "nom inconnu", ., fixed = TRUE) %>%
|
||||
gsub("unknown kingdom", "r\u00e8gme inconnu", ., fixed = TRUE) %>%
|
||||
gsub("unknown phylum", "embranchement inconnu", ., fixed = TRUE) %>%
|
||||
gsub("unknown class", "classe inconnue", ., fixed = TRUE) %>%
|
||||
gsub("unknown order", "ordre inconnu", ., fixed = TRUE) %>%
|
||||
gsub("unknown family", "famille inconnue", ., fixed = TRUE) %>%
|
||||
gsub("unknown genus", "genre inconnu", ., fixed = TRUE) %>%
|
||||
gsub("unknown species", "esp\u00e8ce inconnue", ., fixed = TRUE) %>%
|
||||
gsub("unknown subspecies", "sous-esp\u00e8ce inconnue", ., fixed = TRUE) %>%
|
||||
gsub("unknown rank", "rang inconnu", ., fixed = TRUE) %>%
|
||||
gsub("Gram negative", "Gram n\u00e9gatif", ., fixed = TRUE) %>%
|
||||
gsub("Gram positive", "Gram positif", ., fixed = TRUE) %>%
|
||||
gsub("Bacteria", "Bact\u00e9ries", ., fixed = TRUE) %>%
|
||||
gsub("Fungi", "Champignons", ., fixed = TRUE) %>%
|
||||
gsub("Protozoa", "Protozoaires", ., fixed = TRUE) %>%
|
||||
gsub("biogroup", "biogroupe", ., fixed = TRUE) %>%
|
||||
# gsub("biotype", "biotype", ., fixed = TRUE) %>%
|
||||
gsub("vegetative", "v\u00e9g\u00e9tatif", ., fixed = TRUE) %>%
|
||||
gsub("([([ ]*?)group", "\\1groupe", .) %>%
|
||||
gsub("([([ ]*?)Group", "\\1Groupe", .) %>%
|
||||
iconv(to = "UTF-8"),
|
||||
|
||||
# Portuguese
|
||||
language == "pt" ~ x[x_tobetranslated] %>%
|
||||
gsub("Coagulase-negative Staphylococcus","Staphylococcus coagulase negativo", ., fixed = TRUE) %>%
|
||||
gsub("Coagulase-positive Staphylococcus","Staphylococcus coagulase positivo", ., fixed = TRUE) %>%
|
||||
gsub("Beta-haemolytic Streptococcus", "Streptococcus Beta-hemol\u00edtico", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram negatives", "Gram negativos desconhecidos", ., fixed = TRUE) %>%
|
||||
gsub("unknown Gram positives", "Gram positivos desconhecidos", ., fixed = TRUE) %>%
|
||||
gsub("unknown name", "nome desconhecido", ., fixed = TRUE) %>%
|
||||
gsub("unknown kingdom", "reino desconhecido", ., fixed = TRUE) %>%
|
||||
gsub("unknown phylum", "filo desconhecido", ., fixed = TRUE) %>%
|
||||
gsub("unknown class", "classe desconhecida", ., fixed = TRUE) %>%
|
||||
gsub("unknown order", "ordem desconhecido", ., fixed = TRUE) %>%
|
||||
gsub("unknown family", "fam\u00edlia desconhecida", ., fixed = TRUE) %>%
|
||||
gsub("unknown genus", "g\u00eanero desconhecido", ., fixed = TRUE) %>%
|
||||
gsub("unknown species", "esp\u00e9cies desconhecida", ., fixed = TRUE) %>%
|
||||
gsub("unknown subspecies", "subesp\u00e9cies desconhecida", ., fixed = TRUE) %>%
|
||||
gsub("unknown rank", "classifica\u00e7\u00e3o desconhecido", ., fixed = TRUE) %>%
|
||||
gsub("Gram negative", "Gram negativo", ., fixed = TRUE) %>%
|
||||
gsub("Gram positive", "Gram positivo", ., fixed = TRUE) %>%
|
||||
gsub("Bacteria", "Bact\u00e9rias", ., fixed = TRUE) %>%
|
||||
gsub("Fungi", "Fungos", ., fixed = TRUE) %>%
|
||||
gsub("Protozoa", "Protozo\u00e1rios", ., fixed = TRUE) %>%
|
||||
gsub("biogroup", "biogrupo", ., fixed = TRUE) %>%
|
||||
gsub("biotype", "bi\u00f3tipo", ., fixed = TRUE) %>%
|
||||
gsub("vegetative", "vegetativo", ., fixed = TRUE) %>%
|
||||
gsub("([([ ]*?)group", "\\1grupo", .) %>%
|
||||
gsub("([([ ]*?)Group", "\\1Grupo", .) %>%
|
||||
iconv(to = "UTF-8"))
|
||||
|
||||
x
|
||||
}
|
||||
|
||||
mo_validate <- function(x, property, ...) {
|
||||
|
||||
dots <- list(...)
|
||||
Becker <- dots$Becker
|
||||
if (is.null(Becker)) {
|
||||
Becker <- FALSE
|
||||
}
|
||||
Lancefield <- dots$Lancefield
|
||||
if (is.null(Lancefield)) {
|
||||
Lancefield <- FALSE
|
||||
}
|
||||
|
||||
if (!"AMR" %in% base::.packages()) {
|
||||
library("AMR")
|
||||
# check onLoad() in R/zzz.R: data tables are created there.
|
||||
}
|
||||
|
||||
# try to catch an error when inputting an invalid parameter
|
||||
# so the call can be set to FALSE
|
||||
tryCatch(x[1L] %in% AMR::microorganisms[1, property],
|
||||
error = function(e) stop(e$message, call. = FALSE))
|
||||
|
||||
if (!all(x %in% AMR::microorganisms[, property])
|
||||
| Becker %in% c(TRUE, "all")
|
||||
| Lancefield %in% c(TRUE, "all")) {
|
||||
exec_as.mo(x, property = property, ...)
|
||||
} else {
|
||||
if (property == "mo") {
|
||||
return(structure(x, class = "mo"))
|
||||
} else {
|
||||
return(x)
|
||||
}
|
||||
}
|
||||
}
|
||||
-221
@@ -1,221 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Use predefined reference data set
|
||||
#'
|
||||
#' @description These functions can be used to predefine your own reference to be used in \code{\link{as.mo}} and consequently all \code{mo_*} functions like \code{\link{mo_genus}} and \code{\link{mo_gramstain}}.
|
||||
#'
|
||||
#' This is \strong{the fastest way} to have your organisation (or analysis) specific codes picked up and translated by this package.
|
||||
#' @param path location of your reference file, see Details
|
||||
#' @rdname mo_source
|
||||
#' @name mo_source
|
||||
#' @aliases set_mo_source get_mo_source
|
||||
#' @details The reference file can be a text file seperated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you need to have the \code{readxl} package installed.
|
||||
#'
|
||||
#' \code{set_mo_source} will check the file for validity: it must be a \code{data.frame}, must have a column named \code{"mo"} which contains values from \code{microorganisms$mo} and must have a reference column with your own defined values. If all tests pass, \code{set_mo_source} will read the file into R and export it to \code{"~/.mo_source.rds"}. This compressed data file will then be used at default for MO determination (function \code{\link{as.mo}} and consequently all \code{mo_*} functions like \code{\link{mo_genus}} and \code{\link{mo_gramstain}}). The location of the original file will be saved as option with \code{\link{options}(mo_source = path)}. Its timestamp will be saved with \code{\link{options}(mo_source_datetime = ...)}.
|
||||
#'
|
||||
#' \code{get_mo_source} will return the data set by reading \code{"~/.mo_source.rds"} with \code{\link{readRDS}}. If the original file has changed (the file defined with \code{path}), it will call \code{set_mo_source} to update the data file automatically.
|
||||
#'
|
||||
#' Reading an Excel file (\code{.xlsx}) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by \code{get_mo_source} in only a couple of microseconds (a millionth of a second).
|
||||
#' @section How it works:
|
||||
#' Imagine this data on a sheet of an Excel file (mo codes were looked up in the `microorganisms` data set). The first column contains the organisation specific codes, the second column contains an MO code from this package:
|
||||
#' \preformatted{
|
||||
#' | A | B |
|
||||
#' --|--------------------|-------------|
|
||||
#' 1 | Organisation XYZ | mo |
|
||||
#' 2 | lab_mo_ecoli | B_ESCHR_COL |
|
||||
#' 3 | lab_mo_kpneumoniae | B_KLBSL_PNE |
|
||||
#' 4 | | |
|
||||
#' }
|
||||
#'
|
||||
#' We save it as \code{'home/me/ourcodes.xlsx'}. Now we have to set it as a source:
|
||||
#' \preformatted{
|
||||
#' set_mo_source("home/me/ourcodes.xlsx")
|
||||
#' # Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
|
||||
#' }
|
||||
#'
|
||||
#' It has now created a file "~/.mo_source.rds" with the contents of our Excel file, but only the first column with foreign values and the 'mo' column will be kept.
|
||||
#'
|
||||
#' And now we can use it in our functions:
|
||||
#' \preformatted{
|
||||
#' as.mo("lab_mo_ecoli")
|
||||
#' [1] B_ESCHR_COL
|
||||
#'
|
||||
#' mo_genus("lab_mo_kpneumoniae")
|
||||
#' [1] "Klebsiella"
|
||||
#'
|
||||
#' # other input values still work too
|
||||
#' as.mo(c("Escherichia coli", "E. coli", "lab_mo_ecoli"))
|
||||
#' [1] B_ESCHR_COL B_ESCHR_COL B_ESCHR_COL
|
||||
#' }
|
||||
#'
|
||||
#' If we edit the Excel file to, let's say, this:
|
||||
#' \preformatted{
|
||||
#' | A | B |
|
||||
#' --|--------------------|-------------|
|
||||
#' 1 | Organisation XYZ | mo |
|
||||
#' 2 | lab_mo_ecoli | B_ESCHR_COL |
|
||||
#' 3 | lab_mo_kpneumoniae | B_KLBSL_PNE |
|
||||
#' 4 | lab_Staph_aureus | B_STPHY_AUR |
|
||||
#' 5 | | |
|
||||
#' }
|
||||
#'
|
||||
#' ...any new usage of an MO function in this package will update your data:
|
||||
#' \preformatted{
|
||||
#' as.mo("lab_mo_ecoli")
|
||||
#' # Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
|
||||
#' [1] B_ESCHR_COL
|
||||
#'
|
||||
#' mo_genus("lab_Staph_aureus")
|
||||
#' [1] "Staphylococcus"
|
||||
#' }
|
||||
#'
|
||||
#' To remove the reference completely, just use any of these:
|
||||
#' \preformatted{
|
||||
#' set_mo_source("")
|
||||
#' set_mo_source(NULL)
|
||||
#' # Removed mo_source file '~/.mo_source.rds'.
|
||||
#' }
|
||||
#' @importFrom dplyr select everything
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
set_mo_source <- function(path) {
|
||||
|
||||
file_location <- path.expand('~/mo_source.rds')
|
||||
|
||||
if (!is.character(path) | length(path) > 1) {
|
||||
stop("`path` must be a character of length 1.")
|
||||
}
|
||||
|
||||
if (path %in% c(NULL, "")) {
|
||||
options(mo_source = NULL)
|
||||
options(mo_source_timestamp = NULL)
|
||||
if (file.exists(file_location)) {
|
||||
unlink(file_location)
|
||||
message("Removed mo_source file '", file_location, "'.")
|
||||
}
|
||||
return(invisible())
|
||||
}
|
||||
|
||||
if (!file.exists(path)) {
|
||||
stop("File not found: ", path)
|
||||
}
|
||||
|
||||
if (path %like% '[.]rds$') {
|
||||
df <- readRDS(path)
|
||||
|
||||
} else if (path %like% '[.]xlsx?$') {
|
||||
# is Excel file (old or new)
|
||||
if (!"readxl" %in% utils::installed.packages()) {
|
||||
stop("Install the 'readxl' package first.")
|
||||
}
|
||||
df <- readxl::read_excel(path)
|
||||
|
||||
} else if (path %like% '[.]tsv$') {
|
||||
df <- utils::read.table(header = TRUE, sep = "\t", stringsAsFactors = FALSE)
|
||||
|
||||
} else {
|
||||
# try comma first
|
||||
try(
|
||||
df <- utils::read.table(header = TRUE, sep = ",", stringsAsFactors = FALSE),
|
||||
silent = TRUE)
|
||||
if (!mo_source_isvalid(df)) {
|
||||
# try tab
|
||||
try(
|
||||
df <- utils::read.table(header = TRUE, sep = "\t", stringsAsFactors = FALSE),
|
||||
silent = TRUE)
|
||||
}
|
||||
if (!mo_source_isvalid(df)) {
|
||||
# try pipe
|
||||
try(
|
||||
df <- utils::read.table(header = TRUE, sep = "|", stringsAsFactors = FALSE),
|
||||
silent = TRUE)
|
||||
}
|
||||
}
|
||||
|
||||
if (!mo_source_isvalid(df)) {
|
||||
stop("File must contain a column with self-defined values and a reference column `mo` with valid values from the `microorganisms` data set.")
|
||||
}
|
||||
|
||||
df <- df %>% filter(!is.na(mo))
|
||||
|
||||
# keep only first two columns, second must be mo
|
||||
if (colnames(df)[1] == "mo") {
|
||||
df <- df[, c(2, 1)]
|
||||
} else {
|
||||
df <- df[, c(1, 2)]
|
||||
}
|
||||
|
||||
df <- as.data.frame(df, stringAsFactors = FALSE)
|
||||
|
||||
# success
|
||||
if (file.exists(file_location)) {
|
||||
action <- "Updated"
|
||||
} else {
|
||||
action <- "Created"
|
||||
}
|
||||
saveRDS(df, file_location)
|
||||
options(mo_source = path)
|
||||
options(mo_source_timestamp = as.character(file.info(path)$mtime))
|
||||
message(action, " mo_source file '", file_location, "' from '", path, "'.")
|
||||
}
|
||||
|
||||
#' @rdname mo_source
|
||||
#' @export
|
||||
get_mo_source <- function() {
|
||||
if (is.null(getOption("mo_source", NULL))) {
|
||||
NULL
|
||||
} else {
|
||||
old_time <- as.POSIXct(getOption("mo_source_timestamp"))
|
||||
new_time <- as.POSIXct(as.character(file.info(getOption("mo_source", ""))$mtime))
|
||||
|
||||
if (is.na(new_time)) {
|
||||
# source file was deleted, remove reference too
|
||||
set_mo_source("")
|
||||
return(NULL)
|
||||
}
|
||||
if (new_time != old_time) {
|
||||
# set updated source
|
||||
set_mo_source(getOption("mo_source"))
|
||||
}
|
||||
file_location <- path.expand('~/mo_source.rds')
|
||||
readRDS(file_location)
|
||||
}
|
||||
}
|
||||
|
||||
mo_source_isvalid <- function(x) {
|
||||
if (deparse(substitute(x)) == "get_mo_source()") {
|
||||
return(TRUE)
|
||||
}
|
||||
if (identical(x, get_mo_source())) {
|
||||
return(TRUE)
|
||||
}
|
||||
if (is.null(x)) {
|
||||
return(TRUE)
|
||||
}
|
||||
if (!is.data.frame(x)) {
|
||||
return(FALSE)
|
||||
}
|
||||
if (!"mo" %in% colnames(x)) {
|
||||
return(FALSE)
|
||||
}
|
||||
all(x$mo %in% c("", AMR::microorganisms$mo))
|
||||
}
|
||||
@@ -1,60 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Symbol of a p value
|
||||
#'
|
||||
#' Return the symbol related to the p value: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1. Values above \code{p = 1} will return \code{NA}.
|
||||
#' @param p p value
|
||||
#' @param emptychar text to show when \code{p > 0.1}
|
||||
#' @return Text
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
p.symbol <- function(p, emptychar = " ") {
|
||||
setting.bak <- options()$scipen
|
||||
options(scipen = 999)
|
||||
s <- vector(mode = "character", length = length(p))
|
||||
for (i in 1:length(p)) {
|
||||
if (is.na(p[i])) {
|
||||
s[i] <- NA_character_
|
||||
next
|
||||
}
|
||||
if (p[i] > 1) {
|
||||
s[i] <- NA_character_
|
||||
next
|
||||
} else {
|
||||
p_test <- p[i]
|
||||
}
|
||||
|
||||
if (p_test > 0.1) {
|
||||
s[i] <- emptychar
|
||||
} else if (p_test > 0.05) {
|
||||
s[i] <- '.'
|
||||
} else if (p_test > 0.01) {
|
||||
s[i] <- '*'
|
||||
} else if (p_test > 0.001) {
|
||||
s[i] <- '**'
|
||||
} else if (p_test >= 0) {
|
||||
s[i] <- '***'
|
||||
}
|
||||
}
|
||||
options(scipen = setting.bak)
|
||||
s
|
||||
}
|
||||
-336
@@ -1,336 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Calculate resistance of isolates
|
||||
#'
|
||||
#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
|
||||
#'
|
||||
#' \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
|
||||
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
|
||||
#' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
|
||||
#' @param as_percent a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.
|
||||
#' @param also_single_tested a logical to indicate whether (in combination therapies) also observations should be included where not all antibiotics were tested, but at least one of the tested antibiotics contains a target interpretation (e.g. S in case of \code{portion_S} and R in case of \code{portion_R}). \strong{This would lead to selection bias in almost all cases.}
|
||||
#' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
|
||||
#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
|
||||
#' @param combine_IR a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
|
||||
#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
|
||||
#'
|
||||
#' These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. \emph{Low counts can infuence the outcome - these \code{portion} functions may camouflage this, since they only return the portion albeit being dependent on the \code{minimum} parameter.}
|
||||
#'
|
||||
#' \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
|
||||
#'
|
||||
#' The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
|
||||
#' \if{html}{
|
||||
# (created with https://www.latex4technics.com/)
|
||||
#' \cr\cr
|
||||
#' To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
|
||||
#' \out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
|
||||
#' To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
|
||||
#' \cr
|
||||
#' For two antibiotics:
|
||||
#' \out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
|
||||
#' \cr
|
||||
#' For three antibiotics:
|
||||
#' \out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
|
||||
#' \cr
|
||||
#' And so on.
|
||||
#' }
|
||||
#'
|
||||
#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
|
||||
#'
|
||||
#' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
|
||||
#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptible isolates.
|
||||
#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
|
||||
#' @return Double or, when \code{as_percent = TRUE}, a character.
|
||||
#' @rdname portion
|
||||
#' @name portion
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
|
||||
#' ?septic_patients
|
||||
#'
|
||||
#' # Calculate resistance
|
||||
#' portion_R(septic_patients$amox)
|
||||
#' portion_IR(septic_patients$amox)
|
||||
#'
|
||||
#' # Or susceptibility
|
||||
#' portion_S(septic_patients$amox)
|
||||
#' portion_SI(septic_patients$amox)
|
||||
#'
|
||||
|
||||
#' # Do the above with pipes:
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>% portion_R(amox)
|
||||
#' septic_patients %>% portion_IR(amox)
|
||||
#' septic_patients %>% portion_S(amox)
|
||||
#' septic_patients %>% portion_SI(amox)
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(p = portion_S(cipr),
|
||||
#' n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(R = portion_R(cipr, as_percent = TRUE),
|
||||
#' I = portion_I(cipr, as_percent = TRUE),
|
||||
#' S = portion_S(cipr, as_percent = TRUE),
|
||||
#' n1 = count_all(cipr), # the actual total; sum of all three
|
||||
#' n2 = n_rsi(cipr), # same - analogous to n_distinct
|
||||
#' total = n()) # NOT the number of tested isolates!
|
||||
#'
|
||||
#' # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
|
||||
#' # so we can see that combination therapy does a lot more than mono therapy:
|
||||
#' septic_patients %>% portion_S(amcl) # S = 67.1%
|
||||
#' septic_patients %>% count_all(amcl) # n = 1576
|
||||
#'
|
||||
#' septic_patients %>% portion_S(gent) # S = 74.0%
|
||||
#' septic_patients %>% count_all(gent) # n = 1855
|
||||
#'
|
||||
#' septic_patients %>% portion_S(amcl, gent) # S = 92.0%
|
||||
#' septic_patients %>% count_all(amcl, gent) # n = 1517
|
||||
#'
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
|
||||
#' cipro_n = count_all(cipr),
|
||||
#' genta_p = portion_S(gent, as_percent = TRUE),
|
||||
#' genta_n = count_all(gent),
|
||||
#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
|
||||
#' combination_n = count_all(cipr, gent))
|
||||
#'
|
||||
#' # Get portions S/I/R immediately of all rsi columns
|
||||
#' septic_patients %>%
|
||||
#' select(amox, cipr) %>%
|
||||
#' portion_df(translate = FALSE)
|
||||
#'
|
||||
#' # It also supports grouping variables
|
||||
#' septic_patients %>%
|
||||
#' select(hospital_id, amox, cipr) %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' portion_df(translate = FALSE)
|
||||
#'
|
||||
#'
|
||||
#' \dontrun{
|
||||
#'
|
||||
#' # calculate current empiric combination therapy of Helicobacter gastritis:
|
||||
#' my_table %>%
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' genus == "Helicobacter") %>%
|
||||
#' summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
|
||||
#' n = count_all(amox, metr))
|
||||
#' }
|
||||
portion_R <- function(...,
|
||||
minimum = 30,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "R",
|
||||
include_I = FALSE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent,
|
||||
also_single_tested = also_single_tested,
|
||||
only_count = FALSE)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_IR <- function(...,
|
||||
minimum = 30,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "R",
|
||||
include_I = TRUE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent,
|
||||
also_single_tested = also_single_tested,
|
||||
only_count = FALSE)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_I <- function(...,
|
||||
minimum = 30,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "I",
|
||||
include_I = FALSE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent,
|
||||
also_single_tested = also_single_tested,
|
||||
only_count = FALSE)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_SI <- function(...,
|
||||
minimum = 30,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "S",
|
||||
include_I = TRUE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent,
|
||||
also_single_tested = also_single_tested,
|
||||
only_count = FALSE)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_S <- function(...,
|
||||
minimum = 30,
|
||||
as_percent = FALSE,
|
||||
also_single_tested = FALSE) {
|
||||
rsi_calc(...,
|
||||
type = "S",
|
||||
include_I = FALSE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent,
|
||||
also_single_tested = also_single_tested,
|
||||
only_count = FALSE)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
|
||||
#' @export
|
||||
portion_df <- function(data,
|
||||
translate_ab = getOption("get_antibiotic_names", "official"),
|
||||
minimum = 30,
|
||||
as_percent = FALSE,
|
||||
combine_IR = FALSE) {
|
||||
|
||||
if (!"data.frame" %in% class(data)) {
|
||||
stop("`portion_df` must be called on a data.frame")
|
||||
}
|
||||
|
||||
if (data %>% select_if(is.rsi) %>% ncol() == 0) {
|
||||
stop("No columns with class 'rsi' found. See ?as.rsi.")
|
||||
}
|
||||
|
||||
if (as.character(translate_ab) == "TRUE") {
|
||||
translate_ab <- "official"
|
||||
}
|
||||
options(get_antibiotic_names = translate_ab)
|
||||
|
||||
resS <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = portion_S,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent) %>%
|
||||
mutate(Interpretation = "S") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
if (combine_IR == FALSE) {
|
||||
resI <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = portion_I,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent) %>%
|
||||
mutate(Interpretation = "I") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
resR <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = portion_R,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent) %>%
|
||||
mutate(Interpretation = "R") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
data.groups <- group_vars(data)
|
||||
|
||||
res <- bind_rows(resS, resI, resR) %>%
|
||||
mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
|
||||
tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
|
||||
} else {
|
||||
resIR <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = portion_IR,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent) %>%
|
||||
mutate(Interpretation = "IR") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
data.groups <- group_vars(data)
|
||||
|
||||
res <- bind_rows(resS, resIR) %>%
|
||||
mutate(Interpretation = factor(Interpretation, levels = c("IR", "S"), ordered = TRUE)) %>%
|
||||
tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
|
||||
}
|
||||
|
||||
if (!translate_ab == FALSE) {
|
||||
if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
|
||||
stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
|
||||
}
|
||||
res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
|
||||
}
|
||||
|
||||
res
|
||||
}
|
||||
|
||||
|
||||
#' Calculate resistance of isolates
|
||||
#'
|
||||
#' This function is deprecated. Use the \code{\link{portion}} functions instead.
|
||||
#' @inheritParams portion
|
||||
#' @param ab1,ab2 vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.
|
||||
#' @param interpretation antimicrobial interpretation to check for
|
||||
#' @param ... deprecated parameters to support usage on older versions
|
||||
#' @importFrom dplyr tibble case_when
|
||||
#' @export
|
||||
rsi <- function(ab1,
|
||||
ab2 = NULL,
|
||||
interpretation = "IR",
|
||||
minimum = 30,
|
||||
as_percent = FALSE,
|
||||
...) {
|
||||
|
||||
.Deprecated(new = paste0("portion_", interpretation))
|
||||
|
||||
if (all(is.null(ab2))) {
|
||||
df <- tibble(ab1 = ab1)
|
||||
} else {
|
||||
df <- tibble(ab1 = ab1,
|
||||
ab2 = ab2)
|
||||
}
|
||||
|
||||
if (!interpretation %in% c("S", "SI", "IS", "I", "RI", "IR", "R")) {
|
||||
stop("invalid interpretation")
|
||||
}
|
||||
|
||||
result <- case_when(
|
||||
interpretation == "S" ~ portion_S(df, minimum = minimum, as_percent = FALSE),
|
||||
interpretation %in% c("SI", "IS") ~ portion_SI(df, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "I" ~ portion_I(df, minimum = minimum, as_percent = FALSE),
|
||||
interpretation %in% c("RI", "IR") ~ portion_IR(df, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "R" ~ portion_R(df, minimum = minimum, as_percent = FALSE))
|
||||
|
||||
if (as_percent == TRUE) {
|
||||
percent(result, force_zero = TRUE)
|
||||
} else {
|
||||
result
|
||||
}
|
||||
}
|
||||
-176
@@ -1,176 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Read data from 4D database
|
||||
#'
|
||||
#' This function is only useful for the MMB department of the UMCG. Use this function to \strong{import data by just defining the \code{file} parameter}. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with \code{\link{as.mo}} and transform all antimicrobial columns with \code{\link{as.rsi}}.
|
||||
#' @inheritParams utils::read.table
|
||||
#' @param info a logical to indicate whether info about the import should be printed, defaults to \code{TRUE} in interactive sessions
|
||||
#' @details Column names will be transformed, but the original column names are set as a "label" attribute and can be seen in e.g. RStudio Viewer.
