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@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -56,16 +56,24 @@ jobs:
|
||||
- {os: windows-latest, r: 'devel', allowfail: false}
|
||||
- {os: windows-latest, r: 'release', allowfail: false}
|
||||
- {os: windows-latest, r: 'oldrel', allowfail: false}
|
||||
- {os: ubuntu-16.04, r: 'devel', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-20.04, r: 'devel', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
|
||||
- {os: ubuntu-20.04, r: 'release', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
|
||||
- {os: ubuntu-20.04, r: 'oldrel', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
|
||||
- {os: ubuntu-20.04, r: '3.2', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
|
||||
- {os: ubuntu-20.04, r: '3.1', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
|
||||
- {os: ubuntu-20.04, r: '3.0', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
|
||||
- {os: ubuntu-16.04, r: 'devel', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: 'release', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: 'oldrel', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: 'oldrel', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '4.0', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '3.6', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '3.5', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '3.4', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '3.3', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
# - {os: ubuntu-16.04, r: '3.2', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
# older R versions cannot be tested, since tidyverse only supports last 4 R x.x versions
|
||||
- {os: ubuntu-16.04, r: '3.4', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '3.3', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '3.2', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '3.1', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
- {os: ubuntu-16.04, r: '3.0', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
|
||||
|
||||
env:
|
||||
R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
|
||||
RSPM: ${{ matrix.config.rspm }}
|
||||
@@ -80,34 +88,45 @@ jobs:
|
||||
- uses: r-lib/actions/setup-pandoc@master
|
||||
|
||||
- name: Query dependencies
|
||||
if: matrix.config.r != '3.0' && matrix.config.r != '3.1' && matrix.config.r != '3.2'
|
||||
run: |
|
||||
install.packages('remotes')
|
||||
saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
|
||||
shell: Rscript {0}
|
||||
|
||||
- name: Cache R packages
|
||||
if: runner.os != 'Windows'
|
||||
if: runner.os != 'Windows' && matrix.config.r != '3.0' && matrix.config.r != '3.1' && matrix.config.r != '3.2'
|
||||
uses: actions/cache@v1
|
||||
with:
|
||||
path: ${{ env.R_LIBS_USER }}
|
||||
key: ${{ runner.os }}-r-${{ matrix.config.r }}-3-${{ hashFiles('.github/depends.Rds') }}
|
||||
restore-keys: ${{ runner.os }}-r-${{ matrix.config.r }}-3-
|
||||
key: ${{ matrix.config.os }}-r-${{ matrix.config.r }}-3-${{ hashFiles('.github/depends.Rds') }}
|
||||
restore-keys: ${{ matrix.config.os }}-r-${{ matrix.config.r }}-3-
|
||||
|
||||
- name: Install Linux dependencies
|
||||
if: runner.os == 'Linux'
|
||||
if: runner.os == 'Linux' && matrix.config.r != '3.0' && matrix.config.r != '3.1' && matrix.config.r != '3.2'
|
||||
env:
|
||||
RHUB_PLATFORM: linux-x86_64-ubuntu-gcc
|
||||
run: |
|
||||
Rscript -e "remotes::install_github('r-hub/sysreqs')"
|
||||
sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))")
|
||||
sudo -s eval "$sysreqs"
|
||||
|
||||
- name: Install Linux dependencies on old R versions
|
||||
if: matrix.config.r == '3.0' || matrix.config.r == '3.1' || matrix.config.r == '3.2'
|
||||
env:
|
||||
RHUB_PLATFORM: linux-x86_64-ubuntu-gcc
|
||||
# update the below with sysreqs::sysreqs("DESCRIPTION") and check the "DEB" entries (for Ubuntu).
|
||||
# we don't want to depend on the sysreqs pkg here, as it requires a quite new R version
|
||||
run: |
|
||||
sudo apt install -y libssl-dev pandoc pandoc-citeproc libxml2-dev libicu-dev libcurl4-openssl-dev
|
||||
|
||||
- name: Install macOS dependencies
|
||||
if: matrix.config.os == 'macOS-latest' && matrix.config.r == 'devel'
|
||||
run: |
|
||||
brew install mariadb-connector-c
|
||||
|
||||
- name: Install dependencies
|
||||
- name: Install package dependencies
|
||||
if: matrix.config.r != '3.0' && matrix.config.r != '3.1' && matrix.config.r != '3.2'
|
||||
run: |
|
||||
remotes::install_deps(dependencies = TRUE)
|
||||
remotes::install_cran("rcmdcheck")
|
||||
@@ -116,15 +135,26 @@ jobs:
|
||||
- name: Session info
|
||||
run: |
|
||||
options(width = 100)
|
||||
pkgs <- installed.packages()[, "Package"]
|
||||
sessioninfo::session_info(pkgs, include_base = TRUE)
|
||||
utils::sessionInfo()
|
||||
as.data.frame(utils::installed.packages())[, "Version", drop = FALSE]
|
||||
shell: Rscript {0}
|
||||
|
||||
- name: Run Check
|
||||
- name: Run R CMD check
|
||||
if: matrix.config.r != '3.0' && matrix.config.r != '3.1' && matrix.config.r != '3.2'
|
||||
env:
|
||||
_R_CHECK_CRAN_INCOMING_: false
|
||||
run: rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check")
|
||||
shell: Rscript {0}
|
||||
|
||||
- name: Run R CMD check on older R versions
|
||||
if: matrix.config.r == '3.0' || matrix.config.r == '3.1' || matrix.config.r == '3.2'
|
||||
env:
|
||||
_R_CHECK_CRAN_INCOMING_: false
|
||||
_R_CHECK_FORCE_SUGGESTS_: false
|
||||
_R_CHECK_LENGTH_1_CONDITION_: verbose
|
||||
_R_CHECK_LENGTH_1_LOGIC2_: verbose
|
||||
run: |
|
||||
R CMD check data-raw/AMR_*.tar.gz --no-manual --no-build-vignettes
|
||||
|
||||
- name: Show testthat output
|
||||
if: always()
|
||||
@@ -135,5 +165,5 @@ jobs:
|
||||
if: failure()
|
||||
uses: actions/upload-artifact@master
|
||||
with:
|
||||
name: ${{ runner.os }}-r${{ matrix.config.r }}-results
|
||||
name: ${{ matrix.config.os }}-r${{ matrix.config.r }}-results
|
||||
path: check
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -66,5 +66,5 @@ jobs:
|
||||
shell: Rscript {0}
|
||||
|
||||
- name: Lint
|
||||
run: lintr::lint_package(linters = lintr::with_defaults(line_length_linter = NULL, trailing_whitespace_linter = NULL, object_name_linter = NULL, cyclocomp_linter = NULL, object_usage_linter = NULL, object_length_linter = lintr::object_length_linter(length = 50L)), exclusions = list("R/aa_helper_pm_functions.R"))
|
||||
run: lintr::lint_package(linters = lintr::with_defaults(line_length_linter = NULL, trailing_whitespace_linter = NULL, object_name_linter = NULL, cyclocomp_linter = NULL, object_length_linter = lintr::object_length_linter(length = 50L)), exclusions = list("R/aa_helper_pm_functions.R"))
|
||||
shell: Rscript {0}
|
||||
+4
-3
@@ -1,6 +1,6 @@
|
||||
Package: AMR
|
||||
Version: 1.4.0.9041
|
||||
Date: 2020-12-17
|
||||
Version: 1.5.0
|
||||
Date: 2021-01-05
|
||||
Title: Antimicrobial Resistance Analysis
|
||||
Authors@R: c(
|
||||
person(role = c("aut", "cre"),
|
||||
@@ -29,6 +29,8 @@ Authors@R: c(
|
||||
family = "Meijer", given = c("Bart", "C."), email = "b.meijerg@certe.nl"),
|
||||
person(role = "ctb",
|
||||
family = "Ny", given = "Sofia", email = "sofia.ny@folkhalsomyndigheten.se"),
|
||||
person(role = "ctb",
|
||||
family = "Schade", given = c("Rogier", "P."), email = "r.schade@amsterdamumc.nl"),
|
||||
person(role = "ctb",
|
||||
family = "Souverein", given = "Dennis", email = "d.souvereing@streeklabhaarlem.nl"),
|
||||
person(role = "ctb",
|
||||
@@ -55,7 +57,6 @@ Suggests:
|
||||
skimr,
|
||||
testthat,
|
||||
tidyr,
|
||||
tidyselect,
|
||||
xml2
|
||||
VignetteBuilder: knitr,rmarkdown
|
||||
URL: https://msberends.github.io/AMR/, https://github.com/msberends/AMR
|
||||
|
||||
@@ -137,6 +137,7 @@ export(fluoroquinolones)
|
||||
export(full_join_microorganisms)
|
||||
export(g.test)
|
||||
export(geom_rsi)
|
||||
export(get_episode)
|
||||
export(get_locale)
|
||||
export(get_mo_source)
|
||||
export(ggplot_pca)
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
# AMR 1.4.0.9041
|
||||
## <small>Last updated: 17 December 2020</small>
|
||||
# AMR 1.5.0
|
||||
|
||||
Note: some changes in this version were suggested by anonymous reviewers from the journal we submitted our manuscript about this package to. We are those reviewers very grateful for going through our code so thoroughly!
|
||||
*Note: the rules of 'EUCAST Clinical Breakpoints v11.0 (2021)' will be added in the next release, to be expected in February/March 2021.*
|
||||
|
||||
### New
|
||||
* Function `is_new_episode()` to determine patient episodes which are not necessarily based on microorganisms. It also supports grouped variables with e.g. `mutate()`, `filter()` and `summarise()` of the `dplyr` package:
|
||||
* Functions `get_episode()` and `is_new_episode()` to determine (patient) episodes which are not necessarily based on microorganisms. The `get_episode()` function returns the index number of the episode per group, while the `is_new_episode()` function returns values `TRUE`/`FALSE` to indicate whether an item in a vector is the start of a new episode. They also support `dplyr`s grouping (i.e. using `group_by()`):
|
||||
```r
|
||||
library(dplyr)
|
||||
example_isolates %>%
|
||||
@@ -13,11 +12,32 @@ Note: some changes in this version were suggested by anonymous reviewers from th
|
||||
```
|
||||
* Functions `mo_is_gram_negative()` and `mo_is_gram_positive()` as wrappers around `mo_gramstain()`. They always return `TRUE` or `FALSE` (except when the input is `NA` or the MO code is `UNKNOWN`), thus always return `FALSE` for species outside the taxonomic kingdom of Bacteria.
|
||||
* Function `mo_is_intrinsic_resistant()` to test for intrinsic resistance, based on [EUCAST Intrinsic Resistance and Unusual Phenotypes v3.2](https://www.eucast.org/expert_rules_and_intrinsic_resistance/) from 2020.
|
||||
* Functions `random_mic()`, `random_disk()` and `random_rsi()` for random number generation. They take microorganism names and antibiotic names as input to make generation more realistic.
|
||||
* Functions `random_mic()`, `random_disk()` and `random_rsi()` for random value generation. The functions `random_mic()` and `random_disk()` take microorganism names and antibiotic names as input to make generation more realistic.
|
||||
|
||||
### Changed
|
||||
* Reference data used for `as.rsi()` can now be set by the user, using the `reference_data` parameter. This allows for using own interpretation guidelines. The user-set data must have the same structure as `rsi_translation`.
|
||||
* Some functions are now context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the data parameter does not need to be set anymore. This is the case for the new functions `mo_is_gram_negative()`, `mo_is_gram_positive()`, `mo_is_intrinsic_resistant()` and for the existing functions `first_isolate()`, `key_antibiotics()`, `mdro()`, `brmo()`, `mrgn()`, `mdr_tb()`, `mdr_cmi2012()`, `eucast_exceptional_phenotypes()`. This was already the case for antibiotic selection functions (such as using `penicillins()` in `dplyr::select()`).
|
||||
* New argument `ampc_cephalosporin_resistance` in `eucast_rules()` to correct for AmpC de-repressed cephalosporin-resistant mutants
|
||||
* Interpretation of antimicrobial resistance - `as.rsi()`:
|
||||
* Reference data used for `as.rsi()` can now be set by the user, using the `reference_data` argument. This allows for using own interpretation guidelines. The user-set data must have the same structure as `rsi_translation`.
|
||||
* Better determination of disk zones and MIC values when running `as.rsi()` on a data.frame
|
||||
* Fix for using `as.rsi()` on a data.frame in older R versions
|
||||
* `as.rsi()` on a data.frame will not print a message anymore if the values are already clean R/SI values
|
||||
* If using `as.rsi()` on MICs or disk diffusion while there is intrinsic antimicrobial resistance, a warning will be thrown to remind about this
|
||||
* Fix for using `as.rsi()` on a `data.frame` that only contains one column for antibiotic interpretations
|
||||
* Some functions are now context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the data argument does not need to be set anymore. This is the case for the new functions:
|
||||
* `mo_is_gram_negative()`
|
||||
* `mo_is_gram_positive()`
|
||||
* `mo_is_intrinsic_resistant()`
|
||||
|
||||
... and for the existing functions:
|
||||
* `first_isolate()`,
|
||||
* `key_antibiotics()`,
|
||||
* `mdro()`,
|
||||
* `brmo()`,
|
||||
* `mrgn()`,
|
||||
* `mdr_tb()`,
|
||||
* `mdr_cmi2012()`,
|
||||
* `eucast_exceptional_phenotypes()`
|
||||
|
||||
```r
|
||||
# to select first isolates that are Gram-negative
|
||||
# and view results of cephalosporins and aminoglycosides:
|
||||
@@ -26,40 +46,46 @@ Note: some changes in this version were suggested by anonymous reviewers from th
|
||||
filter(first_isolate(), mo_is_gram_negative()) %>%
|
||||
select(mo, cephalosporins(), aminoglycosides()) %>%
|
||||
as_tibble()
|
||||
```
|
||||
* For all function parameters in the code, it is now defined what the exact type of user input should be (inspired by the [`typed`](https://github.com/moodymudskipper/typed) package). If the user input for a certain function does not meet the requirements for a specific parameter (such as the class or length), an informative error will be thrown. This makes the package more robust and the use of it more reproducible and reliable. In total, more than 400 arguments were defined.
|
||||
```
|
||||
* For antibiotic selection functions (such as `cephalosporins()`, `aminoglycosides()`) to select columns based on a certain antibiotic group, the dependency on the `tidyselect` package was removed, meaning that they can now also be used without the need to have this package installed and now also work in base R function calls (they rely on R 3.2 or later):
|
||||
```r
|
||||
# above example in base R:
|
||||
example_isolates[which(first_isolate() & mo_is_gram_negative()),
|
||||
c("mo", cephalosporins(), aminoglycosides())]
|
||||
```
|
||||
* For all function arguments in the code, it is now defined what the exact type of user input should be (inspired by the [`typed`](https://github.com/moodymudskipper/typed) package). If the user input for a certain function does not meet the requirements for a specific argument (such as the class or length), an informative error will be thrown. This makes the package more robust and the use of it more reproducible and reliable. In total, more than 420 arguments were defined.
|
||||
* Fix for `set_mo_source()`, that previously would not remember the file location of the original file
|
||||
* Deprecated function `p_symbol()` that not really fits the scope of this package. It will be removed in a future version. See [here](https://github.com/msberends/AMR/blob/v1.4.0/R/p_symbol.R) for the source code to preserve it.
|
||||
* Better determination of disk zones and MIC values when running `as.rsi()` on a data.frame
|
||||
* Updated coagulase-negative staphylococci determination with Becker *et al.* 2020 (PMID 32056452), meaning that the species *S. argensis*, *S. caeli*, *S. debuckii*, *S. edaphicus* and *S. pseudoxylosus* are now all considered CoNS
|
||||
* Fix for using parameter `reference_df` in `as.mo()` and `mo_*()` functions that contain old microbial codes (from previous package versions)
|
||||
* Fix for using `as.rsi()` on a data.frame in older R versions
|
||||
* `as.rsi()` on a data.frame will not print a message anymore if the values are already clean R/SI values
|
||||
* Fix for using argument `reference_df` in `as.mo()` and `mo_*()` functions that contain old microbial codes (from previous package versions)
|
||||
* Fixed a bug where `mo_uncertainties()` would not return the results based on the MO matching score
|
||||
* Fixed a bug where `as.mo()` would not return results for known laboratory codes for microorganisms
|
||||
* Fixed a bug where `as.ab()` would sometimes fail
|
||||
* If using `as.rsi()` on MICs or disk diffusion while there is intrinsic antimicrobial resistance, a warning will be thrown to remind about this
|
||||
* Better tibble printing for MIC values
|
||||
* Fix for plotting MIC values with `plot()`
|
||||
* Added `plot()` generic to class `<disk>`
|
||||
* LA-MRSA and CA-MRSA are now recognised as an abbreviation for *Staphylococcus aureus*, meaning that e.g. `mo_genus("LA-MRSA")` will return `"Staphylococcus"` and `mo_is_gram_positive("LA-MRSA")` will return `TRUE`.
|
||||
* Fix for using `as.rsi()` on a `data.frame` that only contains one column for antibiotic interpretations
|
||||
* Fix for printing class <mo> in tibbles when all values are `NA`
|
||||
* Fix for `mo_shortname()` when the input contains `NA`
|
||||
* If `as.mo()` takes more than 30 seconds, some suggestions will be done to improve speed
|
||||
|
||||
### Other
|
||||
* All messages and warnings thrown by this package now break sentences on whole words
|
||||
* More extensive unit tests
|
||||
* Internal calls to `options()` were all removed in favour of a new internal environment `mo_env`
|
||||
* Internal calls to `options()` were all removed in favour of a new internal environment `pkg_env`
|
||||
* Improved internal type setting (among other things: replaced all `sapply()` calls with `vapply()`)
|
||||
* Added CodeFactor as a continuous code review to this package: <https://www.codefactor.io/repository/github/msberends/amr/>
|
||||
* Added Dr. Rogier Schade as contributor
|
||||
|
||||
# AMR 1.4.0
|
||||
|
||||
Note: some changes in this version were suggested by anonymous reviewers from the journal we submitted our manuscript about this package to. We are those reviewers very grateful for going through our code so thoroughly!
|
||||
|
||||
### New
|
||||
* Support for 'EUCAST Expert Rules' / 'EUCAST Intrinsic Resistance and Unusual Phenotypes' version 3.2 of May 2020. With this addition to the previously implemented version 3.1 of 2016, the `eucast_rules()` function can now correct for more than 180 different antibiotics and the `mdro()` function can determine multidrug resistance based on more than 150 different antibiotics. All previously implemented versions of the EUCAST rules are now maintained and kept available in this package. The `eucast_rules()` function consequently gained the parameters `version_breakpoints` (at the moment defaults to v10.0, 2020) and `version_expertrules` (at the moment defaults to v3.2, 2020). The `example_isolates` data set now also reflects the change from v3.1 to v3.2. The `mdro()` function now accepts `guideline == "EUCAST3.1"` and `guideline == "EUCAST3.2"`.
|
||||
* Support for 'EUCAST Expert Rules' / 'EUCAST Intrinsic Resistance and Unusual Phenotypes' version 3.2 of May 2020. With this addition to the previously implemented version 3.1 of 2016, the `eucast_rules()` function can now correct for more than 180 different antibiotics and the `mdro()` function can determine multidrug resistance based on more than 150 different antibiotics. All previously implemented versions of the EUCAST rules are now maintained and kept available in this package. The `eucast_rules()` function consequently gained the arguments `version_breakpoints` (at the moment defaults to v10.0, 2020) and `version_expertrules` (at the moment defaults to v3.2, 2020). The `example_isolates` data set now also reflects the change from v3.1 to v3.2. The `mdro()` function now accepts `guideline == "EUCAST3.1"` and `guideline == "EUCAST3.2"`.
|
||||
* A new vignette and website page with info about all our public and freely available data sets, that can be downloaded as flat files or in formats for use in R, SPSS, SAS, Stata and Excel: https://msberends.github.io/AMR/articles/datasets.html
|
||||
* Data set `intrinsic_resistant`. This data set contains all bug-drug combinations where the 'bug' is intrinsic resistant to the 'drug' according to the latest EUCAST insights. It contains just two columns: `microorganism` and `antibiotic`.
|
||||
|
||||
Curious about which enterococci are actually intrinsic resistant to vancomycin?
|
||||
|
||||
```r
|
||||
library(AMR)
|
||||
library(dplyr)
|
||||
@@ -86,7 +112,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
|
||||
```
|
||||
* Cleaning columns in a data.frame now allows you to specify those columns with tidy selection, e.g. `as.rsi(df, col1:col9)`
|
||||
* Big speed improvement for interpreting MIC values and disk zone diameters. When interpreting 5,000 MIC values of two antibiotics (10,000 values in total), our benchmarks showed a total run time going from 80.7-85.1 seconds to 1.8-2.0 seconds.
|
||||
* Added parameter 'add_intrinsic_resistance' (defaults to `FALSE`), that considers intrinsic resistance according to EUCAST
|
||||
* Added argument 'add_intrinsic_resistance' (defaults to `FALSE`), that considers intrinsic resistance according to EUCAST
|
||||
* Fixed a bug where in EUCAST rules the breakpoint for R would be interpreted as ">=" while this should have been "<"
|
||||
* Added intelligent data cleaning to `as.disk()`, so numbers can also be extracted from text and decimal numbers will always be rounded up:
|
||||
```r
|
||||
@@ -97,7 +123,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
|
||||
* Improvements for `as.mo()`:
|
||||
* A completely new matching score for ambiguous user input, using `mo_matching_score()`. Any user input value that could mean more than one taxonomic entry is now considered 'uncertain'. Instead of a warning, a message will be thrown and the accompanying `mo_uncertainties()` has been changed completely; it now prints all possible candidates with their matching score.
|
||||
* Big speed improvement for already valid microorganism ID. This also means an significant speed improvement for using `mo_*` functions like `mo_name()` on microoganism IDs.
|
||||
* Added parameter `ignore_pattern` to `as.mo()` which can also be given to `mo_*` functions like `mo_name()`, to exclude known non-relevant input from analysing. This can also be set with the option `AMR_ignore_pattern`.
|
||||
* Added argument `ignore_pattern` to `as.mo()` which can also be given to `mo_*` functions like `mo_name()`, to exclude known non-relevant input from analysing. This can also be set with the option `AMR_ignore_pattern`.
|
||||
* `get_locale()` now uses at default `Sys.getenv("LANG")` or, if `LANG` is not set, `Sys.getlocale()`. This can be overwritten by setting the option `AMR_locale`.
|
||||
* Big speed improvement for `eucast_rules()`
|
||||
* Overall speed improvement by tweaking joining functions
|
||||
@@ -108,7 +134,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
|
||||
* Updated the documentation of the `WHONET` data set to clarify that all patient names are fictitious
|
||||
* Small `as.ab()` algorithm improvements
|
||||
* Fix for combining MIC values with raw numbers, i.e. `c(as.mic(2), 2)` previously failed but now returns a valid MIC class
|
||||
* `ggplot_rsi()` and `geom_rsi()` gained parameters `minimum` and `language`, to influence the internal use of `rsi_df()`
|
||||
* `ggplot_rsi()` and `geom_rsi()` gained arguments `minimum` and `language`, to influence the internal use of `rsi_df()`
|
||||
* Changes in the `antibiotics` data set:
|
||||
* Updated oral and parental DDDs from the WHOCC
|
||||
* Added abbreviation "piptazo" to 'Piperacillin/tazobactam' (TZP)
|
||||
@@ -116,7 +142,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
|
||||
* 'Penicillin V' (for oral use, code `PNV`) was removed, since its actual entry 'Phenoxymethylpenicillin' (code `PHN`) already existed
|
||||
* The group name (`antibiotics$group`) of 'Linezolid' (`LNZ`), 'Cycloserine' (`CYC`), 'Tedizolid' (`TZD`) and 'Thiacetazone' (`THA`) is now "Oxazolidinones" instead of "Other antibacterials"
|
||||
* Added support for using `unique()` on classes `<rsi>`, `<mic>`, `<disk>`, `<ab>` and `<mo>`
|
||||
* Added parameter `excess` to the `kurtosis()` function (defaults to `FALSE`), to return the *excess kurtosis*, defined as the kurtosis minus three.
|
||||
* Added argument `excess` to the `kurtosis()` function (defaults to `FALSE`), to return the *excess kurtosis*, defined as the kurtosis minus three.
|
||||
|
||||
### Other
|
||||
* Removed functions `portion_R()`, `portion_S()` and `portion_I()` that were deprecated since version 0.9.0 (November 2019) and were replaced with `proportion_R()`, `proportion_S()` and `proportion_I()`
|
||||
@@ -141,7 +167,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
|
||||
* Added official antimicrobial names to all `filter_ab_class()` functions, such as `filter_aminoglycosides()`
|
||||
* Added antibiotics code "FOX1" for cefoxitin screening (abbreviation "cfsc") to the `antibiotics` data set
|
||||
* Added Monuril as trade name for fosfomycin
|
||||
* Added parameter `conserve_capped_values` to `as.rsi()` for interpreting MIC values - it makes sure that values starting with "<" (but not "<=") will always return "S" and values starting with ">" (but not ">=") will always return "R". The default behaviour of `as.rsi()` has not changed, so you need to specifically do `as.rsi(..., conserve_capped_values = TRUE)`.
|
||||
* Added argument `conserve_capped_values` to `as.rsi()` for interpreting MIC values - it makes sure that values starting with "<" (but not "<=") will always return "S" and values starting with ">" (but not ">=") will always return "R". The default behaviour of `as.rsi()` has not changed, so you need to specifically do `as.rsi(..., conserve_capped_values = TRUE)`.
|
||||
|
||||
### Changed
|
||||
* Big speed improvement for using any function on microorganism codes from earlier package versions (prior to `AMR` v1.2.0), such as `as.mo()`, `mo_name()`, `first_isolate()`, `eucast_rules()`, `mdro()`, etc.
|
||||
@@ -249,7 +275,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
|
||||
mutate_at(vars(antibiotic1:antibiotic25), as.rsi, mo = .$mybacteria)
|
||||
```
|
||||
* Added antibiotic abbreviations for a laboratory manufacturer (GLIMS) for cefuroxime, cefotaxime, ceftazidime, cefepime, cefoxitin and trimethoprim/sulfamethoxazole
|
||||
* Added `uti` (as abbreviation of urinary tract infections) as parameter to `as.rsi()`, so interpretation of MIC values and disk zones can be made dependent on isolates specifically from UTIs
|
||||
* Added `uti` (as abbreviation of urinary tract infections) as argument to `as.rsi()`, so interpretation of MIC values and disk zones can be made dependent on isolates specifically from UTIs
|
||||
* Info printing in functions `eucast_rules()`, `first_isolate()`, `mdro()` and `resistance_predict()` will now at default only print when R is in an interactive mode (i.e. not in RMarkdown)
|
||||
|
||||
# AMR 1.0.0
|
||||
@@ -369,7 +395,7 @@ This software is now out of beta and considered stable. Nonetheless, this packag
|
||||
# AMR 0.8.0
|
||||
|
||||
### Breaking
|
||||
* Determination of first isolates now **excludes** all 'unknown' microorganisms at default, i.e. microbial code `"UNKNOWN"`. They can be included with the new parameter `include_unknown`:
|
||||
* Determination of first isolates now **excludes** all 'unknown' microorganisms at default, i.e. microbial code `"UNKNOWN"`. They can be included with the new argument `include_unknown`:
|
||||
```r
|
||||
first_isolate(..., include_unknown = TRUE)
|
||||
```
|
||||
@@ -420,7 +446,7 @@ This software is now out of beta and considered stable. Nonetheless, this packag
|
||||
```r
|
||||
format(x, combine_IR = FALSE)
|
||||
```
|
||||
* Additional way to calculate co-resistance, i.e. when using multiple antimicrobials as input for `portion_*` functions or `count_*` functions. This can be used to determine the empiric susceptibility of a combination therapy. A new parameter `only_all_tested` (**which defaults to `FALSE`**) replaces the old `also_single_tested` and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the `portion` and `count` help pages), where the %SI is being determined:
|
||||
* Additional way to calculate co-resistance, i.e. when using multiple antimicrobials as input for `portion_*` functions or `count_*` functions. This can be used to determine the empiric susceptibility of a combination therapy. A new argument `only_all_tested` (**which defaults to `FALSE`**) replaces the old `also_single_tested` and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the `portion` and `count` help pages), where the %SI is being determined:
|
||||
|
||||
```r
|
||||
# --------------------------------------------------------------------
|
||||
@@ -476,13 +502,13 @@ This software is now out of beta and considered stable. Nonetheless, this packag
|
||||
* Removed deprecated functions `abname()`, `ab_official()`, `atc_name()`, `atc_official()`, `atc_property()`, `atc_tradenames()`, `atc_trivial_nl()`
|
||||
* Fix and speed improvement for `mo_shortname()`
|
||||
* Fix for using `mo_*` functions where the coercion uncertainties and failures would not be available through `mo_uncertainties()` and `mo_failures()` anymore
|
||||
* Deprecated the `country` parameter of `mdro()` in favour of the already existing `guideline` parameter to support multiple guidelines within one country
|
||||
* Deprecated the `country` argument of `mdro()` in favour of the already existing `guideline` argument to support multiple guidelines within one country
|
||||
* The `name` of `RIF` is now Rifampicin instead of Rifampin
|
||||
* The `antibiotics` data set is now sorted by name and all cephalosporins now have their generation between brackets
|
||||
* Speed improvement for `guess_ab_col()` which is now 30 times faster for antibiotic abbreviations
|
||||
* Improved `filter_ab_class()` to be more reliable and to support 5th generation cephalosporins
|
||||
* Function `availability()` now uses `portion_R()` instead of `portion_IR()`, to comply with EUCAST insights
|
||||
* Functions `age()` and `age_groups()` now have a `na.rm` parameter to remove empty values
|
||||
* Functions `age()` and `age_groups()` now have a `na.rm` argument to remove empty values
|
||||
* Renamed function `p.symbol()` to `p_symbol()` (the former is now deprecated and will be removed in a future version)
|
||||
* Using negative values for `x` in `age_groups()` will now introduce `NA`s and not return an error anymore
|
||||
* Fix for determining the system's language
|
||||
@@ -577,12 +603,12 @@ This software is now out of beta and considered stable. Nonetheless, this packag
|
||||
* All `atc_*` functions are superceded by `ab_*` functions
|
||||
* All output will be translated by using an included translation file which [can be viewed here](https://github.com/msberends/AMR/blob/master/data-raw/translations.tsv)
|
||||
* Improvements to plotting AMR results with `ggplot_rsi()`:
|
||||
* New parameter `colours` to set the bar colours
|
||||
* New parameters `title`, `subtitle`, `caption`, `x.title` and `y.title` to set titles and axis descriptions
|
||||
* New argument `colours` to set the bar colours
|
||||
* New arguments `title`, `subtitle`, `caption`, `x.title` and `y.title` to set titles and axis descriptions
|
||||
* Improved intelligence of looking up antibiotic columns in a data set using `guess_ab_col()`
|
||||
* Added ~5,000 more old taxonomic names to the `microorganisms.old` data set, which leads to better results finding when using the `as.mo()` function
|
||||
* This package now honours the new EUCAST insight (2019) that S and I are but classified as susceptible, where I is defined as 'increased exposure' and not 'intermediate' anymore. For functions like `portion_df()` and `count_df()` this means that their new parameter `combine_SI` is TRUE at default. Our plotting function `ggplot_rsi()` also reflects this change since it uses `count_df()` internally.
|
||||
* The `age()` function gained a new parameter `exact` to determine ages with decimals
|
||||
* This package now honours the new EUCAST insight (2019) that S and I are but classified as susceptible, where I is defined as 'increased exposure' and not 'intermediate' anymore. For functions like `portion_df()` and `count_df()` this means that their new argument `combine_SI` is TRUE at default. Our plotting function `ggplot_rsi()` also reflects this change since it uses `count_df()` internally.
|
||||
* The `age()` function gained a new argument `exact` to determine ages with decimals
|
||||
* Removed deprecated functions `guess_mo()`, `guess_atc()`, `EUCAST_rules()`, `interpretive_reading()`, `rsi()`
|
||||
* Frequency tables (`freq()`):
|
||||
* speed improvement for microbial IDs
|
||||
@@ -626,11 +652,11 @@ This software is now out of beta and considered stable. Nonetheless, this packag
|
||||
|
||||
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 the complete manual of this package and all of its functions with an explanation of their arguments
|
||||
* 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.
|
||||
* **BREAKING**: removed deprecated functions, arguments 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*)
|
||||
@@ -643,7 +669,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
* 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://www.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.
|
||||
* Exported files from WHONET can be read and used in this package. For functions like `first_isolate()` and `eucast_rules()`, all arguments 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:
|
||||
@@ -764,14 +790,14 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
* 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)
|
||||
* Will now use a column named like "patid" for the patient ID (argument `col_patientid`), when this argument was left blank
|
||||
* Will now use a column named like "key(...)ab" or "key(...)antibiotics" for the key antibiotics (argument `col_keyantibiotics()`), when this argument was left blank
|
||||
* Removed argument `output_logical`, the function will now always return a logical value
|
||||
* Renamed argument `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` argument)
|
||||
* 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
|
||||
* Reduce false positives for `is.rsi.eligible()` using the new `threshold` argument
|
||||
* 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`
|
||||
@@ -796,16 +822,16 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
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
|
||||
* The argument `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
|
||||
* The new `big.mark` argument 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
|
||||
* New argument `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()`
|
||||
* Function `scale_y_percent()` now contains the `limits` argument
|
||||
* Automatic argument 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
|
||||
@@ -818,7 +844,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
#### New
|
||||
* Repository moved to GitLab
|
||||
* 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.
|
||||
* Function `get_locale` to determine language for language-dependent output for some `mo_*` functions. This is now the default value for their `language` argument, 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
|
||||
@@ -828,12 +854,12 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
* `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, https://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
|
||||
* New argument `rules` to specify which rules should be applied (expert rules, breakpoints, others or all)
|
||||
* New argument `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
|
||||
* Added argument `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*
|
||||
@@ -845,10 +871,10 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
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)
|
||||
* Added argument `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`
|
||||
* Added argument `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 argument `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:
|
||||
@@ -867,17 +893,17 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
* 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
|
||||
* New argument `na`, to choose which character to print for empty values
|
||||
* New argument `header` to turn the header info off (default when `markdown = TRUE`)
|
||||
* New argument `title` to manually setbthe title of the frequency table
|
||||
* `first_isolate` now tries to find columns to use as input when arguments 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
|
||||
* `ggplot_rsi` and `scale_y_percent` have `breaks` argument
|
||||
* AI improvements for `as.mo`:
|
||||
* `"CRS"` -> *Stenotrophomonas maltophilia*
|
||||
* `"CRSM"` -> *Stenotrophomonas maltophilia*
|
||||
@@ -944,7 +970,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
# 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
|
||||
* Added argument `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`
|
||||
@@ -973,7 +999,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
* 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`
|
||||
* Added arguments `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()
|
||||
@@ -984,12 +1010,12 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
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`.
|
||||
* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` argument 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
|
||||
* Added possibility to set any argument 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 argument `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`
|
||||
@@ -1046,7 +1072,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
|
||||
#### Changed
|
||||
* Improvements for forecasting with `resistance_predict` and added more examples
|
||||
* More antibiotics added as parameters for EUCAST rules
|
||||
* More antibiotics added as arguments 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
|
||||
@@ -1054,7 +1080,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
* 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.
|
||||
* Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the argument `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"`
|
||||
@@ -1102,7 +1128,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
|
||||
* 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
|
||||
* For arguments 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
|
||||
|
||||
+131
-48
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -119,8 +119,8 @@ search_type_in_df <- function(x, type, info = TRUE) {
|
||||
|
||||
# -- mo
|
||||
if (type == "mo") {
|
||||
if (any(sapply(x, is.mo))) {
|
||||
found <- sort(colnames(x)[sapply(x, is.mo)])[1]
|
||||
if (any(vapply(FUN.VALUE = logical(1), x, is.mo))) {
|
||||
found <- sort(colnames(x)[vapply(FUN.VALUE = logical(1), x, is.mo)])[1]
|
||||
} else if ("mo" %in% colnames(x) &
|
||||
suppressWarnings(
|
||||
all(x$mo %in% c(NA,
|
||||
@@ -152,8 +152,8 @@ search_type_in_df <- function(x, type, info = TRUE) {
|
||||
"`, but this column contains no valid dates. Transform its values to valid dates first.")),
|
||||
call. = FALSE)
|
||||
}
|
||||
} else if (any(sapply(x, function(x) inherits(x, c("Date", "POSIXct"))))) {
|
||||
found <- sort(colnames(x)[sapply(x, function(x) inherits(x, c("Date", "POSIXct")))])[1]
|
||||
} else if (any(vapply(FUN.VALUE = logical(1), x, function(x) inherits(x, c("Date", "POSIXct"))))) {
|
||||
found <- sort(colnames(x)[vapply(FUN.VALUE = logical(1), x, function(x) inherits(x, c("Date", "POSIXct")))])[1]
|
||||
}
|
||||
}
|
||||
# -- patient id
|
||||
@@ -187,19 +187,22 @@ search_type_in_df <- function(x, type, info = TRUE) {
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
if (!is.null(found) & info == TRUE) {
|
||||
msg <- paste0("Using column '", font_bold(found), "' as input for `col_", type, "`.")
