mirror of
https://github.com/msberends/AMR.git
synced 2025-09-04 20:09:38 +02:00
(v3.0.0.9022) postpone new features - we like a clearly focussed bugfix release first
This commit is contained in:
@@ -40,3 +40,4 @@
|
||||
^CRAN-SUBMISSION$
|
||||
^PythonPackage$
|
||||
^README\.Rmd$
|
||||
\.no_include$
|
||||
|
||||
14
.github/workflows/check-old-tinytest.yaml
vendored
14
.github/workflows/check-old-tinytest.yaml
vendored
@@ -49,13 +49,13 @@ jobs:
|
||||
# Test all old versions of R >= 3.0, we support them all!
|
||||
# For these old versions, dependencies and vignettes will not be checked.
|
||||
# For recent R versions, see check-recent.yaml (r-lib and tidyverse support the latest 5 major R releases).
|
||||
- {os: ubuntu-latest, r: '3.6', allowfail: true}
|
||||
# - {os: windows-latest, r: '3.5', allowfail: true} # always fails, horrible with UTF-8
|
||||
- {os: ubuntu-latest, r: '3.4', allowfail: true}
|
||||
- {os: ubuntu-latest, r: '3.3', allowfail: true}
|
||||
- {os: ubuntu-latest, r: '3.2', allowfail: true}
|
||||
- {os: ubuntu-latest, r: '3.1', allowfail: true}
|
||||
- {os: ubuntu-latest, r: '3.0', allowfail: true}
|
||||
- {os: ubuntu-latest, r: '3.6', allowfail: false}
|
||||
# - {os: windows-latest, r: '3.5', allowfail: false} # always fails, horrible with UTF-8
|
||||
# - {os: ubuntu-latest, r: '3.4', allowfail: false} # 3.1-3.4 now always fails with Error in grep(warn_re, lines, invert = TRUE, value = TRUE) attempt to set index 46/46 in SET_STRING_ELT
|
||||
# - {os: ubuntu-latest, r: '3.3', allowfail: false}
|
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# - {os: ubuntu-latest, r: '3.2', allowfail: false}
|
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# - {os: ubuntu-latest, r: '3.1', allowfail: false}
|
||||
- {os: ubuntu-latest, r: '3.0', allowfail: false}
|
||||
|
||||
env:
|
||||
R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
|
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|
||||
7
.github/workflows/publish-to-pypi.yml
vendored
7
.github/workflows/publish-to-pypi.yml
vendored
@@ -39,7 +39,7 @@ jobs:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
env:
|
||||
PYPI_PAT: ${{ secrets.PYPI_PAT }}
|
||||
GH_REPO_SCOPE: ${{ secrets.GH_REPO_SCOPE }}
|
||||
|
||||
steps:
|
||||
- name: Checkout code
|
||||
@@ -78,6 +78,7 @@ jobs:
|
||||
cd PythonPackage/AMR
|
||||
python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*
|
||||
|
||||
# TODO - Support Miniconda and Anaconda too
|
||||
# - name: Set up Miniconda
|
||||
# continue-on-error: true
|
||||
# uses: conda-incubator/setup-miniconda@v2
|
||||
@@ -117,7 +118,7 @@ jobs:
|
||||
rm -rf PythonPackage
|
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|
||||
git init
|
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git remote add origin https://$PYPI_PAT@github.com/msberends/AMR
|
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git remote add origin https://$GH_REPO_SCOPE@github.com/msberends/AMR
|
||||
git checkout --orphan python-wrapper
|
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git config user.name "github-actions[bot]"
|
||||
git config user.email "github-actions[bot]@users.noreply.github.com"
|
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@@ -125,4 +126,4 @@ jobs:
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git rm -rf . || true
|
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git add .