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
read.4D <- function(file,
|
||||
info = interactive(),
|
||||
header = TRUE,
|
||||
row.names = NULL,
|
||||
sep = "\t",
|
||||
quote = "\"'",
|
||||
dec = ",",
|
||||
na.strings = c("NA", "", "."),
|
||||
skip = 2,
|
||||
check.names = TRUE,
|
||||
strip.white = TRUE,
|
||||
fill = TRUE,
|
||||
blank.lines.skip = TRUE,
|
||||
stringsAsFactors = FALSE,
|
||||
fileEncoding = "UTF-8",
|
||||
encoding = "UTF-8") {
|
||||
|
||||
if (info == TRUE) {
|
||||
message("Importing ", file, "... ", appendLF = FALSE)
|
||||
}
|
||||
data_4D <- utils::read.table(file = file,
|
||||
row.names = row.names,
|
||||
header = header,
|
||||
sep = sep,
|
||||
quote = quote,
|
||||
dec = dec,
|
||||
na.strings = na.strings,
|
||||
skip = skip,
|
||||
check.names = check.names,
|
||||
strip.white = strip.white,
|
||||
fill = fill,
|
||||
blank.lines.skip = blank.lines.skip,
|
||||
stringsAsFactors = stringsAsFactors,
|
||||
fileEncoding = fileEncoding,
|
||||
encoding = encoding)
|
||||
|
||||
# helper function for dates
|
||||
to_date_4D <- function(x) {
|
||||
date_regular <- as.Date(x, format = "%d-%m-%y")
|
||||
posixlt <- as.POSIXlt(date_regular)
|
||||
# born after today will be born 100 years ago
|
||||
# based on https://stackoverflow.com/a/3312971/4575331
|
||||
posixlt[date_regular > Sys.Date() & !is.na(posixlt)]$year <- posixlt[date_regular > Sys.Date() & !is.na(posixlt)]$year - 100
|
||||
as.Date(posixlt)
|
||||
}
|
||||
|
||||
if (info == TRUE) {
|
||||
message("OK\nTransforming column names... ", appendLF = FALSE)
|
||||
}
|
||||
if ("row.names" %in% colnames(data_4D) & all(is.na(data_4D[, ncol(data_4D)]))) {
|
||||
# remove first column name "row.names" and remove last empty column
|
||||
colnames(data_4D) <- c(colnames(data_4D)[2:ncol(data_4D)], "_skip_last")
|
||||
data_4D <- data_4D[, -ncol(data_4D)]
|
||||
}
|
||||
|
||||
colnames(data_4D) <- tolower(colnames(data_4D))
|
||||
if (all(c("afnamedat", "gebdatum") %in% colnames(data_4D))) {
|
||||
# add age
|
||||
data_4D$age <- NA_integer_
|
||||
}
|
||||
cols_wanted <- c("patientnr", "gebdatum", "age", "mv", "monsternr", "afnamedat", "bepaling",
|
||||
"afd.", "spec", "mat", "matbijz.", "mocode",
|
||||
"amfo", "amox", "anid", "azit", "casp", "cecl", "cefe", "cfcl",
|
||||
"cfot", "cfox", "cfta", "cftr", "cfur", "chlo", "cipr", "clin",
|
||||
"cocl", "ctta", "dapt", "doxy", "eryt", "fluo", "fluz", "fosf",
|
||||
"fusi", "gehi", "gent", "imip", "kana", "levo", "line", "mero",
|
||||
"metr", "mico", "mino", "moxi", "mupi", "nali", "nitr", "norf",
|
||||
"oxac", "peni", "pipe", "pita", "poly", "posa", "quda", "rifa",
|
||||
"spat", "teic", "tige", "tobr", "trim", "trsu", "vana", "vanb",
|
||||
"vanc", "vori")
|
||||
# this ones actually exist
|
||||
cols_wanted <- cols_wanted[cols_wanted %in% colnames(data_4D)]
|
||||
# order of columns
|
||||
data_4D <- data_4D[, cols_wanted]
|
||||
|
||||
# backup original column names
|
||||
colnames.bak <- toupper(colnames(data_4D))
|
||||
colnames.bak[colnames.bak == "AGE"] <- NA_character_
|
||||
|
||||
# rename of columns
|
||||
colnames(data_4D) <- gsub("patientnr", "patient_id", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("gebdatum", "date_birth", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("mv", "gender", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("monsternr", "sample_id", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("afnamedat", "date_received", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("bepaling", "sample_test", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("afd.", "department", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("spec", "specialty", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("matbijz.", "specimen_type", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("mat", "specimen_group", colnames(data_4D), fixed = TRUE)
|
||||
colnames(data_4D) <- gsub("mocode", "mo", colnames(data_4D), fixed = TRUE)
|
||||
|
||||
if (info == TRUE) {
|
||||
message("OK\nTransforming dates and age... ", appendLF = FALSE)
|
||||
}
|
||||
if ("date_birth" %in% colnames(data_4D)) {
|
||||
data_4D$date_birth <- to_date_4D(data_4D$date_birth)
|
||||
}
|
||||
if ("date_received" %in% colnames(data_4D)) {
|
||||
data_4D$date_received <- to_date_4D(data_4D$date_received)
|
||||
}
|
||||
if ("age" %in% colnames(data_4D)) {
|
||||
data_4D$age <- age(data_4D$date_birth, data_4D$date_received)
|
||||
}
|
||||
if ("gender" %in% colnames(data_4D)) {
|
||||
data_4D$gender[data_4D$gender == "V"] <- "F"
|
||||
}
|
||||
|
||||
if (info == TRUE) {
|
||||
message("OK\nTransforming MO codes... ", appendLF = FALSE)
|
||||
}
|
||||
if ("mo" %in% colnames(data_4D)) {
|
||||
data_4D$mo <- as.mo(data_4D$mo)
|
||||
# column right of mo is:
|
||||
drug1 <- colnames(data_4D)[grep("^mo$", colnames(data_4D)) + 1]
|
||||
if (!is.na(drug1)) {
|
||||
# and last is:
|
||||
drug_last <- colnames(data_4D)[length(data_4D)]
|
||||
# transform those to rsi:
|
||||
data_4D <- suppressWarnings(mutate_at(data_4D, vars(drug1:drug_last), as.rsi))
|
||||
}
|
||||
}
|
||||
|
||||
# set original column names as label (can be seen in RStudio Viewer)
|
||||
if (info == TRUE) {
|
||||
message("OK\nSetting original column names as label... ", appendLF = FALSE)
|
||||
}
|
||||
for (i in 1:ncol(data_4D)) {
|
||||
if (!is.na(colnames.bak[i])) {
|
||||
attr(data_4D[, i], "label") <- colnames.bak[i]
|
||||
}
|
||||
}
|
||||
|
||||
if (info == TRUE) {
|
||||
message("OK\nSetting query as label to data.frame... ", appendLF = FALSE)
|
||||
}
|
||||
qry <- readLines(con <- file(file, open="r"))[1]
|
||||
close(con)
|
||||
attr(data_4D, "label") <- qry
|
||||
|
||||
if (info == TRUE) {
|
||||
message("OK")
|
||||
}
|
||||
|
||||
data_4D
|
||||
}
|
||||
|
||||
@@ -1,382 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Predict antimicrobial resistance
|
||||
#'
|
||||
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
|
||||
#' @inheritParams first_isolate
|
||||
#' @inheritParams graphics::plot
|
||||
#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})
|
||||
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
|
||||
#' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in \code{col_date}
|
||||
#' @param year_max highest year to use in the prediction model, defaults to 10 years after today
|
||||
#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
|
||||
#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
|
||||
#' @param model the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.
|
||||
#' @param I_as_R a logical to indicate whether values \code{I} should be treated as \code{R}
|
||||
#' @param preserve_measurements a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be \code{NA}.
|
||||
#' @param info a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.
|
||||
#' @param main title of the plot
|
||||
#' @param ribbon a logical to indicate whether a ribbon should be shown (default) or error bars
|
||||
#' @details Valid options for the statistical model are:
|
||||
#' \itemize{
|
||||
#' \item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution}
|
||||
#' \item{\code{"loglin"} or \code{"poisson"}: a generalised log-linear regression model with poisson distribution}
|
||||
#' \item{\code{"lin"} or \code{"linear"}: a linear regression model}
|
||||
#' }
|
||||
#' @return \code{data.frame} with extra class \code{"resistance_predict"} with columns:
|
||||
#' \itemize{
|
||||
#' \item{\code{year}}
|
||||
#' \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
|
||||
#' \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0} (so the standard error will never go below 0\%)}
|
||||
#' \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1} (so the standard error will never go above 100\%)}
|
||||
#' \item{\code{observations}, the total number of available observations in that year, i.e. S + I + R}
|
||||
#' \item{\code{observed}, the original observed resistant percentages}
|
||||
#' \item{\code{estimated}, the estimated resistant percentages, calculated by the model}
|
||||
#' }
|
||||
#' Furthermore, the model itself is available as an attribute: \code{attributes(x)$model}, see Examples.
|
||||
#' @seealso The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
|
||||
#' @rdname resistance_predict
|
||||
#' @export
|
||||
#' @importFrom stats predict glm lm
|
||||
#' @importFrom dplyr %>% pull mutate mutate_at n group_by_at summarise filter filter_at all_vars n_distinct arrange case_when n_groups transmute
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
|
||||
#' plot(x)
|
||||
#' ggplot_rsi_predict(x)
|
||||
#'
|
||||
#' # use dplyr so you can actually read it:
|
||||
#' library(dplyr)
|
||||
#' x <- septic_patients %>%
|
||||
#' filter_first_isolate() %>%
|
||||
#' filter(mo_genus(mo) == "Staphylococcus") %>%
|
||||
#' resistance_predict("peni")
|
||||
#' plot(x)
|
||||
#'
|
||||
#'
|
||||
#' # get the model from the object
|
||||
#' mymodel <- attributes(x)$model
|
||||
#' summary(mymodel)
|
||||
#'
|
||||
#'
|
||||
#' # create nice plots with ggplot2 yourself
|
||||
#' if (!require(ggplot2)) {
|
||||
#'
|
||||
#' data <- septic_patients %>%
|
||||
#' filter(mo == as.mo("E. coli")) %>%
|
||||
#' resistance_predict(col_ab = "amox",
|
||||
#' col_date = "date",
|
||||
#' info = FALSE,
|
||||
#' minimum = 15)
|
||||
#'
|
||||
#' ggplot(data,
|
||||
#' aes(x = year)) +
|
||||
#' geom_col(aes(y = value),
|
||||
#' fill = "grey75") +
|
||||
#' geom_errorbar(aes(ymin = se_min,
|
||||
#' ymax = se_max),
|
||||
#' colour = "grey50") +
|
||||
#' scale_y_continuous(limits = c(0, 1),
|
||||
#' breaks = seq(0, 1, 0.1),
|
||||
#' labels = paste0(seq(0, 100, 10), "%")) +
|
||||
#' labs(title = expression(paste("Forecast of amoxicillin resistance in ",
|
||||
#' italic("E. coli"))),
|
||||
#' y = "%IR",
|
||||
#' x = "Year") +
|
||||
#' theme_minimal(base_size = 13)
|
||||
#' }
|
||||
resistance_predict <- function(tbl,
|
||||
col_ab,
|
||||
col_date = NULL,
|
||||
year_min = NULL,
|
||||
year_max = NULL,
|
||||
year_every = 1,
|
||||
minimum = 30,
|
||||
model = 'binomial',
|
||||
I_as_R = TRUE,
|
||||
preserve_measurements = TRUE,
|
||||
info = TRUE) {
|
||||
|
||||
if (nrow(tbl) == 0) {
|
||||
stop('This table does not contain any observations.')
|
||||
}
|
||||
|
||||
if (!col_ab %in% colnames(tbl)) {
|
||||
stop('Column ', col_ab, ' not found.')
|
||||
}
|
||||
|
||||
# -- date
|
||||
if (is.null(col_date)) {
|
||||
col_date <- search_type_in_df(tbl = tbl, type = "date")
|
||||
}
|
||||
if (is.null(col_date)) {
|
||||
stop("`col_date` must be set.", call. = FALSE)
|
||||
}
|
||||
|
||||
if (!col_date %in% colnames(tbl)) {
|
||||
stop('Column ', col_date, ' not found.')
|
||||
}
|
||||
|
||||
if (n_groups(tbl) > 1) {
|
||||
# no grouped tibbles please, mutate will throw errors
|
||||
tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
|
||||
}
|
||||
|
||||
year <- function(x) {
|
||||
if (all(grepl('^[0-9]{4}$', x))) {
|
||||
x
|
||||
} else {
|
||||
as.integer(format(as.Date(x), '%Y'))
|
||||
}
|
||||
}
|
||||
|
||||
df <- tbl %>%
|
||||
mutate_at(col_ab, as.rsi) %>%
|
||||
mutate_at(col_ab, droplevels) %>%
|
||||
mutate_at(col_ab, funs(
|
||||
if (I_as_R == TRUE) {
|
||||
gsub("I", "R", .)
|
||||
} else {
|
||||
gsub("I", "S", .)
|
||||
}
|
||||
)) %>%
|
||||
filter_at(col_ab, all_vars(!is.na(.))) %>%
|
||||
mutate(year = pull(., col_date) %>% year()) %>%
|
||||
group_by_at(c('year', col_ab)) %>%
|
||||
summarise(n())
|
||||
|
||||
if (df %>% pull(col_ab) %>% n_distinct(na.rm = TRUE) < 2) {
|
||||
stop("No variety in antimicrobial interpretations - all isolates are '",
|
||||
df %>% pull(col_ab) %>% unique(), "'.",
|
||||
call. = FALSE)
|
||||
}
|
||||
|
||||
colnames(df) <- c('year', 'antibiotic', 'observations')
|
||||
df <- df %>%
|
||||
filter(!is.na(antibiotic)) %>%
|
||||
tidyr::spread(antibiotic, observations, fill = 0) %>%
|
||||
filter((R + S) >= minimum)
|
||||
df_matrix <- df %>%
|
||||
ungroup() %>%
|
||||
select(R, S) %>%
|
||||
as.matrix()
|
||||
|
||||
if (NROW(df) == 0) {
|
||||
stop('There are no observations.')
|
||||
}
|
||||
|
||||
year_lowest <- min(df$year)
|
||||
if (is.null(year_min)) {
|
||||
year_min <- year_lowest
|
||||
} else {
|
||||
year_min <- max(year_min, year_lowest, na.rm = TRUE)
|
||||
}
|
||||
if (is.null(year_max)) {
|
||||
year_max <- year(Sys.Date()) + 10
|
||||
}
|
||||
|
||||
years <- list(year = seq(from = year_min, to = year_max, by = year_every))
|
||||
|
||||
if (model %in% c('binomial', 'binom', 'logit')) {
|
||||
model <- "binomial"
|
||||
model_lm <- with(df, glm(df_matrix ~ year, family = binomial))
|
||||
if (info == TRUE) {
|
||||
cat('\nLogistic regression model (logit) with binomial distribution')
|
||||
cat('\n------------------------------------------------------------\n')
|
||||
print(summary(model_lm))
|
||||
}
|
||||
|
||||
predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
} else if (model %in% c('loglin', 'poisson')) {
|
||||
model <- "poisson"
|
||||
model_lm <- with(df, glm(R ~ year, family = poisson))
|
||||
if (info == TRUE) {
|
||||
cat('\nLog-linear regression model (loglin) with poisson distribution')
|
||||
cat('\n--------------------------------------------------------------\n')
|
||||
print(summary(model_lm))
|
||||
}
|
||||
|
||||
predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
} else if (model %in% c('lin', 'linear')) {
|
||||
model <- "linear"
|
||||
model_lm <- with(df, lm((R / (R + S)) ~ year))
|
||||
if (info == TRUE) {
|
||||
cat('\nLinear regression model')
|
||||
cat('\n-----------------------\n')
|
||||
print(summary(model_lm))
|
||||
}
|
||||
|
||||
predictmodel <- predict(model_lm, newdata = years, se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
} else {
|
||||
stop('No valid model selected.')
|
||||
}
|
||||
|
||||
# prepare the output dataframe
|
||||
df_prediction <- data.frame(year = unlist(years),
|
||||
value = prediction,
|
||||
stringsAsFactors = FALSE) %>%
|
||||
|
||||
mutate(se_min = value - se,
|
||||
se_max = value + se)
|
||||
|
||||
if (model == 'poisson') {
|
||||
df_prediction <- df_prediction %>%
|
||||
mutate(value = value %>%
|
||||
format(scientific = FALSE) %>%
|
||||
as.integer(),
|
||||
se_min = as.integer(se_min),
|
||||
se_max = as.integer(se_max))
|
||||
} else {
|
||||
df_prediction <- df_prediction %>%
|
||||
# se_max not above 1
|
||||
mutate(se_max = ifelse(se_max > 1, 1, se_max))
|
||||
}
|
||||
df_prediction <- df_prediction %>%
|
||||
# se_min not below 0
|
||||
mutate(se_min = ifelse(se_min < 0, 0, se_min))
|
||||
|
||||
df_observations <- df %>%
|
||||
ungroup() %>%
|
||||
transmute(year,
|
||||
observations = R + S,
|
||||
observed = R / (R + S))
|
||||
df_prediction <- df_prediction %>%
|
||||
left_join(df_observations, by = "year") %>%
|
||||
mutate(estimated = value)
|
||||
|
||||
if (preserve_measurements == TRUE) {
|
||||
# replace estimated data by observed data
|
||||
df_prediction <- df_prediction %>%
|
||||
mutate(value = ifelse(!is.na(observed), observed, value),
|
||||
se_min = ifelse(!is.na(observed), NA, se_min),
|
||||
se_max = ifelse(!is.na(observed), NA, se_max))
|
||||
}
|
||||
|
||||
df_prediction <- df_prediction %>%
|
||||
mutate(value = case_when(value > 1 ~ 1,
|
||||
value < 0 ~ 0,
|
||||
TRUE ~ value)) %>%
|
||||
arrange(year)
|
||||
|
||||
structure(
|
||||
.Data = df_prediction,
|
||||
class = c("resistance_predict", "data.frame"),
|
||||
I_as_R = I_as_R,
|
||||
model_title = model,
|
||||
model = model_lm,
|
||||
ab = col_ab
|
||||
)
|
||||
}
|
||||
|
||||
#' @rdname resistance_predict
|
||||
#' @export
|
||||
rsi_predict <- resistance_predict
|
||||
|
||||
#' @exportMethod plot.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr filter
|
||||
#' @importFrom graphics plot axis arrows points
|
||||
#' @rdname resistance_predict
|
||||
plot.resistance_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
|
||||
if (attributes(x)$I_as_R == TRUE) {
|
||||
ylab <- "%IR"
|
||||
} else {
|
||||
ylab <- "%R"
|
||||
}
|
||||
plot(x = x$year,
|
||||
y = x$value,
|
||||
ylim = c(0, 1),
|
||||
yaxt = "n", # no y labels
|
||||
pch = 19, # closed dots
|
||||
ylab = paste0("Percentage (", ylab, ")"),
|
||||
xlab = "Year",
|
||||
main = main,
|
||||
sub = paste0("(n = ", sum(x$observations, na.rm = TRUE),
|
||||
", model: ", attributes(x)$model_title, ")"),
|
||||
cex.sub = 0.75)
|
||||
|
||||
|
||||
axis(side = 2, at = seq(0, 1, 0.1), labels = paste0(0:10 * 10, "%"))
|
||||
|
||||
# hack for error bars: https://stackoverflow.com/a/22037078/4575331
|
||||
arrows(x0 = x$year,
|
||||
y0 = x$se_min,
|
||||
x1 = x$year,
|
||||
y1 = x$se_max,
|
||||
length = 0.05, angle = 90, code = 3, lwd = 1.5)
|
||||
|
||||
# overlay grey points for prediction
|
||||
points(x = filter(x, is.na(observations))$year,
|
||||
y = filter(x, is.na(observations))$value,
|
||||
pch = 19,
|
||||
col = "grey40")
|
||||
}
|
||||
|
||||
#' @rdname resistance_predict
|
||||
#' @importFrom dplyr filter
|
||||
#' @export
|
||||
ggplot_rsi_predict <- function(x,
|
||||
main = paste("Resistance prediction of", attributes(x)$ab),
|
||||
ribbon = TRUE,
|
||||
...) {
|
||||
|
||||
if (!"resistance_predict" %in% class(x)) {
|
||||
stop("`x` must be a resistance prediction model created with resistance_predict().")
|
||||
}
|
||||
|
||||
if (attributes(x)$I_as_R == TRUE) {
|
||||
ylab <- "%IR"
|
||||
} else {
|
||||
ylab <- "%R"
|
||||
}
|
||||
|
||||
p <- ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +
|
||||
ggplot2::geom_point(data = filter(x, !is.na(observations)),
|
||||
size = 2) +
|
||||
scale_y_percent(limits = c(0, 1)) +
|
||||
ggplot2::labs(title = main,
|
||||
y = paste0("Percentage (", ylab, ")"),
|
||||
x = "Year",
|
||||
caption = paste0("(n = ", sum(x$observations, na.rm = TRUE),
|
||||
", model: ", attributes(x)$model_title, ")"))
|
||||
|
||||
if (ribbon == TRUE) {
|
||||
p <- p + ggplot2::geom_ribbon(ggplot2::aes(ymin = se_min, ymax = se_max), alpha = 0.25)
|
||||
} else {
|
||||
p <- p + ggplot2::geom_errorbar(ggplot2::aes(ymin = se_min, ymax = se_max), na.rm = TRUE, width = 0.5)
|
||||
}
|
||||
p <- p +
|
||||
# overlay grey points for prediction
|
||||
ggplot2::geom_point(data = filter(x, is.na(observations)),
|
||||
size = 2,
|
||||
colour = "grey40")
|
||||
p
|
||||
}
|
||||
@@ -1,257 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Class 'rsi'
|
||||
#'
|
||||
#' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
|
||||
#' @rdname as.rsi
|
||||
#' @param x vector
|
||||
#' @param threshold maximum fraction of \code{x} that is allowed to fail transformation, see Examples
|
||||
#' @details The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
|
||||
#' @return Ordered factor with new class \code{rsi}
|
||||
#' @keywords rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
#' @seealso \code{\link{as.mic}}
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
|
||||
#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
|
||||
#' is.rsi(rsi_data)
|
||||
#'
|
||||
#' # this can also coerce combined MIC/RSI values:
|
||||
#' as.rsi("<= 0.002; S") # will return S
|
||||
#'
|
||||
#' plot(rsi_data) # for percentages
|
||||
#' barplot(rsi_data) # for frequencies
|
||||
#' freq(rsi_data) # frequency table with informative header
|
||||
#'
|
||||
#' # using dplyr's mutate
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' mutate_at(vars(peni:rifa), as.rsi)
|
||||
#'
|
||||
#'
|
||||
#' # fastest way to transform all columns with already valid AB results to class `rsi`:
|
||||
#' septic_patients %>%
|
||||
#' mutate_if(is.rsi.eligible,
|
||||
#' as.rsi)
|
||||
#'
|
||||
#' # default threshold of `is.rsi.eligible` is 5%.