|
||||
if (type %in% c("keyantibiotics", "specimen")) {
|
||||
msg <- paste(msg, "Use", font_bold(paste0("col_", type), "= FALSE"), "to prevent this.")
|
||||
if (message_not_thrown_before(fn = paste0("search_", type))) {
|
||||
msg <- paste0("Using column '", font_bold(found), "' as input for `col_", type, "`.")
|
||||
if (type %in% c("keyantibiotics", "specimen")) {
|
||||
msg <- paste(msg, "Use", font_bold(paste0("col_", type), "= FALSE"), "to prevent this.")
|
||||
}
|
||||
message_(msg)
|
||||
remember_thrown_message(fn = paste0("search_", type))
|
||||
}
|
||||
message_(msg)
|
||||
}
|
||||
found
|
||||
}
|
||||
|
||||
is_possibly_regex <- function(x) {
|
||||
tryCatch(sapply(strsplit(x, ""),
|
||||
tryCatch(vapply(FUN.VALUE = character(1), strsplit(x, ""),
|
||||
function(y) any(y %in% c("$", "(", ")", "*", "+", "-", ".", "?", "[", "]", "^", "{", "|", "}", "\\"), na.rm = TRUE)),
|
||||
error = function(e) rep(TRUE, length(x)))
|
||||
}
|
||||
@@ -207,7 +210,7 @@ is_possibly_regex <- function(x) {
|
||||
stop_ifnot_installed <- function(package) {
|
||||
# no "utils::installed.packages()" since it requires non-staged install since R 3.6.0
|
||||
# https://developer.r-project.org/Blog/public/2019/02/14/staged-install/index.html
|
||||
sapply(package, function(pkg)
|
||||
vapply(FUN.VALUE = character(1), package, function(pkg)
|
||||
tryCatch(get(".packageName", envir = asNamespace(pkg)),
|
||||
error = function(e) {
|
||||
if (package == "rstudioapi") {
|
||||
@@ -250,13 +253,23 @@ word_wrap <- function(...,
|
||||
width = 0.95 * getOption("width"),
|
||||
extra_indent = 0) {
|
||||
msg <- paste0(c(...), collapse = "")
|
||||
# replace new lines to add them again later
|
||||
msg <- gsub("\n", "*|*", msg, fixed = TRUE)
|
||||
|
||||
if (isTRUE(as_note)) {
|
||||
msg <- paste0("NOTE: ", gsub("^note:? ?", "", msg, ignore.case = TRUE))
|
||||
}
|
||||
|
||||
if (msg %like% "\n") {
|
||||
# run word_wraps() over every line here, bind them and return again
|
||||
return(paste0(vapply(FUN.VALUE = character(1),
|
||||
trimws(unlist(strsplit(msg, "\n")), which = "right"),
|
||||
word_wrap,
|
||||
add_fn = add_fn,
|
||||
as_note = FALSE,
|
||||
width = width,
|
||||
extra_indent = extra_indent),
|
||||
collapse = "\n"))
|
||||
}
|
||||
|
||||
# we need to correct for already applied style, that adds text like "\033[31m\"
|
||||
msg_stripped <- font_stripstyle(msg)
|
||||
# where are the spaces now?
|
||||
@@ -284,7 +297,6 @@ word_wrap <- function(...,
|
||||
indentation <- 0 + extra_indent
|
||||
}
|
||||
msg <- gsub("\n", paste0("\n", strrep(" ", indentation)), msg, fixed = TRUE)
|
||||
msg <- gsub("*|*", paste0("*|*", strrep(" ", indentation)), msg, fixed = TRUE)
|
||||
# remove trailing empty characters
|
||||
msg <- gsub("(\n| )+$", "", msg)
|
||||
|
||||
@@ -297,9 +309,6 @@ word_wrap <- function(...,
|
||||
}
|
||||
}
|
||||
|
||||
# place back spaces
|
||||
msg <- gsub("*|*", "\n", msg, fixed = TRUE)
|
||||
|
||||
# format backticks
|
||||
msg <- gsub("(`.+?`)", font_grey_bg("\\1"), msg)
|
||||
|
||||
@@ -449,7 +458,7 @@ meet_criteria <- function(object,
|
||||
stop_if(allow_NULL == FALSE, "argument `", obj_name, "` must not be NULL", call = call_depth)
|
||||
return(invisible())
|
||||
}
|
||||
if (is.null(dim(object)) && length(object) == 1 && is.na(object)) {
|
||||
if (is.null(dim(object)) && length(object) == 1 && suppressWarnings(is.na(object))) { # suppressWarnings for functions
|
||||
stop_if(allow_NA == FALSE, "argument `", obj_name, "` must not be NA", call = call_depth)
|
||||
return(invisible())
|
||||
}
|
||||
@@ -504,7 +513,11 @@ meet_criteria <- function(object,
|
||||
call = call_depth)
|
||||
}
|
||||
if (!is.null(contains_column_class)) {
|
||||
stop_ifnot(any(sapply(object, function(col, columns_class = contains_column_class) inherits(col, columns_class)), na.rm = TRUE),
|
||||
stop_ifnot(any(vapply(FUN.VALUE = logical(1),
|
||||
object,
|
||||
function(col, columns_class = contains_column_class) {
|
||||
inherits(col, columns_class)
|
||||
}), na.rm = TRUE),
|
||||
"the data provided in argument `", obj_name,
|
||||
"` must contain at least one column of class <", contains_column_class, ">. ",
|
||||
"See ?as.", contains_column_class, ".",
|
||||
@@ -514,22 +527,97 @@ meet_criteria <- function(object,
|
||||
}
|
||||
|
||||
get_current_data <- function(arg_name, call) {
|
||||
# this mimics dplyr::cur_data_all for users that use our content-aware functions in dplyr verbs
|
||||
cur_data_all_dplyr <- import_fn("cur_data_all", "dplyr", error_on_fail = FALSE)
|
||||
if (is.null(cur_data_all_dplyr)) {
|
||||
# dplyr not installed
|
||||
stop_("argument `", arg_name, "` is missing, with no default", call = call)
|
||||
if (as.double(R.Version()$major) + (as.double(R.Version()$minor) / 10) < 3.2) {
|
||||
if (is.na(arg_name)) {
|
||||
warning_("this function can only be used in R >= 3.2", call = call)
|
||||
return(data.frame())
|
||||
} else {
|
||||
stop_("argument `", arg_name, "` is missing with no default", call = call)
|
||||
}
|
||||
}
|
||||
tryCatch(cur_data_all_dplyr(),
|
||||
# dplyr installed, but not used inside dplyr verb
|
||||
error = function(e) stop_("argument `", arg_name, "` is missing with no default ",
|
||||
"or function not used inside a valid dplyr verb",
|
||||
# tryCatch adds 4 system calls, subtract them
|
||||
call = call - 4))
|
||||
|
||||
# try a (base R) method, by going over the complete system call stack with sys.frames()
|
||||
not_set <- TRUE
|
||||
frms <- lapply(sys.frames(), function(el) {
|
||||
if (".Generic" %in% names(el)) {
|
||||
if (tryCatch(not_set == TRUE && ".data" %in% names(el) && is.data.frame(el$`.data`), error = function(e) FALSE)) {
|
||||
# dplyr? - an element `.data` will be in the system call stack
|
||||
not_set <<- FALSE
|
||||
el$`.data`
|
||||
} else if (tryCatch(not_set == TRUE && any(c("x", "xx") %in% names(el)), error = function(e) FALSE)) {
|
||||
# otherwise try base R:
|
||||
# an element `x` will be in this environment for only cols, e.g. `example_isolates[, carbapenems()]`
|
||||
# an element `xx` will be in this environment for rows + cols, e.g. `example_isolates[c(1:3), carbapenems()]`
|
||||
if (tryCatch(is.data.frame(el$xx), error = function(e) FALSE)) {
|
||||
not_set <<- FALSE
|
||||
el$xx
|
||||
} else if (tryCatch(is.data.frame(el$x))) {
|
||||
not_set <<- FALSE
|
||||
el$x
|
||||
} else {
|
||||
NULL
|
||||
}
|
||||
} else {
|
||||
NULL
|
||||
}
|
||||
} else {
|
||||
NULL
|
||||
}
|
||||
})
|
||||
vars_df <- tryCatch(frms[[which(!vapply(FUN.VALUE = logical(1), frms, is.null))]], error = function(e) NULL)
|
||||
if (is.data.frame(vars_df)) {
|
||||
return(vars_df)
|
||||
}
|
||||
|
||||
# nothing worked, so:
|
||||
if (is.na(arg_name)) {
|
||||
stop_("this function must be used inside valid dplyr selection verbs or inside a data.frame call",
|
||||
call = call)
|
||||
} else {
|
||||
stop_("argument `", arg_name, "` is missing with no default ",
|
||||
"or function not used inside a valid dplyr verb",
|
||||
call = call)
|
||||
}
|
||||
}
|
||||
|
||||
unique_call_id <- function(entire_session = FALSE) {
|
||||
if (entire_session == TRUE) {
|
||||
c(envir = "session",
|
||||
call = "session")
|
||||
} else {
|
||||
# combination of environment ID (like "0x7fed4ee8c848")
|
||||
# and highest system call
|
||||
c(envir = gsub("<environment: (.*)>", "\\1", utils::capture.output(sys.frames()[[1]])),
|
||||
call = paste0(deparse(sys.calls()[[1]]), collapse = ""))
|
||||
}
|
||||
}
|
||||
|
||||
remember_thrown_message <- function(fn, entire_session = FALSE) {
|
||||
# this is to prevent that messages/notes will be printed for every dplyr group
|
||||
# e.g. this would show a msg 4 times: example_isolates %>% group_by(hospital_id) %>% filter(mo_is_gram_negative())
|
||||
assign(x = paste0("thrown_msg_", fn),
|
||||
value = unique_call_id(entire_session = entire_session),
|
||||
envir = pkg_env)
|
||||
}
|
||||
|
||||
message_not_thrown_before <- function(fn, entire_session = FALSE) {
|
||||
is.null(pkg_env[[paste0("thrown_msg_", fn)]]) || !identical(pkg_env[[paste0("thrown_msg_", fn)]], unique_call_id(entire_session))
|
||||
}
|
||||
|
||||
reset_all_thrown_messages <- function() {
|
||||
# for unit tests, where the environment and highest system call do not change
|
||||
pkg_env_contents <- ls(envir = pkg_env)
|
||||
rm(list = pkg_env_contents[pkg_env_contents %like% "^thrown_msg_"],
|
||||
envir = pkg_env)
|
||||
}
|
||||
|
||||
has_colour <- function() {
|
||||
# this is a base R version of crayon::has_color
|
||||
# this is a base R version of crayon::has_color, but disables colours on emacs
|
||||
|
||||
if (Sys.getenv("EMACS") != "" || Sys.getenv("INSIDE_EMACS") != "") {
|
||||
# disable on emacs, which only supports 8 colours
|
||||
return(FALSE)
|
||||
}
|
||||
enabled <- getOption("crayon.enabled")
|
||||
if (!is.null(enabled)) {
|
||||
return(isTRUE(enabled))
|
||||
@@ -559,20 +647,6 @@ has_colour <- function() {
|
||||
}
|
||||
return(FALSE)
|
||||
}
|
||||
emacs_version <- function() {
|
||||
ver <- Sys.getenv("INSIDE_EMACS")
|
||||
if (ver == "") {
|
||||
return(NA_integer_)
|
||||
}
|
||||
ver <- gsub("'", "", ver)
|
||||
ver <- strsplit(ver, ",", fixed = TRUE)[[1]]
|
||||
ver <- strsplit(ver, ".", fixed = TRUE)[[1]]
|
||||
as.numeric(ver)
|
||||
}
|
||||
if ((Sys.getenv("EMACS") != "" || Sys.getenv("INSIDE_EMACS") != "") &&
|
||||
!is.na(emacs_version()[1]) && emacs_version()[1] >= 23) {
|
||||
return(TRUE)
|
||||
}
|
||||
if ("COLORTERM" %in% names(Sys.getenv())) {
|
||||
return(TRUE)
|
||||
}
|
||||
@@ -629,11 +703,20 @@ font_grey <- function(..., collapse = " ") {
|
||||
try_colour(..., before = "\033[38;5;249m", after = "\033[39m", collapse = collapse)
|
||||
}
|
||||
font_grey_bg <- function(..., collapse = " ") {
|
||||
try_colour(..., before = "\033[48;5;255m", after = "\033[49m", collapse = collapse)
|
||||
try_colour(..., before = "\033[48;5;254m", after = "\033[49m", collapse = collapse)
|
||||
}
|
||||
font_green_bg <- function(..., collapse = " ") {
|
||||
try_colour(..., before = "\033[42m", after = "\033[49m", collapse = collapse)
|
||||
}
|
||||
font_rsi_R_bg <- function(..., collapse = " ") {
|
||||
try_colour(..., before = "\033[48;5;202m", after = "\033[49m", collapse = collapse)
|
||||
}
|
||||
font_rsi_S_bg <- function(..., collapse = " ") {
|
||||
try_colour(..., before = "\033[48;5;76m", after = "\033[49m", collapse = collapse)
|
||||
}
|
||||
font_rsi_I_bg <- function(..., collapse = " ") {
|
||||
try_colour(..., before = "\033[48;5;148m", after = "\033[49m", collapse = collapse)
|
||||
}
|
||||
font_red_bg <- function(..., collapse = " ") {
|
||||
try_colour(..., before = "\033[41m", after = "\033[49m", collapse = collapse)
|
||||
}
|
||||
@@ -875,6 +958,6 @@ str2lang <- function(s) {
|
||||
isNamespaceLoaded <- function(pkg) {
|
||||
pkg %in% loadedNamespaces()
|
||||
}
|
||||
lengths = function(x, use.names = TRUE) {
|
||||
lengths <- function(x, use.names = TRUE) {
|
||||
vapply(x, length, FUN.VALUE = NA_integer_, USE.NAMES = use.names)
|
||||
}
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
+41
-20
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -25,11 +25,11 @@
|
||||
|
||||
#' Antibiotic class selectors
|
||||
#'
|
||||
#' Use these selection helpers inside any function that allows [Tidyverse selection helpers](https://tidyselect.r-lib.org/reference/language.html), such as [`select()`][dplyr::select()] and [`pivot_longer()`][tidyr::pivot_longer()]. They help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations.
|
||||
#' These functions help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations.
|
||||
#' @inheritParams filter_ab_class
|
||||
#' @details All columns will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.) in the [antibiotics] data set. This means that a selector like e.g. [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
|
||||
#' @details \strong{\Sexpr{ifelse(as.double(R.Version()$major) + (as.double(R.Version()$minor) / 10) < 3.2, paste0("NOTE: THESE FUNCTIONS DO NOT WORK ON YOUR CURRENT R VERSION. These functions require R version 3.2 or later - you have ", R.version.string, "."), "")}}
|
||||
#'
|
||||
#' **N.B. These functions require the `tidyselect` package to be installed**, that comes with the `dplyr` package. An error will be thrown if the `tidyselect` package is not installed, or if the functions are used outside a function that allows [Tidyverse selection helpers](https://tidyselect.r-lib.org/reference/language.html) such as [`select()`][dplyr::select()] and [`pivot_longer()`][tidyr::pivot_longer()]`.
|
||||
#' All columns will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.) in the [antibiotics] data set. This means that a selector like e.g. [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
|
||||
#' @rdname antibiotic_class_selectors
|
||||
#' @seealso [filter_ab_class()] for the `filter()` equivalent.
|
||||
#' @name antibiotic_class_selectors
|
||||
@@ -37,6 +37,14 @@
|
||||
#' @inheritSection AMR Reference data publicly available
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # `example_isolates` is a dataset available in the AMR package.
|
||||
#' # See ?example_isolates.
|
||||
#'
|
||||
#' # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
|
||||
#' example_isolates[, c(carbapenems())]
|
||||
#' # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
|
||||
#' example_isolates[, c("mo", aminoglycosides())]
|
||||
#'
|
||||
#' if (require("dplyr")) {
|
||||
#'
|
||||
#' # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
|
||||
@@ -63,7 +71,12 @@
|
||||
#' data.frame(some_column = "some_value",
|
||||
#' J01CA01 = "S") %>% # ATC code of ampicillin
|
||||
#' select(penicillins()) # only the 'J01CA01' column will be selected
|
||||
#'
|
||||
#'
|
||||
#'
|
||||
#' # with dplyr 1.0.0 and higher (that adds 'across()'), this is equal:
|
||||
#' # (though the row names on the first are more correct)
|
||||
#' example_isolates %>% filter_carbapenems("R", "all")
|
||||
#' example_isolates %>% filter(across(carbapenems(), ~. == "R"))
|
||||
#' }
|
||||
ab_class <- function(ab_class) {
|
||||
ab_selector(ab_class, function_name = "ab_class")
|
||||
@@ -150,11 +163,15 @@ tetracyclines <- function() {
|
||||
ab_selector <- function(ab_class, function_name) {
|
||||
meet_criteria(ab_class, allow_class = "character", has_length = 1, .call_depth = 1)
|
||||
meet_criteria(function_name, allow_class = "character", has_length = 1, .call_depth = 1)
|
||||
|
||||
peek_vars_tidyselect <- import_fn("peek_vars", "tidyselect")
|
||||
vars_vct <- peek_vars_tidyselect(fn = function_name)
|
||||
vars_df <- data.frame(as.list(vars_vct), stringsAsFactors = FALSE)[1, , drop = FALSE]
|
||||
colnames(vars_df) <- vars_vct
|
||||
|
||||
if (as.double(R.Version()$major) + (as.double(R.Version()$minor) / 10) < 3.2) {
|
||||
warning_("antibiotic class selectors such as ", function_name,
|
||||
"() require R version 3.2 or later - you have ", R.version.string,
|
||||
call = FALSE)
|
||||
return(NULL)
|
||||
}
|
||||
|
||||
vars_df <- get_current_data(arg_name = NA, call = -3)
|
||||
ab_in_data <- get_column_abx(vars_df, info = FALSE)
|
||||
|
||||
if (length(ab_in_data) == 0) {
|
||||
@@ -175,15 +192,19 @@ ab_selector <- function(ab_class, function_name) {
|
||||
}
|
||||
# get the columns with a group names in the chosen ab class
|
||||
agents <- ab_in_data[names(ab_in_data) %in% ab_reference$ab]
|
||||
if (length(agents) == 0) {
|
||||
message_("No antimicrobial agents of class ", ab_group, " found", examples, ".")
|
||||
} else {
|
||||
message_("Selecting ", ab_group, ": ",
|
||||
paste(paste0("'", font_bold(agents, collapse = NULL),
|
||||
"' (", ab_name(names(agents), tolower = TRUE, language = NULL), ")"),
|
||||
collapse = ", "),
|
||||
as_note = FALSE,
|
||||
extra_indent = nchar(paste0("Selecting ", ab_group, ": ")))
|
||||
}
|
||||
if (message_not_thrown_before(function_name)) {
|
||||
if (length(agents) == 0) {
|
||||
message_("No antimicrobial agents of class ", ab_group, " found", examples, ".")
|
||||
} else {
|
||||
agents_formatted <- paste0("column '", font_bold(agents, collapse = NULL), "'")
|
||||
agents_names <- ab_name(names(agents), tolower = TRUE, language = NULL)
|
||||
agents_formatted[agents != agents_names] <- paste0(agents_formatted[agents != agents_names],
|
||||
" (", agents_names[agents != agents_names], ")")
|
||||
message_("Selecting ", ab_group, ": ", paste(agents_formatted, collapse = ", "),
|
||||
as_note = FALSE,
|
||||
extra_indent = nchar(paste0("Selecting ", ab_group, ": ")))
|
||||
}
|
||||
remember_thrown_message(function_name)
|
||||
}
|
||||
unname(agents)
|
||||
}
|
||||
+8
-8
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -32,21 +32,21 @@
|
||||
#' @param collapse character to pass on to `paste(..., collapse = ...)` to only return one character per element of `text`, see *Examples*
|
||||
#' @param translate_ab if `type = "drug"`: a column name of the [antibiotics] data set to translate the antibiotic abbreviations to, using [ab_property()]. Defaults to `FALSE`. Using `TRUE` is equal to using "name".
|
||||
#' @param thorough_search logical to indicate whether the input must be extensively searched for misspelling and other faulty input values. Setting this to `TRUE` will take considerably more time than when using `FALSE`. At default, it will turn `TRUE` when all input elements contain a maximum of three words.
|
||||
#' @param ... parameters passed on to [as.ab()]
|
||||
#' @details This function is also internally used by [as.ab()], although it then only searches for the first drug name and will throw a note if more drug names could have been returned.
|
||||
#' @param ... arguments passed on to [as.ab()]
|
||||
#' @details This function is also internally used by [as.ab()], although it then only searches for the first drug name and will throw a note if more drug names could have been returned. Note: the [as.ab()] function may use very long regular expression to match brand names of antimicrobial agents. This may fail on some systems.
|
||||
#'
|
||||
#' ## Parameter `type`
|
||||
#' ## Argument `type`
|
||||
#' At default, the function will search for antimicrobial drug names. All text elements will be searched for official names, ATC codes and brand names. As it uses [as.ab()] internally, it will correct for misspelling.
|
||||
#'
|
||||
#' With `type = "dose"` (or similar, like "dosing", "doses"), all text elements will be searched for numeric values that are higher than 100 and do not resemble years. The output will be numeric. It supports any unit (g, mg, IE, etc.) and multiple values in one clinical text, see *Examples*.
|
||||
#'
|
||||
#' With `type = "administration"` (or abbreviations, like "admin", "adm"), all text elements will be searched for a form of drug administration. It supports the following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal and vaginal. Abbreviations for oral (such as 'po', 'per os') will become "oral", all values for intravenous (such as 'iv', 'intraven') will become "iv". It supports multiple values in one clinical text, see *Examples*.
|
||||
#'
|
||||
#' ## Parameter `collapse`
|
||||
#' ## Argument `collapse`
|
||||
#' Without using `collapse`, this function will return a [list]. This can be convenient to use e.g. inside a `mutate()`):\cr
|
||||
#' `df %>% mutate(abx = ab_from_text(clinical_text))`
|
||||
#'
|
||||
#' The returned AB codes can be transformed to official names, groups, etc. with all [`ab_*`][ab_property()] functions such as [ab_name()] and [ab_group()], or by using the `translate_ab` parameter.
|
||||
#' The returned AB codes can be transformed to official names, groups, etc. with all [`ab_*`][ab_property()] functions such as [ab_name()] and [ab_group()], or by using the `translate_ab` argument.
|
||||
#'
|
||||
#' With using `collapse`, this function will return a [character]:\cr
|
||||
#' `df %>% mutate(abx = ab_from_text(clinical_text, collapse = "|"))`
|
||||
@@ -115,7 +115,7 @@ ab_from_text <- function(text,
|
||||
translate_ab <- get_translate_ab(translate_ab)
|
||||
|
||||
if (isTRUE(thorough_search) |
|
||||
(isTRUE(is.null(thorough_search)) & max(sapply(text_split_all, length), na.rm = TRUE) <= 3)) {
|
||||
(isTRUE(is.null(thorough_search)) & max(vapply(FUN.VALUE = double(1), text_split_all, length), na.rm = TRUE) <= 3)) {
|
||||
text_split_all <- text_split_all[nchar(text_split_all) >= 4 & grepl("[a-z]+", text_split_all)]
|
||||
result <- lapply(text_split_all, function(text_split) {
|
||||
progress$tick()
|
||||
@@ -203,7 +203,7 @@ ab_from_text <- function(text,
|
||||
|
||||
# collapse text if needed
|
||||
if (!is.null(collapse)) {
|
||||
result <- sapply(result, function(x) {
|
||||
result <- vapply(FUN.VALUE = character(1), result, function(x) {
|
||||
if (length(x) == 1 & all(is.na(x))) {
|
||||
NA_character_
|
||||
} else {
|
||||
|
||||
+3
-3
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -34,7 +34,7 @@
|
||||
#' @param administration way of administration, either `"oral"` or `"iv"`
|
||||
#' @param units a logical to indicate whether the units instead of the DDDs itself must be returned, see Examples
|
||||
#' @param open browse the URL using [utils::browseURL()]
|
||||
#' @param ... other parameters passed on to [as.ab()]
|
||||
#' @param ... other arguments passed on to [as.ab()]
|
||||
#' @details All output will be [translate]d where possible.
|
||||
#'
|
||||
#' The function [ab_url()] will return the direct URL to the official WHO website. A warning will be returned if the required ATC code is not available.
|
||||
@@ -252,7 +252,7 @@ ab_validate <- function(x, property, ...) {
|
||||
|
||||
check_dataset_integrity()
|
||||
|
||||
# try to catch an error when inputting an invalid parameter
|
||||
# try to catch an error when inputting an invalid argument
|
||||
# so the 'call.' can be set to FALSE
|
||||
tryCatch(x[1L] %in% antibiotics[1, property],
|
||||
error = function(e) stop(e$message, call. = FALSE))
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -31,7 +31,7 @@
|
||||
#' @param reference reference date(s) (defaults to today), will be coerced with [as.POSIXlt()]
|
||||
#' @param exact a logical to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of [year-to-date](https://en.wikipedia.org/wiki/Year-to-date) (YTD) of `x` by the number of days in the year of `reference` (either 365 or 366).
|
||||
#' @param na.rm a logical to indicate whether missing values should be removed
|
||||
#' @param ... parameters passed on to [as.POSIXlt()], such as `origin`
|
||||
#' @param ... arguments passed on to [as.POSIXlt()], such as `origin`
|
||||
#' @details Ages below 0 will be returned as `NA` with a warning. Ages above 120 will only give a warning.
|
||||
#' @return An [integer] (no decimals) if `exact = FALSE`, a [double] (with decimals) otherwise
|
||||
#' @seealso To split ages into groups, use the [age_groups()] function.
|
||||
@@ -98,12 +98,12 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
|
||||
|
||||
#' Split ages into age groups
|
||||
#'
|
||||
#' Split ages into age groups defined by the `split` parameter. This allows for easier demographic (antimicrobial resistance) analysis.
|
||||
#' Split ages into age groups defined by the `split` argument. This allows for easier demographic (antimicrobial resistance) analysis.
|
||||
#' @inheritSection lifecycle Stable lifecycle
|
||||
#' @param x age, e.g. calculated with [age()]
|
||||
#' @param split_at values to split `x` at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See Details.
|
||||
#' @param na.rm a [logical] to indicate whether missing values should be removed
|
||||
#' @details To split ages, the input for the `split_at` parameter can be:
|
||||
#' @details To split ages, the input for the `split_at` argument can be:
|
||||
#'
|
||||
#' * A numeric vector. A value of e.g. `c(10, 20)` will split `x` on 0-9, 10-19 and 20+. A value of only `50` will split `x` on 0-49 and 50+.
|
||||
#' The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -27,11 +27,11 @@
|
||||
#'
|
||||
#' Welcome to the `AMR` package.
|
||||
#' @details
|
||||
#' `AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
|
||||
#' `AMR` is a free, open-source and independent \R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
|
||||
#'
|
||||
#' After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
|
||||
#' After installing this package, \R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
|
||||
#'
|
||||
#' This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. 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 is fully independent of any other \R package and works on Windows, macOS and Linux with all versions of \R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. 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:
|
||||
#' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature
|
||||
@@ -79,7 +79,7 @@ NULL
|
||||
#' Functions to print classes of the `AMR` package.
|
||||
#' @inheritSection lifecycle Stable lifecycle
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @param ... Parameters passed on to functions
|
||||
#' @param ... Arguments passed on to functions
|
||||
#' @inheritParams base::plot
|
||||
#' @inheritParams graphics::barplot
|
||||
#' @name plot
|
||||
|
||||
+3
-3
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -32,9 +32,9 @@
|
||||
#' @param administration type of administration when using `property = "Adm.R"`, see Details
|
||||
#' @param url url of website of the WHOCC. The sign `%s` can be used as a placeholder for ATC codes.
|
||||
#' @param url_vet url of website of the WHOCC for veterinary medicine. The sign `%s` can be used as a placeholder for ATC_vet codes (that all start with "Q").
|
||||
#' @param ... parameters to pass on to `atc_property`
|
||||
#' @param ... arguments to pass on to `atc_property`
|
||||
#' @details
|
||||
#' Options for parameter `administration`:
|
||||
#' Options for argument `administration`:
|
||||
#'
|
||||
#' - `"Implant"` = Implant
|
||||
#' - `"Inhal"` = Inhalation
|
||||
|
||||
+4
-4
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -46,11 +46,11 @@ availability <- function(tbl, width = NULL) {
|
||||
meet_criteria(tbl, allow_class = "data.frame")
|
||||
meet_criteria(width, allow_class = "numeric", allow_NULL = TRUE)
|
||||
|
||||
x <- sapply(tbl, function(x) {
|
||||
x <- vapply(FUN.VALUE = double(1), tbl, function(x) {
|
||||
1 - sum(is.na(x)) / length(x)
|
||||
})
|
||||
n <- sapply(tbl, function(x) length(x[!is.na(x)]))
|
||||
R <- sapply(tbl, function(x) ifelse(is.rsi(x), resistance(x, minimum = 0), NA))
|
||||
n <- vapply(FUN.VALUE = double(1), tbl, function(x) length(x[!is.na(x)]))
|
||||
R <- vapply(FUN.VALUE = double(1), tbl, function(x) ifelse(is.rsi(x), resistance(x, minimum = 0), NA_real_))
|
||||
R_print <- character(length(R))
|
||||
R_print[!is.na(R)] <- percentage(R[!is.na(R)])
|
||||
R_print[is.na(R)] <- ""
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -75,7 +75,7 @@ bug_drug_combinations <- function(x,
|
||||
x_class <- class(x)
|
||||
x <- as.data.frame(x, stringsAsFactors = FALSE)
|
||||
x[, col_mo] <- FUN(x[, col_mo, drop = TRUE], ...)
|
||||
x <- x[, c(col_mo, names(which(sapply(x, is.rsi)))), drop = FALSE]
|
||||
x <- x[, c(col_mo, names(which(vapply(FUN.VALUE = logical(1), x, is.rsi)))), drop = FALSE]
|
||||
|
||||
unique_mo <- sort(unique(x[, col_mo, drop = TRUE]))
|
||||
|
||||
@@ -89,7 +89,7 @@ bug_drug_combinations <- function(x,
|
||||
|
||||
for (i in seq_len(length(unique_mo))) {
|
||||
# filter on MO group and only select R/SI columns
|
||||
x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(sapply(x, is.rsi))), drop = FALSE]
|
||||
x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(vapply(FUN.VALUE = logical(1), x, is.rsi))), drop = FALSE]
|
||||
# turn and merge everything
|
||||
pivot <- lapply(x_mo_filter, function(x) {
|
||||
m <- as.matrix(table(x))
|
||||
@@ -165,7 +165,7 @@ format.bug_drug_combinations <- function(x,
|
||||
|
||||
remove_NAs <- function(.data) {
|
||||
cols <- colnames(.data)
|
||||
.data <- as.data.frame(sapply(.data, function(x) ifelse(is.na(x), "", x), simplify = FALSE),
|
||||
.data <- as.data.frame(lapply(.data, function(x) ifelse(is.na(x), "", x)),
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(.data) <- cols
|
||||
.data
|
||||
@@ -235,7 +235,7 @@ format.bug_drug_combinations <- function(x,
|
||||
}
|
||||
|
||||
if (remove_intrinsic_resistant == TRUE) {
|
||||
y <- y[, !sapply(y, function(col) all(col %like% "100", na.rm = TRUE) & !any(is.na(col))), drop = FALSE]
|
||||
y <- y[, !vapply(FUN.VALUE = logical(1), y, function(col) all(col %like% "100", na.rm = TRUE) & !any(is.na(col))), drop = FALSE]
|
||||
}
|
||||
|
||||
rownames(y) <- NULL
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -44,7 +44,7 @@ format_included_data_number <- function(data) {
|
||||
#' 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 (~70,000 species) from the authoritative and comprehensive Catalogue of Life (CoL, <http://www.catalogueoflife.org>). The CoL is the most comprehensive and authoritative global index of species currently available. Nonetheless, we supplemented the CoL data with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, [lpsn.dsmz.de](https://lpsn.dsmz.de)). This supplementation is needed until the [CoL+ project](https://github.com/Sp2000/colplus) is finished, which we await.
|
||||
#' This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (CoL, <http://www.catalogueoflife.org>). The CoL is the most comprehensive and authoritative global index of species currently available. Nonetheless, we supplemented the CoL data with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, [lpsn.dsmz.de](https://lpsn.dsmz.de)). This supplementation is needed until the [CoL+ project](https://github.com/CatalogueOfLife/general) is finished, which we await.
|
||||
#'
|
||||
#' [Click here][catalogue_of_life] for more information about the included taxa. Check which versions of the CoL and LSPN were included in this package with [catalogue_of_life_version()].
|
||||
#' @section Included taxa:
|
||||
@@ -58,7 +58,7 @@ format_included_data_number <- function(data) {
|
||||
#'
|
||||
#' The Catalogue of Life (<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 1.9 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: <https://github.com/msberends/AMR/blob/master/data-raw/reproduction_of_microorganisms.R>.
|
||||
#' The syntax used to transform the original data to a cleansed \R format, can be found here: <https://github.com/msberends/AMR/blob/master/data-raw/reproduction_of_microorganisms.R>.