|
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git commit -m "Python wrapper update"
|
||||
git push https://$PYPI_PAT@github.com/msberends/AMR.git python-wrapper --force
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||||
git push https://$GH_REPO_SCOPE@github.com/msberends/AMR.git python-wrapper --force
|
||||
|
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@@ -39,7 +39,7 @@ jobs:
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||||
runs-on: ubuntu-latest
|
||||
|
||||
env:
|
||||
PYPI_PAT: ${{ secrets.PYPI_PAT }}
|
||||
GH_REPO_SCOPE: ${{ secrets.GH_REPO_SCOPE }}
|
||||
|
||||
steps:
|
||||
- name: Checkout code
|
||||
@@ -63,4 +63,4 @@ jobs:
|
||||
git config user.email "github-actions[bot]@users.noreply.github.com"
|
||||
git add latest_training_data.txt
|
||||
git commit -m "GPT training data update"
|
||||
git push https://$PYPI_PAT@github.com/msberends/amr-for-r-assistant.git main --force
|
||||
git push https://$GH_REPO_SCOPE@github.com/msberends/amr-for-r-assistant.git main --force
|
||||
|
||||
75
.github/workflows/todo-tracker.yml
vendored
Normal file
75
.github/workflows/todo-tracker.yml
vendored
Normal file
@@ -0,0 +1,75 @@
|
||||
# ==================================================================== #
|
||||
# TITLE: #
|
||||
# AMR: An R Package for Working with Antimicrobial Resistance Data #
|
||||
# #
|
||||
# SOURCE CODE: #
|
||||
# https://github.com/msberends/AMR #
|
||||
# #
|
||||
# PLEASE CITE THIS SOFTWARE AS: #
|
||||
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
|
||||
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
|
||||
# Journal of Statistical Software, 104(3), 1-31. #
|
||||
# https://doi.org/10.18637/jss.v104.i03 #
|
||||
# #
|
||||
# Developed at the University of Groningen and the University Medical #
|
||||
# Center Groningen in The Netherlands, in collaboration with many #
|
||||
# colleagues from around the world, see our website. #
|
||||
# #
|
||||
# This R package is free software; you can freely use and distribute #
|
||||
# it for both personal and commercial purposes under the terms of the #
|
||||
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
||||
# the Free Software Foundation. #
|
||||
# We created this package for both routine data analysis and academic #
|
||||
# research and it was publicly released in the hope that it will be #
|
||||
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
|
||||
# #
|
||||
# Visit our website for the full manual and a complete tutorial about #
|
||||
# how to conduct AMR data analysis: https://amr-for-r.org #
|
||||
# ==================================================================== #
|
||||
|
||||
on:
|
||||
push:
|
||||
# only on main
|
||||
branches: "main"
|
||||
|
||||
name: Update TODO Tracker
|
||||
|
||||
jobs:
|
||||
update-todo:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Generate TODO list from R/
|
||||
run: |
|
||||
echo "## TODO Report" > todo.md
|
||||
echo "" >> todo.md
|
||||
echo "_This issue is automatically updated on each push to `main`._" >> todo.md
|
||||
echo "" >> todo.md
|
||||
todos=$(find R/ -type f ! -name "sysdata.rda" -exec grep -nH "TODO" {} + || true)
|
||||
if [ -z "$todos" ]; then
|
||||
echo "✅ No TODOs found." >> todo.md
|
||||
else
|
||||
echo "$todos" | awk -F: '
|
||||
{
|
||||
file = $1
|
||||
line = $2
|
||||
text = substr($0, index($0,$3))
|
||||
if (file != last_file) {
|
||||
if (last_file != "") print ""
|
||||
print "### " file
|
||||
last_file = file
|
||||
}
|
||||
printf "L%s: %s\n", line, text
|
||||
}
|
||||
' >> todo.md
|
||||
fi
|
||||
|
||||
- name: Update GitHub issue
|
||||
uses: peter-evans/create-or-update-comment@v4
|
||||
with:
|
||||
token: ${{ secrets.GH_REPO_SCOPE }}
|
||||
issue-number: 231
|
||||
body-file: todo.md
|
||||
edit-mode: replace
|
||||
@@ -1,5 +1,5 @@
|
||||
Package: AMR
|
||||
Version: 3.0.0.9021
|
||||
Version: 3.0.0.9022
|
||||
Date: 2025-09-03
|
||||
Title: Antimicrobial Resistance Data Analysis
|
||||
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
|
||||
|
||||
14
NAMESPACE
14
NAMESPACE
@@ -106,8 +106,6 @@ S3method(print,mo_uncertainties)
|
||||
S3method(print,pca)
|
||||
S3method(print,sir)
|
||||
S3method(print,sir_log)
|
||||
S3method(print,step_mic_log2)
|
||||
S3method(print,step_sir_numeric)
|
||||
S3method(quantile,mic)