|
||||
#' is.rsi.eligible(WHONET$`First name`) # fails, >80% is invalid
|
||||
#' is.rsi.eligible(WHONET$`First name`, threshold = 0.9) # succeeds
|
||||
as.rsi <- function(x) {
|
||||
if (is.rsi(x)) {
|
||||
x
|
||||
} else if (identical(levels(x), c("S", "I", "R"))) {
|
||||
structure(x, class = c('rsi', 'ordered', 'factor'))
|
||||
} else {
|
||||
if (mic_like(x) > 0.5) {
|
||||
warning("`as.rsi` is intended to clean antimicrobial interpretations - not to interpret MIC values.", call. = FALSE)
|
||||
}
|
||||
|
||||
x <- x %>% unlist()
|
||||
x.bak <- x
|
||||
|
||||
na_before <- x[is.na(x) | x == ''] %>% length()
|
||||
# remove all spaces
|
||||
x <- gsub(' +', '', x)
|
||||
# remove all MIC-like values: numbers, operators and periods
|
||||
x <- gsub('[0-9.,;:<=>]+', '', x)
|
||||
# remove everything between brackets, and 'high' and 'low'
|
||||
x <- gsub("([(].*[)])", "", x)
|
||||
x <- gsub("(high|low)", "", x, ignore.case = TRUE)
|
||||
# disallow more than 3 characters
|
||||
x[nchar(x) > 3] <- NA
|
||||
# set to capitals
|
||||
x <- toupper(x)
|
||||
# remove all invalid characters
|
||||
x <- gsub('[^RSI]+', '', x)
|
||||
# in cases of "S;S" keep S, but in case of "S;I" make it NA
|
||||
x <- gsub('^S+$', 'S', x)
|
||||
x <- gsub('^I+$', 'I', x)
|
||||
x <- gsub('^R+$', 'R', x)
|
||||
x[!x %in% c('S', 'I', 'R')] <- NA
|
||||
na_after <- x[is.na(x) | x == ''] %>% length()
|
||||
|
||||
if (na_before != na_after) {
|
||||
list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
|
||||
unique() %>%
|
||||
sort()
|
||||
list_missing <- paste0('"', list_missing , '"', collapse = ", ")
|
||||
warning(na_after - na_before, ' results truncated (',
|
||||
round(((na_after - na_before) / length(x)) * 100),
|
||||
'%) that were invalid antimicrobial interpretations: ',
|
||||
list_missing, call. = FALSE)
|
||||
}
|
||||
|
||||
x <- factor(x, levels = c("S", "I", "R"), ordered = TRUE)
|
||||
class(x) <- c('rsi', 'ordered', 'factor')
|
||||
x
|
||||
}
|
||||
}
|
||||
|
||||
mic_like <- function(x) {
|
||||
mic <- x %>%
|
||||
gsub("[^0-9.,]+", "", .) %>%
|
||||
unique()
|
||||
mic_valid <- suppressWarnings(as.mic(mic))
|
||||
sum(!is.na(mic_valid)) / length(mic)
|
||||
}
|
||||
|
||||
#' @rdname as.rsi
|
||||
#' @export
|
||||
is.rsi <- function(x) {
|
||||
identical(class(x),
|
||||
c('rsi', 'ordered', 'factor'))
|
||||
}
|
||||
|
||||
#' @rdname as.rsi
|
||||
#' @export
|
||||
is.rsi.eligible <- function(x, threshold = 0.05) {
|
||||
if (NCOL(x) > 1) {
|
||||
stop('`x` must be a one-dimensional vector.')
|
||||
}
|
||||
if (any(c("logical",
|
||||
"numeric",
|
||||
"integer",
|
||||
"mo",
|
||||
"Date",
|
||||
"POSIXct",
|
||||
"rsi",
|
||||
"raw",
|
||||
"hms")
|
||||
%in% class(x))) {
|
||||
# no transformation needed
|
||||
FALSE
|
||||
} else {
|
||||
x <- x[!is.na(x) & !is.null(x) & !identical(x, "")]
|
||||
if (length(x) == 0) {
|
||||
return(FALSE)
|
||||
}
|
||||
checked <- suppressWarnings(as.rsi(x))
|
||||
outcome <- sum(is.na(checked)) / length(x)
|
||||
outcome <= threshold
|
||||
}
|
||||
}
|
||||
|
||||
#' @exportMethod print.rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
#' @noRd
|
||||
print.rsi <- function(x, ...) {
|
||||
cat("Class 'rsi'\n")
|
||||
print(as.character(x), quote = FALSE)
|
||||
}
|
||||
|
||||
#' @exportMethod droplevels.rsi
|
||||
#' @export
|
||||
#' @noRd
|
||||
droplevels.rsi <- function(x, exclude = if(anyNA(levels(x))) NULL else NA, ...) {
|
||||
x <- droplevels.factor(x, exclude = exclude, ...)
|
||||
class(x) <- c('rsi', 'ordered', 'factor')
|
||||
x
|
||||
}
|
||||
|
||||
#' @exportMethod summary.rsi
|
||||
#' @export
|
||||
#' @noRd
|
||||
summary.rsi <- function(object, ...) {
|
||||
x <- object
|
||||
c(
|
||||
"Class" = 'rsi',
|
||||
"<NA>" = sum(is.na(x)),
|
||||
"Sum S" = sum(x == "S", na.rm = TRUE),
|
||||
"Sum IR" = sum(x %in% c("I", "R"), na.rm = TRUE),
|
||||
"-Sum R" = sum(x == "R", na.rm = TRUE),
|
||||
"-Sum I" = sum(x == "I", na.rm = TRUE)
|
||||
)
|
||||
}
|
||||
|
||||
#' @exportMethod plot.rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
|
||||
#' @importFrom graphics plot text
|
||||
#' @noRd
|
||||
plot.rsi <- function(x, ...) {
|
||||
x_name <- deparse(substitute(x))
|
||||
|
||||
suppressWarnings(
|
||||
data <- data.frame(x = x,
|
||||
y = 1,
|
||||
stringsAsFactors = TRUE) %>%
|
||||
group_by(x) %>%
|
||||
summarise(n = sum(y)) %>%
|
||||
filter(!is.na(x)) %>%
|
||||
mutate(s = round((n / sum(n)) * 100, 1))
|
||||
)
|
||||
|
||||
data$x <- factor(data$x, levels = c('S', 'I', 'R'), ordered = TRUE)
|
||||
|
||||
ymax <- if_else(max(data$s) > 95, 105, 100)
|
||||
|
||||
plot(x = data$x,
|
||||
y = data$s,
|
||||
lwd = 2,
|
||||
col = c('green', 'orange', 'red'),
|
||||
ylim = c(0, ymax),
|
||||
ylab = 'Percentage',
|
||||
xlab = 'Antimicrobial Interpretation',
|
||||
main = paste('Susceptibility Analysis of', x_name),
|
||||
axes = FALSE,
|
||||
...)
|
||||
# x axis
|
||||
axis(side = 1, at = 1:n_distinct(data$x), labels = levels(data$x), lwd = 0)
|
||||
# y axis, 0-100%
|
||||
axis(side = 2, at = seq(0, 100, 5))
|
||||
|
||||
text(x = data$x,
|
||||
y = data$s + 4,
|
||||
labels = paste0(data$s, '% (n = ', data$n, ')'))
|
||||
}
|
||||
|
||||
|
||||
#' @exportMethod barplot.rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
|
||||
#' @importFrom graphics barplot axis
|
||||
#' @noRd
|
||||
barplot.rsi <- function(height, ...) {
|
||||
x <- height
|
||||
x_name <- deparse(substitute(height))
|
||||
|
||||
suppressWarnings(
|
||||
data <- data.frame(rsi = x, cnt = 1) %>%
|
||||
group_by(rsi) %>%
|
||||
summarise(cnt = sum(cnt)) %>%
|
||||
droplevels()
|
||||
)
|
||||
|
||||
barplot(table(x),
|
||||
col = c('green3', 'orange2', 'red3'),
|
||||
xlab = 'Antimicrobial Interpretation',
|
||||
main = paste('Susceptibility Analysis of', x_name),
|
||||
ylab = 'Frequency',
|
||||
axes = FALSE,
|
||||
...)
|
||||
# y axis, 0-100%
|
||||
axis(side = 2, at = seq(0, max(data$cnt) + max(data$cnt) * 1.1, by = 25))
|
||||
}
|
||||
-147
@@ -1,147 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' @importFrom dplyr %>% pull all_vars any_vars filter_all funs mutate_all
|
||||
rsi_calc <- function(...,
|
||||
type,
|
||||
include_I,
|
||||
minimum,
|
||||
as_percent,
|
||||
also_single_tested,
|
||||
only_count) {
|
||||
|
||||
if (!is.logical(include_I)) {
|
||||
stop('`include_I` must be logical', call. = FALSE)
|
||||
}
|
||||
if (!is.numeric(minimum)) {
|
||||
stop('`minimum` must be numeric', call. = FALSE)
|
||||
}
|
||||
if (!is.logical(as_percent)) {
|
||||
stop('`as_percent` must be logical', call. = FALSE)
|
||||
}
|
||||
if (!is.logical(also_single_tested)) {
|
||||
stop('`also_single_tested` must be logical', call. = FALSE)
|
||||
}
|
||||
|
||||
dots_df <- ...elt(1) # it needs this evaluation
|
||||
dots <- base::eval(base::substitute(base::alist(...)))
|
||||
ndots <- length(dots)
|
||||
|
||||
if ("data.frame" %in% class(dots_df)) {
|
||||
# data.frame passed with other columns, like:
|
||||
# septic_patients %>% portion_S(amcl, gent)
|
||||
dots <- as.character(dots)
|
||||
dots <- dots[dots != "."]
|
||||
if (length(dots) == 0 | all(dots == "df")) {
|
||||
# for complete data.frames, like septic_patients %>% select(amcl, gent) %>% portion_S()
|
||||
# and the old rsi function, that has "df" as name of the first parameter
|
||||
x <- dots_df
|
||||
} else {
|
||||
x <- dots_df[, dots]
|
||||
}
|
||||
} else if (ndots == 1) {
|
||||
# only 1 variable passed (can also be data.frame), like:
|
||||
# portion_S(septic_patients$amcl)
|
||||
# septic_patients$amcl %>% portion_S()
|
||||
x <- dots_df
|
||||
} else {
|
||||
# multiple variables passed without pipe, like:
|
||||
# portion_S(septic_patients$amcl, septic_patients$gent)
|
||||
x <- NULL
|
||||
try(x <- as.data.frame(dots), silent = TRUE)
|
||||
if (is.null(x)) {
|
||||
# support for: with(septic_patients, portion_S(amcl, gent))
|
||||
x <- as.data.frame(rlang::list2(...))
|
||||
}
|
||||
}
|
||||
|
||||
print_warning <- FALSE
|
||||
|
||||
type_trans <- as.integer(as.rsi(type))
|
||||
type_others <- base::setdiff(1:3, type_trans)
|
||||
|
||||
if (is.data.frame(x)) {
|
||||
rsi_integrity_check <- character(0)
|
||||
for (i in 1:ncol(x)) {
|
||||
# check integrity of columns: force rsi class
|
||||
if (!is.rsi(x %>% pull(i))) {
|
||||
rsi_integrity_check <- c(rsi_integrity_check, x %>% pull(i) %>% as.character())
|
||||
x[, i] <- suppressWarnings(as.rsi(x[, i])) # warning will be given later
|
||||
print_warning <- TRUE
|
||||
}
|
||||
x[, i] <- x %>% pull(i) %>% as.integer()
|
||||
}
|
||||
if (length(rsi_integrity_check) > 0) {
|
||||
# this will give a warning for invalid results, of all input columns (so only 1 warning)
|
||||
rsi_integrity_check <- as.rsi(rsi_integrity_check)
|
||||
}
|
||||
|
||||
if (include_I == TRUE) {
|
||||
x <- x %>% mutate_all(funs(ifelse(. == 2, type_trans, .)))
|
||||
}
|
||||
|
||||
if (also_single_tested == TRUE) {
|
||||
# THE CHANCE THAT AT LEAST ONE RESULT IS type
|
||||
found <- x %>% filter_all(any_vars(. == type_trans)) %>% nrow()
|
||||
# THE CHANCE THAT AT LEAST ONE RESULT IS type OR ALL ARE TESTED
|
||||
total <- found + x %>% filter_all(all_vars(. %in% type_others)) %>% nrow()
|
||||
} else {
|
||||
x <- apply(X = x,
|
||||
MARGIN = 1,
|
||||
FUN = min)
|
||||
found <- sum(as.integer(x) == type_trans, na.rm = TRUE)
|
||||
total <- length(x) - sum(is.na(x))
|
||||
}
|
||||
} else {
|
||||
if (!is.rsi(x)) {
|
||||
x <- as.rsi(x)
|
||||
print_warning <- TRUE
|
||||
}
|
||||
x <- as.integer(x)
|
||||
if (include_I == TRUE) {
|
||||
x[x == 2] <- type_trans
|
||||
}
|
||||
found <- sum(x == type_trans, na.rm = TRUE)
|
||||
total <- length(x) - sum(is.na(x))
|
||||
}
|
||||
|
||||
if (print_warning == TRUE) {
|
||||
warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_if(is.rsi.eligible, as.rsi)",
|
||||
call. = FALSE)
|
||||
}
|
||||
|
||||
if (only_count == TRUE) {
|
||||
return(found)
|
||||
}
|
||||
|
||||
if (total < minimum) {
|
||||
warning("Introducing NA: only ", total, " results available (minimum set to ", minimum, ").", call. = FALSE)
|
||||
result <- NA
|
||||
} else {
|
||||
result <- found / total
|
||||
}
|
||||
|
||||
if (as_percent == TRUE) {
|
||||
percent(result, force_zero = TRUE)
|
||||
} else {
|
||||
result
|
||||
}
|
||||
}
|
||||
@@ -1,62 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Skewness of the sample
|
||||
#'
|
||||
#' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
|
||||
#'
|
||||
#' When negative: the left tail is longer; the mass of the distribution is concentrated on the right of the figure. When positive: the right tail is longer; the mass of the distribution is concentrated on the left of the figure.
|
||||
#' @param x a vector of values, a \code{matrix} or a \code{data frame}
|
||||
#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
|
||||
#' @exportMethod skewness
|
||||
#' @seealso \code{\link{kurtosis}}
|
||||
#' @rdname skewness
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @export
|
||||
skewness <- function(x, na.rm = FALSE) {
|
||||
UseMethod("skewness")
|
||||
}
|
||||
|
||||
#' @exportMethod skewness.default
|
||||
#' @rdname skewness
|
||||
#' @export
|
||||
skewness.default <- function (x, na.rm = FALSE) {
|
||||
x <- as.vector(x)
|
||||
if (na.rm == TRUE) {
|
||||
x <- x[!is.na(x)]
|
||||
}
|
||||
n <- length(x)
|
||||
(base::sum((x - base::mean(x))^3) / n) / (base::sum((x - base::mean(x))^2) / n)^(3/2)
|
||||
}
|
||||
|
||||
#' @exportMethod skewness.matrix
|
||||
#' @rdname skewness
|
||||
#' @export
|
||||
skewness.matrix <- function (x, na.rm = FALSE) {
|
||||
base::apply(x, 2, skewness.default, na.rm = na.rm)
|
||||
}
|
||||
|
||||
#' @exportMethod skewness.data.frame
|
||||
#' @rdname skewness
|
||||
#' @export
|
||||
skewness.data.frame <- function (x, na.rm = FALSE) {
|
||||
base::sapply(x, skewness.default, na.rm = na.rm)
|
||||
}
|
||||
@@ -1,40 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' WHOCC: WHO Collaborating Centre for Drug Statistics Methodology
|
||||
#'
|
||||
#' All antimicrobial drugs and their official names, ATC codes, ATC groups and defined daily dose (DDD) are included in this package, using the WHO Collaborating Centre for Drug Statistics Methodology.
|
||||
#' @section WHOCC:
|
||||
#' \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
|
||||
#' This package contains \strong{all ~500 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
|
||||
#'
|
||||
#' These have become the gold standard for international drug utilisation monitoring and research.
|
||||
#'
|
||||
#' The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @name WHOCC
|
||||
#' @rdname WHOCC
|
||||
#' @examples
|
||||
#' as.atc("meropenem")
|
||||
#' atc_name("J01DH02")
|
||||
#'
|
||||
#' atc_tradenames("flucloxacillin")
|
||||
NULL
|
||||
@@ -1,282 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' @importFrom data.table as.data.table setkey
|
||||
.onLoad <- function(libname, pkgname) {
|
||||
# get new functions not available in older versions of R
|
||||
backports::import(pkgname)
|
||||
|
||||
# register data
|
||||
microorganisms.oldDT <- as.data.table(AMR::microorganisms.old)
|
||||
microorganisms.oldDT$fullname_lower <- tolower(microorganisms.oldDT$fullname)
|
||||
setkey(microorganisms.oldDT, col_id, fullname)
|
||||
|
||||
assign(x = "microorganisms",
|
||||
value = make(),
|
||||
envir = asNamespace("AMR"))
|
||||
|
||||
assign(x = "microorganismsDT",
|
||||
value = make_DT(),
|
||||
envir = asNamespace("AMR"))
|
||||
|
||||
assign(x = "microorganisms.oldDT",
|
||||
value = microorganisms.oldDT,
|
||||
envir = asNamespace("AMR"))
|
||||
|
||||
assign(x = "mo_codes_v0.5.0",
|
||||
value = make_trans_tbl(),
|
||||
envir = asNamespace("AMR"))
|
||||
}
|
||||
|
||||
#' @importFrom dplyr mutate case_when
|
||||
make <- function() {
|
||||
AMR::microorganisms %>%
|
||||
mutate(prevalence = case_when(
|
||||
class == "Gammaproteobacteria"
|
||||
| genus %in% c("Enterococcus", "Staphylococcus", "Streptococcus")
|
||||
| mo == "UNKNOWN"
|
||||
~ 1,
|
||||
phylum %in% c("Proteobacteria",
|
||||
"Firmicutes",
|
||||
"Actinobacteria",
|
||||
"Sarcomastigophora")
|
||||
| genus %in% c("Aspergillus",
|
||||
"Bacteroides",
|
||||
"Candida",
|
||||
"Capnocytophaga",
|
||||
"Chryseobacterium",
|
||||
"Cryptococcus",
|
||||
"Elisabethkingia",
|
||||
"Flavobacterium",
|
||||
"Fusobacterium",
|
||||
"Giardia",
|
||||
"Leptotrichia",
|
||||
"Mycoplasma",
|
||||
"Prevotella",
|
||||
"Rhodotorula",
|
||||
"Treponema",
|
||||
"Trichophyton",
|
||||
"Ureaplasma")
|
||||
~ 2,
|
||||
TRUE ~ 3
|
||||
))
|
||||
}
|
||||
|
||||
#' @importFrom data.table as.data.table setkey
|
||||
make_DT <- function() {
|
||||
microorganismsDT <- as.data.table(make())
|
||||
microorganismsDT$fullname_lower <- tolower(microorganismsDT$fullname)
|
||||
setkey(microorganismsDT,
|
||||
prevalence,
|
||||
kingdom,
|
||||
fullname)
|
||||
microorganismsDT
|
||||
}
|
||||
|
||||
make_trans_tbl <- function() {
|
||||
# conversion of old MO codes from v0.5.0 (ITIS) to later versions (Catalogue of Life)
|
||||
c(B_ACHRMB = "B_ACHRM", B_ANNMA = "B_ACTNS", B_ACLLS = "B_ALCYC",
|
||||
B_AHNGM = "B_ARCHN", B_ARMTM = "B_ARMTMN", B_ARTHRS = "B_ARTHR",
|
||||
B_APHLS = "B_AZRHZP", B_BRCHA = "B_BRCHY", B_BCTRM = "B_BRVBCT",
|
||||
B_CLRBCT = "B_CLRBC", B_CTRDM = "B_CLSTR", B_CPRMM = "B_CYLND",
|
||||
B_DLCLN = "B_DPLCL", B_DMCLM = "B_DSLFT", B_DSLFVB = "B_DSLFV",
|
||||
B_FCTRM = "B_FSBCT", B_GNRLA = "B_GRDNR", B_HNRBM = "B_HLNRB",
|
||||
B_HPHGA = "B_HNPHGA", B_HCCCS = "B_HYDRC", B_MCRCLS = "B_MCRCL",
|
||||
B_MTHYLS = "B_MLSMA", B_MARCLS = "B_MRCLS", B_MGCLS = "B_MSTGC",
|
||||
B_MCLLA = "B_MTHYLC", B_MYCPLS = "B_MYCPL", B_NBCTR = "B_NTRBC",
|
||||
B_OCLLS = "B_OCNBC", B_PTHRX = "B_PLNKT", B_PCCCS = "B_PRCHL",
|
||||
B_PSPHN = "B_PRPHY", B_PDMNS = "B_PSDMN", B_SCCHRP = "B_SCCHR",
|
||||
B_SRBCTR = "B_SHRBCTR", B_STRPTC = "B_STRPT", B_SHMNS = "B_SYNTR",
|
||||
B_TRBCTR = "B_THRMN", P_ALBMN = "C_ABMNA", F_ACHLY = "C_ACHLY",
|
||||
P_ACINT = "C_ACINT", P_ARTCL = "C_ACLNA", P_ACRVL = "C_ACRVL",
|
||||
P_ADRCT = "C_ADRCT", P_AMPHS = "C_AHSRS", F_ALBUG = "C_ALBUG",
|
||||
P_ALCNT = "C_ALCNT", P_ALFRD = "C_ALFRD", P_ALLGR = "C_ALLGR",
|
||||
P_AMPHL = "C_ALPTS", F_ALTHR = "C_ALTHR", P_AMLLA = "C_AMLLA",
|
||||
P_ANMLN = "C_AMLNA", P_AMMBC = "C_AMMBC", P_AMMDS = "C_AMMDS",
|
||||
P_AMMLG = "C_AMMLG", P_AMMMR = "C_AMMMR", P_AMMMS = "C_AMMMS",
|
||||
P_AMMON = "C_AMMON", P_AMMSC = "C_AMMSC", P_AMMSP = "C_AMMSP",
|
||||
P_AMMST = "C_AMMST", P_AMMTM = "C_AMMTM", F_AMYCS = "C_AMYCS",
|
||||
P_ANARM = "C_ANARM", P_ANGLD = "C_ANGLD", P_ANGLG = "C_ANGLG",
|
||||
P_ANNLC = "C_ANNLC", F_ANSLP = "C_ANSLP", F_APDCH = "C_APDCH",
|
||||
F_APHND = "C_APHND", F_APLNC = "C_APLNC", F_AQLND = "C_AQLND",
|
||||
P_ARCHS = "C_ARCHAS", P_ASTRN = "C_ARNNN", P_ARNPR = "C_ARNPR",
|
||||
F_ARSPR = "C_ARSPR", P_ARTST = "C_ARTSTR", P_AMPHC = "C_ARYNA",
|
||||
P_ASCHM = "C_ASCHM", P_ASPDS = "C_ASPDS", P_ASTCL = "C_ASTCL",
|
||||
P_ASTRG = "C_ASTRGR", P_ASTRM = "C_ASTRMM", P_ASTRR = "C_ASTRR",
|
||||
P_ASTRT = "C_ASTRTR", F_ATKNS = "C_ATKNS", F_AYLLA = "C_AYLLA",
|
||||
P_BAGGN = "C_BAGGN", P_BCCLL = "C_BCCLL", P_BDLLD = "C_BDLLD",
|
||||
P_BGNRN = "C_BGNRN", P_BLCLN = "C_BLCLN", P_BLMND = "C_BLMND",
|
||||
P_BLMNL = "C_BLMNL", P_BLPHR = "C_BLPHR", P_BLVNT = "C_BLVNT",
|
||||
P_BOLVN = "C_BOLVN", P_BORLS = "C_BORLS", P_BRNNM = "C_BRNNM",
|
||||
P_BRSLN = "C_BRSLN", P_BRSRD = "C_BRSRD", F_BRVLG = "C_BRVLG",
|
||||
F_BNLLA = "C_BRVLGN", P_BSCCM = "C_BSCCM", F_BSDPH = "C_BSDPH",
|
||||
P_BTHYS = "C_BTHYS", P_BTLLN = "C_BTLLN", P_BULMN = "C_BULMN",
|
||||
P_CCLDM = "C_CCLDM", P_CDNLL = "C_CDNLL", P_CLPSS = "C_CDNLLP",
|
||||
P_CHLDN = "C_CHLDNL", P_CHLST = "C_CHLST", P_CHNLM = "C_CHNLM",
|
||||
P_CHRYS = "C_CHRYSL", P_CHTSP = "C_CHTSP", P_CBCDS = "C_CIBCDS",
|
||||
P_CLCRN = "C_CLCRN", P_CLMNA = "C_CLMNA", P_CLPDM = "C_CLPDM",
|
||||
P_CLPHR = "C_CLPHRY", P_CLVLN = "C_CLVLN", P_CMPNL = "C_CMPNL",
|
||||
P_CNCRS = "C_CNCRS", P_CNTCH = "C_CNTCH", F_CNTRM = "C_CNTRMY",
|
||||
P_COLPD = "C_COLPD", P_COLPS = "C_COLPS", P_CPRDS = "C_CPRDS",
|
||||
P_CRNSP = "C_CPRMA", P_CRBNL = "C_CRBNL", P_CRBRB = "C_CRBRB",
|
||||