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @name catalogue_of_life
|
||||
#' @rdname catalogue_of_life
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -134,7 +134,10 @@ count_R <- function(..., only_all_tested = FALSE) {
|
||||
#' @rdname count
|
||||
#' @export
|
||||
count_IR <- function(..., only_all_tested = FALSE) {
|
||||
warning_("Using 'count_IR' is discouraged; use 'count_resistant()' instead to not consider \"I\" being resistant.", call = FALSE)
|
||||
if (message_not_thrown_before("count_IR")) {
|
||||
warning_("Using count_IR() is discouraged; use count_resistant() instead to not consider \"I\" being resistant.", call = FALSE)
|
||||
remember_thrown_message("count_IR")
|
||||
}
|
||||
rsi_calc(...,
|
||||
ab_result = c("I", "R"),
|
||||
only_all_tested = only_all_tested,
|
||||
@@ -162,7 +165,10 @@ count_SI <- function(..., only_all_tested = FALSE) {
|
||||
#' @rdname count
|
||||
#' @export
|
||||
count_S <- function(..., only_all_tested = FALSE) {
|
||||
warning_("Using 'count_S' is discouraged; use 'count_susceptible()' instead to also consider \"I\" being susceptible.", call = FALSE)
|
||||
if (message_not_thrown_before("count_S")) {
|
||||
warning_("Using count_S() is discouraged; use count_susceptible() instead to also consider \"I\" being susceptible.", call = FALSE)
|
||||
remember_thrown_message("count_S")
|
||||
}
|
||||
rsi_calc(...,
|
||||
ab_result = "S",
|
||||
only_all_tested = only_all_tested,
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -58,12 +58,12 @@
|
||||
#' Synonyms (i.e. trade names) are derived from the Compound ID (`cid`) and consequently only available where a CID is available.
|
||||
#'
|
||||
#' ### Direct download
|
||||
#' These data sets are available as 'flat files' for use even without R - you can find the files here:
|
||||
#' These data sets are available as 'flat files' for use even without \R - you can find the files here:
|
||||
#'
|
||||
#' * <https://github.com/msberends/AMR/raw/master/data-raw/antibiotics.txt>
|
||||
#' * <https://github.com/msberends/AMR/raw/master/data-raw/antivirals.txt>
|
||||
#'
|
||||
#' Files in R format (with preserved data structure) can be found here:
|
||||
#' Files in \R format (with preserved data structure) can be found here:
|
||||
#'
|
||||
#' * <https://github.com/msberends/AMR/raw/master/data/antibiotics.rda>
|
||||
#' * <https://github.com/msberends/AMR/raw/master/data/antivirals.rda>
|
||||
@@ -106,11 +106,11 @@
|
||||
#' - `r format(nrow(subset(microorganisms, source == "DSMZ")), big.mark = ",")` species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) since the DSMZ contain the latest taxonomic information based on recent publications
|
||||
#'
|
||||
#' ### Direct download
|
||||
#' This data set is available as 'flat file' for use even without R - you can find the file here:
|
||||
#' This data set is available as 'flat file' for use even without \R - you can find the file here:
|
||||
#'
|
||||
#' * <https://github.com/msberends/AMR/raw/master/data-raw/microorganisms.txt>
|
||||
#'
|
||||
#' The file in R format (with preserved data structure) can be found here:
|
||||
#' The file in \R format (with preserved data structure) can be found here:
|
||||
#'
|
||||
#' * <https://github.com/msberends/AMR/raw/master/data/microorganisms.rda>
|
||||
#' @section About the records from DSMZ (see source):
|
||||
@@ -120,7 +120,7 @@
|
||||
#' In February 2020, the DSMZ records were merged with the List of Prokaryotic names with Standing in Nomenclature (LPSN).
|
||||
#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
|
||||
#'
|
||||
#' Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; doi: 10.1099/ijsem.0.002786
|
||||
#' Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
|
||||
#'
|
||||
#' Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, <https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date> and <https://lpsn.dsmz.de> (check included version with [catalogue_of_life_version()]).
|
||||
#' @inheritSection AMR Reference data publicly available
|
||||
@@ -147,7 +147,7 @@ catalogue_of_life <- list(
|
||||
#' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
|
||||
#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
|
||||
#'
|
||||
#' Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; doi: 10.1099/ijsem.0.002786
|
||||
#' Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
|
||||
#' @inheritSection AMR Reference data publicly available
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @seealso [as.mo()] [mo_property()] [microorganisms]
|
||||
@@ -178,7 +178,7 @@ catalogue_of_life <- list(
|
||||
#' - `gender`\cr gender of the patient
|
||||
#' - `patient_id`\cr ID of the patient
|
||||
#' - `mo`\cr ID of microorganism created with [as.mo()], see also [microorganisms]
|
||||
#' - `PEN:RIF`\cr `r sum(sapply(example_isolates, is.rsi))` different antibiotics with class [`rsi`] (see [as.rsi()]); these column names occur in the [antibiotics] data set and can be translated with [ab_name()]
|
||||
#' - `PEN:RIF`\cr `r sum(vapply(FUN.VALUE = logical(1), example_isolates, is.rsi))` different antibiotics with class [`rsi`] (see [as.rsi()]); these column names occur in the [antibiotics] data set and can be translated with [ab_name()]
|
||||
#' @inheritSection AMR Reference data publicly available
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
"example_isolates"
|
||||
@@ -225,7 +225,7 @@ catalogue_of_life <- list(
|
||||
#' - `Inducible clindamycin resistance`\cr Clindamycin can be induced?
|
||||
#' - `Comment`\cr Other comments
|
||||
#' - `Date of data entry`\cr Date this data was entered in WHONET
|
||||
#' - `AMP_ND10:CIP_EE`\cr `r sum(sapply(WHONET, is.rsi))` different antibiotics. You can lookup the abbreviations in the [antibiotics] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using [as.rsi()].
|
||||
#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.rsi))` different antibiotics. You can lookup the abbreviations in the [antibiotics] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using [as.rsi()].
|
||||
#' @inheritSection AMR Reference data publicly available
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
"WHONET"
|
||||
|
||||
+1
-1
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -69,13 +69,13 @@ as.disk <- function(x, na.rm = FALSE) {
|
||||
|
||||
na_before <- length(x[is.na(x)])
|
||||
|
||||
# heavily based on the function from our cleaner package:
|
||||
# heavily based on cleaner::clean_double():
|
||||
clean_double2 <- function(x, remove = "[^0-9.,-]", fixed = FALSE) {
|
||||
x <- gsub(",", ".", x)
|
||||
# remove ending dot/comma
|
||||
x <- gsub("[,.]$", "", x)
|
||||
# only keep last dot/comma
|
||||
reverse <- function(x) sapply(lapply(strsplit(x, NULL), rev), paste, collapse = "")
|
||||
reverse <- function(x) vapply(FUN.VALUE = character(1), lapply(strsplit(x, NULL), rev), paste, collapse = "")
|
||||
x <- sub("{{dot}}", ".",
|
||||
gsub(".", "",
|
||||
reverse(sub(".", "}}tod{{",
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -25,26 +25,34 @@
|
||||
|
||||
#' Determine (new) episodes for patients
|
||||
#'
|
||||
#' This function determines which items in a vector can be considered (the start of) a new episode, based on the parameter `episode_days`. This can be used to determine clinical episodes for any epidemiological analysis.
|
||||
#' These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument `episode_days`. This can be used to determine clinical episodes for any epidemiological analysis. The [get_episode()] function returns the index number of the episode per group, while the [is_new_episode()] function returns values `TRUE`/`FALSE` to indicate whether an item in a vector is the start of a new episode.
|
||||
#' @inheritSection lifecycle Stable lifecycle
|
||||
#' @param x vector of dates (class `Date` or `POSIXt`)
|
||||
#' @param episode_days length of the required episode in days, defaults to 365. Every element in the input will return `TRUE` after this number of days has passed since the last included date, independent of calendar years. Please see *Details*.
|
||||
#' @param episode_days length of the required episode in days, please see *Details*
|
||||
#' @param ... arguments passed on to [as.Date()]
|
||||
#' @details
|
||||
#' Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least `episode_days` days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least `episode_days` days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored.
|
||||
#'
|
||||
#' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but more efficient for data sets containing microorganism codes or names.
|
||||
#' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but is more efficient for data sets containing microorganism codes or names.
|
||||
#'
|
||||
#' The `dplyr` package is not required for this function to work, but this function works conveniently inside `dplyr` verbs such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
|
||||
#' @return a [logical] vector
|
||||
#' The `dplyr` package is not required for these functions to work, but these functions support [variable grouping][dplyr::group_by()] and work conveniently inside `dplyr` verbs such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
|
||||
#' @return
|
||||
#' * [get_episode()]: a [double] vector
|
||||
#' * [is_new_episode()]: a [logical] vector
|
||||
#' @seealso [first_isolate()]
|
||||
#' @rdname get_episode
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' # `example_isolates` is a dataset available in the AMR package.
|
||||
#' # See ?example_isolates.
|
||||
#'
|
||||
#' is_new_episode(example_isolates$date)
|
||||
#' get_episode(example_isolates$date, episode_days = 60)
|
||||
#' is_new_episode(example_isolates$date, episode_days = 60)
|
||||
#'
|
||||
#' # filter on results from the third 60-day episode only, using base R
|
||||
#' example_isolates[which(get_episode(example_isolates$date, 60) == 3), ]
|
||||
#'
|
||||
#' \donttest{
|
||||
#' if (require("dplyr")) {
|
||||
#' # is_new_episode() can also be used in dplyr verbs to determine patient
|
||||
@@ -54,7 +62,15 @@
|
||||
#' size = 2000,
|
||||
#' replace = TRUE)) %>%
|
||||
#' group_by(condition) %>%
|
||||
#' mutate(new_episode = is_new_episode(date))
|
||||
#' mutate(new_episode = is_new_episode(date, 365))
|
||||
#'
|
||||
#' example_isolates %>%
|
||||
#' group_by(hospital_id, patient_id) %>%
|
||||
#' transmute(date,
|
||||
#' patient_id,
|
||||
#' new_index = get_episode(date, 60),
|
||||
#' new_logical = is_new_episode(date, 60))
|
||||
#'
|
||||
#'
|
||||
#' example_isolates %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
@@ -71,7 +87,7 @@
|
||||
#'
|
||||
#' y <- example_isolates %>%
|
||||
#' group_by(patient_id, mo) %>%
|
||||
#' filter(is_new_episode(date))
|
||||
#' filter(is_new_episode(date, 365))
|
||||
#'
|
||||
#' identical(x$patient_id, y$patient_id)
|
||||
#'
|
||||
@@ -79,21 +95,52 @@
|
||||
#' # since you can now group on anything that seems relevant:
|
||||
#' example_isolates %>%
|
||||
#' group_by(patient_id, mo, hospital_id, ward_icu) %>%
|
||||
#' mutate(flag_episode = is_new_episode(date))
|
||||
#' mutate(flag_episode = is_new_episode(date, 365))
|
||||
#' }
|
||||
#' }
|
||||
is_new_episode <- function(x, episode_days = 365, ...) {
|
||||
get_episode <- function(x, episode_days, ...) {
|
||||
meet_criteria(x, allow_class = c("Date", "POSIXt"))
|
||||
meet_criteria(episode_days, allow_class = c("numeric", "double", "integer"), has_length = 1)
|
||||
|
||||
exec_episode(type = "sequential",
|
||||
x = x,
|
||||
episode_days = episode_days,
|
||||
... = ...)
|
||||
}
|
||||
|
||||
#' @rdname get_episode
|
||||
#' @export
|
||||
is_new_episode <- function(x, episode_days, ...) {
|
||||
meet_criteria(x, allow_class = c("Date", "POSIXt"))
|
||||
meet_criteria(episode_days, allow_class = c("numeric", "double", "integer"), has_length = 1)
|
||||
|
||||
exec_episode(type = "logical",
|
||||
x = x,
|
||||
episode_days = episode_days,
|
||||
... = ...)
|
||||
}
|
||||
|
||||
exec_episode <- function(type, x, episode_days, ...) {
|
||||
x <- as.double(as.Date(x, ...)) # as.Date() for POSIX classes
|
||||
if (length(x) == 1) {
|
||||
return(TRUE)
|
||||
if (type == "logical") {
|
||||
return(TRUE)
|
||||
} else if (type == "sequential") {
|
||||
return(1)
|
||||
}
|
||||
} else if (length(x) == 2) {
|
||||
if (max(x) - min(x) >= episode_days) {
|
||||
return(c(TRUE, TRUE))
|
||||
if (type == "logical") {
|
||||
return(c(TRUE, TRUE))
|
||||
} else if (type == "sequential") {
|
||||
return(c(1, 2))
|
||||
}
|
||||
} else {
|
||||
return(c(TRUE, FALSE))
|
||||
if (type == "logical") {
|
||||
return(c(TRUE, FALSE))
|
||||
} else if (type == "sequential") {
|
||||
return(c(1, 1))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -107,13 +154,22 @@ is_new_episode <- function(x, episode_days = 365, ...) {
|
||||
for (i in 2:length(x)) {
|
||||
if (isTRUE((x[i] - start) >= episode_days)) {
|
||||
ind <- ind + 1
|
||||
indices[ind] <- i
|
||||
if (type == "logical") {
|
||||
indices[ind] <- i
|
||||
}
|
||||
start <- x[i]
|
||||
}
|
||||
if (type == "sequential") {
|
||||
indices[i] <- ind
|
||||
}
|
||||
}
|
||||
if (type == "logical") {
|
||||
result <- rep(FALSE, length(x))
|
||||
result[indices] <- TRUE
|
||||
result
|
||||
} else if (type == "sequential") {
|
||||
indices
|
||||
}
|
||||
result <- rep(FALSE, length(x))
|
||||
result[indices] <- TRUE
|
||||
result
|
||||
}
|
||||
|
||||
df <- data.frame(x = x,
|
||||
+47
-19
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -64,6 +64,8 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
|
||||
#' @param verbose a [logical] to turn Verbose mode on and off (default is off). In Verbose mode, the function does not apply rules to the data, but instead returns a data set in logbook form with extensive info about which rows and columns would be effected and in which way. Using Verbose mode takes a lot more time.
|
||||
#' @param version_breakpoints the version number to use for the EUCAST Clinical Breakpoints guideline. Currently supported: `r paste0(names(EUCAST_VERSION_BREAKPOINTS), collapse = ", ")`.
|
||||
#' @param version_expertrules the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Currently supported: `r paste0(names(EUCAST_VERSION_EXPERT_RULES), collapse = ", ")`.
|
||||
#' @param ampc_cephalosporin_resistance a character value that should be applied for AmpC de-repressed cephalosporin-resistant mutants, defaults to `NA`. Currently only works when `version_expertrules` is `3.2`; '*EUCAST Expert Rules v3.2 on Enterobacterales*' states that susceptible (S) results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these agents. A value of `NA` for this argument will remove results for these agents, while e.g. a value of `"R"` will make the results for these agents resistant. Use `NULL` to not alter the results for AmpC de-repressed cephalosporin-resistant mutants. \cr For *EUCAST Expert Rules* v3.2, this rule applies to: *`r gsub("[)(^]", "", gsub("|", ", ", eucast_rules_file[which(eucast_rules_file$reference.version == 3.2 & eucast_rules_file$reference.rule %like% "ampc"), "this_value"][1], fixed = TRUE))`*.
|
||||
#'
|
||||
#' @param ... column name of an antibiotic, please see section *Antibiotics* below
|
||||
#' @inheritParams first_isolate
|
||||
#' @details
|
||||
@@ -81,20 +83,20 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
|
||||
#'
|
||||
#' Important examples include amoxicillin and amoxicillin/clavulanic acid, and trimethoprim and trimethoprim/sulfamethoxazole. Needless to say, for these rules to work, both drugs must be available in the data set.
|
||||
#'
|
||||
#' Since these rules are not officially approved by EUCAST, they are not applied at default. To use these rules, include `"other"` to the `rules` parameter, or use `eucast_rules(..., rules = "all")`.
|
||||
#' Since these rules are not officially approved by EUCAST, they are not applied at default. To use these rules, include `"other"` to the `rules` argument, or use `eucast_rules(..., rules = "all")`. You can also set the option `AMR_eucastrules`, i.e. run `options(AMR_eucastrules = "all")`.
|
||||
#' @section Antibiotics:
|
||||
#' To define antibiotics column names, leave as it is to determine it automatically with [guess_ab_col()] or input a text (case-insensitive), or use `NULL` to skip a column (e.g. `TIC = NULL` to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
|
||||
#'
|
||||
#' The following antibiotics are used for the functions [eucast_rules()] and [mdro()]. These are shown below in the format 'name (`antimicrobial ID`, [ATC code](https://www.whocc.no/atc/structure_and_principles/))', sorted alphabetically:
|
||||
#'
|
||||
#' `r create_ab_documentation(c("AMC", "AMK", "AMP", "AMX", "ATM", "AVO", "AZL", "AZM", "BAM", "BPR", "CAC", "CAP", "CAT", "CAZ", "CCV", "CDR", "CDZ", "CEC", "CED", "CEI", "CEP", "CFM", "CFM1", "CFP", "CFR", "CFS", "CHL", "CID", "CIP", "CLI", "CLR", "CMX", "CMZ", "CND", "COL", "CPD", "CPM", "CPO", "CPR", "CPT", "CRB", "CRD", "CRN", "CRO", "CSL", "CTB", "CTF", "CTL", "CTT", "CTX", "CTZ", "CXM", "CYC", "CZD", "CZO", "CZX", "DAL", "DAP", "DIR", "DIT", "DIZ", "DKB", "DOR", "DOX", "ENX", "EPC", "ERV", "ERY", "ETH", "ETP", "FDX", "FEP", "FLC", "FLE", "FLR1", "FOS", "FOX", "FOX1", "FUS", "GAT", "GEH", "GEM", "GEN", "GRX", "HAP", "HET", "INH", "IPM", "ISE", "JOS", "KAN", "LEX", "LIN", "LNZ", "LOM", "LOR", "LTM", "LVX", "MAN", "MCM", "MEC", "MEM", "MEV", "MEZ", "MFX", "MID", "MNO", "MTM", "MTR", "NAL", "NEO", "NET", "NIT", "NOR", "NOV", "NVA", "OFX", "OLE", "OMC", "ORI", "OXA", "PAZ", "PEF", "PEN", "PHN", "PIP", "PLB", "PME", "PRI", "PRL", "PRU", "PVM", "PZA", "QDA", "RAM", "RFL", "RFP", "RIB", "RID", "RIF", "ROK", "RST", "RXT", "SAM", "SBC", "SDI", "SDM", "SIS", "SLF", "SLF1", "SLF10", "SLF11", "SLF12", "SLF13", "SLF2", "SLF3", "SLF4", "SLF5", "SLF6", "SLF7", "SLF8", "SLF9", "SLT1", "SLT2", "SLT3", "SLT4", "SLT5", "SMX", "SPI", "SPT", "SPX", "STH", "STR", "STR1", "SUD", "SUT", "SXT", "SZO", "TAL", "TCC", "TCM", "TCY", "TEC", "TEM", "TGC", "THA", "TIC", "TLT", "TLV", "TMP", "TMX", "TOB", "TRL", "TVA", "TZD", "TZP", "VAN"))`
|
||||
#' `r create_ab_documentation(c("AMC", "AMK", "AMP", "AMX", "ATM", "AVO", "AZL", "AZM", "BAM", "BPR", "CAC", "CAT", "CAZ", "CCP", "CCV", "CCX", "CDC", "CDR", "CDZ", "CEC", "CED", "CEI", "CEM", "CEP", "CFM", "CFM1", "CFP", "CFR", "CFS", "CFZ", "CHE", "CHL", "CID", "CIP", "CLI", "CLR", "CMX", "CMZ", "CND", "COL", "CPD", "CPI", "CPL", "CPM", "CPO", "CPR", "CPT", "CPX", "CRB", "CRD", "CRN", "CRO", "CSL", "CTB", "CTC", "CTF", "CTL", "CTS", "CTT", "CTX", "CTZ", "CXM", "CYC", "CZA", "CZD", "CZO", "CZP", "CZX", "DAL", "DAP", "DIR", "DIT", "DIX", "DIZ", "DKB", "DOR", "DOX", "ENX", "EPC", "ERY", "ETP", "FEP", "FLC", "FLE", "FLR1", "FOS", "FOV", "FOX", "FOX1", "FUS", "GAT", "GEM", "GEN", "GRX", "HAP", "HET", "IPM", "ISE", "JOS", "KAN", "LEX", "LIN", "LNZ", "LOM", "LOR", "LTM", "LVX", "MAN", "MCM", "MEC", "MEM", "MEV", "MEZ", "MFX", "MID", "MNO", "MTM", "NAL", "NEO", "NET", "NIT", "NOR", "NOV", "NVA", "OFX", "OLE", "ORI", "OXA", "PAZ", "PEF", "PEN", "PHN", "PIP", "PLB", "PME", "PRI", "PRL", "PRU", "PVM", "QDA", "RAM", "RFL", "RID", "RIF", "ROK", "RST", "RXT", "SAM", "SBC", "SDI", "SDM", "SIS", "SLF", "SLF1", "SLF10", "SLF11", "SLF12", "SLF13", "SLF2", "SLF3", "SLF4", "SLF5", "SLF6", "SLF7", "SLF8", "SLF9", "SLT1", "SLT2", "SLT3", "SLT4", "SLT5", "SMX", "SPI", "SPX", "STR", "STR1", "SUD", "SUT", "SXT", "SZO", "TAL", "TCC", "TCM", "TCY", "TEC", "TEM", "TGC", "THA", "TIC", "TIO", "TLT", "TLV", "TMP", "TMX", "TOB", "TRL", "TVA", "TZD", "TZP", "VAN"))`
|
||||
#' @aliases EUCAST
|
||||
#' @rdname eucast_rules
|
||||
#' @export
|
||||
#' @return The input of `x`, possibly with edited values of antibiotics. Or, if `verbose = TRUE`, a [data.frame] with all original and new values of the affected bug-drug combinations.
|
||||
#' @source
|
||||
#' - EUCAST Expert Rules. Version 2.0, 2012.\cr
|
||||
#' Leclercq et al. **EUCAST expert rules in antimicrobial susceptibility testing.** *Clin Microbiol Infect.* 2013;19(2):141-60. [(link)](https://doi.org/10.1111/j.1469-0691.2011.03703.x)
|
||||
#' Leclercq et al. **EUCAST expert rules in antimicrobial susceptibility testing.** *Clin Microbiol Infect.* 2013;19(2):141-60; \doi{https://doi.org/10.1111/j.1469-0691.2011.03703.x}
|
||||
#' - EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf)
|
||||
#' - EUCAST Intrinsic Resistance and Unusual Phenotypes. Version 3.2, 2020. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf)
|
||||
#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_9.0_Breakpoint_Tables.xlsx)
|
||||
@@ -149,6 +151,7 @@ eucast_rules <- function(x,
|
||||
verbose = FALSE,
|
||||
version_breakpoints = 10.0,
|
||||
version_expertrules = 3.2,
|
||||
ampc_cephalosporin_resistance = NA,
|
||||
...) {
|
||||
meet_criteria(x, allow_class = "data.frame")
|
||||
meet_criteria(col_mo, allow_class = "character", has_length = 1, is_in = colnames(x), allow_NULL = TRUE)
|
||||
@@ -157,6 +160,7 @@ eucast_rules <- function(x,
|
||||
meet_criteria(verbose, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(version_breakpoints, allow_class = "numeric", has_length = 1)
|
||||
meet_criteria(version_expertrules, allow_class = "numeric", has_length = 1)
|
||||
meet_criteria(ampc_cephalosporin_resistance, allow_class = c("rsi", "character"), has_length = 1, allow_NA = TRUE, allow_NULL = TRUE, is_in = c("R", "S", "I"))
|
||||
|
||||
x_deparsed <- deparse(substitute(x))
|
||||
if (length(x_deparsed) > 1 || !all(x_deparsed %like% "[a-z]+")) {
|
||||
@@ -278,53 +282,66 @@ eucast_rules <- function(x,
|
||||
CAC <- cols_ab["CAC"]
|
||||
CAT <- cols_ab["CAT"]
|
||||
CAZ <- cols_ab["CAZ"]
|
||||
CCP <- cols_ab["CCP"]
|
||||
CCV <- cols_ab["CCV"]
|
||||
CCX <- cols_ab["CCX"]
|
||||
CDC <- cols_ab["CDC"]
|
||||
CDR <- cols_ab["CDR"]
|
||||
CDZ <- cols_ab["CDZ"]
|
||||
CEC <- cols_ab["CEC"]
|
||||
CED <- cols_ab["CED"]
|
||||
CEI <- cols_ab["CEI"]
|
||||
CEM <- cols_ab["CEM"]
|
||||
CEP <- cols_ab["CEP"]
|
||||
CFM <- cols_ab["CFM"]
|
||||
CFM1 <- cols_ab["CFM1"]
|
||||
CFP <- cols_ab["CFP"]
|
||||
CFR <- cols_ab["CFR"]
|
||||
CFS <- cols_ab["CFS"]
|
||||
CFZ <- cols_ab["CFZ"]
|
||||
CHE <- cols_ab["CHE"]
|
||||
CHL <- cols_ab["CHL"]
|
||||
CID <- cols_ab["CID"]
|
||||
CIP <- cols_ab["CIP"]
|
||||
CLI <- cols_ab["CLI"]
|
||||
CLI <- cols_ab["CLI"]
|
||||
CLR <- cols_ab["CLR"]
|
||||
CMX <- cols_ab["CMX"]
|
||||
CMZ <- cols_ab["CMZ"]
|
||||
CND <- cols_ab["CND"]
|
||||
COL <- cols_ab["COL"]
|
||||
CPD <- cols_ab["CPD"]
|
||||
CPI <- cols_ab["CPI"]
|
||||
CPL <- cols_ab["CPL"]
|
||||
CPM <- cols_ab["CPM"]
|
||||
CPO <- cols_ab["CPO"]
|
||||
CPR <- cols_ab["CPR"]
|
||||
CPT <- cols_ab["CPT"]
|
||||
CPX <- cols_ab["CPX"]
|
||||
CRB <- cols_ab["CRB"]
|
||||
CRD <- cols_ab["CRD"]
|
||||
CRN <- cols_ab["CRN"]
|
||||
CRO <- cols_ab["CRO"]
|
||||
CSL <- cols_ab["CSL"]
|
||||
CTB <- cols_ab["CTB"]
|
||||
CTC <- cols_ab["CTC"]
|
||||
CTF <- cols_ab["CTF"]
|
||||
CTL <- cols_ab["CTL"]
|
||||
CTS <- cols_ab["CTS"]
|
||||
CTT <- cols_ab["CTT"]
|
||||
CTX <- cols_ab["CTX"]
|
||||
CTZ <- cols_ab["CTZ"]
|
||||
CXM <- cols_ab["CXM"]
|
||||
CYC <- cols_ab["CYC"]
|
||||
CZA <- cols_ab["CZA"]
|
||||
CZD <- cols_ab["CZD"]
|
||||
CZO <- cols_ab["CZO"]
|
||||
CZP <- cols_ab["CZP"]
|
||||
CZX <- cols_ab["CZX"]
|
||||
DAL <- cols_ab["DAL"]
|
||||
DAP <- cols_ab["DAP"]
|
||||
DIR <- cols_ab["DIR"]
|
||||
DIT <- cols_ab["DIT"]
|
||||
DIX <- cols_ab["DIX"]
|
||||
DIZ <- cols_ab["DIZ"]
|
||||
DKB <- cols_ab["DKB"]
|
||||
DOR <- cols_ab["DOR"]
|
||||
@@ -338,6 +355,7 @@ eucast_rules <- function(x,
|
||||
FLE <- cols_ab["FLE"]
|
||||
FLR1 <- cols_ab["FLR1"]
|
||||
FOS <- cols_ab["FOS"]
|
||||
FOV <- cols_ab["FOV"]
|
||||
FOX <- cols_ab["FOX"]
|
||||
FOX1 <- cols_ab["FOX1"]
|
||||
FUS <- cols_ab["FUS"]
|
||||
@@ -391,7 +409,6 @@ eucast_rules <- function(x,
|
||||
PRU <- cols_ab["PRU"]
|
||||
PVM <- cols_ab["PVM"]
|
||||
QDA <- cols_ab["QDA"]
|
||||
QDA <- cols_ab["QDA"]
|
||||
RAM <- cols_ab["RAM"]
|
||||
RFL <- cols_ab["RFL"]
|
||||
RID <- cols_ab["RID"]
|
||||
@@ -441,6 +458,7 @@ eucast_rules <- function(x,
|
||||
TGC <- cols_ab["TGC"]
|
||||
THA <- cols_ab["THA"]
|
||||
TIC <- cols_ab["TIC"]
|
||||
TIO <- cols_ab["TIO"]
|
||||
TLT <- cols_ab["TLT"]
|
||||
TLV <- cols_ab["TLV"]
|
||||
TMP <- cols_ab["TMP"]
|
||||
@@ -474,9 +492,10 @@ eucast_rules <- function(x,
|
||||
aminoglycosides <- c(AMK, DKB, GEN, ISE, KAN, NEO, NET, RST, SIS, STR, STR1, TOB)
|
||||
aminopenicillins <- c(AMP, AMX)
|
||||
carbapenems <- c(DOR, ETP, IPM, MEM, MEV)
|
||||
cephalosporins <- c(CDZ, CAC, CEC, CFR, RID, MAN, CTZ, CZD, CZO, CDR, DIT, FEP, CAT, CFM, CMX, CMZ, DIZ, CID, CFP, CSL, CND, CTX, CTT, CTF, FOX, CPM, CPO, CPD, CPR, CRD, CFS, CPT, CAZ, CCV, CTL, CTB, CZX, BPR, CFM1, CEI, CRO, CXM, LEX, CEP, HAP, CED, LTM, LOR)
|
||||
cephalosporins <- c(CDZ, CCP, CAC, CEC, CFR, RID, MAN, CTZ, CZD, CZO, CDR, DIT, FEP, CAT, CFM, CMX, CMZ, DIZ, CID, CFP, CSL, CND, CTX, CTT, CTF, FOX, CPM, CPO, CPD, CPR, CRD, CFS, CPT, CAZ, CCV, CTL, CTB, CZX, BPR, CFM1, CEI, CRO, CXM, LEX, CEP, HAP, CED, LTM, LOR)
|
||||
cephalosporins_1st <- c(CAC, CFR, RID, CTZ, CZD, CZO, CRD, CTL, LEX, CEP, HAP, CED)
|
||||
cephalosporins_2nd <- c(CEC, MAN, CMZ, CID, CND, CTT, CTF, FOX, CPR, CXM, LOR)
|
||||
cephalosporins_3rd <- c(CDZ, CCP, CCX, CDR, DIT, DIX, CAT, CPI, CFM, CMX, DIZ, CFP, CSL, CTX, CTC, CTS, CHE, FOV, CFZ, CPM, CPD, CPX, CDC, CFS, CAZ, CZA, CCV, CEM, CPL, CTB, TIO, CZX, CZP, CRO, LTM)
|
||||
cephalosporins_except_CAZ <- cephalosporins[cephalosporins != ifelse(is.null(CAZ), "", CAZ)]
|
||||
fluoroquinolones <- c(CIP, ENX, FLE, GAT, GEM, GRX, LVX, LOM, MFX, NOR, OFX, PAZ, PEF, PRU, RFL, SPX, TMX, TVA)
|
||||
glycopeptides <- c(AVO, NVA, RAM, TEC, TCM, VAN) # dalba/orita/tela are in lipoglycopeptides
|
||||
@@ -519,7 +538,7 @@ eucast_rules <- function(x,
|
||||
strsplit(",") %pm>%
|
||||
unlist() %pm>%
|
||||
trimws() %pm>%
|
||||
sapply(function(x) if (x %in% antibiotics$ab) ab_name(x, language = NULL, tolower = TRUE) else x) %pm>%
|
||||
vapply(FUN.VALUE = character(1), function(x) if (x %in% antibiotics$ab) ab_name(x, language = NULL, tolower = TRUE) else x) %pm>%
|
||||
sort() %pm>%
|
||||
paste(collapse = ", ")
|
||||
x <- gsub("_", " ", x, fixed = TRUE)
|
||||
@@ -582,13 +601,14 @@ eucast_rules <- function(x,
|
||||
x <- as.data.frame(x, stringsAsFactors = FALSE) # no tibbles, data.tables, etc.
|
||||
rownames(x) <- NULL # will later be restored with old_attributes
|
||||
# create unique row IDs - combination of the MO and all ABx columns (so they will only run once per unique combination)
|
||||
x$`.rowid` <- sapply(as.list(as.data.frame(t(x[, c(col_mo, cols_ab), drop = FALSE]),
|
||||
x$`.rowid` <- vapply(FUN.VALUE = character(1),
|
||||
as.list(as.data.frame(t(x[, c(col_mo, cols_ab), drop = FALSE]),
|
||||
stringsAsFactors = FALSE)),
|
||||
function(x) {
|
||||
x[is.na(x)] <- "."
|
||||
paste0(x, collapse = "")
|
||||
})
|
||||
|
||||
|
||||
# save original table, with the new .rowid column
|
||||
x.bak <- x
|
||||
# keep only unique rows for MO and ABx
|
||||
@@ -716,7 +736,7 @@ eucast_rules <- function(x,
|
||||
# Official EUCAST rules ---------------------------------------------------
|
||||
eucast_notification_shown <- FALSE
|
||||
if (!is.null(list(...)$eucast_rules_df)) {
|
||||
# this allows: eucast_rules(x, eucast_rules_df = AMR:::eucast_rules_file %pm>% filter(is.na(have_these_values)))
|
||||
# this allows: eucast_rules(x, eucast_rules_df = AMR:::eucast_rules_file %>% filter(is.na(have_these_values)))
|
||||
eucast_rules_df <- list(...)$eucast_rules_df
|
||||
} else {
|
||||
# otherwise internal data file, created in data-raw/internals.R
|
||||
@@ -734,6 +754,13 @@ eucast_rules <- function(x,
|
||||
!reference.rule_group %like% "expert" |
|
||||
(reference.rule_group %like% "expert" & reference.version == version_expertrules))
|
||||
}
|
||||
# filter out AmpC de-repressed cephalosporin-resistant mutants ----
|
||||
if (is.null(ampc_cephalosporin_resistance)) {
|
||||
eucast_rules_df <- subset(eucast_rules_df,
|
||||
!reference.rule %like% "ampc")
|
||||
} else {
|
||||
eucast_rules_df[which(eucast_rules_df$reference.rule %like% "ampc"), "to_value"] <- as.character(ampc_cephalosporin_resistance)
|
||||
}
|
||||
|
||||
for (i in seq_len(nrow(eucast_rules_df))) {
|
||||
|
||||
@@ -796,7 +823,7 @@ eucast_rules <- function(x,
|
||||
word_wrap(
|
||||
expertrules_info$title, " (",
|
||||
font_red(paste0(expertrules_info$version_txt, ", ", expertrules_info$year)), ")\n")),
|
||||
""))))
|
||||
""))), "\n")
|
||||
}
|
||||
# Print rule -------------------------------------------------------------
|
||||
if (rule_current != rule_previous) {
|
||||
@@ -931,7 +958,7 @@ eucast_rules <- function(x,
|
||||
by = c(".rowid" = "rowid")) %pm>%
|
||||
pm_select(-`.rowid`) %pm>%
|
||||
pm_select(row, pm_everything()) %pm>%
|
||||
pm_filter(!is.na(new)) %pm>%
|
||||
pm_filter(!is.na(new) | is.na(new) & !is.na(old)) %pm>%
|
||||
pm_arrange(row, rule_group, rule_name, col)
|
||||
rownames(verbose_info) <- NULL
|
||||
}
|
||||
@@ -992,6 +1019,7 @@ eucast_rules <- function(x,
|
||||
if (total_n_changed > 0) {
|
||||
changed_summary <- verbose_info %pm>%
|
||||
pm_filter(!is.na(old)) %pm>%
|
||||
pm_mutate(new = ifelse(is.na(new), "NA", new)) %pm>%
|
||||
pm_count(old, new, name = "n")
|
||||
cat(paste(" -",
|
||||
paste0(formatnr(changed_summary$n), " test result", ifelse(changed_summary$n > 1, "s", ""), " changed from ",
|
||||
@@ -1067,18 +1095,18 @@ edit_rsi <- function(x,
|
||||
|
||||
if (length(rows) > 0 & length(cols) > 0) {
|
||||
new_edits <- x
|
||||
if (any(!sapply(x[, cols, drop = FALSE], is.rsi), na.rm = TRUE)) {
|
||||
track_changes$rsi_warn <- cols[!sapply(x[, cols, drop = FALSE], is.rsi)]
|
||||
if (any(!vapply(FUN.VALUE = logical(1), x[, cols, drop = FALSE], is.rsi), na.rm = TRUE)) {
|
||||
track_changes$rsi_warn <- cols[!vapply(FUN.VALUE = logical(1), x[, cols, drop = FALSE], is.rsi)]
|
||||
}
|
||||
tryCatch(
|
||||
# insert into original table
|
||||
new_edits[rows, cols] <- to,
|
||||
warning = function(w) {
|
||||
if (w$message %like% "invalid factor level") {
|
||||
xyz <- sapply(cols, function(col) {
|
||||
xyz <- vapply(FUN.VALUE = logical(1), cols, function(col) {
|
||||
new_edits[, col] <<- factor(x = as.character(pm_pull(new_edits, col)),
|
||||
levels = unique(c(to, levels(pm_pull(new_edits, col)))))
|
||||
invisible()
|
||||
levels = unique(c(to, levels(pm_pull(new_edits, col)))))
|
||||
TRUE
|
||||
})
|
||||
suppressWarnings(new_edits[rows, cols] <<- to)
|
||||
warning_('Value "', to, '" added to the factor levels of column(s) `', paste(cols, collapse = "`, `"), "` because this value was not an existing factor level. A better way is to use as.rsi() on beforehand on antimicrobial columns to guarantee the right structure.", call = FALSE)
|
||||
@@ -1119,7 +1147,7 @@ edit_rsi <- function(x,
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(verbose_new) <- c("rowid", "col", "mo_fullname", "old", "new",
|
||||
"rule", "rule_group", "rule_name", "rule_source")
|
||||
verbose_new <- verbose_new %pm>% pm_filter(old != new | is.na(old))
|
||||
verbose_new <- verbose_new %pm>% pm_filter(old != new | is.na(old) | is.na(new) & !is.na(old))
|
||||
# save changes to data set 'verbose_info'
|
||||
track_changes$verbose_info <- rbind(track_changes$verbose_info,
|
||||
verbose_new,
|
||||
|
||||
+3
-2
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -71,6 +71,7 @@
|
||||
#' filter_fluoroquinolones("R", "all")
|
||||
#'
|
||||
#' # with dplyr 1.0.0 and higher (that adds 'across()'), this is equal:
|
||||
#' # (though the row names on the first are more correct)
|
||||
#' example_isolates %>% filter_carbapenems("R", "all")
|
||||
#' example_isolates %>% filter(across(carbapenems(), ~. == "R"))
|
||||
#' }
|
||||
@@ -164,7 +165,7 @@ filter_ab_class <- function(x,
|
||||
collapse = scope_txt),
|
||||
operator, toString(result), as_note = FALSE)
|
||||
x_transposed <- as.list(as.data.frame(t(x[, agents, drop = FALSE]), stringsAsFactors = FALSE))
|
||||
filtered <- sapply(x_transposed, function(y) scope_fn(y %in% result, na.rm = TRUE))
|
||||
filtered <- vapply(FUN.VALUE = logical(1), x_transposed, function(y) scope_fn(y %in% result, na.rm = TRUE))
|
||||
x <- x[which(filtered), , drop = FALSE]
|
||||
class(x) <- x_class
|
||||
x
|
||||
|
||||
+10
-10
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -27,7 +27,7 @@
|
||||
#'
|
||||
#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use [is_new_episode()] that also supports grouping with the `dplyr` package.