|
||||
S3method(rep,ab)
|
||||
S3method(rep,av)
|
||||
@@ -161,10 +159,6 @@ export(administrable_per_os)
|
||||
export(age)
|
||||
export(age_groups)
|
||||
export(all_antimicrobials)
|
||||
export(all_mic)
|
||||
export(all_mic_predictors)
|
||||
export(all_sir)
|
||||
export(all_sir_predictors)
|
||||
export(aminoglycosides)
|
||||
export(aminopenicillins)
|
||||
export(amr_class)
|
||||
@@ -358,8 +352,6 @@ export(sir_df)
|
||||
export(sir_interpretation_history)
|
||||
export(sir_predict)
|
||||
export(skewness)
|
||||
export(step_mic_log2)
|
||||
export(step_sir_numeric)
|
||||
export(streptogramins)
|
||||
export(sulfonamides)
|
||||
export(susceptibility)
|
||||
@@ -396,12 +388,6 @@ if(getRversion() >= "3.0.0") S3method(pillar::type_sum, av)
|
||||
if(getRversion() >= "3.0.0") S3method(pillar::type_sum, mic)
|
||||
if(getRversion() >= "3.0.0") S3method(pillar::type_sum, mo)
|
||||
if(getRversion() >= "3.0.0") S3method(pillar::type_sum, sir)
|
||||
if(getRversion() >= "3.0.0") S3method(recipes::bake, step_mic_log2)
|
||||
if(getRversion() >= "3.0.0") S3method(recipes::bake, step_sir_numeric)
|
||||
if(getRversion() >= "3.0.0") S3method(recipes::prep, step_mic_log2)
|
||||
if(getRversion() >= "3.0.0") S3method(recipes::prep, step_sir_numeric)
|
||||
if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_mic_log2)
|
||||
if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_sir_numeric)
|
||||
if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, disk)
|
||||
if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, mic)
|
||||
if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, mo)
|
||||
|
||||
11
NEWS.md
11
NEWS.md
@@ -1,11 +1,6 @@
|
||||
# AMR 3.0.0.9021
|
||||
# AMR 3.0.0.9022
|
||||
|
||||
This is primarily a bugfix release, though we added one nice feature too.
|
||||
|
||||
### New
|
||||
* Integration with the **tidymodels** framework to allow seamless use of MIC and SIR data in modelling pipelines via `recipes`
|
||||
- `step_mic_log2()` to transform `<mic>` columns with log2, and `step_sir_numeric()` to convert `<sir>` columns to numeric
|
||||
- New `tidyselect` helpers: `all_mic()`, `all_mic_predictors()`, `all_sir()`, `all_sir_predictors()`
|
||||
This is a bugfix release following the release of v3.0.0 in June 2025.
|
||||
|
||||
### Changed
|
||||
* Fixed a bug in `antibiogram()` for when no antimicrobials are set
|
||||
@@ -16,7 +11,7 @@ This is primarily a bugfix release, though we added one nice feature too.
|
||||
* Fixed a bug in `as.sir()` to pick right breakpoint when `uti = FALSE` (#216)
|
||||
* Fixed a bug in `ggplot_sir()` when using `combine_SI = FALSE` (#213)
|
||||
* Fixed a bug the `antimicrobials` data set to remove statins (#229)
|
||||
* Fixed a bug in `mdro()` to make sure all genes specified in arguments are acknowledges
|
||||
* Fixed a bug in `mdro()` to make sure all genes specified in arguments are acknowledged
|
||||
* Fixed ATC J01CR05 to map to piperacillin/tazobactam rather than piperacillin/sulbactam (#230)
|
||||
* Fixed all plotting to contain a separate colour for SDD (susceptible dose-dependent) (#223)
|
||||
* Fixed some specific Dutch translations for antimicrobials
|
||||
|
||||
@@ -233,6 +233,7 @@ globalVariables(c(
|
||||
"uti_index",
|
||||
"value",
|
||||
"varname",
|
||||
"where",
|
||||
"x",
|
||||
"xvar",
|
||||
"y",
|
||||
|
||||
22
R/data.R
22
R/data.R
@@ -362,14 +362,14 @@
|
||||
#' dosage
|
||||
"dosage"
|
||||
|
||||
#' Data Set with `r format(nrow(esbl_isolates), big.mark = " ")` ESBL Isolates
|
||||
#'
|
||||
#' A data set containing `r format(nrow(esbl_isolates), big.mark = " ")` microbial isolates with MIC values of common antibiotics and a binary `esbl` column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with [tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.html).
|
||||
#' @format A [tibble][tibble::tibble] with `r format(nrow(esbl_isolates), big.mark = " ")` observations and `r ncol(esbl_isolates)` variables:
|
||||
#' - `esbl`\cr Logical indicator if the isolate is ESBL-producing
|
||||
#' - `genus`\cr Genus of the microorganism
|
||||
#' - `AMC:COL`\cr MIC values for 17 antimicrobial agents, transformed to class [`mic`] (see [as.mic()])
|
||||
#' @details See our [tidymodels integration][amr-tidymodels] for an example using this data set.