P_CRBRG = "C_CRBRG", P_CRBRS = "C_CRBRS", P_CRCHS = "C_CRCHS",
|
||||
P_CRCLC = "C_CRCLC", P_CRNLC = "C_CRNLC", P_CRNTH = "C_CRNTH",
|
||||
P_CRPNT = "C_CRPNT", P_CRSTG = "C_CRSTG", P_CRTHN = "C_CRTHN",
|
||||
P_CRTRN = "C_CRTRN", P_CYMBL = "C_CRTTA", P_CRYPT = "C_CRYPT",
|
||||
P_CSHMN = "C_CSHMNL", P_CSSDL = "C_CSSDL", P_CLNDS = "C_CSSDLN",
|
||||
P_CHRNA = "C_CTHRN", P_CTPSS = "C_CTPSS", P_CUNLN = "C_CUNLN",
|
||||
P_CYLND = "C_CVLNA", P_CYCLC = "C_CYCLCB", P_CDNTA = "C_CYCLD",
|
||||
P_CYCLG = "C_CYCLG", P_CYCLM = "C_CYCLM", P_CYRTL = "C_CYRTL",
|
||||
P_CYSTM = "C_CYSTM", P_DCHLM = "C_DCHLM", P_DCRBS = "C_DCRBS",
|
||||
P_DCTYC = "C_DCTYC", P_DIDNM = "C_DIDNM", P_DLPTS = "C_DLPTS",
|
||||
P_DNTLN = "C_DNTLN", P_DNTST = "C_DNTST", P_DORTH = "C_DORTH",
|
||||
P_DCTYP = "C_DPHMS", F_DPLCY = "C_DPLCY", P_DNDRT = "C_DRTNA",
|
||||
P_DSCMM = "C_DSCMM", P_DSCRB = "C_DSCRB", P_DSCRN = "C_DSCRN",
|
||||
P_DSCSP = "C_DSCSP", P_DSNBR = "C_DSNBR", P_DYCBC = "C_DYCBC",
|
||||
F_DCTYC = "C_DYCHS", F_ECTRG = "C_ECTRG", B_EDWRD = "C_EDWRD",
|
||||
P_EGGRL = "C_EGGRL", P_EHLYS = "C_EHLYS", P_EHRNB = "C_EHRNB",
|
||||
P_ELPHD = "C_ELPHD", P_ENCHL = "C_ELYDM", P_EPHDM = "C_EPHDM",
|
||||
P_EPLTS = "C_EPLTS", P_EPLXL = "C_EPLXL", P_EPNDL = "C_EPNDL",
|
||||
P_EPNDS = "C_EPNDS", P_ENLLA = "C_EPSTM", P_EPSTY = "C_EPSTY",
|
||||
F_ERYCH = "C_ERYCH", F_ESMDM = "C_ESMDM", P_ESSYR = "C_ESSYR",
|
||||
P_FSCHR = "C_FHRNA", P_FLRLS = "C_FLRLS", P_FLNTN = "C_FNTNA",
|
||||
P_FRNDC = "C_FRNDC", P_FRNTN = "C_FRNTN", P_FRSNK = "C_FRSNK",
|
||||
P_FNLLA = "C_FSCHRN", P_FSSRN = "C_FSSRN", P_FVCSS = "C_FVCSS",
|
||||
P_GDRYN = "C_GDRYN", F_GELGN = "C_GELGN", P_GERDA = "C_GERDA",
|
||||
P_GLACM = "C_GLACM", P_GLBBL = "C_GLBBL", P_GLBGR = "C_GLBGR",
|
||||
P_GLBLN = "C_GLBLN", P_GRTLA = "C_GLBRT", P_GLBTX = "C_GLBTX",
|
||||
P_GLLNA = "C_GLLNA", P_GLMSP = "C_GLMSP", P_GLNDL = "C_GLNDL",
|
||||
F_GNMCH = "C_GNMCH", P_GOSLL = "C_GOSLL", P_GRNDS = "C_GRNDS",
|
||||
P_GRNTA = "C_GRNTA", P_GLBRT = "C_GTLLA", P_GTTLN = "C_GTTLN",
|
||||
P_GVLNP = "C_GVLNP", P_GYPSN = "C_GYPSN", P_GYRDN = "C_GYRDN",
|
||||
P_HALTR = "C_HALTR", P_HANZW = "C_HANZW", P_HAURN = "C_HAURN",
|
||||
P_HELNN = "C_HELNN", P_HLPHR = "C_HHRYA", P_HLNTA = "C_HLNTA",
|
||||
F_HLPHT = "C_HLPHT", P_HLSTC = "C_HLSTC", P_HMSPH = "C_HMSPH",
|
||||
P_HMTRM = "C_HMTRM", P_HPKNS = "C_HPKNS", P_HPLPH = "C_HPLPH",
|
||||
P_HPPCR = "C_HPPCR", P_HNLLA = "C_HPPCRP", P_HRMSN = "C_HRMSN",
|
||||
P_HRNLL = "C_HRNLL", F_HRPCH = "C_HRPCH", P_HSTGR = "C_HSTGR",
|
||||
P_HSTTL = "C_HSTTL", P_HTRST = "C_HTGNA", P_HTRLL = "C_HTRLL",
|
||||
P_HTRPH = "C_HTRPH", F_HYPHC = "C_HYPHC", P_HYPRM = "C_HYPRM",
|
||||
P_INTRN = "C_INTRN", P_IRIDI = "C_IRIDI", P_ISLND = "C_ISLND",
|
||||
P_JCLLL = "C_JCLLL", P_KHLLL = "C_KHLLL", P_KRNPS = "C_KRNPS",
|
||||
P_KRRRL = "C_KRRRL", P_LABOE = "C_LABOE", P_LAGEN = "C_LAGEN",
|
||||
P_LBSLL = "C_LBSLL", F_LTHLA = "C_LBYRN", P_LCRYM = "C_LCRYM",
|
||||
P_LEMBS = "C_LEMBS", F_LGNDM = "C_LGNDM", P_LGNMM = "C_LGNMM",
|
||||
P_LGNPH = "C_LGNPHR", F_LGNSM = "C_LGNSM", P_LGYNP = "C_LGYNP",
|
||||
P_LITTB = "C_LITTB", P_LITUL = "C_LITUL", P_LMBDN = "C_LMBDN",
|
||||
P_LMRCK = "C_LMRCK", F_LBYRN = "C_LMYXA", P_LNGLN = "C_LNGLN",
|
||||
P_LNTCL = "C_LNTCL", P_LOXDS = "C_LOXDS", F_LPTLG = "C_LPTLG",
|
||||
F_LNLLA = "C_LPTLGN", F_LPTMT = "C_LPTMT", P_LRYNG = "C_LRYNG",
|
||||
P_LTCRN = "C_LTCRN", P_LTHPL = "C_LTHPL", P_LTNTS = "C_LTNTS",
|
||||
F_LTRST = "C_LTRST", P_LXPHY = "C_LXPHY", P_MCRTH = "C_MCRTH",
|
||||
P_MELNS = "C_MELNS", P_MSDNM = "C_MESDNM", P_METPS = "C_METPS",
|
||||
P_MIMSN = "C_MIMSN", P_MINCN = "C_MINCN", P_MLLNL = "C_MLLNL",
|
||||
P_MLMMN = "C_MLMMN", F_MNDNL = "C_MNDNL", P_MNLYS = "C_MNLYS",
|
||||
P_MNPSS = "C_MNPSS", P_MRGNL = "C_MRGNL", P_MRGNP = "C_MRGNP",
|
||||
P_MRSPL = "C_MRSPL", P_MRTNT = "C_MRTNT", P_MSSLN = "C_MSSLN",
|
||||
P_MSSSS = "C_MSSSS", P_MTCNT = "C_MTCNT", P_MYCHS = "C_MYCHS",
|
||||
P_MYSCH = "C_MYSCH", F_MYZCY = "C_MYZCY", P_NASSL = "C_NASSL",
|
||||
P_NBCLN = "C_NBCLN", P_NBCLR = "C_NBCLR", P_NCNRB = "C_NCNRB",
|
||||
P_NDBCL = "C_NDBCL", P_NRLLA = "C_NDBCLR", P_NMMLC = "C_NMMLC",
|
||||
F_NMTPH = "C_NMTPH", P_NNNLL = "C_NNNLL", P_NODSR = "C_NODSR",
|
||||
P_NONIN = "C_NONIN", P_NOURI = "C_NOURI", P_OCLNA = "C_OCLNA",
|
||||
P_OGLNA = "C_OGLNA", P_OPHTH = "C_OLMDM", F_OLPDP = "C_OLPDP",
|
||||
P_ONYCH = "C_OMPSS", P_OOLIN = "C_OOLIN", P_OPRCL = "C_OPRCL",
|
||||
P_ORBLN = "C_ORBLN", F_ORCAD = "C_ORCAD", P_ORDRS = "C_ORDRS",
|
||||
P_OPHRY = "C_ORYDM", P_OSNGL = "C_OSNGL", P_OXYTR = "C_OXYTR",
|
||||
P_PARRN = "C_PARRN", P_PATRS = "C_PATRS", P_PAVNN = "C_PAVNN",
|
||||
P_PTYCH = "C_PCYLS", P_PDPHR = "C_PDPHR", P_PELSN = "C_PELSN",
|
||||
F_PHGMY = "C_PHGMY", F_PSDSP = "C_PHRTA", P_PHRYG = "C_PHRYG",
|
||||
P_PHYSL = "C_PHYSL", F_PHYTP = "C_PHYTP", P_PLACS = "C_PLACS",
|
||||
P_PLCPS = "C_PLCPS", P_PLCPSL = "C_PLCPSL", P_PLCTN = "C_PLCTN",
|
||||
P_PLGPH = "C_PLGPH", B_PLGTH = "C_PLGTH", P_PLMRN = "C_PLMRN",
|
||||
P_PLNCT = "C_PLNCT", P_PLNDSC = "C_PLNDSC", P_PLNGY = "C_PLNGY",
|
||||
P_PLNRBL = "C_PLNLLA", P_PLNLN = "C_PLNLN", P_PLNLR = "C_PLNLR",
|
||||
P_PLNRB = "C_PLNRB", P_PLNSP = "C_PLNSPR", P_PLRNM = "C_PLRNM",
|
||||
P_PLRST = "C_PLRST", P_PLRTR = "C_PLRTR", F_PLSMD = "C_PLSMD",
|
||||
P_PLTYC = "C_PLTYC", P_PSDBL = "C_PLVNA", P_PLYMR = "C_PLYMR",
|
||||
P_PLTYN = "C_PNMTM", P_PNRPL = "C_PNRPL", F_PNTSM = "C_PNTSM",
|
||||
P_PRCNT = "C_PRCNT", P_PRFSS = "C_PRFSS", P_PRMCM = "C_PRMCUM",
|
||||
F_PRNSP = "C_PRNSP", P_PRPND = "C_PRPND", P_PRPYX = "C_PRPYX",
|
||||
P_PRRDN = "C_PRRDN", P_PSDDF = "C_PSDDF", P_PSDMC = "C_PSDMC",
|
||||
P_PSDND = "C_PSDND", P_PSDNN = "C_PSDNN", P_PSDPL = "C_PSDPLY",
|
||||
P_PSMMS = "C_PSMMS", P_PTLLN = "C_PTLLN", P_PTLLND = "C_PTLLND",
|
||||
F_PTRSN = "C_PTRSN", P_PULLN = "C_PULLN", P_PUTLN = "C_PUTLN",
|
||||
P_PRTTR = "C_PYMNA", P_PYRGL = "C_PYRGL", P_PYRGO = "C_PYRGO",
|
||||
P_PYRLN = "C_PYRLN", F_PYTHM = "C_PYTHIM", F_PYTHL = "C_PYTHL",
|
||||
P_PYXCL = "C_PYXCL", P_QNQLC = "C_QNQLC", P_RAMLN = "C_RAMLN",
|
||||
P_RBRTN = "C_RBRTN", P_RCRVD = "C_RCRVD", P_RCTBL = "C_RCTBL",
|
||||
P_RCTCB = "C_RCTCB", P_RCTGL = "C_RCTGL", P_RCTVG = "C_RCTVG",
|
||||
P_RDGDR = "C_RDGDR", P_REMNC = "C_REMNC", P_REPHX = "C_REPHX",
|
||||
P_RHBDM = "C_RHBDMM", F_RHBDS = "C_RHBDSP", P_RHPDD = "C_RHPDD",
|
||||
F_RHPDM = "C_RHPDM", F_RHZDMY = "C_RHZDM", P_RHZMM = "C_RHZMM",
|
||||
P_RIVRN = "C_RIVRN", P_ROSLN = "C_ROSLN", P_ROTAL = "C_ROTAL",
|
||||
P_RPHDP = "C_RPHDP", P_RPRTN = "C_RPRTN", P_RSSLL = "C_RSSLL",
|
||||
P_RTLMM = "C_RTLMM", P_RTYLA = "C_RTYLA", P_RUGID = "C_RUGID",
|
||||
F_RZLLP = "C_RZLLP", P_SAGRN = "C_SAGRN", P_SCCMM = "C_SCCMM",
|
||||
P_SCCRH = "C_SCCRH", P_SCHLM = "C_SCHLM", F_SCLRS = "C_SCLRS",
|
||||
P_SCTLR = "C_SCTLR", P_SEBRK = "C_SEBRK", P_SGMLN = "C_SGMLN",
|
||||
P_SGMLP = "C_SGMLP", P_SGMMR = "C_SGMMR", P_SGMVR = "C_SGMVR",
|
||||
F_SMMRS = "C_SMMRS", P_SNNDS = "C_SNNDS", P_SORTS = "C_SORTS",
|
||||
P_SPHGN = "C_SPHGN", P_SPHNN = "C_SPHNN", P_SNLLA = "C_SPHNNL",
|
||||
P_SPHTR = "C_SPHTR", P_SPHTX = "C_SPHTX", P_SPHVG = "C_SPHVG",
|
||||
P_SPRDT = "C_SPRDT", P_SPRLC = "C_SPRLC", F_SPRLG = "C_SPRLG",
|
||||
P_SPRLL = "C_SPRLL", F_SPRMY = "C_SPRMY", P_SPRPL = "C_SPRPL",
|
||||
P_SPRSG = "C_SPRSG", P_SPRST = "C_SPRST", P_SPHNP = "C_SPRTA",
|
||||
P_SPRZN = "C_SPRZN", P_SPHRG = "C_SPSNA", P_STHDM = "C_SPTHD",
|
||||
P_SRCNR = "C_SRCNR", F_SRLPD = "C_SRLPD", F_SPNGS = "C_SSPRA",
|
||||
F_STEIN = "C_STEIN", P_SPTHD = "C_STHDDS", P_STHRP = "C_STHRP",
|
||||
P_STNFR = "C_STNFR", P_STNSM = "C_STNSM", P_STNTR = "C_STNTR",
|
||||
P_STRBL = "C_STRBL", P_STRMB = "C_STRMB", P_STTSN = "C_STTSN",
|
||||
P_STYLN = "C_SYCHA", F_SCHZC = "C_SYTRM", P_TBNLL = "C_TBNLL",
|
||||
P_TRCHL = "C_TCHLS", P_TCHNT = "C_TCHNT", P_THRCL = "C_THRCL",
|
||||
P_THRMM = "C_THRMM", P_TIARN = "C_TIARN", P_TKPHR = "C_TKPHR",
|
||||
P_TLNMA = "C_TLNMA", P_TLYPM = "C_TLYPM", P_TMNDS = "C_TMNDS",
|
||||
P_TMNTA = "C_TMNTA", P_TNTNN = "C_TNNDM", P_TTNNS = "C_TNTNN",
|
||||
P_TNPSS = "C_TNTNNP", P_TONTN = "C_TONTN", P_TOSAI = "C_TOSAI",
|
||||
P_TPHTR = "C_TPHTR", P_TRCHH = "C_TRCHH", P_TRPHS = "C_TRCHLR",
|
||||
P_TMMNA = "C_TRCHM", P_TRCHS = "C_TRCHSP", P_TRFRN = "C_TRFRN",
|
||||
P_TRLCL = "C_TRLCL", P_TRTXL = "C_TRTXL", P_TRTXS = "C_TRTXS",
|
||||
P_TTRHY = "C_TTRHY", F_TTRMY = "C_TTRMY", P_TXTLR = "C_TXTLR",
|
||||
F_THRST = "C_TYTRM", P_URLPT = "C_ULPTS", P_UNGLT = "C_UNGLT",
|
||||
P_URCNT = "C_URCNT", P_URONM = "C_URONM", P_UROSM = "C_UROSM",
|
||||
P_URTRC = "C_URTRC", P_URSTY = "C_UTYLA", P_UVGRN = "C_UVGRN",
|
||||
P_VLVLN = "C_VALVLN", P_VGNLN = "C_VGNLN", P_VGNLNP = "C_VGNLNP",
|
||||
P_VLNRA = "C_VLVLN", P_VGNCL = "C_VNCLA", P_VRGLN = "C_VRGLN",
|
||||
P_VRGLNP = "C_VRGLNP", P_VRTCL = "C_VRTCL", P_WBBNL = "C_WBBNL",
|
||||
P_WEBBN = "C_WEBBN", P_WSNRL = "C_WSNRL", P_ZTHMN = "C_ZHMNM",
|
||||
B_ZOOGL = "C_ZOOGL", F_DDSCS = "F_DPDSC", F_SCCHR = "F_SMYCS",
|
||||
P_AMTRN = "P_ACNTH", F_AMBDM = "P_AMBDM", F_ARCYR = "P_ARCYR",
|
||||
F_BADHM = "P_BADHM", F_BDHMP = "P_BDHMP", F_BRBYL = "P_BRBYL",
|
||||
F_BRFLD = "P_BRFLD", F_CLMYX = "P_CLMYX", F_CLSTD = "P_CLSTD",
|
||||
F_CMTRC = "P_CMTRC", F_CRBRR = "P_CRBRR", F_CRTMY = "P_CRTMY",
|
||||
F_CRTRM = "P_CRTRM", F_DCTYD = "P_DCTYD", F_DDYMM = "P_DDYMM",
|
||||
F_DIACH = "P_DIACH", F_DIANM = "P_DIANM", F_DIDRM = "P_DIDRM",
|
||||
F_ELMYX = "P_ELMYX", F_ESTLM = "P_ESTLM", F_FULIG = "P_FULIG",
|
||||
F_HMTRC = "P_HMTRC", F_LCRPS = "P_LCRPS", F_LICEA = "P_LICEA",
|
||||
F_LMPRD = "P_LMPRD", F_LPTDR = "P_LPTDR", F_LSTRL = "P_LSTRL",
|
||||
F_LYCGL = "P_LYCGL", F_MCBRD = "P_MCBRD", F_MNKTL = "P_MNKTL",
|
||||
F_MTTRC = "P_MTTRC", F_MUCLG = "P_MUCLG", F_PHYSR = "P_PHYSR",
|
||||
F_PRCHN = "P_PRCHN", F_PRMBD = "P_PRMBD", F_PRTPH = "P_PRTPH",
|
||||
F_PSRNA = "P_PSRNA", F_PYSRM = "P_PYSRM", F_RTCLR = "P_RTCLR",
|
||||
F_STMNT = "P_STMNT", F_SYMPH = "P_SYMPH", F_TRBRK = "P_TRBRK",
|
||||
F_TRICH = "P_TRICH", F_TUBFR = "P_TUBFR")
|
||||
}
|
||||
@@ -1,92 +1,226 @@
|
||||
% AMR (for R)
|
||||
|
||||
# `AMR` (for R)
|
||||
<img src="man/figures/logo.png" align="right" height="120px" />
|
||||
The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the [`AMR` Python package](https://pypi.org/project/AMR/).
|
||||
|
||||
### Not a developer? Then please visit our website [https://msberends.gitlab.io/AMR](https://msberends.gitlab.io/AMR) to read about this package.
|
||||
**It contains documentation about all of the included functions and also a comprehensive tutorial about how to conduct AMR analysis.**
|
||||
This Python package is a wrapper around the `AMR` R package. It uses the `rpy2` package internally. Despite the need to have R installed, Python users can now easily work with AMR data directly through Python code.
|
||||
|
||||
## Development source
|
||||
# Prerequisites
|
||||
|
||||
*NOTE: the original source code is on GitLab (https://gitlab.com/msberends/AMR), so you can report a bug at https://gitlab.com/msberends/AMR/issues. There is a mirror repository on GitHub (https://github.com/msberends/AMR). As the mirror process is automated by GitLab, both repositories always contain the latest changes.*
|
||||
This package was only tested with a [virtual environment (venv)](https://docs.python.org/3/library/venv.html). You can set up such an environment by running:
|
||||
|
||||
This is the **development source** of `AMR`, a free and open-source [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods.
|
||||
```python
|
||||
# linux and macOS:
|
||||
python -m venv /path/to/new/virtual/environment
|
||||
|
||||
## Authors
|
||||
Matthijs S. Berends <a href="https://orcid.org/0000-0001-7620-1800"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,2,a</sup>,
|
||||
Christian F. Luz <a href="https://orcid.org/0000-0001-5809-5995"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,a</sup>,
|
||||
Erwin E.A. Hassing<sup>2</sup>,
|
||||
Corinna Glasner <a href="https://orcid.org/0000-0003-1241-1328"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,b</sup>,
|
||||
Alex W. Friedrich <a href="https://orcid.org/0000-0003-4881-038X"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,b</sup>,
|
||||
Bhanu N.M. Sinha <a href="https://orcid.org/0000-0003-1634-0010"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,b</sup>
|
||||
|
||||
<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands - [rug.nl](http://www.rug.nl) [umcg.nl](http://www.umcg.nl)<br>
|
||||
<sup>2</sup> Certe Medical Diagnostics & Advice, Groningen, the Netherlands - [certe.nl](http://www.certe.nl)<br>
|
||||
<sup>a</sup> Thesis dissertant<br>
|
||||
<sup>b</sup> Thesis advisor
|
||||
|
||||
<a href="https://www.rug.nl"><img src="man/figures/logo_rug.png" height="60px"></a>
|
||||
<a href="https://www.umcg.nl"><img src="man/figures/logo_umcg.png" height="60px"></a>
|
||||
<a href="https://www.certe.nl"><img src="man/figures/logo_certe.png" height="60px"></a>
|
||||
<a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_eh1h.png" height="60px"></a>
|
||||
<a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_interreg.png" height="60px"></a>
|
||||
|
||||
## How to get this package
|
||||
All stable versions of this package [are published on CRAN](https://CRAN.R-project.org/package=AMR), the official R network with a peer-reviewed submission process.
|
||||
|
||||
### Install from CRAN
|
||||
[](https://CRAN.R-project.org/package=AMR) [](https://CRAN.R-project.org/package=AMR)
|
||||
|
||||
(Note: Downloads measured only by cran.rstudio.com, this excludes e.g. the official [cran.r-project.org](https://cran.r-project.org/package=AMR))
|
||||
|
||||
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> Install using [RStudio](http://www.rstudio.com) (recommended):
|
||||
- Click on `Tools` and then `Install Packages...`
|
||||
- Type in `AMR` and press <kbd>Install</kbd>
|
||||
|
||||
- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> Install in R directly:
|
||||
- `install.packages("AMR")`
|
||||
|
||||
### Install from Zenodo
|
||||
[](https://doi.org/10.5281/zenodo.1305355)
|
||||
|
||||
This package was also published on Zenodo (stable releases only): https://doi.org/10.5281/zenodo.1305355
|
||||
|
||||
### Install from GitLab
|
||||
|
||||
This is the latest **development version**. Although it may contain bugfixes and even new functions compared to the latest released version on CRAN, it is also subject to change and may be unstable or behave unexpectedly. Always consider this a beta version. All below 'badges' should be green:
|
||||
|
||||
Development Test | Result | Reference
|
||||
--- | :---: | ---
|
||||
All functions checked on Linux | [](https://gitlab.com/msberends/AMR/commits/master) | GitLab CI [[ref 1]](https://gitlab.com/msberends/AMR)
|
||||
All functions checked on Windows | [](https://ci.appveyor.com/project/msberends/amr-svxon) | Appveyor Systems Inc. [[ref 2]](https://ci.appveyor.com/project/msberends/amr-svxon)
|
||||
Percentage of syntax lines checked | [](https://codecov.io/gl/msberends/AMR) [](https://codecov.io/gl/msberends/AMR) | Codecov LLC [[ref 3]](https://codecov.io/gl/msberends/AMR)
|
||||
|
||||
If so, try it with:
|
||||
```r
|
||||
install.packages("devtools")
|
||||
devtools::install_gitlab("msberends/AMR")
|
||||
# Windows:
|
||||
python -m venv C:\path\to\new\virtual\environment
|
||||
```
|
||||
|
||||
## Copyright
|
||||
Then you can [activate the environment](https://docs.python.org/3/library/venv.html#how-venvs-work), after which the venv is ready to work with.
|
||||
|
||||
This R package is licensed under the [GNU General Public License (GPL) v2.0](https://gitlab.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:
|
||||
# Install AMR
|
||||
|
||||
- May be used for commercial purposes
|
||||
1. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
|
||||
|
||||
- May be used for private purposes
|
||||
```bash
|
||||
pip install AMR
|
||||
```
|
||||
|
||||
- May **not** be used for patent purposes
|
||||
2. Make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
|
||||
|
||||
- May be modified, although:
|
||||
For Linux:
|
||||
|
||||
- Modifications **must** be released under the same license when distributing the package
|
||||
- Changes made to the code **must** be documented
|
||||
```bash
|
||||
# Ubuntu / Debian
|
||||
sudo apt install r-base
|
||||
# Fedora:
|
||||
sudo dnf install R
|
||||
# CentOS/RHEL
|
||||
sudo yum install R
|
||||
```
|
||||
|
||||
- May be distributed, although:
|
||||
For macOS (using [Homebrew](https://brew.sh)):
|
||||
|
||||
- Source code **must** be made available when the package is distributed
|
||||
- A copy of the license and copyright notice **must** be included with the package.
|
||||
```bash
|
||||
brew install r
|
||||
```
|
||||
|
||||
- Comes with a LIMITATION of liability
|
||||
For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
|
||||
|
||||
- Comes with NO warranty
|
||||
# Examples of Usage
|
||||
|
||||
## Cleaning Taxonomy
|
||||
|
||||
Here’s an example that demonstrates how to clean microorganism and drug names using the `AMR` Python package:
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import AMR
|
||||
|
||||
# Sample data
|
||||
data = {
|
||||
"MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
|
||||
"Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
|
||||
}
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# Use AMR functions to clean microorganism and drug names
|
||||
df['MO_clean'] = AMR.mo_name(df['MOs'])
|
||||
df['Drug_clean'] = AMR.ab_name(df['Drug'])
|
||||
|
||||
# Display the results
|
||||
print(df)
|
||||
```
|
||||
|
||||
| MOs | Drug | MO_clean | Drug_clean |
|
||||
|-------------|-----------|--------------------|---------------|
|
||||
| E. coli | Cipro | Escherichia coli | Ciprofloxacin |
|
||||
| ESCCOL | CIP | Escherichia coli | Ciprofloxacin |
|
||||
| esco | J01MA02 | Escherichia coli | Ciprofloxacin |
|
||||
| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
|
||||
|
||||
### Explanation
|
||||
|
||||
* **mo_name:** This function standardises microorganism names. Here, different variations of *Escherichia coli* (such as "E. coli", "ESCCOL", "esco", and "Esche coli") are all converted into the correct, standardised form, "Escherichia coli".