|
||||
#' @inheritSection lifecycle Stable lifecycle
|
||||
#' @param x a [data.frame] containing isolates. Can be omitted when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
|
||||
#' @param x a [data.frame] containing isolates. Can be left blank when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
|
||||
#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column 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 IDs of the microorganisms (see [as.mo()]), defaults to the first column of class [`mo`]. Values will be coerced using [as.mo()].
|
||||
@@ -37,16 +37,16 @@
|
||||
#' @param col_keyantibiotics column name of the key antibiotics to determine first *weighted* isolates, see [key_antibiotics()]. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use `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. The default of 365 days is based on the guideline by CLSI, see Source.
|
||||
#' @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 `TRUE` in column `col_icu`)
|
||||
#' @param specimen_group value in column `col_specimen` to filter on
|
||||
#' @param icu_exclude logical whether ICU isolates should be excluded (rows with value `TRUE` in the column set with `col_icu`)
|
||||
#' @param specimen_group value in the column set with `col_specimen` to filter on
|
||||
#' @param type type to determine weighed isolates; can be `"keyantibiotics"` or `"points"`, see Details
|
||||
#' @param ignore_I logical to determine whether antibiotic interpretations with `"I"` will be ignored when `type = "keyantibiotics"`, see Details
|
||||
#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when `type = "points"`, see Details
|
||||
#' @param info print progress
|
||||
#' @param include_unknown logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
|
||||
#' @param ... parameters passed on to [first_isolate()] when using [filter_first_isolate()], or parameters passed on to [key_antibiotics()] when using [filter_first_weighted_isolate()]
|
||||
#' @param ... arguments passed on to [first_isolate()] when using [filter_first_isolate()], or arguments passed on to [key_antibiotics()] when using [filter_first_weighted_isolate()]
|
||||
#' @details
|
||||
#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` parameter can be omitted, please see *Examples*.
|
||||
#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be left blank, please see *Examples*.
|
||||
#'
|
||||
#' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but more efficient for data sets containing microorganism codes or names.
|
||||
#'
|
||||
@@ -80,11 +80,11 @@
|
||||
#' @section Key antibiotics:
|
||||
#' There are two ways to determine whether isolates can be included as first *weighted* isolates which will give generally the same results:
|
||||
#'
|
||||
#' 1. Using `type = "keyantibiotics"` and parameter `ignore_I`
|
||||
#' 1. Using `type = "keyantibiotics"` and argument `ignore_I`
|
||||
#'
|
||||
#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With `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. Read more about this in the [key_antibiotics()] function.
|
||||
#'
|
||||
#' 2. Using `type = "points"` and parameter `points_threshold`
|
||||
#' 2. Using `type = "points"` and argument `points_threshold`
|
||||
#'
|
||||
#' 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 `points_threshold`, which default to `2`, an isolate will be (re)selected as a first weighted isolate.
|
||||
#' @rdname first_isolate
|
||||
@@ -184,7 +184,7 @@ first_isolate <- function(x,
|
||||
|
||||
dots <- unlist(list(...))
|
||||
if (length(dots) != 0) {
|
||||
# backwards compatibility with old parameters
|
||||
# backwards compatibility with old arguments
|
||||
dots.names <- dots %pm>% names()
|
||||
if ("filter_specimen" %in% dots.names) {
|
||||
specimen_group <- dots[which(dots.names == "filter_specimen")]
|
||||
@@ -238,7 +238,7 @@ first_isolate <- function(x,
|
||||
check_columns_existance <- function(column, tblname = x) {
|
||||
if (!is.null(column)) {
|
||||
stop_ifnot(column %in% colnames(tblname),
|
||||
"Column `", column, "` not found.", call = FALSE)
|
||||
"Column '", column, "' not found.", call = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
+2
-2
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -200,7 +200,7 @@ g.test <- function(x,
|
||||
if (any(E < 5) && is.finite(PARAMETER))
|
||||
warning("G-statistic approximation may be incorrect due to E < 5")
|
||||
|
||||
structure(list(statistic = STATISTIC, parameter = PARAMETER,
|
||||
structure(list(statistic = STATISTIC, argument = 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")
|
||||
|
||||
+3
-4
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -47,13 +47,13 @@
|
||||
#' @param arrows_textangled a logical whether the text at the end of the arrows should be angled
|
||||
#' @param arrows_alpha the alpha (transparency) of the arrows and their text
|
||||
#' @param base_textsize the text size for all plot elements except the labels and arrows
|
||||
#' @param ... Parameters passed on to functions
|
||||
#' @param ... Arguments passed on to functions
|
||||
#' @source The [ggplot_pca()] function is based on the `ggbiplot()` function from the `ggbiplot` package by Vince Vu, as found on GitHub: <https://github.com/vqv/ggbiplot> (retrieved: 2 March 2020, their latest commit: [`7325e88`](https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9); 12 February 2015).
|
||||
#'
|
||||
#' As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were:
|
||||
#' 1. Rewritten code to remove the dependency on packages `plyr`, `scales` and `grid`
|
||||
#' 2. Parametrised more options, like arrow and ellipse settings
|
||||
#' 3. Hardened all input possibilities by defining the exact type of user input for every parameter
|
||||
#' 3. Hardened all input possibilities by defining the exact type of user input for every argument
|
||||
#' 4. Added total amount of explained variance as a caption in the plot
|
||||
#' 5. Cleaned all syntax based on the `lintr` package, fixed grammatical errors and added integrity checks
|
||||
#' 6. Updated documentation
|
||||
@@ -306,7 +306,6 @@ pca_calculations <- function(pca_model,
|
||||
d <- pca_model$svd
|
||||
u <- predict(pca_model)$x / nobs.factor
|
||||
v <- pca_model$scaling
|
||||
d.total <- sum(d ^ 2)
|
||||
} else {
|
||||
stop("Expected an object of class prcomp, princomp, PCA, or lda")
|
||||
}
|
||||
|
||||
+4
-4
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -45,8 +45,8 @@
|
||||
#' @param caption text to show as caption of the plot
|
||||
#' @param x.title text to show as x axis description
|
||||
#' @param y.title text to show as y axis description
|
||||
#' @param ... other parameters passed on to [geom_rsi()]
|
||||
#' @details At default, the names of antibiotics will be shown on the plots using [ab_name()]. This can be set with the `translate_ab` parameter. See [count_df()].
|
||||
#' @param ... other arguments passed on to [geom_rsi()]
|
||||
#' @details At default, the names of antibiotics will be shown on the plots using [ab_name()]. This can be set with the `translate_ab` argument. See [count_df()].
|
||||
#'
|
||||
#' ## The functions
|
||||
#' [geom_rsi()] will take any variable from the data that has an [`rsi`] class (created with [as.rsi()]) using [rsi_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.
|
||||
@@ -91,7 +91,7 @@
|
||||
#' select(AMX, NIT, FOS, TMP, CIP) %>%
|
||||
#' ggplot_rsi(datalabels = FALSE)
|
||||
#'
|
||||
#' # add other ggplot2 parameters as you like:
|
||||
#' # add other ggplot2 arguments as you like:
|
||||
#' example_isolates %>%
|
||||
#' select(AMX, NIT, FOS, TMP, CIP) %>%
|
||||
#' ggplot_rsi(width = 0.5,
|
||||
|
||||
+2
-1
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -60,6 +60,7 @@ globalVariables(c(".rowid",
|
||||
"old_name",
|
||||
"pattern",
|
||||
"R",
|
||||
"reference.rule",
|
||||
"reference.rule_group",
|
||||
"reference.version",
|
||||
"rsi_translation",
|
||||
|
||||
+6
-6
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -139,13 +139,13 @@ get_column_abx <- function(x,
|
||||
}
|
||||
x_bak <- x
|
||||
# only check columns that are a valid AB code, ATC code, name, abbreviation or synonym,
|
||||
# or already have the rsi class (as.rsi)
|
||||
# and that have no more than 50% invalid values
|
||||
# or already have the <rsi> class (as.rsi)
|
||||
# and that they have no more than 50% invalid values
|
||||
vectr_antibiotics <- unique(toupper(unlist(antibiotics[, c("ab", "atc", "name", "abbreviations", "synonyms")])))
|
||||
vectr_antibiotics <- vectr_antibiotics[!is.na(vectr_antibiotics) & nchar(vectr_antibiotics) >= 3]
|
||||
x_columns <- sapply(colnames(x), function(col, df = x_bak) {
|
||||
if (toupper(col) %in% vectr_antibiotics |
|
||||
is.rsi(as.data.frame(df, stringsAsFactors = FALSE)[, col, drop = TRUE]) |
|
||||
x_columns <- vapply(FUN.VALUE = character(1), colnames(x), function(col, df = x_bak) {
|
||||
if (toupper(col) %in% vectr_antibiotics ||
|
||||
is.rsi(as.data.frame(df, stringsAsFactors = FALSE)[, col, drop = TRUE]) ||
|
||||
is.rsi.eligible(as.data.frame(df, stringsAsFactors = FALSE)[, col, drop = TRUE],
|
||||
threshold = 0.5)) {
|
||||
return(col)
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
+5
-5
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -27,16 +27,16 @@
|
||||
#'
|
||||
#' These function can be used to determine first isolates (see [first_isolate()]). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates can then be called first *weighted* isolates.
|
||||
#' @inheritSection lifecycle Stable lifecycle
|
||||
#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be omitted when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`.
|
||||
#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`.
|
||||
#' @param y,z character vectors to compare
|
||||
#' @inheritParams first_isolate
|
||||
#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of **broad-spectrum** antibiotics, case-insensitive. See details for which antibiotics will be used at default (which are guessed with [guess_ab_col()]).
|
||||
#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for **Gram-positives**, case-insensitive. See details for which antibiotics will be used at default (which are guessed with [guess_ab_col()]).
|
||||
#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for **Gram-negatives**, case-insensitive. See details for which antibiotics will be used at default (which are guessed with [guess_ab_col()]).
|
||||
#' @param warnings give a warning about missing antibiotic columns (they will be ignored)
|
||||
#' @param ... other parameters passed on to functions
|
||||
#' @param ... other arguments passed on to functions
|
||||
#' @details
|
||||
#' The [key_antibiotics()] function is context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` parameter can be omitted, please see *Examples*.
|
||||
#' The [key_antibiotics()] function is context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be left blank, please see *Examples*.
|
||||
#'
|
||||
#' The function [key_antibiotics()] returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using [key_antibiotics_equal()], to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (`"."`) by [key_antibiotics()] and ignored by [key_antibiotics_equal()].
|
||||
#'
|
||||
@@ -157,7 +157,7 @@ key_antibiotics <- function(x,
|
||||
|
||||
dots <- unlist(list(...))
|
||||
if (length(dots) != 0) {
|
||||
# backwards compatibility with old parameters
|
||||
# backwards compatibility with old arguments
|
||||
dots.names <- dots %pm>% names()
|
||||
if ("info" %in% dots.names) {
|
||||
warnings <- dots[which(dots.names == "info")]
|
||||
|
||||
+2
-2
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -71,5 +71,5 @@ kurtosis.matrix <- function(x, na.rm = FALSE, excess = FALSE) {
|
||||
kurtosis.data.frame <- function(x, na.rm = FALSE, excess = FALSE) {
|
||||
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(excess, allow_class = "logical", has_length = 1)
|
||||
sapply(x, kurtosis.default, na.rm = na.rm, excess = excess)
|
||||
vapply(FUN.VALUE = double(1), x, kurtosis.default, na.rm = na.rm, excess = excess)
|
||||
}
|
||||
+2
-2
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -44,7 +44,7 @@
|
||||
#' \if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
|
||||
#' The [lifecycle][AMR::lifecycle] of this function is **stable**. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
|
||||
#'
|
||||
#' If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
|
||||
#' If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
|
||||
#' @section Retired lifecycle:
|
||||
#' \if{html}{\figure{lifecycle_retired.svg}{options: style=margin-bottom:5px} \cr}
|
||||
#' The [lifecycle][AMR::lifecycle] of this function is **retired**. A retired function is no longer under active development, and (if appropiate) a better alternative is available. No new arguments will be added, and only the most critical bugs will be fixed. In a future version, this function will be removed.
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -102,7 +102,7 @@ like <- function(x, pattern, ignore.case = TRUE) {
|
||||
res[i] <- grepl(pattern[i], x[i], ignore.case = FALSE, fixed = fixed)
|
||||
}
|
||||
}
|
||||
res <- sapply(pattern, function(pttrn) grepl(pttrn, x, ignore.case = FALSE, fixed = fixed))
|
||||
res <- vapply(FUN.VALUE = logical(1), pattern, function(pttrn) grepl(pttrn, x, ignore.case = FALSE, fixed = fixed))
|
||||
res2 <- as.logical(rowSums(res))
|
||||
# get only first item of every hit in pattern
|
||||
res2[duplicated(res)] <- FALSE
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -27,7 +27,7 @@
|
||||
#'
|
||||
#' Determine which isolates are multidrug-resistant organisms (MDRO) according to international and national guidelines.
|
||||
#' @inheritSection lifecycle Stable lifecycle
|
||||
#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be omitted when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
|
||||
#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
|
||||
#' @param guideline a specific guideline to follow. When left empty, the publication by Magiorakos *et al.* (2012, Clinical Microbiology and Infection) will be followed, please see *Details*.
|
||||
#' @inheritParams eucast_rules
|
||||
#' @param pct_required_classes minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for *S. aureus*. Setting this `pct_required_classes` argument to `0.5` (default) means that for every *S. aureus* isolate at least 8 different classes must be available. Any lower number of available classes will return `NA` for that isolate.
|
||||
@@ -35,7 +35,7 @@
|
||||
#' @param verbose a logical to turn Verbose mode on and off (default is off). In Verbose mode, the function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.
|
||||
#' @inheritSection eucast_rules Antibiotics
|
||||
#' @details
|
||||
#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` parameter can be omitted, please see *Examples*.
|
||||
#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be left blank, please see *Examples*.
|
||||
#'
|
||||
#' For the `pct_required_classes` argument, values above 1 will be divided by 100. This is to support both fractions (`0.75` or `3/4`) and percentages (`75`).
|
||||
#'
|
||||
@@ -59,7 +59,7 @@
|
||||
#'
|
||||
#' * `guideline = "MRGN"`
|
||||
#'
|
||||
#' The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
|
||||
#' The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7; \doi{10.1186/s13756-015-0047-6}
|
||||
#'
|
||||
#' * `guideline = "BRMO"`
|
||||
#'
|
||||
@@ -192,44 +192,44 @@ mdro <- function(x,
|
||||
if (guideline$code == "cmi2012") {
|
||||
guideline$name <- "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance."
|
||||
guideline$author <- "Magiorakos AP, Srinivasan A, Carey RB, ..., Vatopoulos A, Weber JT, Monnet DL"
|
||||
guideline$version <- "N/A"
|
||||
guideline$source <- "Clinical Microbiology and Infection 18:3, 2012. DOI: 10.1111/j.1469-0691.2011.03570.x"
|
||||
guideline$version <- NA
|
||||
guideline$source_url <- "Clinical Microbiology and Infection 18:3, 2012; doi: 10.1111/j.1469-0691.2011.03570.x"
|
||||
guideline$type <- "MDRs/XDRs/PDRs"
|
||||
|
||||
} else if (guideline$code == "eucast3.1") {
|
||||
guideline$name <- "EUCAST Expert Rules, \"Intrinsic Resistance and Exceptional Phenotypes Tables\""
|
||||
guideline$author <- "EUCAST (European Committee on Antimicrobial Susceptibility Testing)"
|
||||
guideline$version <- "3.1, 2016"
|
||||
guideline$source <- "https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf"
|
||||
guideline$source_url <- "https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf"
|
||||
guideline$type <- "EUCAST Exceptional Phenotypes"
|
||||
|
||||
} else if (guideline$code == "eucast3.2") {
|
||||
guideline$name <- "EUCAST Expert Rules, \"Intrinsic Resistance and Unusual Phenotypes\""
|
||||
guideline$author <- "EUCAST (European Committee on Antimicrobial Susceptibility Testing)"
|
||||
guideline$version <- "3.2, 2020"
|
||||
guideline$source <- "https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf"
|
||||
guideline$source_url <- "https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf"
|
||||
guideline$type <- "EUCAST Unusual Phenotypes"
|
||||
|
||||
} else if (guideline$code == "tb") {
|
||||
guideline$name <- "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis"
|
||||
guideline$author <- "WHO (World Health Organization)"
|
||||
guideline$version <- "WHO/HTM/TB/2014.11, 2014"
|
||||
guideline$source <- "https://www.who.int/tb/publications/pmdt_companionhandbook/en/"
|
||||
guideline$source_url <- "https://www.who.int/tb/publications/pmdt_companionhandbook/en/"
|
||||
guideline$type <- "MDR-TB's"
|
||||
|
||||
# support per country:
|
||||
} else if (guideline$code == "mrgn") {
|
||||
guideline$name <- "Cross-border comparison of the Dutch and German guidelines on multidrug-resistant Gram-negative microorganisms"
|
||||
guideline$author <- "M\u00fcller J, Voss A, K\u00f6ck R, ..., Kern WV, Wendt C, Friedrich AW"
|
||||
guideline$version <- "N/A"
|
||||
guideline$source <- "Antimicrobial Resistance and Infection Control 4:7, 2015. DOI: 10.1186/s13756-015-0047-6"
|
||||
guideline$version <- NA
|
||||
guideline$source_url <- "Antimicrobial Resistance and Infection Control 4:7, 2015; doi: 10.1186/s13756-015-0047-6"
|
||||
guideline$type <- "MRGNs"
|
||||
|
||||
} else if (guideline$code == "brmo") {
|
||||
guideline$name <- "WIP-Richtlijn Bijzonder Resistente Micro-organismen (BRMO)"
|
||||
guideline$author <- "RIVM (Rijksinstituut voor de Volksgezondheid)"
|
||||
guideline$version <- "Revision as of December 2017"
|
||||
guideline$source <- "https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH"
|
||||
guideline$source_url <- "https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH"
|
||||
guideline$type <- "BRMOs"
|
||||
} else {
|
||||
stop("This guideline is currently unsupported: ", guideline$code, call. = FALSE)
|
||||
@@ -413,6 +413,7 @@ mdro <- function(x,
|
||||
...)
|
||||
}
|
||||
|
||||
# nolint start
|
||||
AMC <- cols_ab["AMC"]
|
||||
AMK <- cols_ab["AMK"]
|
||||
AMP <- cols_ab["AMP"]
|
||||
@@ -555,6 +556,7 @@ mdro <- function(x,
|
||||
abx_tb <- c(CAP, ETH, GAT, INH, PZA, RIF, RIB, RFP)
|
||||
abx_tb <- abx_tb[!is.na(abx_tb)]
|
||||
stop_if(guideline$code == "tb" & length(abx_tb) == 0, "no antimycobacterials found in data set")
|
||||
# nolint end
|
||||
|
||||
if (combine_SI == TRUE) {
|
||||
search_result <- "R"
|
||||
@@ -568,12 +570,14 @@ mdro <- function(x,
|
||||
} else {
|
||||
cat(font_red("\nResults with 'R' or 'I' are considered as resistance. Use `combine_SI = TRUE` to only consider 'R' as resistance.\n"))
|
||||
}
|
||||
cat("\nDetermining multidrug-resistant organisms (MDRO), according to:\n",
|
||||
font_bold("Guideline: "), font_italic(guideline$name), "\n",
|
||||
font_bold("Version: "), guideline$version, "\n",
|
||||
font_bold("Author: "), guideline$author, "\n",
|
||||
font_bold("Source: "), guideline$source, "\n",
|
||||
"\n", sep = "")
|
||||
cat("\n", word_wrap("Determining multidrug-resistant organisms (MDRO), according to:"), "\n",
|
||||
word_wrap(paste0(font_bold("Guideline: "), font_italic(guideline$name)), extra_indent = 11, as_note = FALSE), "\n",
|
||||
word_wrap(paste0(font_bold("Author(s): "), guideline$author), extra_indent = 11, as_note = FALSE), "\n",
|
||||
ifelse(!is.na(guideline$version),
|
||||
paste0(word_wrap(paste0(font_bold("Version: "), guideline$version), extra_indent = 11, as_note = FALSE), "\n"),
|
||||
""),
|
||||
paste0(font_bold("Source: "), guideline$source_url),
|
||||
"\n\n", sep = "")
|
||||
}
|
||||
|
||||
ab_missing <- function(ab) {
|
||||
@@ -583,9 +587,8 @@ mdro <- function(x,
|
||||
x[!is.na(x)]
|
||||
}
|
||||
|
||||
verbose_df <- NULL
|
||||
|
||||
# antibiotic classes
|
||||
# nolint start
|
||||
aminoglycosides <- c(TOB, GEN)
|
||||
cephalosporins <- c(CDZ, CAC, CEC, CFR, RID, MAN, CTZ, CZD, CZO, CDR, DIT, FEP, CAT, CFM, CMX, CMZ, DIZ, CID, CFP, CSL, CND, CTX, CTT, CTF, FOX, CPM, CPO, CPD, CPR, CRD, CFS, CPT, CAZ, CCV, CTL, CTB, CZX, BPR, CFM1, CEI, CRO, CXM, LEX, CEP, HAP, CED, LTM, LOR)
|
||||
cephalosporins_1st <- c(CAC, CFR, RID, CTZ, CZD, CZO, CRD, CTL, LEX, CEP, HAP, CED)
|
||||
@@ -593,6 +596,7 @@ mdro <- function(x,
|
||||
cephalosporins_3rd <- c(CDZ, CDR, DIT, CAT, CFM, CMX, DIZ, CFP, CSL, CTX, CPM, CPD, CFS, CAZ, CCV, CTB, CZX, CRO, LTM)
|
||||
carbapenems <- c(DOR, ETP, IPM, MEM, MEV)
|
||||
fluoroquinolones <- c(CIP, ENX, FLE, GAT, GEM, GRX, LVX, LOM, MFX, NOR, OFX, PAZ, PEF, PRU, RFL, SPX, TMX, TVA)
|
||||
# nolint end
|
||||
|
||||
# helper function for editing the table
|
||||
trans_tbl <- function(to, rows, cols, any_all) {
|
||||
@@ -602,9 +606,10 @@ mdro <- function(x,
|
||||
x[, cols] <- as.data.frame(lapply(x[, cols, drop = FALSE],
|
||||
function(col) as.rsi(col)),
|
||||
stringsAsFactors = FALSE)
|
||||
x[rows, "columns_nonsusceptible"] <<- sapply(rows,
|
||||
x[rows, "columns_nonsusceptible"] <<- vapply(FUN.VALUE = character(1),
|
||||
rows,
|
||||
function(row, group_vct = cols) {
|
||||
cols_nonsus <- sapply(x[row, group_vct, drop = FALSE],
|
||||
cols_nonsus <- vapply(FUN.VALUE = logical(1), x[row, group_vct, drop = FALSE],
|
||||
function(y) y %in% search_result)
|
||||
paste(sort(c(unlist(strsplit(x[row, "columns_nonsusceptible", drop = TRUE], ", ")),
|
||||
names(cols_nonsus)[cols_nonsus])),
|
||||
@@ -618,7 +623,7 @@ mdro <- function(x,
|
||||
}
|
||||
x_transposed <- as.list(as.data.frame(t(x[, cols, drop = FALSE]),
|
||||
stringsAsFactors = FALSE))
|
||||
row_filter <- sapply(x_transposed, function(y) search_function(y %in% search_result, na.rm = TRUE))
|
||||
row_filter <- vapply(FUN.VALUE = logical(1), x_transposed, function(y) search_function(y %in% search_result, na.rm = TRUE))
|
||||
row_filter <- x[which(row_filter), "row_number", drop = TRUE]
|
||||
rows <- rows[rows %in% row_filter]
|
||||
x[rows, "MDRO"] <<- to
|
||||
@@ -636,21 +641,27 @@ mdro <- function(x,
|
||||
function(col) as.rsi(col)),
|
||||
stringsAsFactors = FALSE)
|
||||
x[rows, "classes_in_guideline"] <<- length(lst)
|
||||
x[rows, "classes_available"] <<- sapply(rows,
|
||||
x[rows, "classes_available"] <<- vapply(FUN.VALUE = double(1),
|
||||
rows,
|
||||
function(row, group_tbl = lst) {
|
||||
sum(sapply(group_tbl, function(group) any(unlist(x[row, group[!is.na(group)], drop = TRUE]) %in% c("S", "I", "R"))))
|
||||
sum(vapply(FUN.VALUE = logical(1),
|
||||
group_tbl,
|
||||
function(group) any(unlist(x[row, group[!is.na(group)], drop = TRUE]) %in% c("S", "I", "R"))))
|
||||
})
|
||||
|
||||
if (verbose == TRUE) {
|
||||
x[rows, "columns_nonsusceptible"] <<- sapply(rows,
|
||||
x[rows, "columns_nonsusceptible"] <<- vapply(FUN.VALUE = character(1),
|
||||
rows,
|
||||
function(row, group_vct = lst_vector) {
|
||||
cols_nonsus <- sapply(x[row, group_vct, drop = FALSE], function(y) y %in% search_result)
|
||||
cols_nonsus <- vapply(FUN.VALUE = logical(1), x[row, group_vct, drop = FALSE], function(y) y %in% search_result)
|
||||
paste(sort(names(cols_nonsus)[cols_nonsus]), collapse = ", ")
|
||||
})
|
||||
}
|
||||
x[rows, "classes_affected"] <<- sapply(rows,
|
||||
x[rows, "classes_affected"] <<- vapply(FUN.VALUE = double(1),
|
||||
rows,
|
||||
function(row, group_tbl = lst) {
|
||||
sum(sapply(group_tbl,
|
||||
sum(vapply(FUN.VALUE = logical(1),
|
||||
group_tbl,
|
||||
function(group) {
|
||||
any(unlist(x[row, group[!is.na(group)], drop = TRUE]) %in% search_result, na.rm = TRUE)
|
||||
}),
|
||||
@@ -659,7 +670,7 @@ mdro <- function(x,
|
||||
# for PDR; all agents are R (or I if combine_SI = FALSE)
|
||||
x_transposed <- as.list(as.data.frame(t(x[rows, lst_vector, drop = FALSE]),
|
||||
stringsAsFactors = FALSE))
|
||||
row_filter <- sapply(x_transposed, function(y) all(y %in% search_result, na.rm = TRUE))
|
||||
row_filter <- vapply(FUN.VALUE = logical(1), x_transposed, function(y) all(y %in% search_result, na.rm = TRUE))
|
||||
x[which(row_filter), "classes_affected"] <<- 999
|
||||
}
|
||||
|
||||
@@ -1237,7 +1248,7 @@ mdro <- function(x,
|
||||
if (guideline$code == "cmi2012") {
|
||||
if (any(x$MDRO == -1, na.rm = TRUE)) {
|
||||
warning_("NA introduced for isolates where the available percentage of antimicrobial classes was below ",
|
||||
percentage(pct_required_classes), " (set with `pct_required_classes`)")
|
||||
percentage(pct_required_classes), " (set with `pct_required_classes`)", call = FALSE)
|
||||
# set these -1s to NA
|
||||
x[which(x$MDRO == -1), "MDRO"] <- NA_integer_
|
||||
}
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -107,14 +107,14 @@ as.mic <- function(x, na.rm = FALSE) {
|
||||
|
||||
# these are allowed MIC values and will become factor levels
|
||||
ops <- c("<", "<=", "", ">=", ">")
|
||||
lvls <- c(c(t(sapply(ops, function(x) paste0(x, "0.00", 1:9)))),
|
||||
unique(c(t(sapply(ops, function(x) paste0(x, sort(as.double(paste0("0.0",
|
||||
lvls <- c(c(t(vapply(FUN.VALUE = character(9), ops, function(x) paste0(x, "0.00", 1:9)))),
|
||||
unique(c(t(vapply(FUN.VALUE = character(104), ops, function(x) paste0(x, sort(as.double(paste0("0.0",
|
||||
sort(c(1:99, 125, 128, 256, 512, 625)))))))))),
|
||||
unique(c(t(sapply(ops, function(x) paste0(x, sort(as.double(paste0("0.",
|
||||
unique(c(t(vapply(FUN.VALUE = character(103), ops, function(x) paste0(x, sort(as.double(paste0("0.",
|
||||
c(1:99, 125, 128, 256, 512))))))))),
|
||||
c(t(sapply(ops, function(x) paste0(x, sort(c(1:9, 1.5)))))),
|
||||
c(t(sapply(ops, function(x) paste0(x, c(10:98)[9:98 %% 2 == TRUE])))),
|
||||
c(t(sapply(ops, function(x) paste0(x, sort(c(2 ^ c(7:10), 80 * c(2:12))))))))
|
||||
c(t(vapply(FUN.VALUE = character(10), ops, function(x) paste0(x, sort(c(1:9, 1.5)))))),
|
||||
c(t(vapply(FUN.VALUE = character(45), ops, function(x) paste0(x, c(10:98)[9:98 %% 2 == TRUE])))),
|
||||
c(t(vapply(FUN.VALUE = character(15), ops, function(x) paste0(x, sort(c(2 ^ c(7:10), 80 * c(2:12))))))))
|
||||
|
||||
na_before <- x[is.na(x) | x == ""] %pm>% length()
|
||||
x[!x %in% lvls] <- NA
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -38,7 +38,7 @@
|
||||
#' @param reference_df a [data.frame] to be used for extra reference when translating `x` to a valid [`mo`]. See [set_mo_source()] and [get_mo_source()] to automate the usage of your own codes (e.g. used in your analysis or organisation).
|
||||
#' @param ignore_pattern a regular expression (case-insensitive) of which all matches in `x` must return `NA`. This can be convenient to exclude known non-relevant input and can also be set with the option `AMR_ignore_pattern`, e.g. `options(AMR_ignore_pattern = "(not reported|contaminated flora)")`.
