(v2.1.1.9121) support tidymodels

.Rbuildignore

@@ -25,6 +25,7 @@
 ^tests/testthat/_snaps$
 ^vignettes/AMR\.Rmd$
 ^vignettes/AMR_intro\.png$
+^vignettes/AMR_with_tidymodels\.Rmd$
 ^vignettes/benchmarks\.Rmd$
 ^vignettes/benchmarks\.Rmd\.not$
 ^vignettes/datasets\.Rmd$

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9120
-Date: 2024-12-15
+Version: 2.1.1.9121
+Date: 2024-12-19
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by
@@ -47,6 +47,7 @@ Suggests:
   rvest,
   skimr,
   tibble,
+  tidymodels,
   tidyselect,
   tinytest,
   vctrs,

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9120
+# AMR 2.1.1.9121
 
 *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
 
@@ -30,6 +30,8 @@ This package now supports not only tools for AMR data analysis in clinical setti
   * New function `rescale_mic()`, which allows users to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used independently to, for instance, compare equality in MIC distributions by rescaling them to the same range first.
 * **Support for Python**
   * While using R for the heavy lifting, [our 'AMR' Python Package](https://pypi.org/project/AMR/) was developed to run the AMR R package natively in Python. The Python package will always have the same version number as the R package, as it is built automatically with every code change.
+* **Support for `tidymodels`**
+  * All antimicrobial selectors (such as `aminoglycosides()` and `betalactams()`) are now supported in `tidymodels` packages such as `recipe` and `parsnip`. See for more info [our tutorial](https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html) on using AMR function for predictive modelling.
 * **Other**
   * New function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that belong to that group.
 
@@ -73,7 +75,9 @@ This package now supports not only tools for AMR data analysis in clinical setti
 * Updated the prevalence calculation to include genera from the World Health Organization's (WHO) Priority Pathogen List
 * Improved algorithm of `first_isolate()` when using the phenotype-based method, to prioritise records with the highest availability of SIR values
 * `scale_y_percent()` can now cope with ranges outside the 0-100% range
-* Support for new Dutch national MDRO guideline (SRI-richtlijn BRMO, Nov 2024)
+* MDRO determination (using `mdro()`)
+  * Implemented the new Dutch national MDRO guideline (SRI-richtlijn BRMO, Nov 2024)
+  * Added arguments `esbl`, `carbapenemase`, `mecA`, `mecC`, `vanA`, `vanB` to denote column names or logical values indicating presence of these genes (or production of their proteins)
 
 ## Other
 * Greatly improved `vctrs` integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as `dplyr`'s `bind_rows()`, `rowwise()` and `c_across()` are now supported for e.g. columns of class `mic`. Despite this, this `AMR` package is still zero-dependent on any other package, including `dplyr` and `vctrs`.

PythonPackage/AMR/AMR.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: AMR
-Version: 2.1.1.9120
+Version: 2.1.1.9121
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends

PythonPackage/AMR/AMR/functions.py

@@ -441,9 +441,9 @@ def mdr_tb(x = None, *args, **kwargs):
 def mdr_cmi2012(x = None, *args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
     return convert_to_python(amr_r.mdr_cmi2012(x = None, *args, **kwargs))
-def eucast_exceptional_phenotypes(x = None, *args, **kwargs):
+def eucast_exceptional_phenotypes(*args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
-    return convert_to_python(amr_r.eucast_exceptional_phenotypes(x = None, *args, **kwargs))
+    return convert_to_python(amr_r.eucast_exceptional_phenotypes(*args, **kwargs))
 def mean_amr_distance(x, *args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
     return convert_to_python(amr_r.mean_amr_distance(x, *args, **kwargs))

PythonPackage/AMR/setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='AMR',
-    version='2.1.1.9120',
+    version='2.1.1.9121',
     packages=find_packages(),
     install_requires=[
         'rpy2',

R/aa_helper_functions.R

@@ -988,7 +988,7 @@ get_current_data <- function(arg_name, call) {
   for (env in frms[which(with_mask)]) {
     if (is.function(env$mask$current_rows) && (valid_df(env$data) || valid_df(env$`.data`))) {
       # an element `.data` or `data` (containing all data) and `mask` (containing functions) will be in the environment when using dplyr verbs
-      # we use their mask$current_rows() to get the group rows, since dplyr::cur_data_all() is deprecated and will be removed in the future
+      # we use their mask$current_rows() below to get the group rows, since dplyr::cur_data_all() is deprecated and will be removed in the future
       # e.g. for `example_isolates %>% group_by(ward) %>% mutate(first = first_isolate(.))`
       if (valid_df(env$data)) {
         # support for dplyr 1.1.x
@@ -1008,6 +1008,9 @@ get_current_data <- function(arg_name, call) {
     if (valid_df(env$`.data`)) {
       # an element `.data` will be in the environment when using dplyr::select()
       return(env$`.data`)
+    } else if (valid_df(env$data)) {
+      # an element `data` will be in the environment when using older dplyr versions, or tidymodels
+      return(env$data)
     } else if (valid_df(env$xx)) {
       # an element `xx` will be in the environment for rows + cols in base R, e.g. `example_isolates[c(1:3), carbapenems()]`
       return(env$xx)