|
||||
|
||||
* **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
|
||||
|
||||
## Calculating AMR
|
||||
|
||||
```python
|
||||
import AMR
|
||||
import pandas as pd
|
||||
|
||||
df = AMR.example_isolates
|
||||
result = AMR.resistance(df["AMX"])
|
||||
print(result)
|
||||
```
|
||||
|
||||
```
|
||||
[0.59555556]
|
||||
```
|
||||
|
||||
## Generating Antibiograms
|
||||
|
||||
One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:
|
||||
|
||||
```python
|
||||
result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
|
||||
print(result2a)
|
||||
```
|
||||
|
||||
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|
||||
|-----------------|-----------------|-----------------|--------------------------|
|
||||
| CoNS | 7% (10/142) | 73% (183/252) | 30% (10/33) |
|
||||
| E. coli | 50% (196/392) | 88% (399/456) | 94% (393/416) |
|
||||
| K. pneumoniae | 0% (0/58) | 96% (53/55) | 89% (47/53) |
|
||||
| P. aeruginosa | 0% (0/30) | 100% (30/30) | None |
|
||||
| P. mirabilis | None | 94% (34/36) | None |
|
||||
| S. aureus | 6% (8/131) | 90% (171/191) | None |
|
||||
| S. epidermidis | 1% (1/91) | 64% (87/136) | None |
|
||||
| S. hominis | None | 80% (56/70) | None |
|
||||
| S. pneumoniae | 100% (112/112) | None | 100% (112/112) |
|
||||
|
||||
|
||||
```python
|
||||
result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
|
||||
print(result2b)
|
||||
```
|
||||
|
||||
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|
||||
|----------------|-----------------|------------------|--------------------------|
|
||||
| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641) |
|
||||
| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345) |
|
||||
|
||||
|
||||
In this example, we generate an antibiogram by selecting various antibiotics.
|
||||
|
||||
## Taxonomic Data Sets Now in Python!
|
||||
|
||||
As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antimicrobials`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
|
||||
|
||||
```python
|
||||
AMR.microorganisms
|
||||
```
|
||||
|
||||
| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
|
||||
|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
|
||||
| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| ... | ... | ... | ... | ... | ... | ... | ... |
|
||||
| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
|
||||
| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
|
||||
| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
|
||||
| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
|
||||
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
|
||||
|
||||
```python
|
||||
AMR.antimicrobials
|
||||
```
|
||||
|
||||
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|
||||
|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
|
||||
| AMA | 4649.0 | 4-aminosalicylic acid| Antimycobacterials | 12.00 | g | NaN | None |
|
||||
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
|
||||
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
|
||||
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
|
||||
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
|
||||
| ... | ... | ... | ... | ... | ... | ... | ... |
|
||||
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
|
||||
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
|
||||
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
|
||||
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
|
||||
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
|
||||
|
||||
|
||||
# Installation Channels
|
||||
|
||||
## Stable Release (CRAN)
|
||||
|
||||
The default `AMR` Python package uses the latest stable version of the `AMR` R package, published on CRAN. After running `pip install AMR`, import it as usual:
|
||||
|
||||
```python
|
||||
import AMR
|
||||
|
||||
AMR.example_isolates
|
||||
```
|
||||
|
||||
## Development Version (GitHub)
|
||||
|
||||
To use the latest development version of the `AMR` R package (sourced directly from GitHub), import the `beta` sub-package and alias it as `AMR`:
|
||||
|
||||
```python
|
||||
import AMR.beta as AMR
|
||||
|
||||
AMR.example_isolates
|
||||
```
|
||||
|
||||
Aliasing with `as AMR` keeps all downstream code identical to the stable import. Switching between the stable release and the development version requires changing only the import line — nothing else in your script needs to change.
|
||||
|
||||
# SIR Classification with `as_sir()`
|
||||
|
||||
## Using `enforce_method`
|
||||
|
||||
The `as_sir()` function in R uses S3 method dispatch to select the correct calculation method based on the input class: `<mic>` for MIC values and `<disk>` for disk diffusion values. Because Python objects do not carry R class attributes through the `rpy2` bridge, this automatic dispatch may not resolve correctly.
|
||||
|
||||
To explicitly specify the input type, use the `enforce_method` argument:
|
||||
|
||||
```python
|
||||
# Treat the column as MIC values — maps to R's as.sir.mic()
|
||||
AMR.as_sir(df["MIC_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="mic")
|
||||
|
||||
# Treat the column as disk diffusion values — maps to R's as.sir.disk()
|
||||
AMR.as_sir(df["disk_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="disk")
|
||||
```
|
||||
|
||||
Without `enforce_method`, R falls back to class-based dispatch on the raw Python input, which may fail or return unexpected results. Always supply `enforce_method` when calling `as_sir()` from Python.
|
||||
|
||||
# Conclusion
|
||||
|
||||
With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
|
||||
|
||||
By just running `import AMR`, users can seamlessly integrate the robust features of the R `AMR` package into Python workflows.
|
||||
|
||||
Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.
|
||||
-186
@@ -1,186 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
url: 'https://msberends.gitlab.io/AMR'
|
||||
|
||||
title: 'AMR (for R)'
|
||||
|
||||
navbar:
|
||||
title: 'AMR (for R)'
|
||||
left:
|
||||
- text: 'Home'
|
||||
icon: 'fa-home'
|
||||
href: 'index.html'
|
||||
- text: 'How to'
|
||||
icon: 'fa-question-circle'
|
||||
menu:
|
||||
- text: 'Conduct AMR analysis'
|
||||
icon: 'fa-directions'
|
||||
href: 'articles/AMR.html'
|
||||
- text: 'Predict antimicrobial resistance'
|
||||
icon: 'fa-dice'
|
||||
href: 'articles/resistance_predict.html'
|
||||
- text: 'Work with WHONET data'
|
||||
icon: 'fa-globe-americas'
|
||||
href: 'articles/WHONET.html'
|
||||
- text: 'Import data from SPSS/SAS/Stata'
|
||||
icon: 'fa-file-upload'
|
||||
href: 'articles/SPSS.html'
|
||||
- text: 'Apply EUCAST rules'
|
||||
icon: 'fa-exchange-alt'
|
||||
href: 'articles/EUCAST.html'
|
||||
- text: 'Get properties of a microorganism'
|
||||
icon: 'fa-bug'
|
||||
# href: 'articles/mo_property.html'
|
||||
href: 'reference/mo_property.html'
|
||||
- text: 'Get properties of an antibiotic'
|
||||
icon: 'fa-capsules'
|
||||
# href: 'articles/atc_property.html'
|
||||
href: 'reference/atc_property.html'
|
||||
- text: 'Create frequency tables'
|
||||
icon: 'fa-sort-amount-down'
|
||||
href: 'articles/freq.html'
|
||||
- text: 'Use the G-test'
|
||||
icon: 'fa-clipboard-check'
|
||||
href: 'articles/G_test.html'
|
||||
- text: 'Other: benchmarks'
|
||||
icon: 'fa-shipping-fast'
|
||||
href: 'articles/benchmarks.html'
|
||||
- text: 'Manual'
|
||||
icon: 'fa-book-open'
|
||||
href: 'reference/'
|
||||
- text: 'Authors'
|
||||
icon: 'fa-users'
|
||||
href: 'authors.html'
|
||||
- text: 'Changelog'
|
||||
icon: 'far fa-newspaper'
|
||||
href: 'news/'
|
||||
right:
|
||||
- text: 'Source Code'
|
||||
icon: 'fab fa-gitlab'
|
||||
href: 'https://gitlab.com/msberends/AMR'
|
||||
- text: 'Licence'
|
||||
icon: 'fa-book'
|
||||
href: 'LICENSE-text.html'
|
||||
|
||||
reference:
|
||||
- title: 'Background information'
|
||||
desc: >
|
||||
Some pages about our package and its external sources. Be sure to read our [How To's](./../articles/index.html)
|
||||
for more information about how to work with functions in this package.
|
||||
contents:
|
||||
- '`AMR`'
|
||||
- '`catalogue_of_life`'
|
||||
- '`catalogue_of_life_version`'
|
||||
- '`WHOCC`'
|
||||
- title: 'Cleaning your data'
|
||||
desc: >
|
||||
Functions for cleaning and optimising your data, to be able to add
|
||||
variables later on (like taxonomic properties) or to fix and extend
|
||||
antibiotic interpretations by applying [EUCAST rules](http://www.eucast.org/expert_rules_and_intrinsic_resistance/).
|
||||
contents:
|
||||
- starts_with("as.")
|
||||
- '`mo_source`'
|
||||
- '`eucast_rules`'
|
||||
- '`guess_ab_col`'
|
||||
- '`read.4D`'
|
||||
- title: 'Adding variables to your data'
|
||||
desc: >
|
||||
Functions to add new data to existing data, like the determination
|
||||
of first isolates, multi-drug resistant microorganisms (MDRO), getting
|
||||
properties of microorganisms or antibiotics and determining the age of
|
||||
patients or divide ages into age groups.
|
||||
contents:
|
||||
- '`first_isolate`'
|
||||
- '`mdro`'
|
||||
- '`key_antibiotics`'
|
||||
- '`mo_property`'
|
||||
- '`atc_property`'
|
||||
- '`atc_online_property`'
|
||||
- '`abname`'
|
||||
- '`age`'
|
||||
- '`age_groups`'
|
||||
- '`p.symbol`'
|
||||
- '`join`'
|
||||
- title: 'Analysing your data'
|
||||
desc: >
|
||||
Functions for conducting AMR analysis, like counting isolates, calculating
|
||||
resistance or susceptibility, creating frequency tables or make plots.
|
||||
contents:
|
||||
- '`availability`'
|
||||
- '`count`'
|
||||
- '`portion`'
|
||||
- '`filter_ab_class`'
|
||||
- '`freq`'
|
||||
- '`g.test`'
|
||||
- '`ggplot_rsi`'
|
||||
- '`kurtosis`'
|
||||
- '`resistance_predict`'
|
||||
- '`rsi`'
|
||||
- '`skewness`'
|
||||
- title: 'Included data sets'
|
||||
desc: >
|
||||
References for microorganisms and antibiotics, and even a
|
||||
genuine data set with isolates from septic patients.
|
||||
contents:
|
||||
- '`antibiotics`'
|
||||
- '`microorganisms`'
|
||||
- '`septic_patients`'
|
||||
- '`WHONET`'
|
||||
- '`microorganisms.codes`'
|
||||
- '`microorganisms.old`'
|
||||
- title: Other functions
|
||||
desc: >
|
||||
These functions are mostly for internal use, but some of
|
||||
them may also be suitable for your analysis. Especially the
|
||||
'like' function can be useful: `if (x %like% y) {...}`.
|
||||
contents:
|
||||
- '`get_locale`'
|
||||
- '`like`'
|
||||
- title: Deprecated functions
|
||||
desc: >
|
||||
These functions are deprecated, meaning that they still
|
||||
work but show a warning with every use and will be removed
|
||||
in a future version.
|
||||
contents:
|
||||
- '`AMR-deprecated`'
|
||||
|
||||
authors:
|
||||
Matthijs S. Berends:
|
||||
href: https://www.rug.nl/staff/m.s.berends/
|
||||
Christian F. Luz:
|
||||
href: https://www.rug.nl/staff/c.f.luz/
|
||||
Corinna Glasner:
|
||||
href: https://www.rug.nl/staff/c.glasner/
|
||||
Alex W. Friedrich:
|
||||
href: https://www.rug.nl/staff/a.w.friedrich/
|
||||
Bhanu N. M. Sinha:
|
||||
href: https://www.rug.nl/staff/b.sinha/
|
||||
|
||||
template:
|
||||
assets: pkgdown/logos # use logos in this folder
|
||||
params:
|
||||
noindex: false
|
||||
bootswatch: flatly
|
||||
docsearch:
|
||||
# using algolia.com
|
||||
api_key: 'f737050abfd4d726c63938e18f8c496e'
|
||||
index_name: 'amr'
|
||||
@@ -1,75 +0,0 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# SOURCE #
|
||||
# https://gitlab.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# #
|
||||
# This R package was created for academic research and was publicly #
|
||||
# released in the hope that it will be useful, but it comes WITHOUT #
|
||||
# ANY WARRANTY OR LIABILITY. #
|
||||
# Visit our website for more info: https://msberends.gitab.io/AMR. #
|
||||
# ==================================================================== #
|
||||
|
||||
# Download script file from GitHub
|
||||
init:
|
||||
ps: |
|
||||
$ErrorActionPreference = "Stop"
|
||||
Invoke-WebRequest https://raw.githubusercontent.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "..\appveyor-tool.ps1"
|
||||
Import-Module '..\appveyor-tool.ps1'
|
||||
|
||||
install:
|
||||
ps: Bootstrap
|
||||
|
||||
cache:
|
||||
- C:\RLibrary
|
||||
|
||||
# Adapt as necessary starting from here
|
||||
|
||||
environment:
|
||||
R_ARCH: x64
|
||||
GCC_PATH: mingw_64
|
||||
WARNINGS_ARE_ERRORS: 1
|
||||
PKGTYPE: win.binary
|
||||
# USE_RTOOLS: true
|
||||
|
||||
matrix:
|
||||
- R_VERSION: release
|
||||
- R_VERSION: devel
|
||||
|
||||
build_script:
|
||||
- travis-tool.sh install_deps
|
||||
|
||||
test_script:
|
||||
- travis-tool.sh run_tests
|
||||
|
||||
on_failure:
|
||||
- 7z a failure.zip *.Rcheck\*
|
||||
- appveyor PushArtifact failure.zip
|
||||
|
||||
artifacts:
|
||||
- path: '*.Rcheck\**\*.log'
|
||||
name: Logs
|
||||
|
||||
- path: '*.Rcheck\**\*.out'
|
||||
name: Logs
|
||||
|
||||
- path: '*.Rcheck\**\*.fail'
|
||||
name: Logs
|
||||
|
||||
- path: '*.Rcheck\**\*.Rout'
|
||||
name: Logs
|
||||
|
||||
- path: '\*_*.tar.gz'
|
||||
name: Bits
|
||||
|
||||
- path: '\*_*.zip'
|
||||
name: Bits
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Vendored
BIN
Binary file not shown.
Vendored
BIN
Binary file not shown.
@@ -1,612 +0,0 @@
|
||||
<!-- Generated by pkgdown: do not edit by hand -->
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta http-equiv="X-UA-Compatible" content="IE=edge">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>License • AMR (for R)</title>
|
||||
|
||||
<!-- favicons -->
|
||||
<link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png">
|
||||
<link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png">
|
||||
<link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png" />
|
||||
<link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png" />
|
||||
<link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png" />
|
||||
<link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png" />
|
||||
<!-- jquery -->
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
|
||||
<!-- Bootstrap -->
|
||||
<link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.3.7/flatly/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous" />
|
||||
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script>
|
||||
|
||||
<!-- Font Awesome icons -->
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" integrity="sha256-eZrrJcwDc/3uDhsdt61sL2oOBY362qM3lon1gyExkL0=" crossorigin="anonymous" />
|
||||
|
||||
<!-- clipboard.js -->
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script>
|
||||
|
||||
<!-- sticky kit -->
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script>
|
||||
|
||||
<!-- pkgdown -->
|
||||
<link href="pkgdown.css" rel="stylesheet">
|
||||
<script src="pkgdown.js"></script>
|
||||
|
||||
|
||||
<!-- docsearch -->
|
||||
<script src="docsearch.js"></script>
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/docsearch.js/2.6.1/docsearch.min.css" integrity="sha256-QOSRU/ra9ActyXkIBbiIB144aDBdtvXBcNc3OTNuX/Q=" crossorigin="anonymous" />
|
||||
<link href="docsearch.css" rel="stylesheet">
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/jquery.mark.min.js" integrity="sha256-4HLtjeVgH0eIB3aZ9mLYF6E8oU5chNdjU6p6rrXpl9U=" crossorigin="anonymous"></script>
|
||||
|
||||
|
||||
<link href="extra.css" rel="stylesheet">
|
||||
<script src="extra.js"></script>
|
||||
<meta property="og:title" content="License" />
|
||||
|
||||
<meta property="og:image" content="https://msberends.gitlab.io/AMR/logo.png" />
|
||||
<meta name="twitter:card" content="summary" />
|
||||
|
||||
|
||||
|
||||
<!-- mathjax -->
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
|
||||
<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="container template-title-body">
|
||||
<header>
|
||||
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
|
||||
<div class="container">
|
||||
<div class="navbar-header">
|
||||
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
|
||||
<span class="sr-only">Toggle navigation</span>
|
||||
<span class="icon-bar"></span>
|
||||
<span class="icon-bar"></span>
|
||||
<span class="icon-bar"></span>
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div id="navbar" class="navbar-collapse collapse">
|
||||
<ul class="nav navbar-nav">
|
||||
<li>
|
||||
<a href="index.html">
|
||||
<span class="fa fa-home"></span>
|
||||
|
||||
Home
|
||||
</a>
|
||||
</li>
|
||||
<li class="dropdown">
|
||||
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
|
||||
<span class="fa fa-question-circle"></span>
|
||||
|
||||
How to
|
||||
|
||||
<span class="caret"></span>
|
||||
</a>
|
||||
<ul class="dropdown-menu" role="menu">
|
||||
<li>
|
||||
<a href="articles/AMR.html">
|
||||
<span class="fa fa-directions"></span>
|
||||
|
||||
Conduct AMR analysis
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="articles/resistance_predict.html">
|
||||
<span class="fa fa-dice"></span>
|
||||
|
||||
Predict antimicrobial resistance
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="articles/WHONET.html">
|
||||
<span class="fa fa-globe-americas"></span>
|
||||
|
||||
Work with WHONET data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="articles/SPSS.html">
|
||||
<span class="fa fa-file-upload"></span>
|
||||
|
||||
Import data from SPSS/SAS/Stata
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="articles/EUCAST.html">
|
||||
<span class="fa fa-exchange-alt"></span>
|
||||
|
||||
Apply EUCAST rules
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="reference/mo_property.html">
|
||||
<span class="fa fa-bug"></span>
|
||||
|
||||
Get properties of a microorganism
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="reference/atc_property.html">
|
||||
<span class="fa fa-capsules"></span>
|
||||
|
||||
Get properties of an antibiotic
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="articles/freq.html">
|
||||
<span class="fa fa-sort-amount-down"></span>
|
||||
|
||||
Create frequency tables
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="articles/G_test.html">
|
||||
<span class="fa fa-clipboard-check"></span>
|
||||
|
||||
Use the G-test
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="articles/benchmarks.html">
|
||||
<span class="fa fa-shipping-fast"></span>
|
||||
|
||||
Other: benchmarks
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<a href="reference/">
|
||||
<span class="fa fa-book-open"></span>
|
||||
|
||||
Manual
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="authors.html">
|
||||
<span class="fa fa-users"></span>
|
||||
|
||||
Authors
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="news/">
|
||||
<span class="far fa far fa-newspaper"></span>
|
||||
|
||||
Changelog
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="https://gitlab.com/msberends/AMR">
|
||||
<span class="fab fa fab fa-gitlab"></span>
|
||||
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LICENSE-text.html">
|
||||
<span class="fa fa-book"></span>
|
||||
|
||||
Licence
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
<form class="navbar-form navbar-right" role="search">
|
||||
<div class="form-group">
|
||||
<input type="search" class="form-control" name="search-input" id="search-input" placeholder="Search..." aria-label="Search for..." autocomplete="off">
|
||||
</div>
|
||||
</form>
|
||||
|
||||
</div><!--/.nav-collapse -->
|
||||
</div><!--/.container -->
|
||||
</div><!--/.navbar -->
|
||||
|
||||
|
||||
</header>
|
||||
|
||||
<div class="row">
|
||||
<div class="contents col-md-9">
|
||||
<div class="page-header">
|
||||
<h1>License</h1>
|
||||
</div>
|
||||
|
||||
<pre> GNU GENERAL PUBLIC LICENSE
|
||||
Version 2, June 1991
|
||||
|
||||
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The licenses for most software are designed to take away your
|
||||
freedom to share and change it. By contrast, the GNU General Public
|
||||
License is intended to guarantee your freedom to share and change free
|
||||
software--to make sure the software is free for all its users. This
|
||||
General Public License applies to most of the Free Software
|
||||
Foundation's software and to any other program whose authors commit to
|
||||
using it. (Some other Free Software Foundation software is covered by
|
||||
the GNU Lesser General Public License instead.) You can apply it to
|
||||
your programs, too.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
this service if you wish), that you receive source code or can get it
|
||||
if you want it, that you can change the software or use pieces of it
|
||||
in new free programs; and that you know you can do these things.
|
||||
|
||||
To protect your rights, we need to make restrictions that forbid
|
||||
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|
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|
||||
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||||
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||||
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|
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|
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|
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||||
It is not the purpose of this section to induce you to infringe any
|
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|
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||||
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||||
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||||
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||||
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|
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|
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|
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|
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||||
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|
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|
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|
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|
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END OF TERMS AND CONDITIONS
|
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||||
How to Apply These Terms to Your New Programs
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|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
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||||
To do so, attach the following notices to the program. It is safest
|
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|
||||
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||||
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||||
{description}
|
||||
Copyright (C) {year} {fullname}
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||||
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||||
This program is free software; you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
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|
||||
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||||
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This program is distributed in the hope that it will be useful,
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||||
You should have received a copy of the GNU General Public License along
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|
||||
Also add information on how to contact you by electronic and paper mail.
|
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|
||||
If the program is interactive, make it output a short notice like this
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|
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|
||||
Gnomovision version 69, Copyright (C) year name of author
|
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Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
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This is free software, and you are welcome to redistribute it
|
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||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
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|
||||
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|
||||
|
||||
You should also get your employer (if you work as a programmer) or your
|
||||
school, if any, to sign a "copyright disclaimer" for the program, if
|
||||
necessary. Here is a sample; alter the names:
|
||||
|
||||
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
|
||||
`Gnomovision' (which makes passes at compilers) written by James Hacker.
|
||||
|
||||
{signature of Ty Coon}, 1 April 1989
|
||||
Ty Coon, President of Vice
|
||||
|
||||
This General Public License does not permit incorporating your program into
|
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|
||||
consider it more useful to permit linking proprietary applications with the
|
||||
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|
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|
||||
</pre>
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<h1>How to apply EUCAST rules</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">27 March 2019</h4>
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<p>What are EUCAST rules? The European Committee on Antimicrobial Susceptibility Testing (EUCAST) states <a href="http://www.eucast.org/expert_rules_and_intrinsic_resistance/">on their website</a>:</p>
|
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<blockquote>
|
||||
<p><em>EUCAST expert rules are a tabulated collection of expert knowledge on intrinsic resistances, exceptional resistance phenotypes and interpretive rules that may be applied to antimicrobial susceptibility testing in order to reduce errors and make appropriate recommendations for reporting particular resistances.</em></p>
|
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|
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<p>In Europe, a lot of medical microbiological laboratories already apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008">Brown <em>et al.</em>, 2015</a>). Our package features their latest insights on intrinsic resistance and exceptional phenotypes (version 9.0, 2019). Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function we use for this purpose can also apply additional rules, like forcing <help title="ATC: J01CA01">ampicillin</help> = R in isolates when <help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
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<h1>How to predict antimicrobial resistance</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">09 February 2019</h4>
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<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
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<p>As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by <a href="https://www.linkedin.com/in/hadleywickham/">Dr Hadley Wickham</a>. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
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<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
|
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<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr)</a>
|
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<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2)</a>
|
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<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR)</a>
|
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<a class="sourceLine" id="cb1-4" data-line-number="4"></a>
|
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<a class="sourceLine" id="cb1-5" data-line-number="5"><span class="co"># (if not yet installed, install with:)</span></a>
|
||||
<a class="sourceLine" id="cb1-6" data-line-number="6"><span class="co"># install.packages(c("tidyverse", "AMR"))</span></a></code></pre></div>
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<div id="prediction-analysis" class="section level2">
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<h2 class="hasAnchor">
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<a href="#prediction-analysis" class="anchor"></a>Prediction analysis</h2>
|
||||
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./articles/AMR.html">other AMR analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
|
||||
<p>It is basically as easy as:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="co"># resistance prediction of piperacillin/tazobactam (pita):</span></a>
|
||||
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">tbl =</span> septic_patients, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a>
|
||||
<a class="sourceLine" id="cb2-3" data-line-number="3"></a>
|
||||
<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="co"># or:</span></a>
|
||||
<a class="sourceLine" id="cb2-5" data-line-number="5">septic_patients <span class="op">%>%</span><span class="st"> </span></a>
|
||||
<a class="sourceLine" id="cb2-6" data-line-number="6"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a>
|
||||
<a class="sourceLine" id="cb2-7" data-line-number="7"></a>
|
||||
<a class="sourceLine" id="cb2-8" data-line-number="8"><span class="co"># to bind it to object 'predict_pita' for example:</span></a>
|
||||
<a class="sourceLine" id="cb2-9" data-line-number="9">predict_pita <-<span class="st"> </span>septic_patients <span class="op">%>%</span><span class="st"> </span></a>
|
||||
<a class="sourceLine" id="cb2-10" data-line-number="10"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a></code></pre></div>
|
||||
<pre><code># NOTE: Using column `date` as input for `col_date`.