|
||||
#' @param language language to translate text like "no growth", which defaults to the system language (see [get_locale()])
|
||||
#' @param ... other parameters passed on to functions
|
||||
#' @param ... other arguments passed on to functions
|
||||
#' @rdname as.mo
|
||||
#' @aliases mo
|
||||
#' @keywords mo Becker becker Lancefield lancefield guess
|
||||
@@ -102,10 +102,10 @@
|
||||
#' @inheritSection catalogue_of_life Catalogue of Life
|
||||
# (source as a section here, so it can be inherited by other man pages:)
|
||||
#' @section Source:
|
||||
#' 1. Becker K *et al.* **Coagulase-Negative Staphylococci**. 2014. Clin Microbiol Rev. 27(4): 870–926. <https://dx.doi.org/10.1128/CMR.00109-13>
|
||||
#' 2. Becker K *et al.* **Implications of identifying the recently defined members of the *S. aureus* complex, *S. argenteus* and *S. schweitzeri*: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).** 2019. Clin Microbiol Infect. <https://doi.org/10.1016/j.cmi.2019.02.028>
|
||||
#' 3. Becker K *et al.* **Emergence of coagulase-negative staphylococci** 2020. Expert Rev Anti Infect Ther. 18(4):349-366. <https://dx.doi.org/10.1080/14787210.2020.1730813>
|
||||
#' 4. Lancefield RC **A serological differentiation of human and other groups of hemolytic streptococci**. 1933. J Exp Med. 57(4): 571–95. <https://dx.doi.org/10.1084/jem.57.4.571>
|
||||
#' 1. Becker K *et al.* **Coagulase-Negative Staphylococci**. 2014. Clin Microbiol Rev. 27(4): 870–926; \doi{10.1128/CMR.00109-13}
|
||||
#' 2. Becker K *et al.* **Implications of identifying the recently defined members of the *S. aureus* complex, *S. argenteus* and *S. schweitzeri*: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).** 2019. Clin Microbiol Infect; \doi{10.1016/j.cmi.2019.02.028}
|
||||
#' 3. Becker K *et al.* **Emergence of coagulase-negative staphylococci** 2020. Expert Rev Anti Infect Ther. 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
|
||||
#' 4. Lancefield RC **A serological differentiation of human and other groups of hemolytic streptococci**. 1933. J Exp Med. 57(4): 571–95; \doi{10.1084/jem.57.4.571}
|
||||
#' 5. Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
|
||||
#' @export
|
||||
#' @return A [character] [vector] with additional class [`mo`]
|
||||
@@ -200,7 +200,7 @@ as.mo <- function(x,
|
||||
uncertainty_level <- translate_allow_uncertain(allow_uncertain)
|
||||
|
||||
if (!is.null(reference_df)
|
||||
&& mo_source_isvalid(reference_df)
|
||||
&& check_validity_mo_source(reference_df)
|
||||
&& isFALSE(Becker)
|
||||
&& isFALSE(Lancefield)
|
||||
&& all(x %in% unlist(reference_df), na.rm = TRUE)) {
|
||||
@@ -276,7 +276,7 @@ exec_as.mo <- function(x,
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
check_dataset_integrity()
|
||||
|
||||
|
||||
lookup <- function(needle,
|
||||
column = property,
|
||||
haystack = reference_data_to_use,
|
||||
@@ -358,11 +358,14 @@ exec_as.mo <- function(x,
|
||||
x[trimws2(x) %like% paste0("^(", translate_AMR("no|not", language = language), ") [a-z]+")] <- "UNKNOWN"
|
||||
|
||||
if (initial_search == TRUE) {
|
||||
mo_env$mo_failures <- NULL
|
||||
mo_env$mo_uncertainties <- NULL
|
||||
mo_env$mo_renamed <- NULL
|
||||
# keep track of time - give some hints to improve speed if it takes a long time
|
||||
start_time <- Sys.time()
|
||||
|
||||
pkg_env$mo_failures <- NULL
|
||||
pkg_env$mo_uncertainties <- NULL
|
||||
pkg_env$mo_renamed <- NULL
|
||||
}
|
||||
mo_env$mo_renamed_last_run <- NULL
|
||||
pkg_env$mo_renamed_last_run <- NULL
|
||||
|
||||
failures <- character(0)
|
||||
uncertainty_level <- translate_allow_uncertain(allow_uncertain)
|
||||
@@ -388,7 +391,7 @@ exec_as.mo <- function(x,
|
||||
|
||||
# defined df to check for
|
||||
if (!is.null(reference_df)) {
|
||||
mo_source_isvalid(reference_df)
|
||||
check_validity_mo_source(reference_df)
|
||||
reference_df <- repair_reference_df(reference_df)
|
||||
}
|
||||
|
||||
@@ -435,7 +438,7 @@ exec_as.mo <- function(x,
|
||||
|
||||
strip_whitespace <- function(x, dyslexia_mode) {
|
||||
# all whitespaces (tab, new lines, etc.) should be one space
|
||||
# and spaces before and after should be omitted
|
||||
# and spaces before and after should be left blank
|
||||
trimmed <- trimws2(x)
|
||||
# also, make sure the trailing and leading characters are a-z or 0-9
|
||||
# in case of non-regex
|
||||
@@ -595,7 +598,7 @@ exec_as.mo <- function(x,
|
||||
} else {
|
||||
x[i] <- lookup(fullname == found["fullname_new"], haystack = MO_lookup)
|
||||
}
|
||||
mo_env$mo_renamed_last_run <- found["fullname"]
|
||||
pkg_env$mo_renamed_last_run <- found["fullname"]
|
||||
was_renamed(name_old = found["fullname"],
|
||||
name_new = lookup(fullname == found["fullname_new"], "fullname", haystack = MO_lookup),
|
||||
ref_old = found["ref"],
|
||||
@@ -970,7 +973,7 @@ exec_as.mo <- function(x,
|
||||
} else {
|
||||
x[i] <- lookup(fullname == found["fullname_new"], haystack = MO_lookup)
|
||||
}
|
||||
mo_env$mo_renamed_last_run <- found["fullname"]
|
||||
pkg_env$mo_renamed_last_run <- found["fullname"]
|
||||
was_renamed(name_old = found["fullname"],
|
||||
name_new = lookup(fullname == found["fullname_new"], "fullname", haystack = MO_lookup),
|
||||
ref_old = found["ref"],
|
||||
@@ -1022,7 +1025,7 @@ exec_as.mo <- function(x,
|
||||
ref_old = found["ref"],
|
||||
ref_new = lookup(fullname == found["fullname_new"], "ref", haystack = MO_lookup),
|
||||
mo = lookup(fullname == found["fullname_new"], "mo", haystack = MO_lookup))
|
||||
mo_env$mo_renamed_last_run <- found["fullname"]
|
||||
pkg_env$mo_renamed_last_run <- found["fullname"]
|
||||
uncertainties <<- rbind(uncertainties,
|
||||
format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
|
||||
input = a.x_backup,
|
||||
@@ -1393,7 +1396,7 @@ exec_as.mo <- function(x,
|
||||
# handling failures ----
|
||||
failures <- failures[!failures %in% c(NA, NULL, NaN)]
|
||||
if (length(failures) > 0 & initial_search == TRUE) {
|
||||
mo_env$mo_failures <- sort(unique(failures))
|
||||
pkg_env$mo_failures <- sort(unique(failures))
|
||||
plural <- c("value", "it", "was")
|
||||
if (pm_n_distinct(failures) > 1) {
|
||||
plural <- c("values", "them", "were")
|
||||
@@ -1408,10 +1411,10 @@ exec_as.mo <- function(x,
|
||||
msg <- paste0(msg, ": ", paste('"', unique(failures), '"', sep = "", collapse = ", "))
|
||||
}
|
||||
msg <- paste0(msg,
|
||||
".\nUse mo_failures() to review ", plural[2], ". Edit the `allow_uncertain` parameter if needed (see ?as.mo).\n",
|
||||
".\nUse mo_failures() to review ", plural[2], ". Edit the `allow_uncertain` argument if needed (see ?as.mo).\n",
|
||||
"You can also use your own reference data, e.g.:\n",
|
||||
' as.mo("mycode", reference_df = data.frame(own = "mycode", mo = "B_ESCHR_COLI"))\n',
|
||||
' mo_name("mycode", reference_df = data.frame(own = "mycode", mo = "B_ESCHR_COLI"))\n')
|
||||
' as.mo("mycode", reference_df = data.frame(own = "mycode", mo = "', MO_lookup$mo[match("Escherichia coli", MO_lookup$fullname)], '"))\n',
|
||||
' mo_name("mycode", reference_df = data.frame(own = "mycode", mo = "', MO_lookup$mo[match("Escherichia coli", MO_lookup$fullname)], '"))\n')
|
||||
warning_(paste0("\n", msg),
|
||||
add_fn = font_red,
|
||||
call = FALSE,
|
||||
@@ -1420,7 +1423,7 @@ exec_as.mo <- function(x,
|
||||
# handling uncertainties ----
|
||||
if (NROW(uncertainties) > 0 & initial_search == TRUE) {
|
||||
uncertainties <- as.list(pm_distinct(uncertainties, input, .keep_all = TRUE))
|
||||
mo_env$mo_uncertainties <- uncertainties
|
||||
pkg_env$mo_uncertainties <- uncertainties
|
||||
|
||||
plural <- c("", "it", "was")
|
||||
if (length(uncertainties$input) > 1) {
|
||||
@@ -1524,8 +1527,24 @@ exec_as.mo <- function(x,
|
||||
}
|
||||
# this will save the uncertain items as attribute, so they can be bound to `uncertainties` in the uncertain_fn() function
|
||||
x <- structure(x, uncertainties = uncertainties)
|
||||
} else {
|
||||
# keep track of time - give some hints to improve speed if it takes a long time
|
||||
end_time <- Sys.time()
|
||||
delta_time <- difftime(end_time, start_time, units = "secs")
|
||||
if (delta_time >= 30) {
|
||||
message_("Using `as.mo()` took ", delta_time, " seconds, which is a long time. Some suggestions to improve speed include:")
|
||||
message_(word_wrap("- Try to use as many valid taxonomic names as possible for your input.",
|
||||
extra_indent = 2),
|
||||
as_note = FALSE)
|
||||
message_(word_wrap("- Save the output and use it as input for future calculations, e.g. create a new variable to your data using `as.mo()`. All functions in this package that rely on microorganism codes will automatically use that new column where possible. All `mo_*()` functions also do not require you to set their `x` argument as long as you have the dplyr package installed and you have a column of class <mo>.",
|
||||
extra_indent = 2),
|
||||
as_note = FALSE)
|
||||
message_(word_wrap("- Use `set_mo_source()` to continually transform your organisation codes to microorganisms codes used by this package, please see `?mo_source`.",
|
||||
extra_indent = 2),
|
||||
as_note = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
x
|
||||
}
|
||||
|
||||
@@ -1540,13 +1559,13 @@ was_renamed <- function(name_old, name_new, ref_old = "", ref_new = "", mo = "")
|
||||
new_ref = ref_new,
|
||||
mo = mo,
|
||||
stringsAsFactors = FALSE)
|
||||
already_set <- mo_env$mo_renamed
|
||||
already_set <- pkg_env$mo_renamed
|
||||
if (!is.null(already_set)) {
|
||||
mo_env$mo_renamed = rbind(already_set,
|
||||
pkg_env$mo_renamed = rbind(already_set,
|
||||
newly_set,
|
||||
stringsAsFactors = FALSE)
|
||||
} else {
|
||||
mo_env$mo_renamed <- newly_set
|
||||
pkg_env$mo_renamed <- newly_set
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1554,9 +1573,9 @@ format_uncertainty_as_df <- function(uncertainty_level,
|
||||
input,
|
||||
result_mo,
|
||||
candidates = NULL) {
|
||||
if (!is.null(mo_env$mo_renamed_last_run)) {
|
||||
fullname <- mo_env$mo_renamed_last_run
|
||||
mo_env$mo_renamed_last_run <- NULL
|
||||
if (!is.null(pkg_env$mo_renamed_last_run)) {
|
||||
fullname <- pkg_env$mo_renamed_last_run
|
||||
pkg_env$mo_renamed_last_run <- NULL
|
||||
renamed_to <- MO_lookup[match(result_mo, MO_lookup$mo), "fullname", drop = TRUE][1]
|
||||
} else {
|
||||
fullname <- MO_lookup[match(result_mo, MO_lookup$mo), "fullname", drop = TRUE][1]
|
||||
@@ -1585,9 +1604,13 @@ pillar_shaft.mo <- function(x, ...) {
|
||||
out[x == "UNKNOWN"] <- font_na(" UNKNOWN")
|
||||
|
||||
# make it always fit exactly
|
||||
max_char <- max(nchar(x))
|
||||
if (is.na(max_char)) {
|
||||
max_char <- 7
|
||||
}
|
||||
create_pillar_column(out,
|
||||
align = "left",
|
||||
width = max(nchar(x)) + ifelse(any(x %in% c(NA, "UNKNOWN")), 2, 0))
|
||||
width = max_char + ifelse(any(x %in% c(NA, "UNKNOWN")), 2, 0))
|
||||
}
|
||||
|
||||
# will be exported using s3_register() in R/zzz.R
|
||||
@@ -1741,16 +1764,16 @@ unique.mo <- function(x, incomparables = FALSE, ...) {
|
||||
#' @rdname as.mo
|
||||
#' @export
|
||||
mo_failures <- function() {
|
||||
mo_env$mo_failures
|
||||
pkg_env$mo_failures
|
||||
}
|
||||
|
||||
#' @rdname as.mo
|
||||
#' @export
|
||||
mo_uncertainties <- function() {
|
||||
if (is.null(mo_env$mo_uncertainties)) {
|
||||
if (is.null(pkg_env$mo_uncertainties)) {
|
||||
return(NULL)
|
||||
}
|
||||
set_clean_class(as.data.frame(mo_env$mo_uncertainties,
|
||||
set_clean_class(as.data.frame(pkg_env$mo_uncertainties,
|
||||
stringsAsFactors = FALSE),
|
||||
new_class = c("mo_uncertainties", "data.frame"))
|
||||
}
|
||||
@@ -1819,7 +1842,7 @@ print.mo_uncertainties <- function(x, ...) {
|
||||
#' @rdname as.mo
|
||||
#' @export
|
||||
mo_renamed <- function() {
|
||||
items <- mo_env$mo_renamed
|
||||
items <- pkg_env$mo_renamed
|
||||
if (is.null(items)) {
|
||||
items <- data.frame(stringsAsFactors = FALSE)
|
||||
} else {
|
||||
@@ -1883,20 +1906,20 @@ translate_allow_uncertain <- function(allow_uncertain) {
|
||||
}
|
||||
|
||||
get_mo_failures_uncertainties_renamed <- function() {
|
||||
remember <- list(failures = mo_env$mo_failures,
|
||||
uncertainties = mo_env$mo_uncertainties,
|
||||
renamed = mo_env$mo_renamed)
|
||||
remember <- list(failures = pkg_env$mo_failures,
|
||||
uncertainties = pkg_env$mo_uncertainties,
|
||||
renamed = pkg_env$mo_renamed)
|
||||
# empty them, otherwise mo_shortname("Chlamydophila psittaci") will give 3 notes
|
||||
mo_env$mo_failures <- NULL
|
||||
mo_env$mo_uncertainties <- NULL
|
||||
mo_env$mo_renamed <- NULL
|
||||
pkg_env$mo_failures <- NULL
|
||||
pkg_env$mo_uncertainties <- NULL
|
||||
pkg_env$mo_renamed <- NULL
|
||||
remember
|
||||
}
|
||||
|
||||
load_mo_failures_uncertainties_renamed <- function(metadata) {
|
||||
mo_env$mo_failures <- metadata$failures
|
||||
mo_env$mo_uncertainties <- metadata$uncertainties
|
||||
mo_env$mo_renamed <- metadata$renamed
|
||||
pkg_env$mo_failures <- metadata$failures
|
||||
pkg_env$mo_uncertainties <- metadata$uncertainties
|
||||
pkg_env$mo_renamed <- metadata$renamed
|
||||
}
|
||||
|
||||
trimws2 <- function(x) {
|
||||
@@ -1983,5 +2006,3 @@ repair_reference_df <- function(reference_df) {
|
||||
reference_df[, "mo"] <- as.mo(reference_df[, "mo", drop = TRUE])
|
||||
reference_df
|
||||
}
|
||||
|
||||
mo_env <- new.env(hash = FALSE)
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
+157
-65
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -27,12 +27,12 @@
|
||||
#'
|
||||
#' Use these functions to return a specific property of a microorganism based on the latest accepted taxonomy. All input values will be evaluated internally with [as.mo()], which makes it possible to use microbial abbreviations, codes and names as input. Please see *Examples*.
|
||||
#' @inheritSection lifecycle Stable lifecycle
|
||||
#' @param x any character (vector) that can be coerced to a valid microorganism code with [as.mo()]. Can be omitted for auto-guessing in `mo_is_*()` functions when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()], please see *Examples*.
|
||||
#' @param x any character (vector) that can be coerced to a valid microorganism code with [as.mo()]. Can be left blank for auto-guessing the column containing microorganism codes when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()], please see *Examples*.
|
||||
#' @param property one of the column names of the [microorganisms] data set: `r paste0('"``', colnames(microorganisms), '\``"', collapse = ", ")`, or must be `"shortname"`
|
||||
#' @param language language of the returned text, defaults to system language (see [get_locale()]) and can be overwritten by setting the option `AMR_locale`, e.g. `options(AMR_locale = "de")`, see [translate]. Also used to translate text like "no growth". Use `language = NULL` or `language = ""` to prevent translation.
|
||||
#' @param ... other parameters passed on to [as.mo()], such as 'allow_uncertain' and 'ignore_pattern'
|
||||
#' @param ... other arguments passed on to [as.mo()], such as 'allow_uncertain' and 'ignore_pattern'
|
||||
#' @param ab any (vector of) text that can be coerced to a valid antibiotic code with [as.ab()]
|
||||
#' @param open browse the URL using [utils::browseURL()]
|
||||
#' @param open browse the URL using [`browseURL()`][utils::browseURL()]
|
||||
#' @details All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for [mo_ref()], [mo_authors()] and [mo_year()]. Please refer to this example, knowing that *Escherichia blattae* was renamed to *Shimwellia blattae* in 2010:
|
||||
#' - `mo_name("Escherichia blattae")` will return `"Shimwellia blattae"` (with a message about the renaming)
|
||||
#' - `mo_ref("Escherichia blattae")` will return `"Burgess et al., 1973"` (with a message about the renaming)
|
||||
@@ -44,7 +44,7 @@
|
||||
#'
|
||||
#' The Gram stain - [mo_gramstain()] - will be determined based on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002, [PMID 11837318](https://pubmed.ncbi.nlm.nih.gov/11837318)), 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 `NA`. Functions [mo_is_gram_negative()] and [mo_is_gram_positive()] always return `TRUE` or `FALSE` (except when the input is `NA` or the MO code is `UNKNOWN`), thus always return `FALSE` for species outside the taxonomic kingdom of Bacteria.
|
||||
#'
|
||||
#' Intrinsic resistance - [mo_is_intrinsic_resistant()] - will be determined based on the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(3.2)`. The [mo_is_intrinsic_resistant()] can be vectorised over parameters `x` (input for microorganisms) and over `ab` (input for antibiotics).
|
||||
#' Intrinsic resistance - [mo_is_intrinsic_resistant()] - will be determined based on the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(3.2)`. The [mo_is_intrinsic_resistant()] can be vectorised over arguments `x` (input for microorganisms) and over `ab` (input for antibiotics).
|
||||
#'
|
||||
#' All output will be [translate]d where possible.
|
||||
#'
|
||||
@@ -161,9 +161,13 @@
|
||||
#' mo_info("E. coli")
|
||||
#' }
|
||||
mo_name <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_name")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "fullname", language = language, ...), language = language, only_unknown = FALSE)
|
||||
}
|
||||
|
||||
@@ -174,22 +178,26 @@ mo_fullname <- mo_name
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_shortname <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_shortname")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x.mo <- as.mo(x, language = language, ...)
|
||||
|
||||
|
||||
metadata <- get_mo_failures_uncertainties_renamed()
|
||||
|
||||
|
||||
replace_empty <- function(x) {
|
||||
x[x == ""] <- "spp."
|
||||
x
|
||||
}
|
||||
|
||||
|
||||
# get first char of genus and complete species in English
|
||||
genera <- mo_genus(x.mo, language = NULL)
|
||||
shortnames <- paste0(substr(genera, 1, 1), ". ", replace_empty(mo_species(x.mo, language = NULL)))
|
||||
|
||||
|
||||
# exceptions for where no species is known
|
||||
shortnames[shortnames %like% ".[.] spp[.]"] <- genera[shortnames %like% ".[.] spp[.]"]
|
||||
# exceptions for staphylococci
|
||||
@@ -199,7 +207,8 @@ mo_shortname <- function(x, language = get_locale(), ...) {
|
||||
shortnames[shortnames %like% "S. group [ABCDFGHK]"] <- paste0("G", gsub("S. group ([ABCDFGHK])", "\\1", shortnames[shortnames %like% "S. group [ABCDFGHK]"]), "S")
|
||||
# unknown species etc.
|
||||
shortnames[shortnames %like% "unknown"] <- paste0("(", trimws(gsub("[^a-zA-Z -]", "", shortnames[shortnames %like% "unknown"])), ")")
|
||||
|
||||
|
||||
shortnames[is.na(x.mo)] <- NA_character_
|
||||
load_mo_failures_uncertainties_renamed(metadata)
|
||||
translate_AMR(shortnames, language = language, only_unknown = FALSE)
|
||||
}
|
||||
@@ -207,72 +216,104 @@ mo_shortname <- function(x, language = get_locale(), ...) {
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_subspecies <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_subspecies")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "subspecies", language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_species <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_species")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "species", language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_genus <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_genus")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "genus", language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_family <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_family")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "family", language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_order <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_order")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "order", language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_class <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_class")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "class", language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_phylum <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_phylum")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "phylum", language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_kingdom <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_kingdom")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "kingdom", language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
@@ -283,21 +324,29 @@ mo_domain <- mo_kingdom
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_type <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_type")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = "kingdom", language = language, ...), language = language, only_unknown = FALSE)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_gramstain <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_gramstain")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x.mo <- as.mo(x, language = language, ...)
|
||||
metadata <- get_mo_failures_uncertainties_renamed()
|
||||
|
||||
|
||||
x.phylum <- mo_phylum(x.mo)
|
||||
# DETERMINE GRAM STAIN FOR BACTERIA
|
||||
# Source: https://itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=956097
|
||||
@@ -318,7 +367,7 @@ mo_gramstain <- function(x, language = get_locale(), ...) {
|
||||
"Firmicutes",
|
||||
"Tenericutes")
|
||||
| x.mo == "B_GRAMP"] <- "Gram-positive"
|
||||
|
||||
|
||||
load_mo_failures_uncertainties_renamed(metadata)
|
||||
translate_AMR(x, language = language, only_unknown = FALSE)
|
||||
}
|
||||
@@ -327,12 +376,12 @@ mo_gramstain <- function(x, language = get_locale(), ...) {
|
||||
#' @export
|
||||
mo_is_gram_negative <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this supports using in dplyr verbs: ... %>% filter(mo_is_gram_negative())
|
||||
x <- find_mo_col("mo_is_gram_negative")
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_is_gram_negative")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x.mo <- as.mo(x, language = language, ...)
|
||||
metadata <- get_mo_failures_uncertainties_renamed()
|
||||
grams <- mo_gramstain(x.mo, language = NULL)
|
||||
@@ -346,12 +395,12 @@ mo_is_gram_negative <- function(x, language = get_locale(), ...) {
|
||||
#' @export
|
||||
mo_is_gram_positive <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this supports using in dplyr verbs: ... %>% filter(mo_is_gram_positive())
|
||||
x <- find_mo_col("mo_is_gram_positive")
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_is_gram_positive")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x.mo <- as.mo(x, language = language, ...)
|
||||
metadata <- get_mo_failures_uncertainties_renamed()
|
||||
grams <- mo_gramstain(x.mo, language = NULL)
|
||||
@@ -365,8 +414,8 @@ mo_is_gram_positive <- function(x, language = get_locale(), ...) {
|
||||
#' @export
|
||||
mo_is_intrinsic_resistant <- function(x, ab, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this supports using in dplyr verbs: ... %>% filter(mo_is_intrinsic_resistant(ab = "amox"))
|
||||
x <- find_mo_col("mo_is_intrinsic_resistant")
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_is_intrinsic_resistant")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(ab, allow_NA = FALSE)
|
||||
@@ -384,12 +433,12 @@ mo_is_intrinsic_resistant <- function(x, ab, language = get_locale(), ...) {
|
||||
stop_("length of `x` and `ab` must be equal, or one of them must be of length 1.")
|
||||
}
|
||||
|
||||
# show used version number once per session
|
||||
if (is.null(getOption("AMR_intrinsic_resistance_note", NULL))) {
|
||||
# show used version number once per session (pkg_env will reload every session)
|
||||
if (message_not_thrown_before("intrinsic_resistant_version", entire_session = TRUE)) {
|
||||
message_("Determining intrinsic resistance based on ",
|
||||
format_eucast_version_nr(3.2, FALSE), ". ",
|
||||
font_bold("This note is shown only once per session."))
|
||||
options(AMR_intrinsic_resistance_note = "shown")
|
||||
format_eucast_version_nr(3.2, markdown = FALSE), ". ",
|
||||
font_red("This note will be shown once per session."))
|
||||
remember_thrown_message("intrinsic_resistant_version", entire_session = TRUE)
|
||||
}
|
||||
|
||||
# runs against internal vector: INTRINSIC_R (see zzz.R)
|
||||
@@ -399,27 +448,39 @@ mo_is_intrinsic_resistant <- function(x, ab, language = get_locale(), ...) {
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_snomed <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_snomed")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
mo_validate(x = x, property = "snomed", language = language, ...)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_ref <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_ref")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
mo_validate(x = x, property = "ref", language = language, ...)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_authors <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_authors")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x <- mo_validate(x = x, property = "ref", language = language, ...)
|
||||
# remove last 4 digits and presumably the comma and space that preceed them
|
||||
x[!is.na(x)] <- gsub(",? ?[0-9]{4}", "", x[!is.na(x)])
|
||||
@@ -429,9 +490,13 @@ mo_authors <- function(x, language = get_locale(), ...) {
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_year <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_year")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x <- mo_validate(x = x, property = "ref", language = language, ...)
|
||||
# get last 4 digits
|
||||
x[!is.na(x)] <- gsub(".*([0-9]{4})$", "\\1", x[!is.na(x)])
|
||||
@@ -441,21 +506,29 @@ mo_year <- function(x, language = get_locale(), ...) {
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_rank <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_rank")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
mo_validate(x = x, property = "rank", language = language, ...)
|
||||
}
|
||||
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_taxonomy <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_taxonomy")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x <- as.mo(x, language = language, ...)
|
||||
metadata <- get_mo_failures_uncertainties_renamed()
|
||||
|
||||
|
||||
result <- list(kingdom = mo_kingdom(x, language = language),
|
||||
phylum = mo_phylum(x, language = language),
|
||||
class = mo_class(x, language = language),
|
||||
@@ -464,7 +537,7 @@ mo_taxonomy <- function(x, language = get_locale(), ...) {
|
||||
genus = mo_genus(x, language = language),
|
||||
species = mo_species(x, language = language),
|
||||
subspecies = mo_subspecies(x, language = language))
|
||||
|
||||
|
||||
load_mo_failures_uncertainties_renamed(metadata)
|
||||
result
|
||||
}
|
||||
@@ -472,12 +545,16 @@ mo_taxonomy <- function(x, language = get_locale(), ...) {
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_synonyms <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_synonyms")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x <- as.mo(x, language = language, ...)
|
||||
metadata <- get_mo_failures_uncertainties_renamed()
|
||||
|
||||
|
||||
IDs <- mo_name(x = x, language = NULL)
|
||||
syns <- lapply(IDs, function(newname) {
|
||||
res <- sort(microorganisms.old[which(microorganisms.old$fullname_new == newname), "fullname"])
|
||||
@@ -493,7 +570,7 @@ mo_synonyms <- function(x, language = get_locale(), ...) {
|
||||
} else {
|
||||
result <- unlist(syns)
|
||||
}
|
||||
|
||||
|
||||
load_mo_failures_uncertainties_renamed(metadata)
|
||||
result
|
||||
}
|
||||
@@ -501,12 +578,16 @@ mo_synonyms <- function(x, language = get_locale(), ...) {
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_info <- function(x, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_info")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
x <- as.mo(x, language = language, ...)
|
||||
metadata <- get_mo_failures_uncertainties_renamed()
|
||||
|
||||
|
||||
info <- lapply(x, function(y)
|
||||
c(mo_taxonomy(y, language = language),
|
||||
list(synonyms = mo_synonyms(y),
|
||||
@@ -519,7 +600,7 @@ mo_info <- function(x, language = get_locale(), ...) {
|
||||
} else {
|
||||
result <- info[[1L]]
|
||||
}
|
||||
|
||||
|
||||
load_mo_failures_uncertainties_renamed(metadata)
|
||||
result
|
||||
}
|
||||
@@ -527,14 +608,18 @@ mo_info <- function(x, language = get_locale(), ...) {
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_url <- function(x, open = FALSE, language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_url")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(open, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
mo <- as.mo(x = x, language = language, ... = ...)
|
||||
mo_names <- mo_name(mo)
|
||||
metadata <- get_mo_failures_uncertainties_renamed()
|
||||
|
||||
|
||||
df <- data.frame(mo, stringsAsFactors = FALSE) %pm>%
|
||||
pm_left_join(pm_select(microorganisms, mo, source, species_id), by = "mo")
|
||||
df$url <- ifelse(df$source == "CoL",
|
||||
@@ -544,14 +629,14 @@ mo_url <- function(x, open = FALSE, language = get_locale(), ...) {
|
||||
NA_character_))
|
||||
u <- df$url
|
||||
names(u) <- mo_names
|
||||
|
||||
|
||||
if (open == TRUE) {
|
||||
if (length(u) > 1) {
|
||||
warning_("Only the first URL will be opened, as `browseURL()` only suports one string.")
|
||||
}
|
||||
utils::browseURL(u[1L])
|
||||
}
|
||||
|
||||
|
||||
load_mo_failures_uncertainties_renamed(metadata)
|
||||
u
|
||||
}
|
||||
@@ -560,21 +645,25 @@ mo_url <- function(x, open = FALSE, language = get_locale(), ...) {
|
||||
#' @rdname mo_property
|
||||
#' @export
|
||||
mo_property <- function(x, property = "fullname", language = get_locale(), ...) {
|
||||
if (missing(x)) {
|
||||
# this tries to find the data and an <mo> column
|
||||
x <- find_mo_col(fn = "mo_property")
|
||||
}
|
||||
meet_criteria(x, allow_NA = TRUE)
|
||||
meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(microorganisms))
|
||||
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
|
||||
|
||||
|
||||
translate_AMR(mo_validate(x = x, property = property, language = language, ...), language = language, only_unknown = TRUE)
|
||||
}
|
||||
|
||||
mo_validate <- function(x, property, language, ...) {
|
||||
check_dataset_integrity()
|
||||
|
||||
|
||||
if (tryCatch(all(x[!is.na(x)] %in% MO_lookup$mo) & length(list(...)) == 0, error = function(e) FALSE)) {
|
||||
# special case for mo_* functions where class is already <mo>
|
||||
return(MO_lookup[match(x, MO_lookup$mo), property, drop = TRUE])
|
||||
}
|
||||
|
||||
|
||||
dots <- list(...)
|
||||
Becker <- dots$Becker
|
||||
if (is.null(Becker)) {
|
||||
@@ -584,12 +673,12 @@ mo_validate <- function(x, property, language, ...) {
|
||||
if (is.null(Lancefield)) {
|
||||
Lancefield <- FALSE
|
||||
}
|
||||
|
||||
# try to catch an error when inputting an invalid parameter
|
||||
|
||||
# try to catch an error when inputting an invalid argument
|
||||
# so the 'call.' can be set to FALSE
|
||||
tryCatch(x[1L] %in% MO_lookup[1, property, drop = TRUE],
|
||||
error = function(e) stop(e$message, call. = FALSE))
|
||||
|
||||
|
||||
if (is.mo(x)
|
||||
& !Becker %in% c(TRUE, "all")
|
||||
& !Lancefield %in% c(TRUE, "all")) {
|
||||
@@ -601,7 +690,7 @@ mo_validate <- function(x, property, language, ...) {
|
||||
| Lancefield %in% c(TRUE, "all")) {
|
||||
x <- exec_as.mo(x, property = property, language = language, ...)
|
||||
}
|
||||
|
||||
|
||||
if (property == "mo") {
|
||||
return(set_clean_class(x, new_class = c("mo", "character")))
|
||||
} else if (property == "snomed") {
|
||||
@@ -612,15 +701,18 @@ mo_validate <- function(x, property, language, ...) {
|
||||
}
|
||||
|
||||
find_mo_col <- function(fn) {
|
||||
# this function tries to find an mo column using dplyr::cur_data_all() for mo_is_*() functions,
|
||||
# this function tries to find an mo column in the data the function was called in,
|
||||
# which is useful when functions are used within dplyr verbs
|
||||
df <- get_current_data("x", call = -3) # will return an error if not found
|
||||
df <- get_current_data(arg_name = "x", call = -3) # will return an error if not found
|
||||
mo <- NULL
|
||||
try({
|
||||
mo <- suppressMessages(search_type_in_df(df, "mo"))
|
||||
}, silent = TRUE)
|
||||
if (!is.null(df) && !is.null(mo) && is.data.frame(df)) {
|
||||
message_("Using column '", font_bold(mo), "' as input for ", fn, "()")
|
||||
if (message_not_thrown_before(fn = fn)) {
|
||||
message_("Using column '", font_bold(mo), "' as input for ", fn, "()")
|
||||
remember_thrown_message(fn = fn)
|
||||
}
|
||||
return(df[, mo, drop = TRUE])
|
||||
} else {
|
||||
stop_("argument `x` is missing and no column with info about microorganisms could be found.", call = -2)
|
||||
|
||||
+26
-25
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -25,18 +25,18 @@
|
||||
|
||||
#' User-defined reference data set for microorganisms
|
||||
#'
|
||||
#' @description These functions can be used to predefine your own reference to be used in [as.mo()] and consequently all `mo_*` functions like [mo_genus()] and [mo_gramstain()].
|
||||
#' @description These functions can be used to predefine your own reference to be used in [as.mo()] and consequently all [`mo_*`][mo_property()] functions (such as [mo_genus()] and [mo_gramstain()]).
|
||||
#'
|
||||
#' This is **the fastest way** to have your organisation (or analysis) specific codes picked up and translated by this package.
|
||||
#' This is **the fastest way** to have your organisation (or analysis) specific codes picked up and translated by this package, since you don't have to bother about it again after setting it up once.
|
||||
#' @inheritSection lifecycle Stable lifecycle
|
||||
#' @param path location of your reference file, see Details. Can be `""`, `NULL` or `FALSE` to delete the reference file.
|
||||
#' @param destination destination of the compressed data file, default to the user's home directory.
|
||||
#' @rdname mo_source
|
||||
#' @name mo_source
|
||||
#' @aliases set_mo_source get_mo_source
|
||||
#' @details The reference file can be a text file separated 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 will need to have the `readxl` package installed.
|
||||
#' @details The reference file can be a text file separated 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 will need to have the `readxl` package installed.
|
||||
#'
|
||||
#' [set_mo_source()] will check the file for validity: it must be a [data.frame], must have a column named `"mo"` which contains values from [`microorganisms$mo`][microorganisms] and must have a reference column with your own defined values. If all tests pass, [set_mo_source()] will read the file into R and will ask to export it to `"~/mo_source.rds"`. The CRAN policy disallows packages to write to the file system, although '*exceptions may be allowed in interactive sessions if the package obtains confirmation from the user*'. For this reason, this function only works in interactive sessions so that the user can **specifically confirm and allow** that this file will be created. The destination of this file can be set with the `destination` parameter and defaults to the user's home directory. It can also be set as an \R option, using `options(AMR_mo_source = "my/location/file.rds)`.
|
||||
#' [set_mo_source()] will check the file for validity: it must be a [data.frame], must have a column named `"mo"` which contains values from [`microorganisms$mo`][microorganisms] and must have a reference column with your own defined values. If all tests pass, [set_mo_source()] will read the file into \R and will ask to export it to `"~/mo_source.rds"`. The CRAN policy disallows packages to write to the file system, although '*exceptions may be allowed in interactive sessions if the package obtains confirmation from the user*'. For this reason, this function only works in interactive sessions so that the user can **specifically confirm and allow** that this file will be created. The destination of this file can be set with the `destination` argument and defaults to the user's home directory. It can also be set as an \R option, using `options(AMR_mo_source = "my/location/file.rds")`.
|
||||
#'
|
||||
#' The created compressed data file `"mo_source.rds"` will be used at default for MO determination (function [as.mo()] and consequently all `mo_*` functions like [mo_genus()] and [mo_gramstain()]). The location and timestamp of the original file will be saved as an attribute to the compressed data file.
|
||||
#'
|
||||
@@ -103,7 +103,7 @@
|
||||
#' ```
|
||||
#' as.mo("lab_mo_ecoli")
|
||||
#' #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from
|
||||
#' #> '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns
|
||||
#' #> '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns
|
||||
#' #> "Organisation XYZ" and "mo"
|
||||
#' #> Class <mo>
|
||||
#' #> [1] B_ESCHR_COLI
|
||||
@@ -119,20 +119,20 @@
|
||||
#' #> Removed mo_source file '/Users/me/mo_source.rds'
|
||||
#' ```
|
||||
#'
|
||||
#' If the original Excel file is moved or deleted, the mo_source file will be removed upon the next use of [as.mo()].
|
||||
#' If the original file (in the previous case an Excel file) is moved or deleted, the `mo_source.rds` file will be removed upon the next use of [as.mo()] or any [`mo_*`][mo_property()] function.
|
||||
#' @export
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_source.rds")) {
|
||||
meet_criteria(path, allow_class = "character", has_length = 1, allow_NULL = TRUE)
|
||||
meet_criteria(destination, allow_class = "character", has_length = 1)
|
||||
stop_ifnot(destination %like% "[.]rds$", "the `destination` must be a file location with file extension .rds")
|
||||
stop_ifnot(destination %like% "[.]rds$", "the `destination` must be a file location with file extension .rds.")
|
||||
|
||||
mo_source_destination <- path.expand(destination)
|
||||
|
||||
stop_ifnot(interactive(), "This function can only be used in interactive mode, since it must ask for the user's permission to write a file to their home folder.")
|
||||
stop_ifnot(interactive(), "this function can only be used in interactive mode, since it must ask for the user's permission to write a file to their home folder.")
|
||||
|
||||
if (is.null(path) || path %in% c(FALSE, "")) {
|
||||
mo_env$mo_source <- NULL
|
||||
pkg_env$mo_source <- NULL
|
||||
if (file.exists(mo_source_destination)) {
|
||||
unlink(mo_source_destination)
|
||||
message_("Removed mo_source file '", font_bold(mo_source_destination), "'",
|
||||
@@ -149,8 +149,8 @@ set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_s
|
||||
|
||||
} else if (path %like% "[.]xlsx?$") {
|
||||
# is Excel file (old or new)
|
||||
read_excel <- import_fn("read_excel", "readxl")
|
||||
df <- read_excel(path)
|
||||
stop_ifnot_installed("readxl")
|
||||
df <- readxl::read_excel(path)
|
||||
|
||||
} else if (path %like% "[.]tsv$") {
|
||||
df <- utils::read.table(header = TRUE, sep = "\t", stringsAsFactors = FALSE)
|
||||
@@ -160,13 +160,13 @@ set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_s
|
||||
try(
|
||||
df <- utils::read.table(header = TRUE, sep = ",", stringsAsFactors = FALSE),
|
||||
silent = TRUE)
|
||||
if (!mo_source_isvalid(df, stop_on_error = FALSE)) {
|
||||
if (!check_validity_mo_source(df, stop_on_error = FALSE)) {
|
||||
# try tab
|
||||
try(
|
||||
df <- utils::read.table(header = TRUE, sep = "\t", stringsAsFactors = FALSE),
|
||||
silent = TRUE)
|
||||
}
|
||||
if (!mo_source_isvalid(df, stop_on_error = FALSE)) {
|
||||
if (!check_validity_mo_source(df, stop_on_error = FALSE)) {
|
||||
# try pipe
|
||||
try(
|
||||
df <- utils::read.table(header = TRUE, sep = "|", stringsAsFactors = FALSE),
|
||||
@@ -175,7 +175,7 @@ set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_s
|
||||
}
|
||||
|
||||
# check integrity
|
||||
mo_source_isvalid(df)
|
||||
check_validity_mo_source(df)
|
||||
|
||||
df <- subset(df, !is.na(mo))
|
||||
|
||||
@@ -211,9 +211,10 @@ set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_s
|
||||
}
|
||||
}
|
||||
attr(df, "mo_source_location") <- path
|
||||
attr(df, "mo_source_destination") <- mo_source_destination
|
||||
attr(df, "mo_source_timestamp") <- file.mtime(path)
|
||||
saveRDS(df, mo_source_destination)
|
||||
mo_env$mo_source <- df
|
||||
pkg_env$mo_source <- df
|
||||
message_(action, " mo_source file '", font_bold(mo_source_destination),
|
||||
"' (", formatted_filesize(mo_source_destination),
|
||||
") from '", font_bold(path),
|
||||
@@ -226,31 +227,31 @@ set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_s
|
||||
get_mo_source <- function(destination = getOption("AMR_mo_source", "~/mo_source.rds")) {
|
||||
if (!file.exists(path.expand(destination))) {
|
||||
if (interactive()) {
|
||||
# source file might have been deleted, update reference
|
||||
# source file might have been deleted, so update reference
|
||||
set_mo_source("")
|
||||
}
|
||||
return(NULL)
|
||||
}
|
||||
if (is.null(mo_env$mo_source)) {
|
||||
mo_env$mo_source <- readRDS(path.expand(destination))
|
||||
if (is.null(pkg_env$mo_source)) {
|
||||
pkg_env$mo_source <- readRDS(path.expand(destination))
|
||||
}
|
||||
|
||||
old_time <- attributes(mo_env$mo_source)$mo_source_timestamp
|
||||
new_time <- file.mtime(attributes(mo_env$mo_source)$mo_source_location)
|
||||
old_time <- attributes(pkg_env$mo_source)$mo_source_timestamp
|
||||
new_time <- file.mtime(attributes(pkg_env$mo_source)$mo_source_location)
|
||||
if (interactive() && !identical(old_time, new_time)) {
|
||||
# source file was updated, also update reference
|
||||
set_mo_source(attributes(mo_env$mo_source)$mo_source_location)
|
||||
set_mo_source(attributes(pkg_env$mo_source)$mo_source_location)
|
||||
}
|
||||
mo_env$mo_source
|
||||
pkg_env$mo_source
|
||||
}
|
||||
|
||||
mo_source_isvalid <- function(x, refer_to_name = "`reference_df`", stop_on_error = TRUE) {
|
||||
check_validity_mo_source <- function(x, refer_to_name = "`reference_df`", stop_on_error = TRUE) {
|
||||
check_dataset_integrity()
|
||||
|
||||
if (paste(deparse(substitute(x)), collapse = "") == "get_mo_source()") {
|
||||
return(TRUE)
|
||||
}
|
||||
if (is.null(mo_env$mo_source) && (identical(x, get_mo_source()))) {
|
||||
if (is.null(pkg_env$mo_source) && (identical(x, get_mo_source()))) {
|
||||
return(TRUE)
|
||||
}
|
||||
if (is.null(x)) {
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -90,14 +90,14 @@ pca <- function(x,
|
||||
# this is to support quoted variables: df %pm>% pca("mycol1", "mycol2")
|
||||
new_list[[i]] <- x[, new_list[[i]]]
|
||||
} else {
|
||||
# remove item - it's a parameter like `center`
|
||||
# remove item - it's a argument like `center`
|
||||
new_list[[i]] <- NULL
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
x <- as.data.frame(new_list, stringsAsFactors = FALSE)
|
||||
if (any(sapply(x, function(y) !is.numeric(y)))) {
|
||||
if (any(vapply(FUN.VALUE = logical(1), x, function(y) !is.numeric(y)))) {
|
||||
warning_("Be sure to first calculate the resistance (or susceptibility) of variables with antimicrobial test results, since PCA works with numeric variables only. Please see Examples in ?pca.")