|
||||
#' @examples
|
||||
#' esbl_isolates
|
||||
"esbl_isolates"
|
||||
# TODO #' Data Set with `r format(nrow(esbl_isolates), big.mark = " ")` ESBL Isolates
|
||||
# TODO #'
|
||||
# TODO #' A data set containing `r format(nrow(esbl_isolates), big.mark = " ")` microbial isolates with MIC values of common antibiotics and a binary `esbl` column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with [tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.html).
|
||||
# TODO #' @format A [tibble][tibble::tibble] with `r format(nrow(esbl_isolates), big.mark = " ")` observations and `r ncol(esbl_isolates)` variables:
|
||||
# TODO #' - `esbl`\cr Logical indicator if the isolate is ESBL-producing
|
||||
# TODO #' - `genus`\cr Genus of the microorganism
|
||||
# TODO #' - `AMC:COL`\cr MIC values for 17 antimicrobial agents, transformed to class [`mic`] (see [as.mic()])
|
||||
# TODO #' @details See our [tidymodels integration][amr-tidymodels] for an example using this data set.
|
||||
# TODO #' @examples
|
||||
# TODO #' esbl_isolates
|
||||
# TODO "esbl_isolates"
|
||||
|
||||
@@ -1,125 +0,0 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/tidymodels.R
|
||||
\name{amr-tidymodels}
|
||||
\alias{amr-tidymodels}
|
||||
\alias{all_mic}
|
||||
\alias{all_mic_predictors}
|
||||
\alias{all_sir}
|
||||
\alias{all_sir_predictors}
|
||||
\alias{step_mic_log2}
|
||||
\alias{step_sir_numeric}
|
||||
\title{AMR Extensions for Tidymodels}
|
||||
\usage{
|
||||
all_mic()
|
||||
|
||||
all_mic_predictors()
|
||||
|
||||
all_sir()
|
||||
|
||||
all_sir_predictors()
|
||||
|
||||
step_mic_log2(recipe, ..., role = NA, trained = FALSE, columns = NULL,
|
||||
skip = FALSE, id = recipes::rand_id("mic_log2"))
|
||||
|
||||
step_sir_numeric(recipe, ..., role = NA, trained = FALSE, columns = NULL,
|
||||
skip = FALSE, id = recipes::rand_id("sir_numeric"))
|
||||
}
|
||||
\arguments{
|
||||
\item{recipe}{A recipe object. The step will be added to the sequence of
|
||||
operations for this recipe.}
|
||||
|
||||
\item{...}{One or more selector functions to choose variables for this step.
|
||||
See \code{\link[recipes:selections]{selections()}} for more details.}
|
||||
|
||||
\item{role}{Not used by this step since no new variables are created.}
|
||||
|
||||
\item{trained}{A logical to indicate if the quantities for preprocessing have
|
||||
been estimated.}
|
||||
|
||||
\item{skip}{A logical. Should the step be skipped when the recipe is baked by
|
||||
\code{\link[recipes:bake]{bake()}}? While all operations are baked when \code{\link[recipes:prep]{prep()}} is run, some
|
||||
operations may not be able to be conducted on new data (e.g. processing the
|
||||
outcome variable(s)). Care should be taken when using \code{skip = TRUE} as it
|
||||
may affect the computations for subsequent operations.}
|
||||
|
||||
\item{id}{A character string that is unique to this step to identify it.}
|
||||
}
|
||||
\description{
|
||||
This family of functions allows using AMR-specific data types such as \verb{<mic>} and \verb{<sir>} inside \code{tidymodels} pipelines.
|
||||
}
|
||||
\details{
|
||||
You can read more in our online \href{https://amr-for-r.org/articles/AMR_with_tidymodels.html}{AMR with tidymodels introduction}.