|
||||
#
|
||||
# Logistic regression model (logit) with binomial distribution
|
||||
# ------------------------------------------------------------
|
||||
#
|
||||
# Call:
|
||||
# glm(formula = df_matrix ~ year, family = binomial)
|
||||
#
|
||||
# Deviance Residuals:
|
||||
# Min 1Q Median 3Q Max
|
||||
# -2.9224 -1.3120 0.0170 0.7586 3.1932
|
||||
#
|
||||
# Coefficients:
|
||||
# Estimate Std. Error z value Pr(>|z|)
|
||||
# (Intercept) -222.92857 45.93922 -4.853 1.22e-06 ***
|
||||
# year 0.10994 0.02284 4.814 1.48e-06 ***
|
||||
# ---
|
||||
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
|
||||
#
|
||||
# (Dispersion parameter for binomial family taken to be 1)
|
||||
#
|
||||
# Null deviance: 59.794 on 14 degrees of freedom
|
||||
# Residual deviance: 35.191 on 13 degrees of freedom
|
||||
# AIC: 93.464
|
||||
#
|
||||
# Number of Fisher Scoring iterations: 4</code></pre>
|
||||
<p>The function will look for a data column itself if <code>col_date</code> is not set. The result is nothing more than a <code>data.frame</code>, containing the years, number of observations, actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">predict_pita</a>
|
||||
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co"># year value se_min se_max observations observed estimated</span></a>
|
||||
<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co"># 1 2003 0.06250000 NA NA 32 0.06250000 0.06177594</span></a>
|
||||
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="co"># 2 2004 0.08536585 NA NA 82 0.08536585 0.06846343</span></a>
|
||||
<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="co"># 3 2005 0.10000000 NA NA 60 0.10000000 0.07581637</span></a>
|
||||
<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="co"># 4 2006 0.05084746 NA NA 59 0.05084746 0.08388789</span></a>
|
||||
<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="co"># 5 2007 0.12121212 NA NA 66 0.12121212 0.09273250</span></a>
|
||||
<a class="sourceLine" id="cb4-8" data-line-number="8"><span class="co"># 6 2008 0.04166667 NA NA 72 0.04166667 0.10240539</span></a>
|
||||
<a class="sourceLine" id="cb4-9" data-line-number="9"><span class="co"># 7 2009 0.01639344 NA NA 61 0.01639344 0.11296163</span></a>
|
||||
<a class="sourceLine" id="cb4-10" data-line-number="10"><span class="co"># 8 2010 0.09433962 NA NA 53 0.09433962 0.12445516</span></a>
|
||||
<a class="sourceLine" id="cb4-11" data-line-number="11"><span class="co"># 9 2011 0.18279570 NA NA 93 0.18279570 0.13693759</span></a>
|
||||
<a class="sourceLine" id="cb4-12" data-line-number="12"><span class="co"># 10 2012 0.30769231 NA NA 65 0.30769231 0.15045682</span></a>
|
||||
<a class="sourceLine" id="cb4-13" data-line-number="13"><span class="co"># 11 2013 0.08620690 NA NA 58 0.08620690 0.16505550</span></a>
|
||||
<a class="sourceLine" id="cb4-14" data-line-number="14"><span class="co"># 12 2014 0.15254237 NA NA 59 0.15254237 0.18076926</span></a>
|
||||
<a class="sourceLine" id="cb4-15" data-line-number="15"><span class="co"># 13 2015 0.27272727 NA NA 55 0.27272727 0.19762493</span></a>
|
||||
<a class="sourceLine" id="cb4-16" data-line-number="16"><span class="co"># 14 2016 0.25000000 NA NA 84 0.25000000 0.21563859</span></a>
|
||||
<a class="sourceLine" id="cb4-17" data-line-number="17"><span class="co"># 15 2017 0.16279070 NA NA 86 0.16279070 0.23481370</span></a>
|
||||
<a class="sourceLine" id="cb4-18" data-line-number="18"><span class="co"># 16 2018 0.25513926 0.2228376 0.2874409 NA NA 0.25513926</span></a>
|
||||
<a class="sourceLine" id="cb4-19" data-line-number="19"><span class="co"># 17 2019 0.27658825 0.2386811 0.3144954 NA NA 0.27658825</span></a>
|
||||
<a class="sourceLine" id="cb4-20" data-line-number="20"><span class="co"># 18 2020 0.29911630 0.2551715 0.3430611 NA NA 0.29911630</span></a>
|
||||
<a class="sourceLine" id="cb4-21" data-line-number="21"><span class="co"># 19 2021 0.32266085 0.2723340 0.3729877 NA NA 0.32266085</span></a>
|
||||
<a class="sourceLine" id="cb4-22" data-line-number="22"><span class="co"># 20 2022 0.34714076 0.2901847 0.4040968 NA NA 0.34714076</span></a>
|
||||
<a class="sourceLine" id="cb4-23" data-line-number="23"><span class="co"># 21 2023 0.37245666 0.3087318 0.4361815 NA NA 0.37245666</span></a>
|
||||
<a class="sourceLine" id="cb4-24" data-line-number="24"><span class="co"># 22 2024 0.39849187 0.3279750 0.4690088 NA NA 0.39849187</span></a>
|
||||
<a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># 23 2025 0.42511415 0.3479042 0.5023241 NA NA 0.42511415</span></a>
|
||||
<a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># 24 2026 0.45217796 0.3684992 0.5358568 NA NA 0.45217796</span></a>
|
||||
<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># 25 2027 0.47952757 0.3897276 0.5693275 NA NA 0.47952757</span></a>
|
||||
<a class="sourceLine" id="cb4-28" data-line-number="28"><span class="co"># 26 2028 0.50700045 0.4115444 0.6024565 NA NA 0.50700045</span></a>
|
||||
<a class="sourceLine" id="cb4-29" data-line-number="29"><span class="co"># 27 2029 0.53443111 0.4338908 0.6349714 NA NA 0.53443111</span></a></code></pre></div>
|
||||
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>(predict_pita)</a></code></pre></div>
|
||||
<p><img src="Predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
|
||||
<p>We also support the <code>ggplot2</code> package with the function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code>:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2)</a>
|
||||
<a class="sourceLine" id="cb6-2" data-line-number="2"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_pita)</a>
|
||||
<a class="sourceLine" id="cb6-3" data-line-number="3"><span class="co"># Warning: Removed 15 rows containing missing values (geom_errorbar).</span></a></code></pre></div>
|
||||
<p><img src="Predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
|
||||
<div id="choosing-the-right-model" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3>
|
||||
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo) <span class="op">==</span><span class="st"> "Gram positive"</span>) <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"vanc"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>) <span class="op">%>%</span><span class="st"> </span></a>
|
||||
<a class="sourceLine" id="cb7-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>()</a>
|
||||
<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
|
||||
<p><img src="Predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
|
||||
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
|
||||
<p>You can define the model with the <code>model</code> parameter. The default model is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
|
||||
<p>Valid values are:</p>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="32%">
|
||||
<col width="25%">
|
||||
<col width="42%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th>Input values</th>
|
||||
<th>Function used by R</th>
|
||||
<th>Type of model</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td>
|
||||
<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
|
||||
</td>
|
||||
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = binomial)</a></code></td>
|
||||
<td>Generalised linear model with binomial distribution</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>
|
||||
<code>"loglin"</code> or <code>"poisson"</code>
|
||||
</td>
|
||||
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = poisson)</a></code></td>
|
||||
<td>Generalised linear model with poisson distribution</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>
|
||||
<code>"lin"</code> or <code>"linear"</code>
|
||||
</td>
|
||||
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/lm">lm()</a></code></td>
|
||||
<td>Linear model</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on observed years:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo) <span class="op">==</span><span class="st"> "Gram positive"</span>) <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"vanc"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>, <span class="dt">model =</span> <span class="st">"linear"</span>) <span class="op">%>%</span><span class="st"> </span></a>
|
||||
<a class="sourceLine" id="cb8-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>()</a>
|
||||
<a class="sourceLine" id="cb8-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
|
||||
<p><img src="Predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
|
||||
<p>This seems more likely, doesn’t it?</p>
|
||||
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<li><a href="#needed-r-packages">Needed R packages</a></li>
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Import data from SPSS/SAS/Stata
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|
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Use the G-test
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</header><div class="row">
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||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1>How to import data from SPSS / SAS / Stata</h1>
|
||||
<h4 class="author">Matthijs S. Berends</h4>
|
||||
|
||||
<h4 class="date">27 March 2019</h4>
|
||||
|
||||
|
||||
<div class="hidden name"><code>SPSS.Rmd</code></div>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div id="spss-sas-stata" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#spss-sas-stata" class="anchor"></a>SPSS / SAS / Stata</h2>
|
||||
<p>SPSS (Statistical Package for the Social Sciences) is probably the most well-known software package for statistical analysis. SPSS is easier to learn than R, because in SPSS you only have to click a menu to run parts of your analysis. Because of its user-friendliness, it is taught at universities and particularly useful for students who are new to statistics. From my experience, I would guess that pretty much all (bio)medical students know it at the time they graduate. SAS and Stata are comparable statistical packages popular in big industries.</p>
|
||||
</div>
|
||||
<div id="compared-to-r" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#compared-to-r" class="anchor"></a>Compared to R</h2>
|
||||
<p>As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major downsides when comparing it with R:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<p><strong>R is highly modular.</strong></p>
|
||||
<p>The <a href="https://cran.r-project.org/">official R network (CRAN)</a> features almost 14,000 packages at the time of writing, our <code>AMR</code> package being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitLab or GitHub. So there may even be a lot more than 14,000 packages out there.</p>
|
||||
<p>Bottom line is, you can really extend it yourself or ask somebody to do this for you. Take for example our <code>AMR</code> package. Among other things, it adds reliable reference data to R to help you with the data cleaning and analysis. SPSS, SAS and Stata will never know what a valid MIC value is or what the Gram stain of <em>E. coli</em> is. Or that all species of <em>Klebiella</em> are resistant to amoxicillin and that Floxapen<sup>®</sup> is a trade name of flucloxacillin. These facts and properties are often needed to clean existing data, which would be very inconvenient in a software package without reliable reference data. See below for a demonstration.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>R is extremely flexible.</strong></p>
|
||||
<p>Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, gathering, grouping, summarising and drawing plots is endless - with SPSS, SAS or Stata you are bound to their algorithms and styles. They may be a bit flexible, but you can probably never create that very specific publication-ready plot without using other (paid) software.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>R can be easily automated.</strong></p>
|
||||
<p>Over the last years, <a href="https://rmarkdown.rstudio.com/">R Markdown</a> has really made an interesting development. With R Markdown, you can very easily reproduce your reports, whether it’s to Word, Powerpoint, a website, a PDF document or just the raw data to Excel. I use this a lot to generate monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.</p>
|
||||
<p>For an even more professional environment, you could create <a href="https://shiny.rstudio.com/">Shiny apps</a>: live manipulation of data using a custom made website. The webdesign knowledge needed (JavaScript, CSS, HTML) is almost <em>zero</em>.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>R has a huge community.</strong></p>
|
||||
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, almost <a href="https://stackoverflow.com/questions/tagged/r?sort=votes">300,000 R-related questions</a> have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>R understands any data type, including SPSS/SAS/Stata.</strong></p>
|
||||
<p>And that’s not vice versa I’m afraid. You can import data from any source into R. From SPSS, SAS and Stata (<a href="https://haven.tidyverse.org/">link</a>), from Minitab, Epi Info and EpiData (<a href="https://cran.r-project.org/package=foreign">link</a>), from Excel (<a href="https://readxl.tidyverse.org/">link</a>), from flat files like CSV, TXT or TSV (<a href="https://readr.tidyverse.org/">link</a>), or directly from databases and datawarehouses from anywhere on the world (<a href="https://dbplyr.tidyverse.org/">link</a>). You can even scrape websites to download tables that are live on the internet (<a href="https://github.com/hadley/rvest">link</a>).</p>
|
||||
<p>And the best part - you can export from R to most data formats as well. So you can import an SPSS file, do your analysis neatly in R and export the resulting tables to Excel files.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>R is completely free and open-source.</strong></p>
|
||||
<p>No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying <a href="https://www.ibm.com/products/spss-statistics/pricing">between USD 1,300 and USD 8,500</a> per computer <em>per year</em>. SAS Analytics Pro costs <a href="https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html">around USD 10,000</a> per computer. Stata also has a business model with subscription fees, varying <a href="https://www.stata.com/order/new/bus/single-user-licenses/dl/">between USD 600 and USD 1,200</a> per computer per year, but lower prices come with a limitation of the number of variables you can work with.</p>
|
||||
<p>If you are working at a midsized or small company, you can save it tens of thousands of dollars by using R instead of e.g. SPSS - gaining even more functions and flexibility. And all R enthousiasts can do as much PR as they want (like I do here), because nobody is officially associated with or affiliated by R. It is really free.</p>
|
||||
</li>
|
||||
</ul>
|
||||
<p>If you sometimes write syntaxes in SPSS to run a complete analysis or to ‘automate’ some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS.</p>
|
||||
<p>To demonstrate the first point:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="co"># not all values are valid MIC values:</span></a>
|
||||
<a class="sourceLine" id="cb1-2" title="2"><span class="kw"><a href="../reference/as.mic.html">as.mic</a></span>(<span class="fl">0.125</span>)</a>
|
||||
<a class="sourceLine" id="cb1-3" title="3"><span class="co">#> Class 'mic'</span></a>
|
||||
<a class="sourceLine" id="cb1-4" title="4"><span class="co">#> [1] 0.125</span></a>
|
||||
<a class="sourceLine" id="cb1-5" title="5"><span class="kw"><a href="../reference/as.mic.html">as.mic</a></span>(<span class="st">"testvalue"</span>)</a>
|
||||
<a class="sourceLine" id="cb1-6" title="6"><span class="co">#> Class 'mic'</span></a>
|
||||
<a class="sourceLine" id="cb1-7" title="7"><span class="co">#> [1] <NA></span></a>
|
||||
<a class="sourceLine" id="cb1-8" title="8"></a>
|
||||
<a class="sourceLine" id="cb1-9" title="9"><span class="co"># the Gram stain is avaiable for all bacteria:</span></a>
|
||||
<a class="sourceLine" id="cb1-10" title="10"><span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="st">"E. coli"</span>)</a>
|
||||
<a class="sourceLine" id="cb1-11" title="11"><span class="co">#> [1] "Gram negative"</span></a>
|
||||
<a class="sourceLine" id="cb1-12" title="12"></a>
|
||||
<a class="sourceLine" id="cb1-13" title="13"><span class="co"># Klebsiella is intrinsic resistant to amoxicllin, according to EUCAST:</span></a>
|
||||
<a class="sourceLine" id="cb1-14" title="14">klebsiella_test <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/data.frame">data.frame</a></span>(<span class="dt">mo =</span> <span class="st">"klebsiella"</span>, </a>
|
||||
<a class="sourceLine" id="cb1-15" title="15"> <span class="dt">amox =</span> <span class="st">"S"</span>,</a>
|
||||
<a class="sourceLine" id="cb1-16" title="16"> <span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>)</a>
|
||||
<a class="sourceLine" id="cb1-17" title="17">klebsiella_test</a>
|
||||
<a class="sourceLine" id="cb1-18" title="18"><span class="co">#> mo amox</span></a>
|
||||
<a class="sourceLine" id="cb1-19" title="19"><span class="co">#> 1 klebsiella S</span></a>
|
||||
<a class="sourceLine" id="cb1-20" title="20"><span class="kw"><a href="../reference/eucast_rules.html">eucast_rules</a></span>(klebsiella_test, <span class="dt">info =</span> <span class="ot">FALSE</span>)</a>
|
||||
<a class="sourceLine" id="cb1-21" title="21"><span class="co">#> mo amox</span></a>
|
||||
<a class="sourceLine" id="cb1-22" title="22"><span class="co">#> 1 klebsiella R</span></a>
|
||||
<a class="sourceLine" id="cb1-23" title="23"></a>
|
||||
<a class="sourceLine" id="cb1-24" title="24"><span class="co"># hundreds of trade names can be translated to an ATC or name:</span></a>
|
||||
<a class="sourceLine" id="cb1-25" title="25"><span class="kw"><a href="../reference/atc_property.html">atc_name</a></span>(<span class="st">"floxapen"</span>)</a>
|
||||
<a class="sourceLine" id="cb1-26" title="26"><span class="co">#> [1] "Flucloxacillin"</span></a>
|
||||
<a class="sourceLine" id="cb1-27" title="27"><span class="kw"><a href="../reference/as.atc.html">as.atc</a></span>(<span class="st">"floxapen"</span>)</a>
|
||||
<a class="sourceLine" id="cb1-28" title="28"><span class="co">#> Class 'atc'</span></a>
|
||||
<a class="sourceLine" id="cb1-29" title="29"><span class="co">#> [1] J01CF05</span></a>
|
||||
<a class="sourceLine" id="cb1-30" title="30"><span class="kw"><a href="../reference/atc_property.html">atc_tradenames</a></span>(<span class="st">"floxapen"</span>)</a>
|
||||
<a class="sourceLine" id="cb1-31" title="31"><span class="co">#> [1] "Flopen" "Floxapen" "Fluclox" "Sesamol" "Softapen" "Staphylex"</span></a></code></pre></div>
|
||||
</div>
|
||||
<div id="import-data-from-spsssasstata" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#import-data-from-spsssasstata" class="anchor"></a>Import data from SPSS/SAS/Stata</h2>
|
||||
<div id="rstudio" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#rstudio" class="anchor"></a>RStudio</h3>
|
||||
<p>To work with R, probably the best option is to use <a href="https://www.rstudio.com/products/rstudio/">RStudio</a>. It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menus to work with other data sources. You can also install <a href="https://www.rstudio.com/products/rstudio/">RStudio Server</a> on a private or corporate server, which brings nothing less than the complete RStudio software to you as a website (at home or at work).</p>
|
||||
<p>To import a data file, just click <em>Import Dataset</em> in the Environment tab:</p>
|
||||
<p><img src="https://gitlab.com/msberends/AMR/raw/master/docs/import1.png"></p>
|
||||
<p>If additional packages are needed, RStudio will ask you if they should be installed on beforehand.</p>
|
||||
<p>In the the window that opens, you can define all options (parameters) that should be used for import and you’re ready to go:</p>
|
||||
<p><img src="https://gitlab.com/msberends/AMR/raw/master/docs/import2.png"></p>
|
||||
<p>If you want named variables to be imported as factors so it resembles SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>.</p>
|
||||
<p>The difference is this:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">SPSS_data</a>
|
||||
<a class="sourceLine" id="cb2-2" title="2"><span class="co"># # A tibble: 4,203 x 4</span></a>
|
||||
<a class="sourceLine" id="cb2-3" title="3"><span class="co"># v001 sex status statusage</span></a>
|
||||
<a class="sourceLine" id="cb2-4" title="4"><span class="co"># <dbl> <dbl+lbl> <dbl+lbl> <dbl></span></a>
|
||||
<a class="sourceLine" id="cb2-5" title="5"><span class="co"># 1 10002 1 1 76.6</span></a>
|
||||
<a class="sourceLine" id="cb2-6" title="6"><span class="co"># 2 10004 0 1 59.1</span></a>
|
||||
<a class="sourceLine" id="cb2-7" title="7"><span class="co"># 3 10005 1 1 54.5</span></a>
|
||||
<a class="sourceLine" id="cb2-8" title="8"><span class="co"># 4 10006 1 1 54.1</span></a>
|
||||
<a class="sourceLine" id="cb2-9" title="9"><span class="co"># 5 10007 1 1 57.7</span></a>
|
||||
<a class="sourceLine" id="cb2-10" title="10"><span class="co"># 6 10008 1 1 62.8</span></a>
|
||||
<a class="sourceLine" id="cb2-11" title="11"><span class="co"># 7 10010 0 1 63.7</span></a>
|
||||
<a class="sourceLine" id="cb2-12" title="12"><span class="co"># 8 10011 1 1 73.1</span></a>
|
||||
<a class="sourceLine" id="cb2-13" title="13"><span class="co"># 9 10017 1 1 56.7</span></a>
|
||||
<a class="sourceLine" id="cb2-14" title="14"><span class="co"># 10 10018 0 1 66.6</span></a>
|
||||
<a class="sourceLine" id="cb2-15" title="15"><span class="co"># # … with 4,193 more rows</span></a>
|
||||
<a class="sourceLine" id="cb2-16" title="16"></a>
|
||||
<a class="sourceLine" id="cb2-17" title="17"><span class="kw">as_factor</span>(SPSS_data)</a>
|
||||
<a class="sourceLine" id="cb2-18" title="18"><span class="co"># # A tibble: 4,203 x 4</span></a>
|
||||
<a class="sourceLine" id="cb2-19" title="19"><span class="co"># v001 sex status statusage</span></a>
|
||||
<a class="sourceLine" id="cb2-20" title="20"><span class="co"># <dbl> <fct> <fct> <dbl></span></a>
|
||||
<a class="sourceLine" id="cb2-21" title="21"><span class="co"># 1 10002 Male alive 76.6</span></a>
|
||||
<a class="sourceLine" id="cb2-22" title="22"><span class="co"># 2 10004 Female alive 59.1</span></a>
|
||||
<a class="sourceLine" id="cb2-23" title="23"><span class="co"># 3 10005 Male alive 54.5</span></a>
|
||||
<a class="sourceLine" id="cb2-24" title="24"><span class="co"># 4 10006 Male alive 54.1</span></a>
|
||||
<a class="sourceLine" id="cb2-25" title="25"><span class="co"># 5 10007 Male alive 57.7</span></a>
|
||||
<a class="sourceLine" id="cb2-26" title="26"><span class="co"># 6 10008 Male alive 62.8</span></a>
|
||||
<a class="sourceLine" id="cb2-27" title="27"><span class="co"># 7 10010 Female alive 63.7</span></a>
|
||||
<a class="sourceLine" id="cb2-28" title="28"><span class="co"># 8 10011 Male alive 73.1</span></a>
|
||||
<a class="sourceLine" id="cb2-29" title="29"><span class="co"># 9 10017 Male alive 56.7</span></a>
|
||||
<a class="sourceLine" id="cb2-30" title="30"><span class="co"># 10 10018 Female alive 66.6</span></a>
|
||||
<a class="sourceLine" id="cb2-31" title="31"><span class="co"># # … with 4,193 more rows</span></a></code></pre></div>
|
||||
</div>
|
||||
<div id="base-r" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#base-r" class="anchor"></a>Base R</h3>
|
||||
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/">great <code>haven</code> package</a> yourself:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="co"># download and install the latest version:</span></a>
|
||||
<a class="sourceLine" id="cb3-2" title="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages</a></span>(<span class="st">"haven"</span>)</a>
|
||||
<a class="sourceLine" id="cb3-3" title="3"><span class="co"># load the package you just installed:</span></a>
|
||||
<a class="sourceLine" id="cb3-4" title="4"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(haven) </a></code></pre></div>
|
||||
<p>You can now import files as follows:</p>
|
||||
<div id="spss" class="section level4">
|
||||
<h4 class="hasAnchor">
|
||||
<a href="#spss" class="anchor"></a>SPSS</h4>
|
||||
<p>To read files from SPSS into R:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="co"># read any SPSS file based on file extension (best way):</span></a>
|
||||
<a class="sourceLine" id="cb4-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_spss</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a>
|
||||
<a class="sourceLine" id="cb4-3" title="3"></a>
|
||||
<a class="sourceLine" id="cb4-4" title="4"><span class="co"># read .sav or .zsav file:</span></a>
|
||||
<a class="sourceLine" id="cb4-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_sav</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a>
|
||||
<a class="sourceLine" id="cb4-6" title="6"></a>
|
||||
<a class="sourceLine" id="cb4-7" title="7"><span class="co"># read .por file:</span></a>
|
||||
<a class="sourceLine" id="cb4-8" title="8"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a></code></pre></div>
|
||||
<p>Do not forget about <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>, as mentioned above.</p>
|
||||
<p>To export your R objects to the SPSS file format:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="co"># save as .sav file:</span></a>
|
||||
<a class="sourceLine" id="cb5-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>)</a>
|
||||
<a class="sourceLine" id="cb5-3" title="3"></a>
|
||||
<a class="sourceLine" id="cb5-4" title="4"><span class="co"># save as compressed .zsav file:</span></a>
|
||||
<a class="sourceLine" id="cb5-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>, <span class="dt">compress =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
|
||||
</div>
|
||||
<div id="sas" class="section level4">
|
||||
<h4 class="hasAnchor">
|
||||
<a href="#sas" class="anchor"></a>SAS</h4>
|
||||
<p>To read files from SAS into R:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="co"># read .sas7bdat + .sas7bcat files:</span></a>
|
||||
<a class="sourceLine" id="cb6-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_sas.html">read_sas</a></span>(<span class="dt">data_file =</span> <span class="st">"path/to/file"</span>, <span class="dt">catalog_file =</span> <span class="ot">NULL</span>)</a>
|
||||
<a class="sourceLine" id="cb6-3" title="3"></a>
|
||||
<a class="sourceLine" id="cb6-4" title="4"><span class="co"># read SAS transport files (version 5 and version 8):</span></a>
|
||||
<a class="sourceLine" id="cb6-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span>(<span class="dt">file =</span> <span class="st">"path/to/file"</span>)</a></code></pre></div>
|
||||
<p>To export your R objects to the SAS file format:</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="co"># save as regular SAS file:</span></a>
|
||||
<a class="sourceLine" id="cb7-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_sas.html">write_sas</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>)</a>
|
||||
<a class="sourceLine" id="cb7-3" title="3"></a>
|
||||
<a class="sourceLine" id="cb7-4" title="4"><span class="co"># the SAS transport format is an open format </span></a>
|
||||
<a class="sourceLine" id="cb7-5" title="5"><span class="co"># (required for submission of the data to the FDA)</span></a>
|
||||
<a class="sourceLine" id="cb7-6" title="6"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"path/to/file"</span>, <span class="dt">version =</span> <span class="dv">8</span>)</a></code></pre></div>
|
||||
</div>
|
||||
<div id="stata" class="section level4">
|
||||
<h4 class="hasAnchor">
|
||||
<a href="#stata" class="anchor"></a>Stata</h4>
|
||||
<p>To read files from Stata into R:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1"><span class="co"># read .dta file:</span></a>
|
||||
<a class="sourceLine" id="cb8-2" title="2"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_stata</a></span>(<span class="dt">file =</span> <span class="st">"/path/to/file"</span>)</a>
|
||||
<a class="sourceLine" id="cb8-3" title="3"></a>
|
||||
<a class="sourceLine" id="cb8-4" title="4"><span class="co"># works exactly the same:</span></a>
|
||||
<a class="sourceLine" id="cb8-5" title="5"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span>(<span class="dt">file =</span> <span class="st">"/path/to/file"</span>)</a></code></pre></div>