|
||||
}
|
||||
|
||||
@@ -106,21 +106,21 @@ pca <- function(x,
|
||||
error = function(e) warning("column names could not be set"))
|
||||
|
||||
# keep only numeric columns
|
||||
x <- x[, sapply(x, function(y) is.numeric(y))]
|
||||
x <- x[, vapply(FUN.VALUE = logical(1), x, function(y) is.numeric(y))]
|
||||
# bind the data set with the non-numeric columns
|
||||
x <- cbind(x.bak[, sapply(x.bak, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE], x)
|
||||
x <- cbind(x.bak[, vapply(FUN.VALUE = logical(1), x.bak, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE], x)
|
||||
}
|
||||
|
||||
x <- pm_ungroup(x) # would otherwise select the grouping vars
|
||||
x <- x[rowSums(is.na(x)) == 0, ] # remove columns containing NAs
|
||||
|
||||
pca_data <- x[, which(sapply(x, function(x) is.numeric(x)))]
|
||||
pca_data <- x[, which(vapply(FUN.VALUE = logical(1), x, function(x) is.numeric(x)))]
|
||||
|
||||
message_("Columns selected for PCA: ", paste0(font_bold(colnames(pca_data)), collapse = "/"),
|
||||
". Total observations available: ", nrow(pca_data), ".")
|
||||
|
||||
pca_model <- prcomp(pca_data, retx = retx, center = center, scale. = scale., tol = tol, rank. = rank.)
|
||||
attr(pca_model, "non_numeric_cols") <- x[, sapply(x, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE]
|
||||
attr(pca_model, "non_numeric_cols") <- x[, vapply(FUN.VALUE = logical(1), x, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE]
|
||||
class(pca_model) <- c("pca", class(pca_model))
|
||||
pca_model
|
||||
}
|
||||
+4
-4
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -36,15 +36,15 @@
|
||||
#' @param data a [data.frame] containing columns with class [`rsi`] (see [as.rsi()])
|
||||
#' @param translate_ab a column name of the [antibiotics] data set to translate the antibiotic abbreviations to, using [ab_property()]
|
||||
#' @inheritParams ab_property
|
||||
#' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter `combine_IR`, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is `TRUE`.
|
||||
#' @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. I+R (susceptible vs. non-susceptible). This is outdated, see parameter `combine_SI`.
|
||||
#' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the argument `combine_IR`, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is `TRUE`.
|
||||
#' @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. I+R (susceptible vs. non-susceptible). This is outdated, see argument `combine_SI`.
|
||||
#' @inheritSection as.rsi Interpretation of R and S/I
|
||||
#' @details
|
||||
#' The function [resistance()] is equal to the function [proportion_R()]. The function [susceptibility()] is equal to the function [proportion_SI()].
|
||||
#'
|
||||
#' **Remember that you should filter your table to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use [first_isolate()] to determine them in your data set.
|
||||
#'
|
||||
#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [`count()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` parameter).*
|
||||
#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [`count()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` argument).*
|
||||
#'
|
||||
#' The function [proportion_df()] takes any variable from `data` that has an [`rsi`] class (created with [as.rsi()]) and calculates the proportions R, I and S. It also supports grouped variables. The function [rsi_df()] works exactly like [proportion_df()], but adds the number of isolates.
|
||||
#' @section Combination therapy:
|
||||
|
||||
+4
-4
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -34,7 +34,7 @@
|
||||
#' @param ... extension for future versions, not used at the moment
|
||||
#' @details The base R function [sample()] is used for generating values.
|
||||
#'
|
||||
#' Generated values are based on the latest EUCAST guideline implemented in the [rsi_translation] data set. To create specific generated values per bug or drug, set the `mo` and/or `ab` parameter.
|
||||
#' Generated values are based on the latest EUCAST guideline implemented in the [rsi_translation] data set. To create specific generated values per bug or drug, set the `mo` and/or `ab` argument.
|
||||
#' @return class `<mic>` for [random_mic()] (see [as.mic()]) and class `<disk>` for [random_disk()] (see [as.disk()])
|
||||
#' @name random
|
||||
#' @rdname random
|
||||
@@ -89,7 +89,7 @@ random_exec <- function(type, size, mo = NULL, ab = NULL) {
|
||||
if (nrow(df_new) > 0) {
|
||||
df <- df_new
|
||||
} else {
|
||||
warning_("No rows found that match mo '", mo, "', ignoring parameter `mo`", call = FALSE)
|
||||
warning_("No rows found that match mo '", mo, "', ignoring argument `mo`", call = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -100,7 +100,7 @@ random_exec <- function(type, size, mo = NULL, ab = NULL) {
|
||||
if (nrow(df_new) > 0) {
|
||||
df <- df_new
|
||||
} else {
|
||||
warning_("No rows found that match ab '", ab, "', ignoring parameter `ab`", call = FALSE)
|
||||
warning_("No rows found that match ab '", ab, "', ignoring argument `ab`", call = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -39,11 +39,11 @@
|
||||
#' @param info a logical to indicate whether textual analysis should be printed with the name and [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
|
||||
#' @param ... parameters passed on to functions
|
||||
#' @param ... arguments passed on to functions
|
||||
#' @inheritSection as.rsi Interpretation of R and S/I
|
||||
#' @inheritParams first_isolate
|
||||
#' @inheritParams graphics::plot
|
||||
#' @details Valid options for the statistical model (parameter `model`) are:
|
||||
#' @details Valid options for the statistical model (argument `model`) are:
|
||||
#' - `"binomial"` or `"binom"` or `"logit"`: a generalised linear regression model with binomial distribution
|
||||
#' - `"loglin"` or `"poisson"`: a generalised log-linear regression model with poisson distribution
|
||||
#' - `"lin"` or `"linear"`: a linear regression model
|
||||
@@ -138,11 +138,11 @@ resistance_predict <- function(x,
|
||||
meet_criteria(preserve_measurements, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(info, allow_class = "logical", has_length = 1)
|
||||
|
||||
stop_if(is.null(model), 'choose a regression model with the `model` parameter, e.g. resistance_predict(..., model = "binomial")')
|
||||
stop_if(is.null(model), 'choose a regression model with the `model` argument, e.g. resistance_predict(..., model = "binomial")')
|
||||
|
||||
dots <- unlist(list(...))
|
||||
if (length(dots) != 0) {
|
||||
# backwards compatibility with old parameters
|
||||
# backwards compatibility with old arguments
|
||||
dots.names <- dots %pm>% names()
|
||||
if ("tbl" %in% dots.names) {
|
||||
x <- dots[which(dots.names == "tbl")]
|
||||
@@ -158,7 +158,7 @@ resistance_predict <- function(x,
|
||||
stop_if(is.null(col_date), "`col_date` must be set")
|
||||
}
|
||||
stop_ifnot(col_date %in% colnames(x),
|
||||
"column `", col_date, "` not found")
|
||||
"column '", col_date, "' not found")
|
||||
|
||||
# no grouped tibbles
|
||||
x <- as.data.frame(x, stringsAsFactors = FALSE)
|
||||
@@ -192,7 +192,9 @@ resistance_predict <- function(x,
|
||||
rownames(df) <- NULL
|
||||
|
||||
df <- subset(df, sum(df$R + df$S, na.rm = TRUE) >= minimum)
|
||||
# nolint start
|
||||
df_matrix <- as.matrix(df[, c("R", "S"), drop = FALSE])
|
||||
# nolint end
|
||||
|
||||
stop_if(NROW(df) == 0, "there are no observations")
|
||||
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -36,9 +36,9 @@
|
||||
#' @param guideline defaults to the latest included EUCAST guideline, see Details for all options
|
||||
#' @param conserve_capped_values a logical to indicate that MIC values starting with `">"` (but not `">="`) must always return "R" , and that MIC values starting with `"<"` (but not `"<="`) must always return "S"
|
||||
#' @param add_intrinsic_resistance *(only useful when using a EUCAST guideline)* a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in *Klebsiella* species. Determination is based on the [intrinsic_resistant] data set, that itself is based on `r format_eucast_version_nr(3.2)`.
|
||||
#' @param reference_data a [data.frame] to be used for interpretation, which defaults to the [rsi_translation] data set. Changing this parameter allows for using own interpretation guidelines. This parameter must contain a data set that is equal in structure to the [rsi_translation] data set (same column names and column types). Please note that the `guideline` parameter will be ignored when `reference_data` is manually set.
|
||||
#' @param reference_data a [data.frame] to be used for interpretation, which defaults to the [rsi_translation] data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the [rsi_translation] data set (same column names and column types). Please note that the `guideline` argument will be ignored when `reference_data` is manually set.
|
||||
#' @param threshold maximum fraction of invalid antimicrobial interpretations of `x`, please see *Examples*
|
||||
#' @param ... for using on a [data.frame]: names of columns to apply [as.rsi()] on (supports tidy selection like `AMX:VAN`). Otherwise: parameters passed on to methods.
|
||||
#' @param ... for using on a [data.frame]: names of columns to apply [as.rsi()] on (supports tidy selection like `AMX:VAN`). Otherwise: arguments passed on to methods.
|
||||
#' @details
|
||||
#' ## How it works
|
||||
#'
|
||||
@@ -46,7 +46,7 @@
|
||||
#'
|
||||
#' 1. For **cleaning raw / untransformed data**. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with R/SI interpretations and MIC values such as `"<0.25; S"` will be coerced to `"S"`. Combined interpretations for multiple test methods (as seen in laboratory records) such as `"S; S"` will be coerced to `"S"`, but a value like `"S; I"` will return `NA` with a warning that the input is unclear.
|
||||
#'
|
||||
#' 2. For **interpreting minimum inhibitory concentration (MIC) values** according to EUCAST or CLSI. You must clean your MIC values first using [as.mic()], that also gives your columns the new data class [`mic`]. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` parameter.
|
||||
#' 2. For **interpreting minimum inhibitory concentration (MIC) values** according to EUCAST or CLSI. You must clean your MIC values first using [as.mic()], that also gives your columns the new data class [`mic`]. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` argument.
|
||||
#' * Using `dplyr`, R/SI interpretation can be done very easily with either:
|
||||
#' ```
|
||||
#' your_data %>% mutate_if(is.mic, as.rsi) # until dplyr 1.0.0
|
||||
@@ -54,7 +54,7 @@
|
||||
#' ```
|
||||
#' * Operators like "<=" will be stripped before interpretation. When using `conserve_capped_values = TRUE`, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (`conserve_capped_values = FALSE`) considers ">2" to be lower than ">=4" and might in this case return "S" or "I".
|
||||
#'
|
||||
#' 3. For **interpreting disk diffusion diameters** according to EUCAST or CLSI. You must clean your disk zones first using [as.disk()], that also gives your columns the new data class [`disk`]. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` parameter.
|
||||
#' 3. For **interpreting disk diffusion diameters** according to EUCAST or CLSI. You must clean your disk zones first using [as.disk()], that also gives your columns the new data class [`disk`]. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` argument.
|
||||
#' * Using `dplyr`, R/SI interpretation can be done very easily with either:
|
||||
#' ```
|
||||
#' your_data %>% mutate_if(is.disk, as.rsi) # until dplyr 1.0.0
|
||||
@@ -65,9 +65,9 @@
|
||||
#'
|
||||
#' ## Supported guidelines
|
||||
#'
|
||||
#' For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the `guideline` parameter are: `r paste0('"', sort(unique(AMR::rsi_translation$guideline)), '"', collapse = ", ")`.
|
||||
#' For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the `guideline` argument are: `r paste0('"', sort(unique(AMR::rsi_translation$guideline)), '"', collapse = ", ")`.
|
||||
#'
|
||||
#' Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline. You can set your own data set using the `reference_data` parameter. The `guideline` parameter will then be ignored.
|
||||
#' Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline. You can set your own data set using the `reference_data` argument. The `guideline` argument will then be ignored.
|
||||
#'
|
||||
#' ## After interpretation
|
||||
#'
|
||||
@@ -79,7 +79,7 @@
|
||||
#'
|
||||
#' ## Other
|
||||
#'
|
||||
#' The function [is.rsi.eligible()] returns `TRUE` when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and `FALSE` otherwise. The threshold of 5% can be set with the `threshold` parameter.
|
||||
#' The function [is.rsi.eligible()] returns `TRUE` when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and `FALSE` otherwise. The threshold of 5% can be set with the `threshold` argument.
|
||||
#' @section Interpretation of R and S/I:
|
||||
#' In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (<https://www.eucast.org/newsiandr/>).
|
||||
#'
|
||||
@@ -113,7 +113,8 @@
|
||||
#' CIP = as.mic(0.256),
|
||||
#' GEN = as.disk(18),
|
||||
#' TOB = as.disk(16),
|
||||
#' NIT = as.mic(32))
|
||||
#' NIT = as.mic(32),
|
||||
#' ERY = "R")
|
||||
#' as.rsi(df)
|
||||
#'
|
||||
#' # for single values
|
||||
@@ -323,25 +324,25 @@ as.rsi.mic <- function(x,
|
||||
mo <- suppressMessages(search_type_in_df(df, "mo"))
|
||||
}, silent = TRUE)
|
||||
if (!is.null(df) && !is.null(mo) && is.data.frame(df)) {
|
||||
mo_var_found <- paste0(" based on column `", font_bold(mo), "`")
|
||||
mo_var_found <- paste0(" based on column '", font_bold(mo), "'")
|
||||
mo <- df[, mo, drop = TRUE]
|
||||
}
|
||||
}, error = function(e)
|
||||
stop_('No information was supplied about the microorganisms (missing parameter "mo"). See ?as.rsi.\n\n',
|
||||
stop_('No information was supplied about the microorganisms (missing argument `mo`). See ?as.rsi.\n\n',
|
||||
"To transform certain columns with e.g. mutate_at(), use `data %>% mutate_at(vars(...), as.rsi, mo = .$x)`, where x is your column with microorganisms.\n",
|
||||
"To tranform all disk diffusion zones in a data set, use `data %>% as.rsi()` or data %>% mutate_if(is.disk, as.rsi).", call = FALSE)
|
||||
)
|
||||
}
|
||||
if (length(ab) == 1 && ab %like% "as.mic") {
|
||||
stop_('No unambiguous name was supplied about the antibiotic (parameter "ab"). See ?as.rsi.', call = FALSE)
|
||||
stop_('No unambiguous name was supplied about the antibiotic (argument `ab`). See ?as.rsi.', call = FALSE)
|
||||
}
|
||||
|
||||
ab_coerced <- suppressWarnings(as.ab(ab))
|
||||
mo_coerced <- suppressWarnings(as.mo(mo))
|
||||
guideline_coerced <- get_guideline(guideline, reference_data)
|
||||
if (is.na(ab_coerced)) {
|
||||
message_("Returning NAs for unknown drug: `", font_bold(ab),
|
||||
"`. Rename this column to a drug name or code, and check the output with as.ab().",
|
||||
message_("Returning NAs for unknown drug: '", font_bold(ab),
|
||||
"'. Rename this column to a drug name or code, and check the output with `as.ab()`.",
|
||||
add_fn = font_red,
|
||||
as_note = FALSE)
|
||||
return(as.rsi(rep(NA, length(x))))
|
||||
@@ -353,7 +354,7 @@ as.rsi.mic <- function(x,
|
||||
uti <- rep(uti, length(x))
|
||||
}
|
||||
|
||||
message_("=> Interpreting MIC values of '", font_bold(ab), "' (",
|
||||
message_("=> Interpreting MIC values of ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""), "'", font_bold(ab), "' (",
|
||||
ifelse(ab_coerced != ab, paste0(ab_coerced, ", "), ""),
|
||||
ab_name(ab_coerced, tolower = TRUE), ")", mo_var_found,
|
||||
" according to ", ifelse(identical(reference_data, AMR::rsi_translation),
|
||||
@@ -412,25 +413,25 @@ as.rsi.disk <- function(x,
|
||||
mo <- suppressMessages(search_type_in_df(df, "mo"))
|
||||
}, silent = TRUE)
|
||||
if (!is.null(df) && !is.null(mo) && is.data.frame(df)) {
|
||||
mo_var_found <- paste0(" based on column `", font_bold(mo), "`")
|
||||
mo_var_found <- paste0(" based on column '", font_bold(mo), "'")
|
||||
mo <- df[, mo, drop = TRUE]
|
||||
}
|
||||
}, error = function(e)
|
||||
stop_('No information was supplied about the microorganisms (missing parameter "mo"). See ?as.rsi.\n\n',
|
||||
stop_('No information was supplied about the microorganisms (missing argument `mo`). See ?as.rsi.\n\n',
|
||||
"To transform certain columns with e.g. mutate_at(), use `data %>% mutate_at(vars(...), as.rsi, mo = .$x)`, where x is your column with microorganisms.\n",
|
||||
"To tranform all disk diffusion zones in a data set, use `data %>% as.rsi()` or data %>% mutate_if(is.disk, as.rsi).", call = FALSE)
|
||||
)
|
||||
}
|
||||
if (length(ab) == 1 && ab %like% "as.disk") {
|
||||
stop_('No unambiguous name was supplied about the antibiotic (parameter "ab"). See ?as.rsi.', call = FALSE)
|
||||
stop_('No unambiguous name was supplied about the antibiotic (argument `ab`). See ?as.rsi.', call = FALSE)
|
||||
}
|
||||
|
||||
ab_coerced <- suppressWarnings(as.ab(ab))
|
||||
mo_coerced <- suppressWarnings(as.mo(mo))
|
||||
guideline_coerced <- get_guideline(guideline, reference_data)
|
||||
if (is.na(ab_coerced)) {
|
||||
message_("Returning NAs for unknown drug: `", font_bold(ab),
|
||||
"`. Rename this column to a drug name or code, and check the output with as.ab().",
|
||||
message_("Returning NAs for unknown drug: '", font_bold(ab),
|
||||
"'. Rename this column to a drug name or code, and check the output with `as.ab()`.",
|
||||
add_fn = font_red,
|
||||
as_note = FALSE)
|
||||
return(as.rsi(rep(NA, length(x))))
|
||||
@@ -442,7 +443,7 @@ as.rsi.disk <- function(x,
|
||||
uti <- rep(uti, length(x))
|
||||
}
|
||||
|
||||
message_("=> Interpreting disk zones of '", font_bold(ab), "' (",
|
||||
message_("=> Interpreting disk zones of ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""), "'", font_bold(ab), "' (",
|
||||
ifelse(ab_coerced != ab, paste0(ab_coerced, ", "), ""),
|
||||
ab_name(ab_coerced, tolower = TRUE), ")", mo_var_found,
|
||||
" according to ", ifelse(identical(reference_data, AMR::rsi_translation),
|
||||
@@ -482,6 +483,7 @@ as.rsi.data.frame <- function(x,
|
||||
meet_criteria(add_intrinsic_resistance, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(reference_data, allow_class = "data.frame")
|
||||
|
||||
x.bak <- x
|
||||
for (i in seq_len(ncol(x))) {
|
||||
# don't keep factors
|
||||
if (is.factor(x[, i, drop = TRUE])) {
|
||||
@@ -527,8 +529,8 @@ as.rsi.data.frame <- function(x,
|
||||
}
|
||||
message_("Assuming value", plural[1], " ",
|
||||
paste(paste0('"', values, '"'), collapse = ", "),
|
||||
" in column `", font_bold(col_specimen),
|
||||
"` reflect", plural[2], " ", plural[3], "urinary tract infection", plural[1],
|
||||
" in column '", font_bold(col_specimen),
|
||||
"' reflect", plural[2], " ", plural[3], "urinary tract infection", plural[1],
|
||||
".\n Use `as.rsi(uti = FALSE)` to prevent this.")
|
||||
} else {
|
||||
# no data about UTI's found
|
||||
@@ -542,7 +544,7 @@ as.rsi.data.frame <- function(x,
|
||||
sel <- sel[sel != col_mo]
|
||||
}
|
||||
|
||||
ab_cols <- colnames(x)[sapply(x, function(y) {
|
||||
ab_cols <- colnames(x)[vapply(FUN.VALUE = logical(1), x, function(y) {
|
||||
i <<- i + 1
|
||||
check <- is.mic(y) | is.disk(y)
|
||||
ab <- colnames(x)[i]
|
||||
@@ -569,11 +571,11 @@ as.rsi.data.frame <- function(x,
|
||||
"no columns with MIC values, disk zones or antibiotic column names found in this data set. Use as.mic() or as.disk() to transform antimicrobial columns.")
|
||||
# set type per column
|
||||
types <- character(length(ab_cols))
|
||||
types[sapply(x[, ab_cols, drop = FALSE], is.disk)] <- "disk"
|
||||
types[types == "" & sapply(x[, ab_cols, drop = FALSE], all_valid_disks)] <- "disk"
|
||||
types[sapply(x[, ab_cols, drop = FALSE], is.mic)] <- "mic"
|
||||
types[types == "" & sapply(x[, ab_cols, drop = FALSE], all_valid_mics)] <- "mic"
|
||||
types[types == "" & !sapply(x[, ab_cols, drop = FALSE], is.rsi)] <- "rsi"
|
||||
types[vapply(FUN.VALUE = logical(1), x.bak[, ab_cols, drop = FALSE], is.disk)] <- "disk"
|
||||
types[vapply(FUN.VALUE = logical(1), x.bak[, ab_cols, drop = FALSE], is.mic)] <- "mic"
|
||||
types[types == "" & vapply(FUN.VALUE = logical(1), x[, ab_cols, drop = FALSE], all_valid_disks)] <- "disk"
|
||||
types[types == "" & vapply(FUN.VALUE = logical(1), x[, ab_cols, drop = FALSE], all_valid_mics)] <- "mic"
|
||||
types[types == "" & !vapply(FUN.VALUE = logical(1), x.bak[, ab_cols, drop = FALSE], is.rsi)] <- "rsi"
|
||||
if (any(types %in% c("mic", "disk"), na.rm = TRUE)) {
|
||||
# now we need an mo column
|
||||
stop_if(is.null(col_mo), "`col_mo` must be set")
|
||||
@@ -597,7 +599,8 @@ as.rsi.data.frame <- function(x,
|
||||
uti = uti,
|
||||
conserve_capped_values = conserve_capped_values,
|
||||
add_intrinsic_resistance = add_intrinsic_resistance,
|
||||
reference_data = reference_data)
|
||||
reference_data = reference_data,
|
||||
is_data.frame = TRUE)
|
||||
} else if (types[i] == "disk") {
|
||||
x[, ab_cols[i]] <- as.rsi(x = x %pm>%
|
||||
pm_pull(ab_cols[i]) %pm>%
|
||||
@@ -608,20 +611,31 @@ as.rsi.data.frame <- function(x,
|
||||
guideline = guideline,
|
||||
uti = uti,
|
||||
add_intrinsic_resistance = add_intrinsic_resistance,
|
||||
reference_data = reference_data)
|
||||
reference_data = reference_data,
|
||||
is_data.frame = TRUE)
|
||||
} else if (types[i] == "rsi") {
|
||||
show_message <- FALSE
|
||||
ab <- ab_cols[i]
|
||||
ab_coerced <- suppressWarnings(as.ab(ab))
|
||||
if (!all(x[, ab_cols[i], drop = TRUE] %in% c("R", "S", "I"), na.rm = TRUE)) {
|
||||
show_message <- TRUE
|
||||
# only print message if values are not already clean
|
||||
message_("=> Cleaning values in column `", font_bold(ab), "` (",
|
||||
message_("=> Cleaning values in column '", font_bold(ab), "' (",
|
||||
ifelse(ab_coerced != ab, paste0(ab_coerced, ", "), ""),
|
||||
ab_name(ab_coerced, tolower = TRUE), ")... ",
|
||||
appendLF = FALSE,
|
||||
as_note = FALSE)
|
||||
} else if (!is.rsi(x.bak[, ab_cols[i], drop = TRUE])) {
|
||||
show_message <- TRUE
|
||||
# only print message if class not already set
|
||||
message_("=> Assigning class <rsi> to already clean column '", font_bold(ab), "' (",
|
||||
ifelse(ab_coerced != ab, paste0(ab_coerced, ", "), ""),
|
||||
ab_name(ab_coerced, tolower = TRUE), ")... ",
|
||||
appendLF = FALSE,
|
||||
as_note = FALSE)
|
||||
}
|
||||
x[, ab_cols[i]] <- as.rsi.default(x = as.character(x[, ab_cols[i], drop = TRUE]))
|
||||
if (!all(x[, ab_cols[i], drop = TRUE] %in% c("R", "S", "I"), na.rm = TRUE)) {
|
||||
if (show_message == TRUE) {
|
||||
message_(" OK.", add_fn = list(font_green, font_bold), as_note = FALSE)
|
||||
}
|
||||
}
|
||||
@@ -695,7 +709,10 @@ exec_as.rsi <- function(method,
|
||||
|
||||
guideline_coerced <- get_guideline(guideline, reference_data)
|
||||
if (guideline_coerced != guideline) {
|
||||
message_("Using guideline ", font_bold(guideline_coerced), " as input for `guideline`.")
|
||||
if (message_not_thrown_before("as.rsi")) {
|
||||
message_("Using guideline ", font_bold(guideline_coerced), " as input for `guideline`.")
|
||||
remember_thrown_message("as.rsi")
|
||||
}
|
||||
}
|
||||
|
||||
new_rsi <- rep(NA_character_, length(x))
|
||||
@@ -719,7 +736,7 @@ exec_as.rsi <- function(method,
|
||||
|
||||
if (all(trans$uti == TRUE, na.rm = TRUE) & all(uti == FALSE)) {
|
||||
message_("WARNING.", add_fn = list(font_yellow, font_bold), as_note = FALSE)
|
||||
warning_("Interpretation of ", font_bold(ab_name(ab, tolower = TRUE)), " for some microorganisms is only available for (uncomplicated) urinary tract infections (UTI). Use parameter 'uti' to set which isolates are from urine. See ?as.rsi.", call = FALSE)
|
||||
warning_("Introducing NA: interpretation of ", font_bold(ab_name(ab, tolower = TRUE)), " for some microorganisms is only available for (uncomplicated) urinary tract infections (UTI). Use argument `uti` to set which isolates are from urine. See ?as.rsi.", call = FALSE)
|
||||
warned <- TRUE
|
||||
}
|
||||
|
||||
@@ -731,7 +748,10 @@ exec_as.rsi <- function(method,
|
||||
|
||||
if (isTRUE(add_intrinsic_resistance) & is_intrinsic_r) {
|
||||
if (!guideline_coerced %like% "EUCAST") {
|
||||
warning_("Using 'add_intrinsic_resistance' is only useful when using EUCAST guidelines, since the rules for intrinsic resistance are based on EUCAST.", call = FALSE)
|
||||
if (message_not_thrown_before("as.rsi2")) {
|
||||
warning_("Using 'add_intrinsic_resistance' is only useful when using EUCAST guidelines, since the rules for intrinsic resistance are based on EUCAST.", call = FALSE)
|
||||
remember_thrown_message("as.rsi2")
|
||||
}
|
||||
} else {
|
||||
new_rsi[i] <- "R"
|
||||
next
|
||||
@@ -797,7 +817,10 @@ exec_as.rsi <- function(method,
|
||||
if (any_is_intrinsic_resistant & guideline_coerced %like% "EUCAST" & !isTRUE(add_intrinsic_resistance)) {
|
||||
# found some intrinsic resistance, but was not applied
|
||||
message_("WARNING.", add_fn = list(font_yellow, font_bold), as_note = FALSE)
|
||||
warning_("Found intrinsic resistance in some bug/drug combinations, although it was not applied.\nUse `as.rsi(..., add_intrinsic_resistance = TRUE)` to apply it.", call = FALSE)
|
||||
if (message_not_thrown_before("as.rsi3")) {
|
||||
warning_("Found intrinsic resistance in some bug/drug combinations, although it was not applied.\nUse `as.rsi(..., add_intrinsic_resistance = TRUE)` to apply it.", call = FALSE)
|
||||
remember_thrown_message("as.rsi3")
|
||||
}
|
||||
warned <- TRUE
|
||||
}
|
||||
|
||||
@@ -821,9 +844,9 @@ exec_as.rsi <- function(method,
|
||||
pillar_shaft.rsi <- function(x, ...) {
|
||||
out <- trimws(format(x))
|
||||
out[is.na(x)] <- font_grey(" NA")
|
||||
out[x == "S"] <- font_green_bg(font_white(" S "))
|
||||
out[x == "I"] <- font_yellow_bg(font_black(" I "))
|
||||
out[x == "R"] <- font_red_bg(font_white(" R "))
|
||||
out[x == "R"] <- font_rsi_R_bg(font_black(" R "))
|
||||
out[x == "S"] <- font_rsi_S_bg(font_black(" S "))
|
||||
out[x == "I"] <- font_rsi_I_bg(font_black(" I "))
|
||||
create_pillar_column(out, align = "left", width = 5)
|
||||
}
|
||||
|
||||
@@ -838,7 +861,8 @@ freq.rsi <- function(x, ...) {
|
||||
x_name <- gsub(".*[$]", "", x_name)
|
||||
if (x_name %in% c("x", ".")) {
|
||||
# try again going through system calls
|
||||
x_name <- stats::na.omit(sapply(sys.calls(),
|
||||
x_name <- stats::na.omit(vapply(FUN.VALUE = character(1),
|
||||
sys.calls(),
|
||||
function(call) {
|
||||
call_txt <- as.character(call)
|
||||
ifelse(call_txt[1] %like% "freq$", call_txt[length(call_txt)], character(0))
|
||||
@@ -883,8 +907,8 @@ get_skimmers.rsi <- function(column) {
|
||||
if (is.null(vars) | is.null(i)) {
|
||||
NA_character_
|
||||
} else {
|
||||
lengths <- sapply(vars, length)
|
||||
when_starts_rsi <- which(names(sapply(vars, length)) == "rsi")
|
||||
lengths <- vapply(FUN.VALUE = double(1), vars, length)
|
||||
when_starts_rsi <- which(names(vapply(FUN.VALUE = double(1), vars, length)) == "rsi")
|
||||
offset <- sum(lengths[c(1:when_starts_rsi - 1)])
|
||||
var <- vars$rsi[i - offset]
|
||||
if (!isFALSE(var == "data")) {
|
||||
@@ -1092,8 +1116,8 @@ unique.rsi <- function(x, incomparables = FALSE, ...) {
|
||||
|
||||
check_reference_data <- function(reference_data) {
|
||||
if (!identical(reference_data, AMR::rsi_translation)) {
|
||||
class_rsi <- sapply(rsi_translation, function(x) paste0("<", class(x), ">", collapse = " and "))
|
||||
class_ref <- sapply(reference_data, function(x) paste0("<", class(x), ">", collapse = " and "))
|
||||
class_rsi <- vapply(FUN.VALUE = character(1), rsi_translation, function(x) paste0("<", class(x), ">", collapse = " and "))
|
||||
class_ref <- vapply(FUN.VALUE = character(1), reference_data, function(x) paste0("<", class(x), ">", collapse = " and "))
|
||||
if (!all(names(class_rsi) == names(class_ref))) {
|
||||
stop_("`reference_data` must have the same column names as the 'rsi_translation' data set.", call = -2)
|
||||
}
|
||||
|
||||
+12
-9
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -70,7 +70,7 @@ rsi_calc <- function(...,
|
||||
}
|
||||
if (length(dots) == 0 | all(dots == "df")) {
|
||||
# for complete data.frames, like example_isolates %pm>% select(AMC, GEN) %pm>% proportion_S()
|
||||
# and the old rsi function, which has "df" as name of the first parameter
|
||||
# and the old rsi function, which has "df" as name of the first argument
|
||||
x <- dots_df
|
||||
} else {
|
||||
# get dots that are in column names already, and the ones that will be once evaluated using dots_df or global env
|
||||
@@ -129,12 +129,12 @@ rsi_calc <- function(...,
|
||||
MARGIN = 1,
|
||||
FUN = min)
|
||||
numerator <- sum(as.integer(y) %in% as.integer(ab_result), na.rm = TRUE)
|
||||
denominator <- sum(sapply(x_transposed, function(y) !(any(is.na(y)))))
|
||||
denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(any(is.na(y)))))
|
||||
} else {
|
||||
# may contain NAs in any column
|
||||
other_values <- setdiff(c(NA, levels(ab_result)), ab_result)
|
||||
numerator <- sum(sapply(x_transposed, function(y) any(y %in% ab_result, na.rm = TRUE)))
|
||||
denominator <- sum(sapply(x_transposed, function(y) !(all(y %in% other_values) & any(is.na(y)))))
|
||||
numerator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) any(y %in% ab_result, na.rm = TRUE)))
|
||||
denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(all(y %in% other_values) & any(is.na(y)))))
|
||||
}
|
||||
} else {
|
||||
# x is not a data.frame
|
||||
@@ -147,8 +147,11 @@ rsi_calc <- function(...,
|
||||
}
|
||||
|
||||
if (print_warning == TRUE) {
|
||||
warning_("Increase speed by transforming to class <rsi> on beforehand: your_data %pm>% mutate_if(is.rsi.eligible, as.rsi)",
|
||||
call = FALSE)
|
||||
if (message_not_thrown_before("rsi_calc")) {
|
||||
warning_("Increase speed by transforming to class <rsi> on beforehand: your_data %>% mutate_if(is.rsi.eligible, as.rsi)",
|
||||
call = FALSE)
|
||||
remember_thrown_message("rsi_calc")
|
||||
}
|
||||
}
|
||||
|
||||
if (only_count == TRUE) {
|
||||
@@ -204,10 +207,10 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
|
||||
if (inherits(data, "grouped_df")) {
|
||||
data_has_groups <- TRUE
|
||||
groups <- setdiff(names(attributes(data)$groups), ".rows")
|
||||
data <- data[, c(groups, colnames(data)[sapply(data, is.rsi)]), drop = FALSE]
|
||||
data <- data[, c(groups, colnames(data)[vapply(FUN.VALUE = logical(1), data, is.rsi)]), drop = FALSE]
|
||||
} else {
|
||||
data_has_groups <- FALSE
|
||||
data <- data[, colnames(data)[sapply(data, is.rsi)], drop = FALSE]
|
||||
data <- data[, colnames(data)[vapply(FUN.VALUE = logical(1), data, is.rsi)], drop = FALSE]
|
||||
}
|
||||
|
||||
data <- as.data.frame(data, stringsAsFactors = FALSE)
|
||||
|
||||
+1
-1
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
+2
-2
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -66,5 +66,5 @@ skewness.matrix <- function(x, na.rm = FALSE) {
|
||||
#' @export
|
||||
skewness.data.frame <- function(x, na.rm = FALSE) {
|
||||
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
|
||||
sapply(x, skewness.default, na.rm = na.rm)
|
||||
vapply(FUN.VALUE = double(1), x, skewness.default, na.rm = na.rm)
|
||||
}
|
||||
Binary file not shown.