|
||||
|
||||
Tidyselect helpers include:
|
||||
\itemize{
|
||||
\item \code{\link[=all_mic]{all_mic()}} and \code{\link[=all_mic_predictors]{all_mic_predictors()}} to select \verb{<mic>} columns
|
||||
\item \code{\link[=all_sir]{all_sir()}} and \code{\link[=all_sir_predictors]{all_sir_predictors()}} to select \verb{<sir>} columns
|
||||
}
|
||||
|
||||
Pre-processing pipeline steps include:
|
||||
\itemize{
|
||||
\item \code{\link[=step_mic_log2]{step_mic_log2()}} to convert MIC columns to numeric (via \code{as.numeric()}) and apply a log2 transform, to be used with \code{\link[=all_mic_predictors]{all_mic_predictors()}}
|
||||
\item \code{\link[=step_sir_numeric]{step_sir_numeric()}} to convert SIR columns to numeric (via \code{as.numeric()}), to be used with \code{\link[=all_sir_predictors]{all_sir_predictors()}}: \code{"S"} = 1, \code{"I"}/\code{"SDD"} = 2, \code{"R"} = 3. All other values are rendered \code{NA}. Keep this in mind for further processing, especially if the model does not allow for \code{NA} values.
|
||||
}
|
||||
|
||||
These steps integrate with \code{recipes::recipe()} and work like standard preprocessing steps. They are useful for preparing data for modelling, especially with classification models.
|
||||
}
|
||||
\examples{
|
||||
if (require("tidymodels")) {
|
||||
|
||||
# The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
|
||||
# Presence of ESBL genes was predicted based on raw MIC values.
|
||||
|
||||
|
||||
# example data set in the AMR package
|
||||
esbl_isolates
|
||||
|
||||
# Prepare a binary outcome and convert to ordered factor
|
||||
data <- esbl_isolates \%>\%
|
||||
mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
|
||||
|
||||
# Split into training and testing sets
|
||||
split <- initial_split(data)
|
||||
training_data <- training(split)
|
||||
testing_data <- testing(split)
|
||||
|
||||
# Create and prep a recipe with MIC log2 transformation
|
||||
mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\%
|
||||
|
||||
# Optionally remove non-predictive variables
|
||||
remove_role(genus, old_role = "predictor") \%>\%
|
||||
|
||||
# Apply the log2 transformation to all MIC predictors
|
||||
step_mic_log2(all_mic_predictors()) \%>\%
|
||||
|
||||
# And apply the preparation steps
|
||||
prep()
|
||||
|
||||
# View prepped recipe
|
||||
mic_recipe
|
||||
|
||||
# Apply the recipe to training and testing data
|
||||
out_training <- bake(mic_recipe, new_data = NULL)
|
||||
out_testing <- bake(mic_recipe, new_data = testing_data)
|
||||
|
||||
# Fit a logistic regression model
|
||||
fitted <- logistic_reg(mode = "classification") \%>\%
|
||||
set_engine("glm") \%>\%
|
||||
fit(esbl ~ ., data = out_training)
|
||||
|
||||
# Generate predictions on the test set
|
||||
predictions <- predict(fitted, out_testing) \%>\%
|
||||
bind_cols(out_testing)
|
||||
|
||||
# Evaluate predictions using standard classification metrics
|
||||
our_metrics <- metric_set(accuracy, kap, ppv, npv)
|
||||
metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
|
||||
|
||||
# Show performance
|
||||
metrics
|
||||
}
|
||||
}
|
||||
\seealso{
|
||||
\code{\link[recipes:recipe]{recipes::recipe()}}, \code{\link[=as.mic]{as.mic()}}, \code{\link[=as.sir]{as.sir()}}
|
||||
}
|
||||
\keyword{internal}
|
||||
@@ -1,27 +0,0 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/data.R
|
||||
\docType{data}
|
||||
\name{esbl_isolates}
|
||||
\alias{esbl_isolates}
|
||||
\title{Data Set with 500 ESBL Isolates}
|
||||
\format{
|
||||
A \link[tibble:tibble]{tibble} with 500 observations and 19 variables:
|
||||
\itemize{
|
||||
\item \code{esbl}\cr Logical indicator if the isolate is ESBL-producing
|
||||
\item \code{genus}\cr Genus of the microorganism
|
||||
\item \code{AMC:COL}\cr MIC values for 17 antimicrobial agents, transformed to class \code{\link{mic}} (see \code{\link[=as.mic]{as.mic()}})
|
||||
}
|
||||
}
|
||||
\usage{
|
||||
esbl_isolates
|
||||
}
|
||||
\description{
|
||||
A data set containing 500 microbial isolates with MIC values of common antibiotics and a binary \code{esbl} column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with \href{https://amr-for-r.org/articles/AMR_with_tidymodels.html}{tidymodels}.
|
||||
}
|
||||
\details{
|
||||
See our \link[=amr-tidymodels]{tidymodels integration} for an example using this data set.
|
||||
}
|
||||
\examples{
|
||||
esbl_isolates
|
||||
}
|
||||
\keyword{datasets}
|
||||
Reference in New Issue
Block a user