|
||||
<p>To export your R objects to the Stata file format:</p>
|
||||
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="co"># save as .dta file, Stata version 14:</span></a>
|
||||
<a class="sourceLine" id="cb9-2" title="2"><span class="co"># (supports Stata v8 until v15 at the time of writing)</span></a>
|
||||
<a class="sourceLine" id="cb9-3" title="3"><span class="kw"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span>(<span class="dt">data =</span> yourdata, <span class="dt">path =</span> <span class="st">"/path/to/file"</span>, <span class="dt">version =</span> <span class="dv">14</span>)</a></code></pre></div>
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<h1>How to work with WHONET data</h1>
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<h4 class="author">Matthijs S. Berends</h4>
|
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<h4 class="date">27 March 2019</h4>
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<h1 class="hasAnchor">
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||||
<a href="#import-of-data" class="anchor"></a>Import of data</h1>
|
||||
<p>This tutorial assumes you already imported the WHONET data with e.g. the <a href="https://readxl.tidyverse.org/"><code>readxl</code> package</a>. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.</p>
|
||||
<p>An example syntax could look like this:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(readxl)</a>
|
||||
<a class="sourceLine" id="cb1-2" title="2">data <-<span class="st"> </span><span class="kw"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span>(<span class="dt">path =</span> <span class="st">"path/to/your/file.xlsx"</span>)</a></code></pre></div>
|
||||
<p>This package comes with an <a href="./reference/WHONET.html">example data set <code>WHONET</code></a>. We will use it for this analysis.</p>
|
||||
</div>
|
||||
<div id="preparation" class="section level1">
|
||||
<h1 class="hasAnchor">
|
||||
<a href="#preparation" class="anchor"></a>Preparation</h1>
|
||||
<p>First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="uri">https://www.tidyverse.org/</a>.</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr) <span class="co"># part of tidyverse</span></a>
|
||||
<a class="sourceLine" id="cb2-2" title="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2) <span class="co"># part of tidyverse</span></a>
|
||||
<a class="sourceLine" id="cb2-3" title="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR) <span class="co"># this package</span></a></code></pre></div>
|
||||
<p>We will have to transform some variables to simplify and automate the analysis:</p>
|
||||
<ul>
|
||||
<li>Microorganisms should be transformed to our own microorganism IDs (called an <code>mo</code>) using <a href="./reference/ITIS.html">the ITIS reference data set</a>, which contains all ~20,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also recognises almost all WHONET abbreviations of microorganisms.</li>
|
||||
<li>Antimicrobial results or interpretations have to be clean and valid. In other words, they should only contain values <code>"S"</code>, <code>"I"</code> or <code>"R"</code>. That is exactly where the <code><a href="../reference/as.rsi.html">as.rsi()</a></code> function is for.</li>
|
||||
</ul>
|
||||
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="co"># transform variables</span></a>
|
||||
<a class="sourceLine" id="cb3-2" title="2">data <-<span class="st"> </span>WHONET <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb3-3" title="3"><span class="st"> </span><span class="co"># get microbial ID based on given organism</span></a>
|
||||
<a class="sourceLine" id="cb3-4" title="4"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="dt">mo =</span> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(Organism)) <span class="op">%>%</span><span class="st"> </span></a>
|
||||
<a class="sourceLine" id="cb3-5" title="5"><span class="st"> </span><span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class</span></a>
|
||||
<a class="sourceLine" id="cb3-6" title="6"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span>(<span class="kw"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span>(AMP_ND10<span class="op">:</span>CIP_EE), as.rsi)</a></code></pre></div>
|
||||
<p>No errors or warnings, so all values are transformed succesfully. Let’s check it though, with a couple of frequency tables:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="co"># our newly created `mo` variable</span></a>
|
||||
<a class="sourceLine" id="cb4-2" title="2">data <span class="op">%>%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(mo, <span class="dt">nmax =</span> <span class="dv">10</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>mo</code> from a <code>data.frame</code> (500 x 54)</strong></p>
|
||||
<p>Class: <code>mo</code> (<code>character</code>)<br>
|
||||
Length: 500 (of which NA: 0 = 0.00%)<br>
|
||||
Unique: 39</p>
|
||||
<p>Families: 10<br>
|
||||
Genera: 17<br>
|
||||
Species: 38</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">B_ESCHR_COL</td>
|
||||
<td align="right">245</td>
|
||||
<td align="right">49.0%</td>
|
||||
<td align="right">245</td>
|
||||
<td align="right">49.0%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">B_STPHY_CNS</td>
|
||||
<td align="right">74</td>
|
||||
<td align="right">14.8%</td>
|
||||
<td align="right">319</td>
|
||||
<td align="right">63.8%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">B_STPHY_EPI</td>
|
||||
<td align="right">38</td>
|
||||
<td align="right">7.6%</td>
|
||||
<td align="right">357</td>
|
||||
<td align="right">71.4%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">B_STRPT_PNE</td>
|
||||
<td align="right">31</td>
|
||||
<td align="right">6.2%</td>
|
||||
<td align="right">388</td>
|
||||
<td align="right">77.6%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">5</td>
|
||||
<td align="left">B_STPHY_HOM</td>
|
||||
<td align="right">21</td>
|
||||
<td align="right">4.2%</td>
|
||||
<td align="right">409</td>
|
||||
<td align="right">81.8%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">6</td>
|
||||
<td align="left">B_PROTS_MIR</td>
|
||||
<td align="right">9</td>
|
||||
<td align="right">1.8%</td>
|
||||
<td align="right">418</td>
|
||||
<td align="right">83.6%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">7</td>
|
||||
<td align="left">B_ENTRC_IUM</td>
|
||||
<td align="right">8</td>
|
||||
<td align="right">1.6%</td>
|
||||
<td align="right">426</td>
|
||||
<td align="right">85.2%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">8</td>
|
||||
<td align="left">B_STPHY_CAP</td>
|
||||
<td align="right">8</td>
|
||||
<td align="right">1.6%</td>
|
||||
<td align="right">434</td>
|
||||
<td align="right">86.8%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">9</td>
|
||||
<td align="left">B_ENTRB_CLO</td>
|
||||
<td align="right">5</td>
|
||||
<td align="right">1.0%</td>
|
||||
<td align="right">439</td>
|
||||
<td align="right">87.8%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">10</td>
|
||||
<td align="left">B_ENTRC_COL</td>
|
||||
<td align="right">4</td>
|
||||
<td align="right">0.8%</td>
|
||||
<td align="right">443</td>
|
||||
<td align="right">88.6%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>(omitted 29 entries, n = 57 [11.4%])</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"></a>
|
||||
<a class="sourceLine" id="cb5-2" title="2"><span class="co"># our transformed antibiotic columns</span></a>
|
||||
<a class="sourceLine" id="cb5-3" title="3"><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></a>
|
||||
<a class="sourceLine" id="cb5-4" title="4">data <span class="op">%>%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(AMC_ND2)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>AMC_ND2</code> from a <code>data.frame</code> (500 x 54)</strong></p>
|
||||
<pre><code># Warning: These values could not be coerced to a valid atc: "AMCND".</code></pre>
|
||||
<p>Class: <code>factor</code> > <code>ordered</code> > <code>rsi</code> (<code>numeric</code>)<br>
|
||||
Length: 500 (of which NA: 19 = 3.80%)<br>
|
||||
Levels: 3: <code>S</code> < <code>I</code> < <code>R</code><br>
|
||||
Unique: 3</p>
|
||||
<p>%IR: 25.99%</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">S</td>
|
||||
<td align="right">356</td>
|
||||
<td align="right">74.0%</td>
|
||||
<td align="right">356</td>
|
||||
<td align="right">74.0%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">R</td>
|
||||
<td align="right">103</td>
|
||||
<td align="right">21.4%</td>
|
||||
<td align="right">459</td>
|
||||
<td align="right">95.4%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">I</td>
|
||||
<td align="right">22</td>
|
||||
<td align="right">4.6%</td>
|
||||
<td align="right">481</td>
|
||||
<td align="right">100.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div id="analysis" class="section level1">
|
||||
<h1 class="hasAnchor">
|
||||
<a href="#analysis" class="anchor"></a>Analysis</h1>
|
||||
<p><em>(more will be available soon)</em></p>
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<a href="#tocnav" class="anchor"></a>Contents</h2>
|
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|
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<li><a href="#import-of-data">Import of data</a></li>
|
||||
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|
||||
<li><a href="#analysis">Analysis</a></li>
|
||||
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|
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Get properties of an antibiotic
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<h1>Benchmarks</h1>
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<h4 class="author">Matthijs S. Berends</h4>
|
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<h4 class="date">27 March 2019</h4>
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<div class="hidden name"><code>benchmarks.Rmd</code></div>
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<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://catalogueoflife.org">Catalogue of Life</a>. We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.</p>
|
||||
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark()</a></code> runs different input expressions independently of each other and measures their time-to-result.</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(microbenchmark)</a>
|
||||
<a class="sourceLine" id="cb1-2" title="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR)</a></code></pre></div>
|
||||
<p>In the next test, we try to ‘coerce’ different input values for <em>Staphylococcus aureus</em>. The actual result is the same every time: it returns its MO code <code>B_STPHY_AUR</code> (<em>B</em> stands for <em>Bacteria</em>, the taxonomic kingdom).</p>
|
||||
<p>But the calculation time differs a lot:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">S.aureus <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"sau"</span>),</a>
|
||||
<a class="sourceLine" id="cb2-2" title="2"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"stau"</span>),</a>
|
||||
<a class="sourceLine" id="cb2-3" title="3"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"staaur"</span>),</a>
|
||||
<a class="sourceLine" id="cb2-4" title="4"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"STAAUR"</span>),</a>
|
||||
<a class="sourceLine" id="cb2-5" title="5"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"S. aureus"</span>),</a>
|
||||
<a class="sourceLine" id="cb2-6" title="6"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"S. aureus"</span>),</a>
|
||||
<a class="sourceLine" id="cb2-7" title="7"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Staphylococcus aureus"</span>),</a>
|
||||
<a class="sourceLine" id="cb2-8" title="8"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
|
||||
<a class="sourceLine" id="cb2-9" title="9"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(S.aureus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
|
||||
<a class="sourceLine" id="cb2-10" title="10"><span class="co">#> Unit: milliseconds</span></a>
|
||||
<a class="sourceLine" id="cb2-11" title="11"><span class="co">#> expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb2-12" title="12"><span class="co">#> as.mo("sau") 18.0 18.0 22 18.0 20.0 48 10</span></a>
|
||||
<a class="sourceLine" id="cb2-13" title="13"><span class="co">#> as.mo("stau") 48.0 48.0 76 67.0 92.0 150 10</span></a>
|
||||
<a class="sourceLine" id="cb2-14" title="14"><span class="co">#> as.mo("staaur") 18.0 18.0 24 19.0 22.0 62 10</span></a>
|
||||
<a class="sourceLine" id="cb2-15" title="15"><span class="co">#> as.mo("STAAUR") 18.0 18.0 23 18.0 20.0 61 10</span></a>
|
||||
<a class="sourceLine" id="cb2-16" title="16"><span class="co">#> as.mo("S. aureus") 29.0 29.0 35 29.0 30.0 76 10</span></a>
|
||||
<a class="sourceLine" id="cb2-17" title="17"><span class="co">#> as.mo("S. aureus") 29.0 29.0 37 29.0 30.0 79 10</span></a>
|
||||
<a class="sourceLine" id="cb2-18" title="18"><span class="co">#> as.mo("Staphylococcus aureus") 8.2 8.2 13 8.2 8.4 52 10</span></a></code></pre></div>
|
||||
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.</p>
|
||||
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Thermus islandicus</em> (<code>B_THERMS_ISL</code>), a bug probably never found before in humans:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1">T.islandicus <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"theisl"</span>),</a>
|
||||
<a class="sourceLine" id="cb3-2" title="2"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"THEISL"</span>),</a>
|
||||
<a class="sourceLine" id="cb3-3" title="3"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"T. islandicus"</span>),</a>
|
||||
<a class="sourceLine" id="cb3-4" title="4"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"T. islandicus"</span>),</a>
|
||||
<a class="sourceLine" id="cb3-5" title="5"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Thermus islandicus"</span>),</a>
|
||||
<a class="sourceLine" id="cb3-6" title="6"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
|
||||
<a class="sourceLine" id="cb3-7" title="7"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(T.islandicus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
|
||||
<a class="sourceLine" id="cb3-8" title="8"><span class="co">#> Unit: milliseconds</span></a>
|
||||
<a class="sourceLine" id="cb3-9" title="9"><span class="co">#> expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb3-10" title="10"><span class="co">#> as.mo("theisl") 460 460 500 510 510 560 10</span></a>
|
||||
<a class="sourceLine" id="cb3-11" title="11"><span class="co">#> as.mo("THEISL") 460 470 490 490 510 530 10</span></a>
|
||||
<a class="sourceLine" id="cb3-12" title="12"><span class="co">#> as.mo("T. islandicus") 74 74 84 75 78 120 10</span></a>
|
||||
<a class="sourceLine" id="cb3-13" title="13"><span class="co">#> as.mo("T. islandicus") 74 75 88 75 120 120 10</span></a>
|
||||
<a class="sourceLine" id="cb3-14" title="14"><span class="co">#> as.mo("Thermus islandicus") 73 73 84 74 75 130 10</span></a></code></pre></div>
|
||||
<p>That takes 7.6 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Thermus islandicus</em>) are almost fast - these are the most probable input from most data sets.</p>
|
||||
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Thermus islandicus</em> (which is very uncommon):</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/par">par</a></span>(<span class="dt">mar =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">5</span>, <span class="dv">16</span>, <span class="dv">4</span>, <span class="dv">2</span>)) <span class="co"># set more space for left margin text (16)</span></a>
|
||||
<a class="sourceLine" id="cb4-2" title="2"></a>
|
||||
<a class="sourceLine" id="cb4-3" title="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/boxplot">boxplot</a></span>(<span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Thermus islandicus"</span>),</a>
|
||||
<a class="sourceLine" id="cb4-4" title="4"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Prevotella brevis"</span>),</a>
|
||||
<a class="sourceLine" id="cb4-5" title="5"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Escherichia coli"</span>),</a>
|
||||
<a class="sourceLine" id="cb4-6" title="6"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"T. islandicus"</span>),</a>
|
||||
<a class="sourceLine" id="cb4-7" title="7"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"P. brevis"</span>),</a>
|
||||
<a class="sourceLine" id="cb4-8" title="8"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"E. coli"</span>),</a>
|
||||
<a class="sourceLine" id="cb4-9" title="9"> <span class="dt">times =</span> <span class="dv">10</span>),</a>
|
||||
<a class="sourceLine" id="cb4-10" title="10"> <span class="dt">horizontal =</span> <span class="ot">TRUE</span>, <span class="dt">las =</span> <span class="dv">1</span>, <span class="dt">unit =</span> <span class="st">"s"</span>, <span class="dt">log =</span> <span class="ot">FALSE</span>,</a>
|
||||
<a class="sourceLine" id="cb4-11" title="11"> <span class="dt">xlab =</span> <span class="st">""</span>, <span class="dt">ylab =</span> <span class="st">"Time in seconds"</span>, <span class="dt">ylim =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">0</span>, <span class="fl">0.5</span>),</a>
|
||||
<a class="sourceLine" id="cb4-12" title="12"> <span class="dt">main =</span> <span class="st">"Benchmarks per prevalence"</span>)</a></code></pre></div>
|
||||
<p><img src="benchmarks_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
|
||||
<p>In reality, the <code><a href="../reference/as.mo.html">as.mo()</a></code> functions <strong>learns from its own output to speed up determinations for next times</strong>. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use <code><a href="../reference/as.mo.html">as.mo()</a></code> yourself:</p>
|
||||
<pre><code>#> File /home/uscloud/.Rhistory_mo removed.</code></pre>
|
||||
<p><img src="benchmarks_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
|
||||
<p>The highest outliers are the first times. All next determinations were done in only thousands of seconds.</p>
|
||||
<p>Still, uncommon microorganisms take a lot more time than common microorganisms, especially the first time. To relieve this pitfall and further improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
|
||||
<div id="repetitive-results" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#repetitive-results" class="anchor"></a>Repetitive results</h3>
|
||||
<p>Repetitive results are unique values that are present more than once. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. We will use <code><a href="../reference/mo_property.html">mo_fullname()</a></code> for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr)</a>
|
||||
<a class="sourceLine" id="cb6-2" title="2"><span class="co"># take all MO codes from the septic_patients data set</span></a>
|
||||
<a class="sourceLine" id="cb6-3" title="3">x <-<span class="st"> </span>septic_patients<span class="op">$</span>mo <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb6-4" title="4"><span class="st"> </span><span class="co"># keep only the unique ones</span></a>
|
||||
<a class="sourceLine" id="cb6-5" title="5"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/unique">unique</a></span>() <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb6-6" title="6"><span class="st"> </span><span class="co"># pick 50 of them at random</span></a>
|
||||
<a class="sourceLine" id="cb6-7" title="7"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="dv">50</span>) <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb6-8" title="8"><span class="st"> </span><span class="co"># paste that 10,000 times</span></a>
|
||||
<a class="sourceLine" id="cb6-9" title="9"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/rep">rep</a></span>(<span class="dv">10000</span>) <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb6-10" title="10"><span class="st"> </span><span class="co"># scramble it</span></a>
|
||||
<a class="sourceLine" id="cb6-11" title="11"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>()</a>
|
||||
<a class="sourceLine" id="cb6-12" title="12"> </a>
|
||||
<a class="sourceLine" id="cb6-13" title="13"><span class="co"># got indeed 50 times 10,000 = half a million?</span></a>
|
||||
<a class="sourceLine" id="cb6-14" title="14"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/length">length</a></span>(x)</a>
|
||||
<a class="sourceLine" id="cb6-15" title="15"><span class="co">#> [1] 500000</span></a>
|
||||
<a class="sourceLine" id="cb6-16" title="16"></a>
|
||||
<a class="sourceLine" id="cb6-17" title="17"><span class="co"># and how many unique values do we have?</span></a>
|
||||
<a class="sourceLine" id="cb6-18" title="18"><span class="kw"><a href="https://dplyr.tidyverse.org/reference/n_distinct.html">n_distinct</a></span>(x)</a>
|
||||
<a class="sourceLine" id="cb6-19" title="19"><span class="co">#> [1] 50</span></a>
|
||||
<a class="sourceLine" id="cb6-20" title="20"></a>
|
||||
<a class="sourceLine" id="cb6-21" title="21"><span class="co"># now let's see:</span></a>
|
||||
<a class="sourceLine" id="cb6-22" title="22">run_it <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(x),</a>
|
||||
<a class="sourceLine" id="cb6-23" title="23"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
|
||||
<a class="sourceLine" id="cb6-24" title="24"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
|
||||
<a class="sourceLine" id="cb6-25" title="25"><span class="co">#> Unit: milliseconds</span></a>
|
||||
<a class="sourceLine" id="cb6-26" title="26"><span class="co">#> expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb6-27" title="27"><span class="co">#> mo_fullname(x) 825 845 887 867 903 1080 10</span></a></code></pre></div>
|
||||
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.87 seconds (867 ms). You only lose time on your unique input values.</p>
|
||||
</div>
|
||||
<div id="precalculated-results" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#precalculated-results" class="anchor"></a>Precalculated results</h3>
|
||||
<p>What about precalculated results? If the input is an already precalculated result of a helper function like <code><a href="../reference/mo_property.html">mo_fullname()</a></code>, it almost doesn’t take any time at all (see ‘C’ below):</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1">run_it <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"B_STPHY_AUR"</span>),</a>
|
||||
<a class="sourceLine" id="cb7-2" title="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"S. aureus"</span>),</a>
|
||||
<a class="sourceLine" id="cb7-3" title="3"> <span class="dt">C =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"Staphylococcus aureus"</span>),</a>
|
||||
<a class="sourceLine" id="cb7-4" title="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
|
||||
<a class="sourceLine" id="cb7-5" title="5"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
|
||||
<a class="sourceLine" id="cb7-6" title="6"><span class="co">#> Unit: milliseconds</span></a>
|
||||
<a class="sourceLine" id="cb7-7" title="7"><span class="co">#> expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb7-8" title="8"><span class="co">#> A 12.400 12.700 13.100 13.000 13.400 13.700 10</span></a>
|
||||
<a class="sourceLine" id="cb7-9" title="9"><span class="co">#> B 26.300 26.600 27.900 28.400 28.700 29.000 10</span></a>
|
||||
<a class="sourceLine" id="cb7-10" title="10"><span class="co">#> C 0.486 0.502 0.727 0.821 0.849 0.982 10</span></a></code></pre></div>
|
||||
<p>So going from <code><a href="../reference/mo_property.html">mo_fullname("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0008 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1">run_it <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),</a>
|
||||
<a class="sourceLine" id="cb8-2" title="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</a>
|
||||
<a class="sourceLine" id="cb8-3" title="3"> <span class="dt">C =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"Staphylococcus aureus"</span>),</a>
|
||||
<a class="sourceLine" id="cb8-4" title="4"> <span class="dt">D =</span> <span class="kw"><a href="../reference/mo_property.html">mo_family</a></span>(<span class="st">"Staphylococcaceae"</span>),</a>
|
||||
<a class="sourceLine" id="cb8-5" title="5"> <span class="dt">E =</span> <span class="kw"><a href="../reference/mo_property.html">mo_order</a></span>(<span class="st">"Bacillales"</span>),</a>
|
||||
<a class="sourceLine" id="cb8-6" title="6"> <span class="dt">F =</span> <span class="kw"><a href="../reference/mo_property.html">mo_class</a></span>(<span class="st">"Bacilli"</span>),</a>
|
||||
<a class="sourceLine" id="cb8-7" title="7"> <span class="dt">G =</span> <span class="kw"><a href="../reference/mo_property.html">mo_phylum</a></span>(<span class="st">"Firmicutes"</span>),</a>
|
||||
<a class="sourceLine" id="cb8-8" title="8"> <span class="dt">H =</span> <span class="kw"><a href="../reference/mo_property.html">mo_kingdom</a></span>(<span class="st">"Bacteria"</span>),</a>
|
||||
<a class="sourceLine" id="cb8-9" title="9"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
|
||||
<a class="sourceLine" id="cb8-10" title="10"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
|
||||
<a class="sourceLine" id="cb8-11" title="11"><span class="co">#> Unit: milliseconds</span></a>
|
||||
<a class="sourceLine" id="cb8-12" title="12"><span class="co">#> expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb8-13" title="13"><span class="co">#> A 0.371 0.432 0.463 0.471 0.500 0.525 10</span></a>
|
||||
<a class="sourceLine" id="cb8-14" title="14"><span class="co">#> B 0.479 0.531 0.557 0.553 0.566 0.631 10</span></a>
|
||||
<a class="sourceLine" id="cb8-15" title="15"><span class="co">#> C 0.511 0.676 0.834 0.904 0.959 1.090 10</span></a>
|
||||
<a class="sourceLine" id="cb8-16" title="16"><span class="co">#> D 0.422 0.444 0.483 0.494 0.501 0.559 10</span></a>
|
||||
<a class="sourceLine" id="cb8-17" title="17"><span class="co">#> E 0.383 0.458 0.502 0.477 0.507 0.778 10</span></a>
|
||||
<a class="sourceLine" id="cb8-18" title="18"><span class="co">#> F 0.362 0.376 0.476 0.426 0.473 0.947 10</span></a>
|
||||
<a class="sourceLine" id="cb8-19" title="19"><span class="co">#> G 0.361 0.368 0.451 0.452 0.500 0.610 10</span></a>
|
||||
<a class="sourceLine" id="cb8-20" title="20"><span class="co">#> H 0.359 0.383 0.431 0.437 0.458 0.505 10</span></a></code></pre></div>
|
||||
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
|
||||
</div>
|
||||
<div id="results-in-other-languages" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#results-in-other-languages" class="anchor"></a>Results in other languages</h3>
|
||||
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost does’t take extra time:</p>
|
||||
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"en"</span>) <span class="co"># or just mo_fullname("CoNS") on an English system</span></a>
|
||||
<a class="sourceLine" id="cb9-2" title="2"><span class="co">#> [1] "Coagulase-negative Staphylococcus (CoNS)"</span></a>
|
||||
<a class="sourceLine" id="cb9-3" title="3"></a>
|
||||
<a class="sourceLine" id="cb9-4" title="4"><span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"es"</span>) <span class="co"># or just mo_fullname("CoNS") on a Spanish system</span></a>
|
||||
<a class="sourceLine" id="cb9-5" title="5"><span class="co">#> [1] "Staphylococcus coagulasa negativo (SCN)"</span></a>
|
||||
<a class="sourceLine" id="cb9-6" title="6"></a>
|
||||
<a class="sourceLine" id="cb9-7" title="7"><span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"nl"</span>) <span class="co"># or just mo_fullname("CoNS") on a Dutch system</span></a>
|
||||
<a class="sourceLine" id="cb9-8" title="8"><span class="co">#> [1] "Coagulase-negatieve Staphylococcus (CNS)"</span></a>
|
||||
<a class="sourceLine" id="cb9-9" title="9"></a>
|
||||
<a class="sourceLine" id="cb9-10" title="10">run_it <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="dt">en =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"en"</span>),</a>
|
||||
<a class="sourceLine" id="cb9-11" title="11"> <span class="dt">de =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"de"</span>),</a>