+2
-2
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -45,7 +45,7 @@
|
||||
#' @name translate
|
||||
#' @export
|
||||
#' @examples
|
||||
#' # The 'language' parameter of below functions
|
||||
#' # The 'language' argument of below functions
|
||||
#' # will be set automatically to your system language
|
||||
#' # with get_locale()
|
||||
#'
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -23,6 +23,9 @@
|
||||
# how to conduct AMR analysis: https://msberends.github.io/AMR/ #
|
||||
# ==================================================================== #
|
||||
|
||||
# set up package environment, used by numerous AMR functions
|
||||
pkg_env <- new.env(hash = FALSE)
|
||||
|
||||
.onLoad <- function(libname, pkgname) {
|
||||
|
||||
assign(x = "AB_lookup",
|
||||
@@ -75,6 +78,13 @@
|
||||
s3_register("skimr::get_skimmers", "rsi")
|
||||
s3_register("skimr::get_skimmers", "mic")
|
||||
s3_register("skimr::get_skimmers", "disk")
|
||||
|
||||
# if mo source exists, fire it up (see mo_source())
|
||||
try({
|
||||
if (file.exists(getOption("AMR_mo_source", "~/mo_source.rds"))) {
|
||||
invisible(get_mo_source())
|
||||
}
|
||||
}, silent = TRUE)
|
||||
}
|
||||
|
||||
.onAttach <- function(...) {
|
||||
|
||||
@@ -2,8 +2,11 @@
|
||||
|
||||
# `AMR` (for R)
|
||||
|
||||
[](https://cran.R-project.org/package=AMR) [](https://cran.R-project.org/package=AMR)
|
||||
[](https://codecov.io/gh/msberends/AMR/branch/master)
|
||||
[](https://cran.r-project.org/package=AMR)
|
||||
[](https://cran.r-project.org/package=AMR)
|
||||
|
||||
[](https://www.codefactor.io/repository/github/msberends/amr)
|
||||
[](https://codecov.io/gh/msberends/AMR?branch=master)
|
||||
|
||||
<img src="https://msberends.github.io/AMR/works_great_on.png" align="center" height="150px" />
|
||||
|
||||
|
||||
+3
-1
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -212,6 +212,8 @@ authors:
|
||||
href: https://www.rug.nl/staff/c.glasner/
|
||||
|
||||
template:
|
||||
# this requires the 'preferably' package, https://github.com/amirmasoudabdol/preferably/
|
||||
# package: preferably
|
||||
assets: "pkgdown/logos" # use logos in this folder
|
||||
params:
|
||||
noindex: false
|
||||
|
||||
+1
-1
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -1,3 +1 @@
|
||||
* Edited the unit tests, so they will run under 10 minutes on CRAN (using testthat::skip_on_cran() on some tests).
|
||||
|
||||
* Since version 0.3.0 (2018-08-14), CHECK returns a NOTE for having a data directory over 3 MB. This is needed to offer users reference data for the complete taxonomy of microorganisms - one of the most important features of this package.
|
||||
Binary file not shown.
@@ -1,6 +1,6 @@
|
||||
# -------------------------------------------------------------------------------------------------------------------------------
|
||||
# For editing this EUCAST reference file, these values can all be used for targeting antibiotics:
|
||||
# 'all_betalactams', 'aminoglycosides', 'aminopenicillins', 'carbapenems', 'cephalosporins', 'cephalosporins_1st', 'cephalosporins_2nd', 'cephalosporins_except_CAZ',
|
||||
# 'all_betalactams', 'aminoglycosides', 'aminopenicillins', 'carbapenems', 'cephalosporins', 'cephalosporins_1st', 'cephalosporins_2nd', 'cephalosporins_3rd', 'cephalosporins_except_CAZ',
|
||||
# 'fluoroquinolones', 'glycopeptides', 'lincosamides', 'lipoglycopeptides', 'macrolides', 'oxazolidinones', 'polymyxins', 'streptogramins', 'tetracyclines', 'ureidopenicillins',
|
||||
# and all separate EARS-Net letter codes like 'AMC'. They can be separated by comma: 'AMC, fluoroquinolones'.
|
||||
# The 'if_mo_property' column can be any column name from the AMR::microorganisms data set, or "genus_species" or "gramstain".
|
||||
@@ -300,3 +300,6 @@ genus_species is Moraxella catarrhalis NAL S fluoroquinolones S Expert Rules on
|
||||
genus_species is Moraxella catarrhalis NAL R fluoroquinolones R Expert Rules on Moraxella catarrhalis Expert Rules 3.2
|
||||
genus is Campylobacter ERY S CLR, AZM S Expert Rules on Campylobacter Expert Rules 3.2
|
||||
genus_species is Campylobacter ERY R CLR, AZM R Expert Rules on Campylobacter Expert Rules 3.2
|
||||
fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter (braakii|freundii|gillenii|murliniae|rodenticum|sedlakii|werkmanii|youngae)|Hafnia alvei|Serratia|Morganella morganii|Providencia) CTX S CTX, CRO, CAZ Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) Expert Rules 3.2 This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
|
||||
fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter (braakii|freundii|gillenii|murliniae|rodenticum|sedlakii|werkmanii|youngae)|Hafnia alvei|Serratia|Morganella morganii|Providencia) CRO S CTX, CRO, CAZ Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) Expert Rules 3.2 This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
|
||||
fullname like ^(Enterobacter|Klebsiella aerogenes|Citrobacter (braakii|freundii|gillenii|murliniae|rodenticum|sedlakii|werkmanii|youngae)|Hafnia alvei|Serratia|Morganella morganii|Providencia) CAZ S CTX, CRO, CAZ Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins) Expert Rules 3.2 This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
|
||||
|
Can't render this file because it contains an unexpected character in line 6 and column 96.
|
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
+1
-1
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
@@ -24,6 +24,7 @@
|
||||
# ==================================================================== #
|
||||
|
||||
library(AMR)
|
||||
library(dplyr)
|
||||
int_resis <- data.frame(microorganism = microorganisms$mo, stringsAsFactors = FALSE)
|
||||
for (i in seq_len(nrow(antibiotics))) {
|
||||
int_resis$new <- as.rsi("S")
|
||||
@@ -45,3 +46,6 @@ int_resis2$microorganism <- mo_name(int_resis2$microorganism, language = NULL)
|
||||
intrinsic_resistant <- as.data.frame(int_resis2, stringsAsFactors = FALSE)
|
||||
usethis::use_data(intrinsic_resistant, internal = FALSE, overwrite = TRUE, version = 2, compress = "xz")
|
||||
rm(intrinsic_resistant)
|
||||
|
||||
# AFTER THIS:
|
||||
# DO NOT FORGET TO UPDATE THE VERSION NUMBER IN mo_is_intrinsic_resistant()
|
||||
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
+1
-1
@@ -6,7 +6,7 @@
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# LICENCE #
|
||||
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
||||
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
||||
# Developed at the University of Groningen, the Netherlands, in #
|
||||
# collaboration with non-profit organisations Certe Medical #
|
||||
# Diagnostics & Advice, and University Medical Center Groningen. #
|
||||
|
||||
+1
-1
@@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="https://msberends.github.io/AMR//index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9041</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@@ -81,7 +81,7 @@
|
||||
</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="Latest development version">1.4.0.9041</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
+409
-424
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|
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|
||||
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
||||
// be compatible with the behavior of Pandoc < 2.8).
|
||||
document.addEventListener('DOMContentLoaded', function(e) {
|
||||
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
||||
var i, h, a;
|
||||
for (i = 0; i < hs.length; i++) {
|
||||
h = hs[i];
|
||||
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
||||
a = h.attributes;
|
||||
while (a.length > 0) h.removeAttribute(a[0].name);
|
||||
}
|
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});
|
||||
+33
-33
@@ -39,7 +39,7 @@
|
||||
</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="Latest development version">1.4.0.9034</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@@ -47,14 +47,14 @@
|
||||
<ul class="nav navbar-nav">
|
||||
<li>
|
||||
<a href="../index.html">
|
||||
<span class="fa fa-home"></span>
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<span class="fas fa-home"></span>
|
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Home
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</a>
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<li class="dropdown">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
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<span class="fa fa-question-circle"></span>
|
||||
<span class="fas fa-question-circle"></span>
|
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How to
|
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|
||||
@@ -63,77 +63,77 @@
|
||||
<ul class="dropdown-menu" role="menu">
|
||||
<li>
|
||||
<a href="../articles/AMR.html">
|
||||
<span class="fa fa-directions"></span>
|
||||
<span class="fas fa-directions"></span>
|
||||
|
||||
Conduct AMR analysis
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/resistance_predict.html">
|
||||
<span class="fa fa-dice"></span>
|
||||
<span class="fas fa-dice"></span>
|
||||
|
||||
Predict antimicrobial resistance
|
||||
</a>
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||||
</li>
|
||||
<li>
|
||||
<a href="../articles/datasets.html">
|
||||
<span class="fa fa-database"></span>
|
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<span class="fas fa-database"></span>
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Data sets for download / own use
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</a>
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||||
</li>
|
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<li>
|
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<a href="../articles/PCA.html">
|
||||
<span class="fa fa-compress"></span>
|
||||
<span class="fas fa-compress"></span>
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|
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Conduct principal component analysis for AMR
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/MDR.html">
|
||||
<span class="fa fa-skull-crossbones"></span>
|
||||
<span class="fas fa-skull-crossbones"></span>
|
||||
|
||||
Determine multi-drug resistance (MDR)
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/WHONET.html">
|
||||
<span class="fa fa-globe-americas"></span>
|
||||
<span class="fas fa-globe-americas"></span>
|
||||
|
||||
Work with WHONET data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/SPSS.html">
|
||||
<span class="fa fa-file-upload"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-exchange-alt"></span>
|
||||
|
||||
Apply EUCAST rules
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/mo_property.html">
|
||||
<span class="fa fa-bug"></span>
|
||||
<span class="fas fa-bug"></span>
|
||||
|
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Get properties of a microorganism
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/ab_property.html">
|
||||
<span class="fa fa-capsules"></span>
|
||||
<span class="fas fa-capsules"></span>
|
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|
||||
Get properties of an antibiotic
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/benchmarks.html">
|
||||
<span class="fa fa-shipping-fast"></span>
|
||||
<span class="fas fa-shipping-fast"></span>
|
||||
|
||||
Other: benchmarks
|
||||
</a>
|
||||
@@ -142,21 +142,21 @@
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/index.html">
|
||||
<span class="fa fa-book-open"></span>
|
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<span class="fas fa-book-open"></span>
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||||
|
||||
Manual
|
||||
</a>
|
||||
</li>
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<li>
|
||||
<a href="../authors.html">
|
||||
<span class="fa fa-users"></span>
|
||||
<span class="fas fa-users"></span>
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||||
Authors
|
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</a>
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</li>
|
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<li>
|
||||
<a href="../news/index.html">
|
||||
<span class="far fa far fa-newspaper"></span>
|
||||
<span class="far fa-newspaper"></span>
|
||||
|
||||
Changelog
|
||||
</a>
|
||||
@@ -165,14 +165,14 @@
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="https://github.com/msberends/AMR">
|
||||
<span class="fab fa fab fa-github"></span>
|
||||
<span class="fab fa-github"></span>
|
||||
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../survey.html">
|
||||
<span class="fa fa-clipboard-list"></span>
|
||||
<span class="fas fa-clipboard-list"></span>
|
||||
|
||||
Survey
|
||||
</a>
|
||||
@@ -187,7 +187,7 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="EUCAST_files/header-attrs-2.4/header-attrs.js"></script><script src="EUCAST_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="EUCAST_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>How to apply EUCAST rules</h1>
|
||||
@@ -215,8 +215,8 @@
|
||||
<a href="#examples" class="anchor"></a>Examples</h2>
|
||||
<p>These rules can be used to discard impossible bug-drug combinations in your data. For example, <em>Klebsiella</em> produces beta-lactamase that prevents ampicillin (or amoxicillin) from working against it. In other words, practically every strain of <em>Klebsiella</em> is resistant to ampicillin.</p>
|
||||
<p>Sometimes, laboratory data can still contain such strains with ampicillin being susceptible to ampicillin. This could be because an antibiogram is available before an identification is available, and the antibiogram is then not re-interpreted based on the identification (namely, <em>Klebsiella</em>). EUCAST expert rules solve this, that can be applied using <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="va">oops</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">oops</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
|
||||
<span class="st">"Escherichia"</span><span class="op">)</span>,
|
||||
ampicillin <span class="op">=</span> <span class="st">"S"</span><span class="op">)</span>
|
||||
<span class="va">oops</span>
|
||||
@@ -227,19 +227,19 @@
|
||||
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
|
||||
<span class="co"># mo ampicillin</span>
|
||||
<span class="co"># 1 Klebsiella R</span>
|
||||
<span class="co"># 2 Escherichia S</span></pre></div>
|
||||
<span class="co"># 2 Escherichia S</span></code></pre></div>
|
||||
<p>A more convenient function is <code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline, but allows to check for one or more specific microorganisms or antibiotics:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>, <span class="st">"Escherichia"</span><span class="op">)</span>,
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>, <span class="st">"Escherichia"</span><span class="op">)</span>,
|
||||
<span class="st">"ampicillin"</span><span class="op">)</span>
|
||||
<span class="co"># [1] TRUE FALSE</span>
|
||||
|
||||
<span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span><span class="op">)</span>
|
||||
<span class="co"># [1] TRUE FALSE</span></pre></div>
|
||||
<span class="co"># [1] TRUE FALSE</span></code></pre></div>
|
||||
<p>EUCAST rules can not only be used for correction, they can also be used for filling in known resistance and susceptibility based on results of other antimicrobials drugs. This process is called <em>interpretive reading</em>, is basically a form of imputation, and is part of the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function as well:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit">
|
||||
<span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span>,
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span>,
|
||||
<span class="st">"Enterococcus faecalis"</span>,
|
||||
<span class="st">"Escherichia coli"</span>,
|
||||
<span class="st">"Klebsiella pneumoniae"</span>,
|
||||
@@ -251,9 +251,9 @@
|
||||
CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span>
|
||||
PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span>
|
||||
FOX <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Cefoxitin</span>
|
||||
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="va">data</span></pre></div>
|
||||
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">data</span></code></pre></div>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left">mo</th>
|
||||
@@ -318,8 +318,8 @@
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></code></pre></div>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left">mo</th>
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
||||
// be compatible with the behavior of Pandoc < 2.8).
|
||||
document.addEventListener('DOMContentLoaded', function(e) {
|
||||
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
||||
var i, h, a;
|
||||
for (i = 0; i < hs.length; i++) {
|
||||
h = hs[i];
|
||||
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
||||
a = h.attributes;
|
||||
while (a.length > 0) h.removeAttribute(a[0].name);
|
||||
}
|
||||
});
|
||||
+83
-90
@@ -39,7 +39,7 @@
|
||||
</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="Latest development version">1.4.0.9034</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@@ -47,14 +47,14 @@
|
||||
<ul class="nav navbar-nav">
|
||||
<li>
|
||||
<a href="../index.html">
|
||||
<span class="fa fa-home"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-question-circle"></span>
|
||||
|
||||
How to
|
||||
|
||||
@@ -63,77 +63,77 @@
|
||||
<ul class="dropdown-menu" role="menu">
|
||||
<li>
|
||||
<a href="../articles/AMR.html">
|
||||
<span class="fa fa-directions"></span>
|
||||
<span class="fas fa-directions"></span>
|
||||
|
||||
Conduct AMR analysis
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/resistance_predict.html">
|
||||
<span class="fa fa-dice"></span>
|
||||
<span class="fas fa-dice"></span>
|
||||
|
||||
Predict antimicrobial resistance
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/datasets.html">
|
||||
<span class="fa fa-database"></span>
|
||||
<span class="fas fa-database"></span>
|
||||
|
||||
Data sets for download / own use
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/PCA.html">
|
||||
<span class="fa fa-compress"></span>
|
||||
<span class="fas fa-compress"></span>
|
||||
|
||||
Conduct principal component analysis for AMR
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/MDR.html">
|
||||
<span class="fa fa-skull-crossbones"></span>
|
||||
<span class="fas fa-skull-crossbones"></span>
|
||||
|
||||
Determine multi-drug resistance (MDR)
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/WHONET.html">
|
||||
<span class="fa fa-globe-americas"></span>
|
||||
<span class="fas fa-globe-americas"></span>
|
||||
|
||||
Work with WHONET data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/SPSS.html">
|
||||
<span class="fa fa-file-upload"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-exchange-alt"></span>
|
||||
|
||||
Apply EUCAST rules
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/mo_property.html">
|
||||
<span class="fa fa-bug"></span>
|
||||
<span class="fas fa-bug"></span>
|
||||
|
||||
Get properties of a microorganism
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/ab_property.html">
|
||||
<span class="fa fa-capsules"></span>
|
||||
<span class="fas fa-capsules"></span>
|
||||
|
||||
Get properties of an antibiotic
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/benchmarks.html">
|
||||
<span class="fa fa-shipping-fast"></span>
|
||||
<span class="fas fa-shipping-fast"></span>
|
||||
|
||||
Other: benchmarks
|
||||
</a>
|
||||
@@ -142,21 +142,21 @@
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/index.html">
|
||||
<span class="fa fa-book-open"></span>
|
||||
<span class="fas fa-book-open"></span>
|
||||
|
||||
Manual
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../authors.html">
|
||||
<span class="fa fa-users"></span>
|
||||
<span class="fas fa-users"></span>
|
||||
|
||||
Authors
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../news/index.html">
|
||||
<span class="far fa far fa-newspaper"></span>
|
||||
<span class="far fa-newspaper"></span>
|
||||
|
||||
Changelog
|
||||
</a>
|
||||
@@ -165,14 +165,14 @@
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="https://github.com/msberends/AMR">
|
||||
<span class="fab fa fab fa-github"></span>
|
||||
<span class="fab fa-github"></span>
|
||||
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../survey.html">
|
||||
<span class="fa fa-clipboard-list"></span>
|
||||
<span class="fas fa-clipboard-list"></span>
|
||||
|
||||
Survey
|
||||
</a>
|
||||
@@ -187,7 +187,7 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="MDR_files/header-attrs-2.4/header-attrs.js"></script><script src="MDR_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="MDR_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>How to determine multi-drug resistance (MDR)</h1>
|
||||
@@ -244,15 +244,15 @@
|
||||
<a href="#examples" class="anchor"></a>Examples</h4>
|
||||
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered <code>factor</code>. For example, the output of the default guideline by Magiorakos <em>et al.</em> returns a <code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.</p>
|
||||
<p>The next example uses the <code>example_isolates</code> data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># to support pipes: %>%</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></pre></div>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit">
|
||||
<span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># to support pipes: %>%</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span>
|
||||
<span class="co"># Warning in warning_("NA introduced for isolates where the available percentage of antimicrobial classes was below ", : NA introduced for isolates where the available percentage of antimicrobial</span>
|
||||
<span class="co"># classes was below 50% (set with `pct_required_classes`)</span></pre></div>
|
||||
<span class="co"># Warning: NA introduced for isolates where the available percentage of antimicrobial</span>
|
||||
<span class="co"># classes was below 50% (set with `pct_required_classes`)</span></code></pre></div>
|
||||
<p><strong>Frequency table</strong></p>
|
||||
<p>Class: factor > ordered (numeric)<br>
|
||||
Length: 2,000<br>
|
||||
@@ -288,60 +288,53 @@ Unique: 2</p>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>For another example, I will create a data set to determine multi-drug resistant TB:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit">
|
||||
<span class="co"># a helper function to get a random vector with values S, I and R</span>
|
||||
<span class="co"># with the probabilities 50% - 10% - 40%</span>
|
||||
<span class="va">sample_rsi</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"S"</span>, <span class="st">"I"</span>, <span class="st">"R"</span><span class="op">)</span>,
|
||||
size <span class="op">=</span> <span class="fl">5000</span>,
|
||||
prob <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">0.1</span>, <span class="fl">0.4</span><span class="op">)</span>,
|
||||
replace <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
|
||||
<span class="op">}</span>
|
||||
|
||||
<span class="va">my_TB_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>rifampicin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
isoniazid <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
gatifloxacin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
ethambutol <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
pyrazinamide <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
moxifloxacin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
kanamycin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># random_rsi() is a helper function to generate</span>
|
||||
<span class="co"># a random vector with values S, I and R</span>
|
||||
<span class="va">my_TB_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>rifampicin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
isoniazid <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
gatifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
ethambutol <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
pyrazinamide <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
moxifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
kanamycin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
|
||||
<p>Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="va">my_TB_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
INH <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
GAT <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
ETH <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
PZA <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
MFX <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
|
||||
KAN <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">my_TB_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
INH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
GAT <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
ETH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
PZA <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
MFX <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
|
||||
KAN <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
|
||||
<p>The data set now looks like this:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit">
|
||||
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
|
||||
<span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span>
|
||||
<span class="co"># 1 R I R S S S</span>
|
||||
<span class="co"># 2 S R I R R S</span>
|
||||
<span class="co"># 3 R S R R R R</span>
|
||||
<span class="co"># 4 R S I R I S</span>
|
||||
<span class="co"># 5 S R S R S R</span>
|
||||
<span class="co"># 6 R R S R S S</span>
|
||||
<span class="co"># 1 S R S S I R</span>
|
||||
<span class="co"># 2 R I S I S I</span>
|
||||
<span class="co"># 3 I I R S I S</span>
|
||||
<span class="co"># 4 R S R I R S</span>
|
||||
<span class="co"># 5 I R I I I R</span>
|
||||
<span class="co"># 6 I R I R S R</span>
|
||||
<span class="co"># kanamycin</span>
|
||||
<span class="co"># 1 R</span>
|
||||
<span class="co"># 2 S</span>
|
||||
<span class="co"># 3 S</span>
|
||||
<span class="co"># 4 S</span>
|
||||
<span class="co"># 5 R</span>
|
||||
<span class="co"># 6 R</span></pre></div>
|
||||
<span class="co"># 2 I</span>
|
||||
<span class="co"># 3 I</span>
|
||||
<span class="co"># 4 I</span>
|
||||
<span class="co"># 5 S</span>
|
||||
<span class="co"># 6 S</span></code></pre></div>
|
||||
<p>We can now add the interpretation of MDR-TB to our data set. You can use:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></code></pre></div>
|
||||
<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit">
|
||||
<span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
|
||||
<span class="co"># NOTE: No column found as input for `col_mo`, assuming all records contain</span>
|
||||
<span class="co"># Mycobacterium tuberculosis.</span></pre></div>
|
||||
<span class="co"># Mycobacterium tuberculosis.</span></code></pre></div>
|
||||
<p>Create a frequency table of the results:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit">
|
||||
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></code></pre></div>
|
||||
<p><strong>Frequency table</strong></p>
|
||||
<p>Class: factor > ordered (numeric)<br>
|
||||
Length: 5,000<br>
|
||||
@@ -361,40 +354,40 @@ Unique: 5</p>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">Mono-resistant</td>
|
||||
<td align="right">3244</td>
|
||||
<td align="right">64.88%</td>
|
||||
<td align="right">3244</td>
|
||||
<td align="right">64.88%</td>
|
||||
<td align="right">3286</td>
|
||||
<td align="right">65.72%</td>
|
||||
<td align="right">3286</td>
|
||||
<td align="right">65.72%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">Negative</td>
|
||||
<td align="right">644</td>
|
||||
<td align="right">12.88%</td>
|
||||
<td align="right">3888</td>
|
||||
<td align="right">77.76%</td>
|
||||
<td align="right">992</td>
|
||||
<td align="right">19.84%</td>
|
||||
<td align="right">4278</td>
|
||||
<td align="right">85.56%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">Multi-drug-resistant</td>
|
||||
<td align="right">613</td>
|
||||
<td align="right">12.26%</td>
|
||||
<td align="right">4501</td>
|
||||
<td align="right">90.02%</td>
|
||||
<td align="right">424</td>
|
||||
<td align="right">8.48%</td>
|
||||
<td align="right">4702</td>
|
||||
<td align="right">94.04%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">Poly-resistant</td>
|
||||
<td align="right">304</td>
|
||||
<td align="right">6.08%</td>
|
||||
<td align="right">4805</td>
|
||||
<td align="right">96.10%</td>
|
||||
<td align="right">214</td>
|
||||
<td align="right">4.28%</td>
|
||||
<td align="right">4916</td>
|
||||
<td align="right">98.32%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">5</td>
|
||||
<td align="left">Extensively drug-resistant</td>
|
||||
<td align="right">195</td>
|
||||
<td align="right">3.90%</td>
|
||||
<td align="right">84</td>
|
||||
<td align="right">1.68%</td>
|
||||
<td align="right">5000</td>
|
||||
<td align="right">100.00%</td>
|
||||
</tr>
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
||||
// be compatible with the behavior of Pandoc < 2.8).
|
||||
document.addEventListener('DOMContentLoaded', function(e) {
|
||||
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
||||
var i, h, a;
|
||||
for (i = 0; i < hs.length; i++) {
|
||||
h = hs[i];
|
||||
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
||||
a = h.attributes;
|
||||
while (a.length > 0) h.removeAttribute(a[0].name);
|
||||
}
|
||||
});
|
||||
+39
-39
@@ -39,7 +39,7 @@
|
||||
</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="Latest development version">1.4.0.9032</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@@ -47,14 +47,14 @@
|
||||
<ul class="nav navbar-nav">
|
||||
<li>
|
||||
<a href="../index.html">
|
||||
<span class="fa fa-home"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-question-circle"></span>
|
||||
|
||||
How to
|
||||
|
||||
@@ -63,77 +63,77 @@
|
||||
<ul class="dropdown-menu" role="menu">
|
||||
<li>
|
||||
<a href="../articles/AMR.html">
|
||||
<span class="fa fa-directions"></span>
|
||||
<span class="fas fa-directions"></span>
|
||||
|
||||
Conduct AMR analysis
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/resistance_predict.html">
|
||||
<span class="fa fa-dice"></span>
|
||||
<span class="fas fa-dice"></span>
|
||||
|
||||
Predict antimicrobial resistance
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/datasets.html">
|
||||
<span class="fa fa-database"></span>
|
||||
<span class="fas fa-database"></span>
|
||||
|
||||
Data sets for download / own use
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/PCA.html">
|
||||
<span class="fa fa-compress"></span>
|
||||
<span class="fas fa-compress"></span>
|
||||
|
||||
Conduct principal component analysis for AMR
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/MDR.html">
|
||||
<span class="fa fa-skull-crossbones"></span>
|
||||
<span class="fas fa-skull-crossbones"></span>
|
||||
|
||||
Determine multi-drug resistance (MDR)
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/WHONET.html">
|
||||
<span class="fa fa-globe-americas"></span>
|
||||
<span class="fas fa-globe-americas"></span>
|
||||
|
||||
Work with WHONET data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/SPSS.html">
|
||||
<span class="fa fa-file-upload"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-exchange-alt"></span>
|
||||
|
||||
Apply EUCAST rules
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/mo_property.html">
|
||||
<span class="fa fa-bug"></span>
|
||||
<span class="fas fa-bug"></span>
|
||||
|
||||
Get properties of a microorganism
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/ab_property.html">
|
||||
<span class="fa fa-capsules"></span>
|
||||
<span class="fas fa-capsules"></span>
|
||||
|
||||
Get properties of an antibiotic
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/benchmarks.html">
|
||||
<span class="fa fa-shipping-fast"></span>
|
||||
<span class="fas fa-shipping-fast"></span>
|
||||
|
||||
Other: benchmarks
|
||||
</a>
|
||||
@@ -142,21 +142,21 @@
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/index.html">
|
||||
<span class="fa fa-book-open"></span>
|
||||
<span class="fas fa-book-open"></span>
|
||||
|
||||
Manual
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../authors.html">
|
||||
<span class="fa fa-users"></span>
|
||||
<span class="fas fa-users"></span>
|
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|
||||
Authors
|
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</a>
|
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</li>
|
||||
<li>
|
||||
<a href="../news/index.html">
|
||||
<span class="far fa far fa-newspaper"></span>
|
||||
<span class="far fa-newspaper"></span>
|
||||
|
||||
Changelog
|
||||
</a>
|
||||
@@ -165,14 +165,14 @@
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="https://github.com/msberends/AMR">
|
||||
<span class="fab fa fab fa-github"></span>
|
||||
<span class="fab fa-github"></span>
|
||||
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../survey.html">
|
||||
<span class="fa fa-clipboard-list"></span>
|
||||
<span class="fas fa-clipboard-list"></span>
|
||||
|
||||
Survey
|
||||
</a>
|
||||
@@ -187,7 +187,7 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="PCA_files/header-attrs-2.4/header-attrs.js"></script><script src="PCA_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="PCA_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
|
||||
@@ -209,8 +209,8 @@
|
||||
<h1 class="hasAnchor">
|
||||
<a href="#transforming" class="anchor"></a>Transforming</h1>
|
||||
<p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
|
||||
<span class="fu"><a href="https://tibble.tidyverse.org/reference/glimpse.html">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
|
||||
<span class="co"># Rows: 2,000</span>
|
||||
@@ -263,10 +263,10 @@
|
||||
<span class="co"># $ CHL <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
|
||||
<span class="co"># $ COL <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span>
|
||||
<span class="co"># $ MUP <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
|
||||
<span class="co"># $ RIF <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></pre></div>
|
||||
<span class="co"># $ RIF <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></code></pre></div>
|
||||
<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit">
|
||||
<span class="va">resistance_data</span> <span class="op"><-</span> <span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">resistance_data</span> <span class="op"><-</span> <span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span>
|
||||
genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op">%>%</span> <span class="co"># and genus as we do here</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op">%>%</span> <span class="co"># then get resistance of all drugs</span>
|
||||
@@ -283,40 +283,40 @@
|
||||
<span class="co"># 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA</span>
|
||||
<span class="co"># 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA</span>
|
||||
<span class="co"># 5 Actinomycetales Micrococcus NA NA NA NA NA NA NA NA</span>
|
||||
<span class="co"># 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA</span></pre></div>
|
||||
<span class="co"># 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA</span></code></pre></div>
|
||||
</div>
|
||||
<div id="perform-principal-component-analysis" class="section level1">
|
||||
<h1 class="hasAnchor">
|
||||
<a href="#perform-principal-component-analysis" class="anchor"></a>Perform principal component analysis</h1>
|
||||
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit">
|
||||
<span class="va">pca_result</span> <span class="op"><-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">pca_result</span> <span class="op"><-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
|
||||
<span class="co"># NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT. Total</span>
|
||||
<span class="co"># observations available: 7.</span></pre></div>
|
||||
<span class="co"># observations available: 7.</span></code></pre></div>
|
||||
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> function:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
|
||||
<span class="co"># Importance of components:</span>
|
||||
<span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span>
|
||||
<span class="co"># Standard deviation 2.154 1.6807 0.61365 0.33902 0.20757 0.03136 1.733e-16</span>
|
||||
<span class="co"># Proportion of Variance 0.580 0.3531 0.04707 0.01437 0.00539 0.00012 0.000e+00</span>
|
||||
<span class="co"># Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00</span></pre></div>
|
||||
<span class="co"># Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00</span></code></pre></div>
|
||||
<p>Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the <em>Proportion of Variance</em>. We can create a so-called biplot with the base R <code><a href="https://rdrr.io/r/stats/biplot.html">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
|
||||
</div>
|
||||
<div id="plotting-the-results" class="section level1">
|
||||
<h1 class="hasAnchor">
|
||||
<a href="#plotting-the-results" class="anchor"></a>Plotting the results</h1>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit">
|
||||
<span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
|
||||
<p>But we can’t see the explanation of the points. Perhaps this works better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that automatically adds the right labels and even groups:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
|
||||
<p>You can also print an ellipse per group, and edit the appearance:</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span>
|
||||
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span>
|
||||
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
||||
// be compatible with the behavior of Pandoc < 2.8).
|
||||
document.addEventListener('DOMContentLoaded', function(e) {
|
||||
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
||||
var i, h, a;
|
||||
for (i = 0; i < hs.length; i++) {
|
||||
h = hs[i];
|
||||
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
||||
a = h.attributes;
|
||||
while (a.length > 0) h.removeAttribute(a[0].name);
|
||||
}
|
||||
});
|
||||
+48
-48
@@ -39,7 +39,7 @@
|
||||
</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="Latest development version">1.4.0.9032</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@@ -47,14 +47,14 @@
|
||||
<ul class="nav navbar-nav">
|
||||
<li>
|
||||
<a href="../index.html">
|
||||
<span class="fa fa-home"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-question-circle"></span>
|
||||
|
||||
How to
|
||||
|
||||
@@ -63,77 +63,77 @@
|
||||
<ul class="dropdown-menu" role="menu">
|
||||
<li>
|
||||
<a href="../articles/AMR.html">
|
||||
<span class="fa fa-directions"></span>
|
||||
<span class="fas fa-directions"></span>
|
||||
|
||||
Conduct AMR analysis
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/resistance_predict.html">
|
||||
<span class="fa fa-dice"></span>
|
||||
<span class="fas fa-dice"></span>
|
||||
|
||||
Predict antimicrobial resistance
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/datasets.html">
|
||||
<span class="fa fa-database"></span>
|
||||
<span class="fas fa-database"></span>
|
||||
|
||||
Data sets for download / own use
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/PCA.html">
|
||||
<span class="fa fa-compress"></span>
|
||||
<span class="fas fa-compress"></span>
|
||||
|
||||
Conduct principal component analysis for AMR
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/MDR.html">
|
||||
<span class="fa fa-skull-crossbones"></span>
|
||||
<span class="fas fa-skull-crossbones"></span>
|
||||
|
||||
Determine multi-drug resistance (MDR)
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/WHONET.html">
|
||||
<span class="fa fa-globe-americas"></span>
|
||||
<span class="fas fa-globe-americas"></span>
|
||||
|
||||
Work with WHONET data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/SPSS.html">
|
||||
<span class="fa fa-file-upload"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-exchange-alt"></span>
|
||||
|
||||
Apply EUCAST rules
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/mo_property.html">
|
||||
<span class="fa fa-bug"></span>
|
||||
<span class="fas fa-bug"></span>
|
||||
|
||||
Get properties of a microorganism
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/ab_property.html">
|
||||
<span class="fa fa-capsules"></span>
|
||||
<span class="fas fa-capsules"></span>
|
||||
|
||||
Get properties of an antibiotic
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/benchmarks.html">
|
||||
<span class="fa fa-shipping-fast"></span>
|
||||
<span class="fas fa-shipping-fast"></span>
|
||||
|
||||
Other: benchmarks
|
||||
</a>
|
||||
@@ -142,21 +142,21 @@
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/index.html">
|
||||
<span class="fa fa-book-open"></span>
|
||||
<span class="fas fa-book-open"></span>
|
||||
|
||||
Manual
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../authors.html">
|
||||
<span class="fa fa-users"></span>
|
||||
<span class="fas fa-users"></span>
|
||||
|
||||
Authors
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../news/index.html">
|
||||
<span class="far fa far fa-newspaper"></span>
|
||||
<span class="far fa-newspaper"></span>
|
||||
|
||||
Changelog
|
||||
</a>
|
||||
@@ -165,14 +165,14 @@
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="https://github.com/msberends/AMR">
|
||||
<span class="fab fa fab fa-github"></span>
|
||||
<span class="fab fa-github"></span>
|
||||
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../survey.html">
|
||||
<span class="fa fa-clipboard-list"></span>
|
||||
<span class="fas fa-clipboard-list"></span>
|
||||
|
||||
Survey
|
||||
</a>
|
||||
@@ -187,13 +187,13 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="SPSS_files/header-attrs-2.4/header-attrs.js"></script><script src="SPSS_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="SPSS_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
|
||||
<h4 class="author">Matthijs S. Berends</h4>
|
||||
|
||||
<h4 class="date">08 December 2020</h4>
|
||||
<h4 class="date">29 December 2020</h4>
|
||||
|
||||
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/SPSS.Rmd"><code>vignettes/SPSS.Rmd</code></a></small>
|
||||
<div class="hidden name"><code>SPSS.Rmd</code></div>
|
||||
@@ -247,8 +247,8 @@
|
||||
</li>
|
||||
</ul>
|
||||
<p>To demonstrate the first point:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="co"># not all values are valid MIC values:</span>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># not all values are valid MIC values:</span>
|
||||
<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span><span class="op">(</span><span class="fl">0.125</span><span class="op">)</span>
|
||||
<span class="co"># Class <mic></span>
|
||||
<span class="co"># [1] 0.125</span>
|
||||
@@ -279,7 +279,7 @@
|
||||
<span class="co"># [4] "fluclox" "flucloxacilina" "flucloxacillin" </span>
|
||||
<span class="co"># [7] "flucloxacilline" "flucloxacillinum" "fluorochloroxacillin"</span>
|
||||
<span class="fu"><a href="../reference/ab_property.html">ab_atc</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
|
||||
<span class="co"># [1] "J01CF05"</span></pre></div>
|
||||
<span class="co"># [1] "J01CF05"</span></code></pre></div>
|
||||
</div>
|
||||
<div id="import-data-from-spsssasstata" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
@@ -295,8 +295,8 @@
|
||||
<p><img src="https://github.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="downlit">
|
||||
<span class="va">SPSS_data</span>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">SPSS_data</span>
|
||||
<span class="co"># # A tibble: 4,203 x 4</span>
|
||||
<span class="co"># v001 sex status statusage</span>
|
||||
<span class="co"># <dbl> <dbl+lbl> <dbl+lbl> <dbl></span>
|
||||
@@ -326,74 +326,74 @@
|
||||
<span class="co"># 8 10011 Male alive 73.1</span>
|
||||
<span class="co"># 9 10017 Male alive 56.7</span>
|
||||
<span class="co"># 10 10018 Female alive 66.6</span>
|
||||
<span class="co"># # … with 4,193 more rows</span></pre></div>
|
||||
<span class="co"># # … with 4,193 more rows</span></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="downlit">
|
||||
<span class="co"># download and install the latest version:</span>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># download and install the latest version:</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"haven"</span><span class="op">)</span>
|
||||
<span class="co"># load the package you just installed:</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://haven.tidyverse.org">haven</a></span><span class="op">)</span> </pre></div>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://haven.tidyverse.org">haven</a></span><span class="op">)</span> </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="downlit">
|
||||
<span class="co"># read any SPSS file based on file extension (best way):</span>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># read any SPSS file based on file extension (best way):</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_spss</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># read .sav or .zsav file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_sav</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># read .por file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></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="downlit">
|
||||
<span class="co"># save as .sav file:</span>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># save as .sav file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># save as compressed .zsav file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, compress <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, compress <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></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="downlit">
|
||||
<span class="co"># read .sas7bdat + .sas7bcat files:</span>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># read .sas7bdat + .sas7bcat files:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html">read_sas</a></span><span class="op">(</span>data_file <span class="op">=</span> <span class="st">"path/to/file"</span>, catalog_file <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># read SAS transport files (version 5 and version 8):</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
|
||||
<p>To export your R objects to the SAS file format:</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit">
|
||||
<span class="co"># save as regular SAS file:</span>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># save as regular SAS file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html">write_sas</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># the SAS transport format is an open format </span>
|
||||
<span class="co"># (required for submission of the data to the FDA)</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, version <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, version <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></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="downlit">
|
||||
<span class="co"># read .dta file:</span>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># read .dta file:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_stata</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span>
|
||||
|
||||
<span class="co"># works exactly the same:</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span></code></pre></div>
|
||||
<p>To export your R objects to the Stata file format:</p>
|
||||
<div class="sourceCode" id="cb9"><pre class="downlit">
|
||||
<span class="co"># save as .dta file, Stata version 14:</span>
|
||||
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># save as .dta file, Stata version 14:</span>
|
||||
<span class="co"># (supports Stata v8 until v15 at the time of writing)</span>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"/path/to/file"</span>, version <span class="op">=</span> <span class="fl">14</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"/path/to/file"</span>, version <span class="op">=</span> <span class="fl">14</span><span class="op">)</span></code></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
||||
// be compatible with the behavior of Pandoc < 2.8).
|
||||
document.addEventListener('DOMContentLoaded', function(e) {
|
||||
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
||||
var i, h, a;
|
||||
for (i = 0; i < hs.length; i++) {
|
||||
h = hs[i];
|
||||
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
||||
a = h.attributes;
|
||||
while (a.length > 0) h.removeAttribute(a[0].name);
|
||||
}
|
||||
});
|
||||
+38
-38
@@ -39,7 +39,7 @@
|
||||
</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="Latest development version">1.4.0.9032</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@@ -47,14 +47,14 @@
|
||||
<ul class="nav navbar-nav">
|
||||
<li>
|
||||
<a href="../index.html">
|
||||
<span class="fa fa-home"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-question-circle"></span>
|
||||
|
||||
How to
|
||||
|
||||
@@ -63,77 +63,77 @@
|
||||
<ul class="dropdown-menu" role="menu">
|
||||
<li>
|
||||
<a href="../articles/AMR.html">
|
||||
<span class="fa fa-directions"></span>
|
||||
<span class="fas fa-directions"></span>
|
||||
|
||||
Conduct AMR analysis
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/resistance_predict.html">
|
||||
<span class="fa fa-dice"></span>
|
||||
<span class="fas fa-dice"></span>
|
||||
|
||||
Predict antimicrobial resistance
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/datasets.html">
|
||||
<span class="fa fa-database"></span>
|
||||
<span class="fas fa-database"></span>
|
||||
|
||||
Data sets for download / own use
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/PCA.html">
|
||||
<span class="fa fa-compress"></span>
|
||||
<span class="fas fa-compress"></span>
|
||||
|
||||
Conduct principal component analysis for AMR
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/MDR.html">
|
||||
<span class="fa fa-skull-crossbones"></span>
|
||||
<span class="fas fa-skull-crossbones"></span>
|
||||
|
||||
Determine multi-drug resistance (MDR)
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/WHONET.html">
|
||||
<span class="fa fa-globe-americas"></span>
|
||||
<span class="fas fa-globe-americas"></span>
|
||||
|
||||
Work with WHONET data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/SPSS.html">
|
||||
<span class="fa fa-file-upload"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-exchange-alt"></span>
|
||||
|
||||
Apply EUCAST rules
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/mo_property.html">
|
||||
<span class="fa fa-bug"></span>
|
||||
<span class="fas fa-bug"></span>
|
||||
|
||||
Get properties of a microorganism
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/ab_property.html">
|
||||
<span class="fa fa-capsules"></span>
|
||||
<span class="fas fa-capsules"></span>
|
||||
|
||||
Get properties of an antibiotic
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/benchmarks.html">
|
||||
<span class="fa fa-shipping-fast"></span>
|
||||
<span class="fas fa-shipping-fast"></span>
|
||||
|
||||
Other: benchmarks
|
||||
</a>
|
||||
@@ -142,21 +142,21 @@
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/index.html">
|
||||
<span class="fa fa-book-open"></span>
|
||||
<span class="fas fa-book-open"></span>
|
||||
|
||||
Manual
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../authors.html">
|
||||
<span class="fa fa-users"></span>
|
||||
<span class="fas fa-users"></span>
|
||||
|
||||
Authors
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../news/index.html">
|
||||
<span class="far fa far fa-newspaper"></span>
|
||||
<span class="far fa-newspaper"></span>
|
||||
|
||||
Changelog
|
||||
</a>
|
||||
@@ -165,14 +165,14 @@
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="https://github.com/msberends/AMR">
|
||||
<span class="fab fa fab fa-github"></span>
|
||||
<span class="fab fa-github"></span>
|
||||
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../survey.html">
|
||||
<span class="fa fa-clipboard-list"></span>
|
||||
<span class="fas fa-clipboard-list"></span>
|
||||
|
||||
Survey
|
||||
</a>
|
||||
@@ -187,7 +187,7 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="WHONET_files/header-attrs-2.4/header-attrs.js"></script><script src="WHONET_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="WHONET_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>How to work with WHONET data</h1>
|
||||
@@ -205,38 +205,38 @@
|
||||
<a href="#import-of-data" class="anchor"></a>Import of data</h3>
|
||||
<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="downlit">
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org">readxl</a></span><span class="op">)</span>
|
||||
<span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org">readxl</a></span><span class="op">)</span>
|
||||
<span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></code></pre></div>
|
||||
<p>This package comes with an <a href="https://msberends.github.io/AMR/reference/WHONET.html">example data set <code>WHONET</code></a>. We will use it for this analysis.</p>
|
||||
</div>
|
||||
<div id="preparation" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#preparation" class="anchor"></a>Preparation</h3>
|
||||
<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="downlit">
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></pre></div>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></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="https://msberends.github.io/AMR/reference/catalogue_of_life">our Catalogue of Life reference data set</a>, which contains all ~70,000 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="downlit">
|
||||
<span class="co"># transform variables</span>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># transform variables</span>
|
||||
<span class="va">data</span> <span class="op"><-</span> <span class="va">WHONET</span> <span class="op">%>%</span>
|
||||
<span class="co"># get microbial ID based on given organism</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></code></pre></div>
|
||||
<p>No errors or warnings, so all values are transformed succesfully.</p>
|
||||
<p>We also created a package dedicated to data cleaning and checking, called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq()</a></code> function can be used to create frequency tables.</p>
|
||||
<p>So let’s check our data, with a couple of frequency tables:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
|
||||
<span class="va">data</span> <span class="op">%>%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
|
||||
<span class="va">data</span> <span class="op">%>%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></code></pre></div>
|
||||
<p><strong>Frequency table</strong></p>
|
||||
<p>Class: character<br>
|
||||
Length: 500<br>
|
||||
@@ -337,10 +337,10 @@ Longest: 40</p>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>(omitted 27 entries, n = 56 [11.20%])</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit">
|
||||
<span class="co"># our transformed antibiotic columns</span>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># our transformed antibiotic columns</span>
|
||||
<span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span>
|
||||
<span class="va">data</span> <span class="op">%>%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></pre></div>
|
||||
<span class="va">data</span> <span class="op">%>%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></code></pre></div>
|
||||
<p><strong>Frequency table</strong></p>
|
||||
<p>Class: factor > ordered > rsi (numeric)<br>
|
||||
Length: 500<br>
|
||||
@@ -391,11 +391,11 @@ Drug group: Beta-lactams/penicillins<br>
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#a-first-glimpse-at-results" class="anchor"></a>A first glimpse at results</h3>
|
||||
<p>An easy <code>ggplot</code> will already give a lot of information, using the included <code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> function:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit">
|
||||
<span class="va">data</span> <span class="op">%>%</span>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">data</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">'ab'</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">'ab'</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
||||
// be compatible with the behavior of Pandoc < 2.8).
|
||||
document.addEventListener('DOMContentLoaded', function(e) {
|
||||
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
||||
var i, h, a;
|
||||
for (i = 0; i < hs.length; i++) {
|
||||
h = hs[i];
|
||||
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
||||
a = h.attributes;
|
||||
while (a.length > 0) h.removeAttribute(a[0].name);
|
||||
}
|
||||
});
|
||||
@@ -39,7 +39,7 @@
|
||||
</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="Latest development version">1.4.0.9032</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@@ -47,14 +47,14 @@
|
||||
<ul class="nav navbar-nav">
|
||||
<li>
|
||||
<a href="../index.html">
|
||||
<span class="fa fa-home"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-question-circle"></span>
|
||||
|
||||
How to
|
||||
|
||||
@@ -63,77 +63,77 @@
|
||||
<ul class="dropdown-menu" role="menu">
|
||||
<li>
|
||||
<a href="../articles/AMR.html">
|
||||
<span class="fa fa-directions"></span>
|
||||
<span class="fas fa-directions"></span>
|
||||
|
||||
Conduct AMR analysis
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/resistance_predict.html">
|
||||
<span class="fa fa-dice"></span>
|
||||
<span class="fas fa-dice"></span>
|
||||
|
||||
Predict antimicrobial resistance
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/datasets.html">
|
||||
<span class="fa fa-database"></span>
|
||||
<span class="fas fa-database"></span>
|
||||
|
||||
Data sets for download / own use
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/PCA.html">
|
||||
<span class="fa fa-compress"></span>
|
||||
<span class="fas fa-compress"></span>
|
||||
|
||||
Conduct principal component analysis for AMR
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/MDR.html">
|
||||
<span class="fa fa-skull-crossbones"></span>
|
||||
<span class="fas fa-skull-crossbones"></span>
|
||||
|
||||
Determine multi-drug resistance (MDR)
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/WHONET.html">
|
||||
<span class="fa fa-globe-americas"></span>
|
||||
<span class="fas fa-globe-americas"></span>
|
||||
|
||||
Work with WHONET data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/SPSS.html">
|
||||
<span class="fa fa-file-upload"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-exchange-alt"></span>
|
||||
|
||||
Apply EUCAST rules
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/mo_property.html">
|
||||
<span class="fa fa-bug"></span>
|
||||
<span class="fas fa-bug"></span>
|
||||
|
||||
Get properties of a microorganism
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/ab_property.html">
|
||||
<span class="fa fa-capsules"></span>
|
||||
<span class="fas fa-capsules"></span>
|
||||
|
||||
Get properties of an antibiotic
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/benchmarks.html">
|
||||
<span class="fa fa-shipping-fast"></span>
|
||||
<span class="fas fa-shipping-fast"></span>
|
||||
|
||||
Other: benchmarks
|
||||
</a>
|
||||
@@ -142,21 +142,21 @@
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/index.html">
|
||||
<span class="fa fa-book-open"></span>
|
||||
<span class="fas fa-book-open"></span>
|
||||
|
||||
Manual
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../authors.html">
|
||||
<span class="fa fa-users"></span>
|
||||
<span class="fas fa-users"></span>
|
||||
|
||||
Authors
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../news/index.html">
|
||||
<span class="far fa far fa-newspaper"></span>
|
||||
<span class="far fa-newspaper"></span>
|
||||
|
||||
Changelog
|
||||
</a>
|
||||
@@ -165,14 +165,14 @@
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="https://github.com/msberends/AMR">
|
||||
<span class="fab fa fab fa-github"></span>
|
||||
<span class="fab fa-github"></span>
|
||||
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../survey.html">
|
||||
<span class="fa fa-clipboard-list"></span>
|
||||
<span class="fas fa-clipboard-list"></span>
|
||||
|
||||
Survey
|
||||
</a>
|
||||
@@ -187,7 +187,7 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="benchmarks_files/header-attrs-2.4/header-attrs.js"></script><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="benchmarks_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>Benchmarks</h1>
|
||||
@@ -202,15 +202,15 @@
|
||||
|
||||
<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>microbenchmark()</code> runs different input expressions independently of each other and measures their time-to-result.</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="va">microbenchmark</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">microbenchmark</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span></pre></div>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span></code></pre></div>
|
||||
<p>In the next test, we try to ‘coerce’ different input values into the microbial code of <em>Staphylococcus aureus</em>. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.</p>
|
||||
<p>The actual result is the same every time: it returns its microorganism code <code>B_STPHY_AURS</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="downlit">
|
||||
<span class="va">S.aureus</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="op">(</span>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">S.aureus</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="op">(</span>
|
||||
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"sau"</span><span class="op">)</span>, <span class="co"># WHONET code</span>
|
||||
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"stau"</span><span class="op">)</span>,
|
||||
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"STAU"</span><span class="op">)</span>,
|
||||
@@ -227,20 +227,20 @@
|
||||
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max</span>
|
||||
<span class="co"># as.mo("sau") 13.0 13.0 17.0 15.0 16.0 43.0</span>
|
||||
<span class="co"># as.mo("stau") 120.0 120.0 140.0 150.0 150.0 160.0</span>
|
||||
<span class="co"># as.mo("STAU") 120.0 150.0 150.0 150.0 160.0 160.0</span>
|
||||
<span class="co"># as.mo("staaur") 13.0 13.0 17.0 14.0 16.0 44.0</span>
|
||||
<span class="co"># as.mo("STAAUR") 13.0 14.0 19.0 16.0 16.0 48.0</span>
|
||||
<span class="co"># as.mo("S. aureus") 34.0 58.0 61.0 63.0 68.0 86.0</span>
|
||||
<span class="co"># as.mo("S aureus") 33.0 34.0 46.0 36.0 63.0 63.0</span>
|
||||
<span class="co"># as.mo("Staphylococcus aureus") 2.3 2.5 2.5 2.5 2.6 2.7</span>
|
||||
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 980.0 980.0 1000.0 1000.0 1000.0 1100.0</span>
|
||||
<span class="co"># as.mo("Sthafilokkockus aaureuz") 430.0 430.0 440.0 440.0 440.0 460.0</span>
|
||||
<span class="co"># as.mo("MRSA") 13.0 14.0 18.0 16.0 17.0 43.0</span>
|
||||
<span class="co"># as.mo("VISA") 21.0 22.0 23.0 22.0 24.0 25.0</span>
|
||||
<span class="co"># as.mo("VRSA") 22.0 23.0 35.0 27.0 52.0 55.0</span>
|
||||
<span class="co"># expr min lq mean median uq max</span>
|
||||
<span class="co"># as.mo("sau") 13.0 14.0 25.0 15.0 44.0 65</span>
|
||||
<span class="co"># as.mo("stau") 120.0 130.0 140.0 140.0 160.0 160</span>
|
||||
<span class="co"># as.mo("STAU") 120.0 130.0 150.0 160.0 160.0 180</span>
|
||||
<span class="co"># as.mo("staaur") 13.0 13.0 14.0 14.0 14.0 15</span>
|
||||
<span class="co"># as.mo("STAAUR") 13.0 14.0 17.0 15.0 15.0 43</span>
|
||||
<span class="co"># as.mo("S. aureus") 30.0 32.0 45.0 34.0 62.0 68</span>
|
||||
<span class="co"># as.mo("S aureus") 31.0 34.0 42.0 35.0 61.0 63</span>
|
||||
<span class="co"># as.mo("Staphylococcus aureus") 2.5 2.6 2.8 2.8 2.9 3</span>
|
||||
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 1200.0 1200.0 1200.0 1200.0 1200.0 1200</span>
|
||||
<span class="co"># as.mo("Sthafilokkockus aaureuz") 550.0 560.0 570.0 560.0 570.0 610</span>
|
||||
<span class="co"># as.mo("MRSA") 13.0 13.0 17.0 15.0 15.0 42</span>
|
||||
<span class="co"># as.mo("VISA") 21.0 22.0 26.0 23.0 23.0 52</span>
|
||||
<span class="co"># as.mo("VRSA") 21.0 23.0 35.0 24.0 52.0 54</span>
|
||||
<span class="co"># neval</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
@@ -254,7 +254,7 @@
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span>
|
||||
<span class="co"># 10</span></pre></div>
|
||||
<span class="co"># 10</span></code></pre></div>
|
||||
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="562.5"></p>
|
||||
<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. It is clear that accepted taxonomic names are extremely fast, but some variations can take up to 500-1000 times as much time.</p>
|
||||
<p>To improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
|
||||
@@ -262,8 +262,8 @@
|
||||
<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_name()</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="cb3"><pre class="downlit">
|
||||
<span class="co"># take all MO codes from the example_isolates data set</span>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="co"># take all MO codes from the example_isolates data set</span>
|
||||
<span class="va">x</span> <span class="op"><-</span> <span class="va">example_isolates</span><span class="op">$</span><span class="va">mo</span> <span class="op">%>%</span>
|
||||
<span class="co"># and copy them a thousand times</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
@@ -284,27 +284,27 @@
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># mo_name(x) 145 190 221 213 248 313 10</span></pre></div>
|
||||
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.213 seconds. You only lose time on your unique input values.</p>
|
||||
<span class="co"># mo_name(x) 141 180 218 207 245 312 10</span></code></pre></div>
|
||||
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.207 seconds. 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_name()</a></code>, it almost doesn’t take any time at all (see ‘C’ below):</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="va">run_it</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">run_it</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
|
||||
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>,
|
||||
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
|
||||
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># A 8.36 8.59 8.99 9.07 9.29 9.62 10</span>
|
||||
<span class="co"># B 26.80 27.50 28.10 28.00 29.10 29.80 10</span>
|
||||
<span class="co"># C 2.06 2.15 6.68 2.50 2.69 45.20 10</span></pre></div>
|
||||
<span class="co"># A 8.76 8.96 9.39 9.31 9.86 10.10 10</span>
|
||||
<span class="co"># B 27.60 28.20 33.00 28.90 29.40 71.20 10</span>
|
||||
<span class="co"># C 2.28 2.32 2.47 2.45 2.48 2.85 10</span></code></pre></div>
|
||||
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0025 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="cb5"><pre class="downlit">
|
||||
<span class="va">run_it</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">run_it</span> <span class="op"><-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
|
||||
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"Staphylococcus"</span><span class="op">)</span>,
|
||||
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
|
||||
D <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_family</a></span><span class="op">(</span><span class="st">"Staphylococcaceae"</span><span class="op">)</span>,
|
||||
@@ -316,22 +316,22 @@
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># A 1.68 1.91 2.13 2.08 2.43 2.62 10</span>
|
||||
<span class="co"># B 1.89 1.99 2.16 2.05 2.30 2.69 10</span>
|
||||
<span class="co"># C 1.87 1.91 2.09 2.00 2.28 2.46 10</span>
|
||||
<span class="co"># D 1.65 1.71 1.92 1.87 1.98 2.58 10</span>
|
||||
<span class="co"># E 1.64 1.74 1.90 1.87 1.98 2.34 10</span>
|
||||
<span class="co"># F 1.86 1.92 2.05 2.05 2.16 2.27 10</span>
|
||||
<span class="co"># G 1.73 1.80 1.99 1.97 2.14 2.29 10</span>
|
||||
<span class="co"># H 1.85 1.93 2.08 1.98 2.15 2.93 10</span></pre></div>
|
||||
<span class="co"># A 1.91 1.95 2.06 1.99 2.09 2.62 10</span>
|
||||
<span class="co"># B 1.83 1.91 2.09 2.04 2.20 2.45 10</span>
|
||||
<span class="co"># C 1.79 1.90 2.03 1.99 2.22 2.30 10</span>
|
||||
<span class="co"># D 1.90 2.01 2.18 2.12 2.25 2.71 10</span>
|
||||
<span class="co"># E 1.91 2.02 2.14 2.08 2.15 2.81 10</span>
|
||||
<span class="co"># F 1.86 1.92 2.00 2.01 2.06 2.16 10</span>
|
||||
<span class="co"># G 1.81 1.96 2.09 2.08 2.22 2.41 10</span>
|
||||
<span class="co"># H 1.90 1.93 2.05 2.00 2.22 2.29 10</span></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> anyway, 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="cb6"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</span> <span class="co"># or just mo_name("CoNS") on an English system</span>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</span> <span class="co"># or just mo_name("CoNS") on an English system</span>
|
||||
<span class="co"># [1] "Coagulase-negative Staphylococcus (CoNS)"</span>
|
||||
|
||||
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span> <span class="co"># or just mo_name("CoNS") on a Spanish system</span>
|
||||
@@ -351,13 +351,13 @@
|
||||
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
|
||||
<span class="co"># Unit: milliseconds</span>
|
||||
<span class="co"># expr min lq mean median uq max neval</span>
|
||||
<span class="co"># en 16.16 17.85 22.42 18.54 19.61 68.28 100</span>
|
||||
<span class="co"># de 19.55 21.38 24.37 21.96 22.95 61.55 100</span>
|
||||
<span class="co"># nl 31.64 35.08 40.58 36.34 37.88 79.00 100</span>
|
||||
<span class="co"># es 18.03 21.46 25.61 22.09 23.30 64.97 100</span>
|
||||
<span class="co"># it 17.82 21.01 23.29 21.80 22.82 58.47 100</span>
|
||||
<span class="co"># fr 18.19 21.29 25.63 21.93 23.20 60.48 100</span>
|
||||
<span class="co"># pt 17.76 21.07 24.81 21.83 23.39 65.00 100</span></pre></div>
|
||||
<span class="co"># en 17.45 18.01 19.69 18.53 19.14 55.30 100</span>
|
||||
<span class="co"># de 20.58 21.54 26.69 22.08 23.96 67.16 100</span>
|
||||
<span class="co"># nl 33.79 34.67 39.13 35.39 36.72 74.60 100</span>
|
||||
<span class="co"># es 20.71 21.42 24.36 21.88 22.65 58.57 100</span>
|
||||
<span class="co"># it 20.65 21.18 26.50 21.53 22.68 61.96 100</span>
|
||||
<span class="co"># fr 20.68 21.27 25.05 21.64 22.37 58.82 100</span>
|
||||
<span class="co"># pt 20.69 21.44 24.36 21.94 22.99 59.66 100</span></code></pre></div>
|
||||
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
|
||||
</div>
|
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</div>
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9041</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9041</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9032</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
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Conduct principal component analysis for AMR
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<a href="../articles/MDR.html">
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<span class="fa fa-skull-crossbones"></span>
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Determine multi-drug resistance (MDR)
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Work with WHONET data
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<span class="fa fa-file-upload"></span>
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Import data from SPSS/SAS/Stata
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Apply EUCAST rules
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<span class="fa fa-bug"></span>
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Get properties of a microorganism
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<a href="../reference/ab_property.html">
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Get properties of an antibiotic
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@@ -142,21 +142,21 @@
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@@ -165,14 +165,14 @@
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Survey
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@@ -187,7 +187,7 @@
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||||
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||||
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||||
|
||||
</header><script src="resistance_predict_files/header-attrs-2.4/header-attrs.js"></script><script src="resistance_predict_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
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</header><script src="resistance_predict_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
|
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<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
|
||||
@@ -205,37 +205,37 @@
|
||||
<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
|
||||
<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 Dr Hadley Wickham. 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>
|
||||
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span>
|
||||
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
|
||||
|
||||
<span class="co"># (if not yet installed, install with:)</span>
|
||||
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></pre></div>
|
||||
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></code></pre></div>
|
||||
</div>
|
||||
<div id="prediction-analysis" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<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="./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"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
|
||||
<span id="cb2-2"><a href="#cb2-2"></a><span class="kw">resistance_predict</span>(<span class="dt">tbl =</span> example_isolates, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</span>
|
||||
<span id="cb2-3"><a href="#cb2-3"></a></span>
|
||||
<span id="cb2-4"><a href="#cb2-4"></a><span class="co"># or:</span></span>
|
||||
<span id="cb2-5"><a href="#cb2-5"></a>example_isolates <span class="op">%>%</span><span class="st"> </span></span>
|
||||
<span id="cb2-6"><a href="#cb2-6"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
|
||||
<span id="cb2-7"><a href="#cb2-7"></a> model <span class="st">"binomial"</span>)</span>
|
||||
<span id="cb2-8"><a href="#cb2-8"></a></span>
|
||||
<span id="cb2-9"><a href="#cb2-9"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
|
||||
<span id="cb2-10"><a href="#cb2-10"></a>predict_TZP <-<span class="st"> </span>example_isolates <span class="op">%>%</span><span class="st"> </span></span>
|
||||
<span id="cb2-11"><a href="#cb2-11"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
|
||||
<span id="cb2-12"><a href="#cb2-12"></a> <span class="dt">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
|
||||
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
|
||||
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">resistance_predict</span>(<span class="at">tbl =</span> example_isolates, <span class="at">col_date =</span> <span class="st">"date"</span>, <span class="at">col_ab =</span> <span class="st">"TZP"</span>, <span class="at">model =</span> <span class="st">"binomial"</span>)</span>
|
||||
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co"># or:</span></span>
|
||||
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>example_isolates <span class="sc">%>%</span> </span>
|
||||
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
|
||||
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> model <span class="st">"binomial"</span>)</span>
|
||||
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
|
||||
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
|
||||
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>predict_TZP <span class="ot"><-</span> example_isolates <span class="sc">%>%</span> </span>
|
||||
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
|
||||
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a> <span class="at">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
|
||||
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
|
||||
<p>When running any of these commands, a summary of the regression model will be printed unless using <code><a href="../reference/resistance_predict.html">resistance_predict(..., info = FALSE)</a></code>.</p>
|
||||
<pre><code># NOTE: Using column 'date' as input for `col_date`.</code></pre>
|
||||
<p>This text is only a printed summary - the actual result (output) of the function is a <code>data.frame</code> containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit">
|
||||
<span class="va">predict_TZP</span>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">predict_TZP</span>
|
||||
<span class="co"># year value se_min se_max observations observed estimated</span>
|
||||
<span class="co"># 1 2002 0.20000000 NA NA 15 0.20000000 0.05616378</span>
|
||||
<span class="co"># 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839</span>
|
||||
@@ -265,31 +265,30 @@
|
||||
<span class="co"># 26 2027 0.41315710 0.3244399 0.5018743 NA NA 0.41315710</span>
|
||||
<span class="co"># 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688</span>
|
||||
<span class="co"># 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755</span>
|
||||
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span></pre></div>
|
||||
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span></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="downlit">
|
||||
<span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
|
||||
<p>This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.</p>
|
||||
<p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit">
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></pre></div>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit">
|
||||
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R">
|
||||
<span class="co"># choose for error bars instead of a ribbon</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.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-positive bacteria, the spread (i.e. standard error) is enormous:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit">
|
||||
<span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
|
||||
<span class="co"># NOTE: Using column 'date' as input for `col_date`.</span></pre></div>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="resistance_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 model chosen above 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>
|
||||
@@ -330,17 +329,16 @@
|
||||
</tbody>
|
||||
</table>
|
||||
<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:</p>
|
||||
<div class="sourceCode" id="cb9"><pre class="downlit">
|
||||
<span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</span><span class="op">)</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
|
||||
<span class="co"># NOTE: Using column 'date' as input for `col_date`.</span></pre></div>
|
||||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span></code></pre></div>
|
||||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
|
||||
<p>This seems more likely, doesn’t it?</p>
|
||||
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
|
||||
<div class="sourceCode" id="cb10"><pre class="downlit">
|
||||
<span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
|
||||
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
|
||||
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">family</span>
|
||||
<span class="co"># </span>
|
||||
@@ -350,7 +348,7 @@
|
||||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span>
|
||||
<span class="co"># Estimate Std. Error z value Pr(>|z|)</span>
|
||||
<span class="co"># (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span>
|
||||
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></pre></div>
|
||||
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></code></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
||||
// be compatible with the behavior of Pandoc < 2.8).
|
||||
document.addEventListener('DOMContentLoaded', function(e) {
|
||||
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
||||
var i, h, a;
|
||||
for (i = 0; i < hs.length; i++) {
|
||||
h = hs[i];
|
||||
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
||||
a = h.attributes;
|
||||
while (a.length > 0) h.removeAttribute(a[0].name);
|
||||
}
|
||||
});
|
||||
@@ -39,7 +39,7 @@
|
||||
</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="Latest development version">1.4.0.9032</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@@ -47,14 +47,14 @@
|
||||
<ul class="nav navbar-nav">
|
||||
<li>
|
||||
<a href="../index.html">
|
||||
<span class="fa fa-home"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-question-circle"></span>
|
||||
|
||||
How to
|
||||
|
||||
@@ -63,77 +63,77 @@
|
||||
<ul class="dropdown-menu" role="menu">
|
||||
<li>
|
||||
<a href="../articles/AMR.html">
|
||||
<span class="fa fa-directions"></span>
|
||||
<span class="fas fa-directions"></span>
|
||||
|
||||
Conduct AMR analysis
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/resistance_predict.html">
|
||||
<span class="fa fa-dice"></span>
|
||||
<span class="fas fa-dice"></span>
|
||||
|
||||
Predict antimicrobial resistance
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/datasets.html">
|
||||
<span class="fa fa-database"></span>
|
||||
<span class="fas fa-database"></span>
|
||||
|
||||
Data sets for download / own use
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/PCA.html">
|
||||
<span class="fa fa-compress"></span>
|
||||
<span class="fas fa-compress"></span>
|
||||
|
||||
Conduct principal component analysis for AMR
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/MDR.html">
|
||||
<span class="fa fa-skull-crossbones"></span>
|
||||
<span class="fas fa-skull-crossbones"></span>
|
||||
|
||||
Determine multi-drug resistance (MDR)
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/WHONET.html">
|
||||
<span class="fa fa-globe-americas"></span>
|
||||
<span class="fas fa-globe-americas"></span>
|
||||
|
||||
Work with WHONET data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/SPSS.html">
|
||||
<span class="fa fa-file-upload"></span>
|
||||
<span class="fas 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>
|
||||
<span class="fas fa-exchange-alt"></span>
|
||||
|
||||
Apply EUCAST rules
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/mo_property.html">
|
||||
<span class="fa fa-bug"></span>
|
||||
<span class="fas fa-bug"></span>
|
||||
|
||||
Get properties of a microorganism
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/ab_property.html">
|
||||
<span class="fa fa-capsules"></span>
|
||||
<span class="fas fa-capsules"></span>
|
||||
|
||||
Get properties of an antibiotic
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../articles/benchmarks.html">
|
||||
<span class="fa fa-shipping-fast"></span>
|
||||
<span class="fas fa-shipping-fast"></span>
|
||||
|
||||
Other: benchmarks
|
||||
</a>
|
||||
@@ -142,21 +142,21 @@
|
||||
</li>
|
||||
<li>
|
||||
<a href="../reference/index.html">
|
||||
<span class="fa fa-book-open"></span>
|
||||
<span class="fas fa-book-open"></span>
|
||||
|
||||
Manual
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../authors.html">
|
||||
<span class="fa fa-users"></span>
|
||||
<span class="fas fa-users"></span>
|
||||
|
||||
Authors
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../news/index.html">
|
||||
<span class="far fa far fa-newspaper"></span>
|
||||
<span class="far fa-newspaper"></span>
|
||||
|
||||
Changelog
|
||||
</a>
|
||||
@@ -165,14 +165,14 @@
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="https://github.com/msberends/AMR">
|
||||
<span class="fab fa fab fa-github"></span>
|
||||
<span class="fab fa-github"></span>
|
||||
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="../survey.html">
|
||||
<span class="fa fa-clipboard-list"></span>
|
||||
<span class="fas fa-clipboard-list"></span>
|
||||
|
||||
Survey
|
||||
</a>
|
||||
@@ -187,7 +187,7 @@
|
||||
|
||||
|
||||
|
||||
</header><script src="welcome_to_AMR_files/header-attrs-2.4/header-attrs.js"></script><script src="welcome_to_AMR_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
|
||||
</header><script src="welcome_to_AMR_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
|
||||
<div class="col-md-9 contents">
|
||||
<div class="page-header toc-ignore">
|
||||
<h1 data-toc-skip>Welcome to the AMR package</h1>
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
||||
// be compatible with the behavior of Pandoc < 2.8).
|
||||
document.addEventListener('DOMContentLoaded', function(e) {
|
||||
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
||||
var i, h, a;
|
||||
for (i = 0; i < hs.length; i++) {
|
||||
h = hs[i];
|
||||
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
||||
a = h.attributes;
|
||||
while (a.length > 0) h.removeAttribute(a[0].name);
|
||||
}
|
||||
});
|
||||
+5
-1
@@ -81,7 +81,7 @@
|
||||
</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="Latest development version">1.4.0.9041</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@@ -303,6 +303,10 @@
|
||||
<p><strong>Sofia Ny</strong>. Contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Rogier P. Schade</strong>. Contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Dennis Souverein</strong>. Contributor.
|
||||
</p>
|
||||
|
||||
@@ -0,0 +1,48 @@
|
||||
pre {
|
||||
word-wrap: normal;
|
||||
word-break: normal;
|
||||
/*border: 1px solid #eee;*/
|
||||
border: 0px !important;
|
||||
}
|
||||
|
||||
pre, code {
|
||||
background-color: #1c1c1c;
|
||||
color: #ccc;
|
||||
}
|
||||
|
||||
pre code {
|
||||
overflow: auto;
|
||||
word-wrap: normal;
|
||||
white-space: pre;
|
||||
}
|
||||
|
||||
pre .img {
|
||||
margin: 5px 0;
|
||||
}
|
||||
|
||||
pre .img img {
|
||||
background-color: #aaa;
|
||||
display: block;
|
||||
height: auto;
|
||||
}
|
||||
|
||||
code a, pre a {
|
||||
font-family: ui-monospace, SFMono-Regular,Consolas,Liberation Mono,Menlo,monospace;
|
||||
color: #85AACC;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
a.sourceLine:hover {
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
.fl {color: #ae81ff;}
|
||||
.fu {color: #ade837;} /* function */
|
||||
.ch,.st {color: #e7db74;} /* string */
|
||||
.kw {color: #f92672;} /* keyword */
|
||||
.co {color: #696d70;} /* comment */
|
||||
.va {color: #fd971f;} /* values */
|
||||
|
||||
.message { color: black; font-weight: bolder;}
|
||||
.error { color: orange; font-weight: bolder;}
|
||||
.warning { color: #6A0366; font-weight: bolder;}
|
||||
@@ -0,0 +1,48 @@
|
||||
pre {
|
||||
word-wrap: normal;
|
||||
word-break: normal;
|
||||
/*border: 1px solid #eee;*/
|
||||
border: 0px !important;
|
||||
}
|
||||
|
||||
pre, code {
|
||||
background-color: #f8f8f8;
|
||||
color: #333;
|
||||
}
|
||||
|
||||
pre code {
|
||||
overflow: auto;
|
||||
word-wrap: normal;
|
||||
white-space: pre;
|
||||
}
|
||||
|
||||
pre .img {
|
||||
margin: 5px 0;
|
||||
}
|
||||
|
||||
pre .img img {
|
||||
background-color: #fff;
|
||||
display: block;
|
||||
height: auto;
|
||||
}
|
||||
|
||||
code a, pre a {
|
||||
font-family: ui-monospace, SFMono-Regular,Consolas,Liberation Mono,Menlo,monospace;
|
||||
color: #375f84;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
a.sourceLine:hover {
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
.fl {color: #1514b5;}
|
||||
.fu {color: #8c60bf;} /* function */
|
||||
.ch,.st {color: #036a07;} /* string */
|
||||
.kw {color: #264D66;} /* keyword */
|
||||
.co {color: #888888;} /* comment */
|
||||
.va {color: #3032da;} /* values */
|
||||
|
||||
.message { color: black; font-weight: bolder;}
|
||||
.error { color: orange; font-weight: bolder;}
|
||||
.warning { color: #6A0366; font-weight: bolder;}
|
||||
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Block a user