|
||||
<a class="sourceLine" id="cb9-12" title="12"> <span class="dt">nl =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"nl"</span>),</a>
|
||||
<a class="sourceLine" id="cb9-13" title="13"> <span class="dt">es =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"es"</span>),</a>
|
||||
<a class="sourceLine" id="cb9-14" title="14"> <span class="dt">it =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"it"</span>),</a>
|
||||
<a class="sourceLine" id="cb9-15" title="15"> <span class="dt">fr =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"fr"</span>),</a>
|
||||
<a class="sourceLine" id="cb9-16" title="16"> <span class="dt">pt =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"pt"</span>),</a>
|
||||
<a class="sourceLine" id="cb9-17" title="17"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
|
||||
<a class="sourceLine" id="cb9-18" title="18"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
|
||||
<a class="sourceLine" id="cb9-19" title="19"><span class="co">#> Unit: milliseconds</span></a>
|
||||
<a class="sourceLine" id="cb9-20" title="20"><span class="co">#> expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb9-21" title="21"><span class="co">#> en 18.83 19.19 24.20 19.53 20.67 63.66 10</span></a>
|
||||
<a class="sourceLine" id="cb9-22" title="22"><span class="co">#> de 31.53 32.10 36.79 32.22 33.81 75.80 10</span></a>
|
||||
<a class="sourceLine" id="cb9-23" title="23"><span class="co">#> nl 31.24 31.78 32.22 32.10 32.24 33.43 10</span></a>
|
||||
<a class="sourceLine" id="cb9-24" title="24"><span class="co">#> es 31.40 32.07 45.82 33.08 75.31 76.95 10</span></a>
|
||||
<a class="sourceLine" id="cb9-25" title="25"><span class="co">#> it 31.17 31.95 36.82 32.09 32.19 79.48 10</span></a>
|
||||
<a class="sourceLine" id="cb9-26" title="26"><span class="co">#> fr 31.48 31.64 31.89 31.96 32.07 32.33 10</span></a>
|
||||
<a class="sourceLine" id="cb9-27" title="27"><span class="co">#> pt 31.27 31.66 36.80 32.06 32.34 80.53 10</span></a></code></pre></div>
|
||||
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
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<div class="page-header toc-ignore">
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<h1>How to create frequency tables</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">27 March 2019</h4>
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<div class="hidden name"><code>freq.Rmd</code></div>
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<div id="introduction" class="section level2">
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<h2 class="hasAnchor">
|
||||
<a href="#introduction" class="anchor"></a>Introduction</h2>
|
||||
<p>Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the <code>freq</code> function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the <code>septic_patients</code> dataset (included in this AMR package) as example.</p>
|
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</div>
|
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<div id="frequencies-of-one-variable" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#frequencies-of-one-variable" class="anchor"></a>Frequencies of one variable</h2>
|
||||
<p>To only show and quickly review the content of one variable, you can just select this variable in various ways. Let’s say we want to get the frequencies of the <code>gender</code> variable of the <code>septic_patients</code> dataset:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1">septic_patients <span class="op">%>%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(gender)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>gender</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
|
||||
<p>Class: <code>character</code> (<code>character</code>)<br>
|
||||
Length: 2,000 (of which NA: 0 = 0.00%)<br>
|
||||
Unique: 2</p>
|
||||
<p>Shortest: 1<br>
|
||||
Longest: 1</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">M</td>
|
||||
<td align="right">1,031</td>
|
||||
<td align="right">51.6%</td>
|
||||
<td align="right">1,031</td>
|
||||
<td align="right">51.6%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">F</td>
|
||||
<td align="right">969</td>
|
||||
<td align="right">48.5%</td>
|
||||
<td align="right">2,000</td>
|
||||
<td align="right">100.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>This immediately shows the class of the variable, its length and availability (i.e. the amount of <code>NA</code>), the amount of unique values and (most importantly) that among septic patients men are more prevalent than women.</p>
|
||||
</div>
|
||||
<div id="frequencies-of-more-than-one-variable" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#frequencies-of-more-than-one-variable" class="anchor"></a>Frequencies of more than one variable</h2>
|
||||
<p>Multiple variables will be pasted into one variable to review individual cases, keeping a univariate frequency table.</p>
|
||||
<p>For illustration, we could add some more variables to the <code>septic_patients</code> dataset to learn about bacterial properties:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">my_patients <-<span class="st"> </span>septic_patients <span class="op">%>%</span><span class="st"> </span><span class="kw"><a href="../reference/join.html">left_join_microorganisms</a></span>()</a>
|
||||
<a class="sourceLine" id="cb2-2" title="2"><span class="co"># Joining, by = "mo"</span></a></code></pre></div>
|
||||
<p>Now all variables of the <code>microorganisms</code> dataset have been joined to the <code>septic_patients</code> dataset. The <code>microorganisms</code> dataset consists of the following variables:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/colnames">colnames</a></span>(microorganisms)</a>
|
||||
<a class="sourceLine" id="cb3-2" title="2"><span class="co"># [1] "mo" "col_id" "fullname" "kingdom" "phylum" </span></a>
|
||||
<a class="sourceLine" id="cb3-3" title="3"><span class="co"># [6] "class" "order" "family" "genus" "species" </span></a>
|
||||
<a class="sourceLine" id="cb3-4" title="4"><span class="co"># [11] "subspecies" "rank" "ref" "species_id" "source" </span></a>
|
||||
<a class="sourceLine" id="cb3-5" title="5"><span class="co"># [16] "prevalence"</span></a></code></pre></div>
|
||||
<p>If we compare the dimensions between the old and new dataset, we can see that these 15 variables were added:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/dim">dim</a></span>(septic_patients)</a>
|
||||
<a class="sourceLine" id="cb4-2" title="2"><span class="co"># [1] 2000 49</span></a>
|
||||
<a class="sourceLine" id="cb4-3" title="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/dim">dim</a></span>(my_patients)</a>
|
||||
<a class="sourceLine" id="cb4-4" title="4"><span class="co"># [1] 2000 64</span></a></code></pre></div>
|
||||
<p>So now the <code>genus</code> and <code>species</code> variables are available. A frequency table of these combined variables can be created like this:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1">my_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb5-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(genus, species, <span class="dt">nmax =</span> <span class="dv">15</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>genus</code> and <code>species</code> from a <code>data.frame</code> (2,000 x 64)</strong></p>
|
||||
<p>Columns: 2<br>
|
||||
Length: 2,000 (of which NA: 0 = 0.00%)<br>
|
||||
Unique: 95</p>
|
||||
<p>Shortest: 8<br>
|
||||
Longest: 34</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">Escherichia coli</td>
|
||||
<td align="right">467</td>
|
||||
<td align="right">23.4%</td>
|
||||
<td align="right">467</td>
|
||||
<td align="right">23.4%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">Staphylococcus coagulase-negative</td>
|
||||
<td align="right">313</td>
|
||||
<td align="right">15.7%</td>
|
||||
<td align="right">780</td>
|
||||
<td align="right">39.0%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">Staphylococcus aureus</td>
|
||||
<td align="right">235</td>
|
||||
<td align="right">11.8%</td>
|
||||
<td align="right">1,015</td>
|
||||
<td align="right">50.7%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">Staphylococcus epidermidis</td>
|
||||
<td align="right">174</td>
|
||||
<td align="right">8.7%</td>
|
||||
<td align="right">1,189</td>
|
||||
<td align="right">59.5%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">5</td>
|
||||
<td align="left">Streptococcus pneumoniae</td>
|
||||
<td align="right">117</td>
|
||||
<td align="right">5.9%</td>
|
||||
<td align="right">1,306</td>
|
||||
<td align="right">65.3%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">6</td>
|
||||
<td align="left">Staphylococcus hominis</td>
|
||||
<td align="right">81</td>
|
||||
<td align="right">4.1%</td>
|
||||
<td align="right">1,387</td>
|
||||
<td align="right">69.4%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">7</td>
|
||||
<td align="left">Klebsiella pneumoniae</td>
|
||||
<td align="right">58</td>
|
||||
<td align="right">2.9%</td>
|
||||
<td align="right">1,445</td>
|
||||
<td align="right">72.3%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">8</td>
|
||||
<td align="left">Enterococcus faecalis</td>
|
||||
<td align="right">39</td>
|
||||
<td align="right">2.0%</td>
|
||||
<td align="right">1,484</td>
|
||||
<td align="right">74.2%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">9</td>
|
||||
<td align="left">Proteus mirabilis</td>
|
||||
<td align="right">36</td>
|
||||
<td align="right">1.8%</td>
|
||||
<td align="right">1,520</td>
|
||||
<td align="right">76.0%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">10</td>
|
||||
<td align="left">Pseudomonas aeruginosa</td>
|
||||
<td align="right">30</td>
|
||||
<td align="right">1.5%</td>
|
||||
<td align="right">1,550</td>
|
||||
<td align="right">77.5%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">11</td>
|
||||
<td align="left">Serratia marcescens</td>
|
||||
<td align="right">25</td>
|
||||
<td align="right">1.3%</td>
|
||||
<td align="right">1,575</td>
|
||||
<td align="right">78.8%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">12</td>
|
||||
<td align="left">Enterobacter cloacae</td>
|
||||
<td align="right">23</td>
|
||||
<td align="right">1.2%</td>
|
||||
<td align="right">1,598</td>
|
||||
<td align="right">79.9%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">13</td>
|
||||
<td align="left">Enterococcus faecium</td>
|
||||
<td align="right">21</td>
|
||||
<td align="right">1.1%</td>
|
||||
<td align="right">1,619</td>
|
||||
<td align="right">81.0%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">14</td>
|
||||
<td align="left">Staphylococcus capitis</td>
|
||||
<td align="right">21</td>
|
||||
<td align="right">1.1%</td>
|
||||
<td align="right">1,640</td>
|
||||
<td align="right">82.0%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">15</td>
|
||||
<td align="left">Bacteroides fragilis</td>
|
||||
<td align="right">20</td>
|
||||
<td align="right">1.0%</td>
|
||||
<td align="right">1,660</td>
|
||||
<td align="right">83.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>(omitted 80 entries, n = 340 [17.0%])</p>
|
||||
</div>
|
||||
<div id="frequencies-of-numeric-values" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#frequencies-of-numeric-values" class="anchor"></a>Frequencies of numeric values</h2>
|
||||
<p>Frequency tables can be created of any input.</p>
|
||||
<p>In case of numeric values (like integers, doubles, etc.) additional descriptive statistics will be calculated and shown into the header:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="co"># # get age distribution of unique patients</span></a>
|
||||
<a class="sourceLine" id="cb6-2" title="2">septic_patients <span class="op">%>%</span><span class="st"> </span></a>
|
||||
<a class="sourceLine" id="cb6-3" title="3"><span class="st"> </span><span class="kw">distinct</span>(patient_id, <span class="dt">.keep_all =</span> <span class="ot">TRUE</span>) <span class="op">%>%</span><span class="st"> </span></a>
|
||||
<a class="sourceLine" id="cb6-4" title="4"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(age, <span class="dt">nmax =</span> <span class="dv">5</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>age</code> from a <code>data.frame</code> (981 x 49)</strong></p>
|
||||
<p>Class: <code>numeric</code> (<code>numeric</code>)<br>
|
||||
Length: 981 (of which NA: 0 = 0.00%)<br>
|
||||
Unique: 73</p>
|
||||
<p>Mean: 71.08<br>
|
||||
SD: 14.05 (CV: 0.20, MAD: 13.34)<br>
|
||||
Five-Num: 14 | 63 | 74 | 82 | 97 (IQR: 19, CQV: 0.13)<br>
|
||||
Outliers: 15 (unique count: 12)</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="right">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="right">83</td>
|
||||
<td align="right">44</td>
|
||||
<td align="right">4.5%</td>
|
||||
<td align="right">44</td>
|
||||
<td align="right">4.5%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="right">76</td>
|
||||
<td align="right">43</td>
|
||||
<td align="right">4.4%</td>
|
||||
<td align="right">87</td>
|
||||
<td align="right">8.9%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="right">75</td>
|
||||
<td align="right">37</td>
|
||||
<td align="right">3.8%</td>
|
||||
<td align="right">124</td>
|
||||
<td align="right">12.6%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="right">82</td>
|
||||
<td align="right">33</td>
|
||||
<td align="right">3.4%</td>
|
||||
<td align="right">157</td>
|
||||
<td align="right">16.0%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">5</td>
|
||||
<td align="right">78</td>
|
||||
<td align="right">32</td>
|
||||
<td align="right">3.3%</td>
|
||||
<td align="right">189</td>
|
||||
<td align="right">19.3%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>(omitted 68 entries, n = 792 [80.7%])</p>
|
||||
<p>So the following properties are determined, where <code>NA</code> values are always ignored:</p>
|
||||
<ul>
|
||||
<li><p><strong>Mean</strong></p></li>
|
||||
<li><p><strong>Standard deviation</strong></p></li>
|
||||
<li><p><strong>Coefficient of variation</strong> (CV), the standard deviation divided by the mean</p></li>
|
||||
<li><p><strong>Five numbers of Tukey</strong> (min, Q1, median, Q3, max)</p></li>
|
||||
<li><p><strong>Coefficient of quartile variation</strong> (CQV, sometimes called coefficient of dispersion), calculated as (Q3 - Q1) / (Q3 + Q1) using quantile with <code>type = 6</code> as quantile algorithm to comply with SPSS standards</p></li>
|
||||
<li><p><strong>Outliers</strong> (total count and unique count)</p></li>
|
||||
</ul>
|
||||
<p>So for example, the above frequency table quickly shows the median age of patients being 74.</p>
|
||||
</div>
|
||||
<div id="frequencies-of-factors" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#frequencies-of-factors" class="anchor"></a>Frequencies of factors</h2>
|
||||
<p>To sort frequencies of factors on factor level instead of item count, use the <code>sort.count</code> parameter.</p>
|
||||
<p><code>sort.count</code> is <code>TRUE</code> by default. Compare this default behaviour…</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb7-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>hospital_id</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
|
||||
<p>Class: <code>factor</code> (<code>numeric</code>)<br>
|
||||
Length: 2,000 (of which NA: 0 = 0.00%)<br>
|
||||
Levels: 4: <code>A</code>, <code>B</code>, <code>C</code>, <code>D</code><br>
|
||||
Unique: 4</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">D</td>
|
||||
<td align="right">762</td>
|
||||
<td align="right">38.1%</td>
|
||||
<td align="right">762</td>
|
||||
<td align="right">38.1%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">B</td>
|
||||
<td align="right">663</td>
|
||||
<td align="right">33.2%</td>
|
||||
<td align="right">1,425</td>
|
||||
<td align="right">71.3%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">A</td>
|
||||
<td align="right">321</td>
|
||||
<td align="right">16.1%</td>
|
||||
<td align="right">1,746</td>
|
||||
<td align="right">87.3%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">C</td>
|
||||
<td align="right">254</td>
|
||||
<td align="right">12.7%</td>
|
||||
<td align="right">2,000</td>
|
||||
<td align="right">100.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>… with this, where items are now sorted on count:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb8-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">sort.count =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>hospital_id</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
|
||||
<p>Class: <code>factor</code> (<code>numeric</code>)<br>
|
||||
Length: 2,000 (of which NA: 0 = 0.00%)<br>
|
||||
Levels: 4: <code>A</code>, <code>B</code>, <code>C</code>, <code>D</code><br>
|
||||
Unique: 4</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">A</td>
|
||||
<td align="right">321</td>
|
||||
<td align="right">16.1%</td>
|
||||
<td align="right">321</td>
|
||||
<td align="right">16.1%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">B</td>
|
||||
<td align="right">663</td>
|
||||
<td align="right">33.2%</td>
|
||||
<td align="right">984</td>
|
||||
<td align="right">49.2%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">C</td>
|
||||
<td align="right">254</td>
|
||||
<td align="right">12.7%</td>
|
||||
<td align="right">1,238</td>
|
||||
<td align="right">61.9%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">D</td>
|
||||
<td align="right">762</td>
|
||||
<td align="right">38.1%</td>
|
||||
<td align="right">2,000</td>
|
||||
<td align="right">100.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>All classes will be printed into the header (default is <code>FALSE</code> when using markdown like this document). Variables with the new <code>rsi</code> class of this AMR package are actually ordered factors and have three classes (look at <code>Class</code> in the header):</p>
|
||||
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb9-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(amox, <span class="dt">header =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>amox</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
|
||||
<p>Class: <code>factor</code> > <code>ordered</code> > <code>rsi</code> (<code>numeric</code>)<br>
|
||||
Length: 2,000 (of which NA: 771 = 38.55%)<br>
|
||||
Levels: 3: <code>S</code> < <code>I</code> < <code>R</code><br>
|
||||
Unique: 3</p>
|
||||
<p>Drug: Amoxicillin<br>
|
||||
%IR: 55.82%</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">R</td>
|
||||
<td align="right">683</td>
|
||||
<td align="right">55.6%</td>
|
||||
<td align="right">683</td>
|
||||
<td align="right">55.6%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">S</td>
|
||||
<td align="right">543</td>
|
||||
<td align="right">44.2%</td>
|
||||
<td align="right">1,226</td>
|
||||
<td align="right">99.8%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">I</td>
|
||||
<td align="right">3</td>
|
||||
<td align="right">0.2%</td>
|
||||
<td align="right">1,229</td>
|
||||
<td align="right">100.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div id="frequencies-of-dates" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#frequencies-of-dates" class="anchor"></a>Frequencies of dates</h2>
|
||||
<p>Frequencies of dates will show the oldest and newest date in the data, and the amount of days between them:</p>
|
||||
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb10-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(date, <span class="dt">nmax =</span> <span class="dv">5</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>date</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
|
||||
<p>Class: <code>Date</code> (<code>numeric</code>)<br>
|
||||
Length: 2,000 (of which NA: 0 = 0.00%)<br>
|
||||
Unique: 1,140</p>
|
||||
<p>Oldest: 2 January 2002<br>
|
||||
Newest: 28 December 2017 (+5,839)<br>
|
||||
Median: 31 July 2009 (47.39%)</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">2016-05-21</td>
|
||||
<td align="right">10</td>
|
||||
<td align="right">0.5%</td>
|
||||
<td align="right">10</td>
|
||||
<td align="right">0.5%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">2004-11-15</td>
|
||||
<td align="right">8</td>
|
||||
<td align="right">0.4%</td>
|
||||
<td align="right">18</td>
|
||||
<td align="right">0.9%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">2013-07-29</td>
|
||||
<td align="right">8</td>
|
||||
<td align="right">0.4%</td>
|
||||
<td align="right">26</td>
|
||||
<td align="right">1.3%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">2017-06-12</td>
|
||||
<td align="right">8</td>
|
||||
<td align="right">0.4%</td>
|
||||
<td align="right">34</td>
|
||||
<td align="right">1.7%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">5</td>
|
||||
<td align="left">2015-11-19</td>
|
||||
<td align="right">7</td>
|
||||
<td align="right">0.4%</td>
|
||||
<td align="right">41</td>
|
||||
<td align="right">2.1%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>(omitted 1,135 entries, n = 1,959 [98.0%])</p>
|
||||
</div>
|
||||
<div id="assigning-a-frequency-table-to-an-object" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#assigning-a-frequency-table-to-an-object" class="anchor"></a>Assigning a frequency table to an object</h2>
|
||||
<p>A frequency table is actaually a regular <code>data.frame</code>, with the exception that it contains an additional class.</p>
|
||||
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1">my_df <-<span class="st"> </span>septic_patients <span class="op">%>%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(age)</a>
|
||||
<a class="sourceLine" id="cb11-2" title="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/class">class</a></span>(my_df)</a></code></pre></div>
|
||||
<p>[1] “frequency_tbl” “data.frame”</p>
|
||||
<p>Because of this additional class, a frequency table prints like the examples above. But the object itself contains the complete table without a row limitation:</p>
|
||||
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/dim">dim</a></span>(my_df)</a></code></pre></div>
|
||||
<p>[1] 74 5</p>
|
||||
</div>
|
||||
<div id="additional-parameters" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#additional-parameters" class="anchor"></a>Additional parameters</h2>
|
||||
<div id="parameter-na-rm" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#parameter-na-rm" class="anchor"></a>Parameter <code>na.rm</code>
|
||||
</h3>
|
||||
<p>With the <code>na.rm</code> parameter (defaults to <code>TRUE</code>, but they will always be shown into the header), you can include <code>NA</code> values in the frequency table:</p>
|
||||
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb13-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(amox, <span class="dt">na.rm =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>amox</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
|
||||
<p>Class: <code>factor</code> > <code>ordered</code> > <code>rsi</code> (<code>numeric</code>)<br>
|
||||
Length: 2,000 (of which NA: 771 = 38.55%)<br>
|
||||
Levels: 3: <code>S</code> < <code>I</code> < <code>R</code><br>
|
||||
Unique: 4</p>
|
||||
<p>Drug: Amoxicillin<br>
|
||||
%IR: 55.82%</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">(NA)</td>
|
||||
<td align="right">771</td>
|
||||
<td align="right">38.6%</td>
|
||||
<td align="right">771</td>
|
||||
<td align="right">38.6%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">R</td>
|
||||
<td align="right">683</td>
|
||||
<td align="right">34.2%</td>
|
||||
<td align="right">1,454</td>
|
||||
<td align="right">72.7%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">S</td>
|
||||
<td align="right">543</td>
|
||||
<td align="right">27.2%</td>
|
||||
<td align="right">1,997</td>
|
||||
<td align="right">99.9%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">I</td>
|
||||
<td align="right">3</td>
|
||||
<td align="right">0.2%</td>
|
||||
<td align="right">2,000</td>
|
||||
<td align="right">100.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div id="parameter-row-names" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#parameter-row-names" class="anchor"></a>Parameter <code>row.names</code>
|
||||
</h3>
|
||||
<p>The default frequency tables shows row indices. To remove them, use <code>row.names = FALSE</code>:</p>
|
||||
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb14-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">row.names =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>hospital_id</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
|
||||
<p>Class: <code>factor</code> (<code>numeric</code>)<br>
|
||||
Length: 2,000 (of which NA: 0 = 0.00%)<br>
|
||||
Levels: 4: <code>A</code>, <code>B</code>, <code>C</code>, <code>D</code><br>
|
||||
Unique: 4</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">D</td>
|
||||
<td align="right">762</td>
|
||||
<td align="right">38.1%</td>
|
||||
<td align="right">762</td>
|
||||
<td align="right">38.1%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">B</td>
|
||||
<td align="right">663</td>
|
||||
<td align="right">33.2%</td>
|
||||
<td align="right">1,425</td>
|
||||
<td align="right">71.3%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">A</td>
|
||||
<td align="right">321</td>
|
||||
<td align="right">16.1%</td>
|
||||
<td align="right">1,746</td>
|
||||
<td align="right">87.3%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">C</td>
|
||||
<td align="right">254</td>
|
||||
<td align="right">12.7%</td>
|
||||
<td align="right">2,000</td>
|
||||
<td align="right">100.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div id="parameter-markdown" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#parameter-markdown" class="anchor"></a>Parameter <code>markdown</code>
|
||||
</h3>
|
||||
<p>The <code>markdown</code> parameter is <code>TRUE</code> at default in non-interactive sessions, like in reports created with R Markdown. This will always print all rows, unless <code>nmax</code> is set.</p>
|
||||
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||||
<a class="sourceLine" id="cb15-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">markdown =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
|
||||
<p><strong>Frequency table of <code>hospital_id</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
|
||||
<p>Class: <code>factor</code> (<code>numeric</code>)<br>
|
||||
Length: 2,000 (of which NA: 0 = 0.00%)<br>
|
||||
Levels: 4: <code>A</code>, <code>B</code>, <code>C</code>, <code>D</code><br>
|
||||
Unique: 4</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">D</td>
|
||||
<td align="right">762</td>
|
||||
<td align="right">38.1%</td>
|
||||
<td align="right">762</td>
|
||||
<td align="right">38.1%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">B</td>
|
||||
<td align="right">663</td>
|
||||
<td align="right">33.2%</td>
|
||||
<td align="right">1,425</td>
|
||||
<td align="right">71.3%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">A</td>
|
||||
<td align="right">321</td>
|
||||
<td align="right">16.1%</td>
|
||||
<td align="right">1,746</td>
|
||||
<td align="right">87.3%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">C</td>
|
||||
<td align="right">254</td>
|
||||
<td align="right">12.7%</td>
|
||||
<td align="right">2,000</td>
|
||||
<td align="right">100.0%</td>
|
||||
</tr>
|
||||
</tbody>
|
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</table>
|
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<a href="#tocnav" class="anchor"></a>Contents</h2>
|
||||
<ul class="nav nav-pills nav-stacked">
|
||||
<li><a href="#introduction">Introduction</a></li>
|
||||
<li><a href="#frequencies-of-one-variable">Frequencies of one variable</a></li>
|
||||
<li><a href="#frequencies-of-more-than-one-variable">Frequencies of more than one variable</a></li>
|
||||
<li><a href="#frequencies-of-numeric-values">Frequencies of numeric values</a></li>
|
||||
<li><a href="#frequencies-of-factors">Frequencies of factors</a></li>
|
||||
<li><a href="#frequencies-of-dates">Frequencies of dates</a></li>
|
||||
<li><a href="#assigning-a-frequency-table-to-an-object">Assigning a frequency table to an object</a></li>
|
||||
<li><a href="#additional-parameters">Additional parameters</a></li>
|
||||
</ul>
|
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