P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2024-12-25 18:06:12 +01:00
Code Releases Activity

(v2.1.1.9095) Python support

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends 2024-10-15 17:12:55 +02:00
parent 94501371cd
commit 5c4d8fcd2a
18 changed files with 513 additions and 435 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.Rbuildignore
View File

@ -30,7 +30,6 @@
^vignettes/datasets\.Rmd$ ^vignettes/datasets\.Rmd$
^vignettes/EUCAST\.Rmd$ ^vignettes/EUCAST\.Rmd$
^vignettes/MDR\.Rmd$ ^vignettes/MDR\.Rmd$
^vignettes/other_pkg.*\.Rmd$
^vignettes/PCA\.Rmd$ ^vignettes/PCA\.Rmd$
^vignettes/resistance_predict\.Rmd$ ^vignettes/resistance_predict\.Rmd$
^vignettes/WHONET\.Rmd$ ^vignettes/WHONET\.Rmd$

11
.github/workflows/publish-to-pypi.yml vendored
View File

@ -30,7 +30,7 @@
on: on:
push: push:
# only on main # only on main
branches: 'main_UPDATE_THIS' branches: "main"
name: Publish Python Package to PyPI name: Publish Python Package to PyPI
@ -53,14 +53,17 @@ jobs:
python -m pip install --upgrade pip python -m pip install --upgrade pip
pip install build twine pip install build twine
- name: Build the package - name: Build the Python package
run: python -m build run: |
cd data-raw/
bash _generate_python_wrapper.sh
- name: Publish to PyPI - name: Publish to PyPI
env: env:
TWINE_USERNAME: "msberends" TWINE_USERNAME: "__token__"
TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
run: | run: |
cd data-raw/python_wrapper/AMR
python -m twine upload dist/* python -m twine upload dist/*
# for test server: # for test server:
# python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/* # python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*

4
DESCRIPTION
View File

@ -1,6 +1,6 @@
Package: AMR Package: AMR
Version: 2.1.1.9094 Version: 2.1.1.9095
Date: 2024-10-10 Date: 2024-10-15
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by data analysis and to work with microbial and antimicrobial properties by

5
NEWS.md
View File

@ -1,4 +1,4 @@
# AMR 2.1.1.9094 # AMR 2.1.1.9095
*(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).)* *(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).)*
@ -28,6 +28,8 @@ This package now supports not only tools for AMR data analysis in clinical setti
* **New forms for MIC plotting and transforming** * **New forms for MIC plotting and transforming**
* New function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels. * New function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels.
* 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. * 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.
* **Other** * **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. * 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.
@ -52,6 +54,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
* Comparisons of MIC values are now more strict. For example, `>32` is higher than (and never equal to) `32`. Thus, `as.mic(">32") == as.mic(32)` now returns `FALSE`, and `as.mic(">32") > as.mic(32)` now returns `TRUE`. * Comparisons of MIC values are now more strict. For example, `>32` is higher than (and never equal to) `32`. Thus, `as.mic(">32") == as.mic(32)` now returns `FALSE`, and `as.mic(">32") > as.mic(32)` now returns `TRUE`.
* Sorting of MIC values (using `sort()`) was fixed in the same manner; `<0.001` now gets sorted before `0.001`, and `>0.001` gets sorted after `0.001`. * Sorting of MIC values (using `sort()`) was fixed in the same manner; `<0.001` now gets sorted before `0.001`, and `>0.001` gets sorted after `0.001`.
* Intermediate log2 levels used for MIC plotting are now more common values instead of following a strict dilution range * Intermediate log2 levels used for MIC plotting are now more common values instead of following a strict dilution range
* Disks of 0 to 5 mm are now allowed, the newly allowed range for disk diffusion (`as.disk()`) is now between 0 and 50 mm
* Updated `italicise_taxonomy()` to support HTML output * Updated `italicise_taxonomy()` to support HTML output
* `custom_eucast_rules()` now supports multiple antibiotics and antibiotic groups to be affected by a single rule * `custom_eucast_rules()` now supports multiple antibiotics and antibiotic groups to be affected by a single rule
* `mo_info()` now contains an extra element `rank` and `group_members` (with the contents of the new `mo_group_members()` function) * `mo_info()` now contains an extra element `rank` and `group_members` (with the contents of the new `mo_group_members()` function)

35
R/aa_helper_functions.R
View File

@ -822,7 +822,6 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
is_positive = NULL, is_positive = NULL,
is_positive_or_zero = NULL, is_positive_or_zero = NULL,
is_finite = NULL, is_finite = NULL,
contains_column_class = NULL,
allow_NULL = FALSE, allow_NULL = FALSE,
allow_NA = FALSE, allow_NA = FALSE,
ignore.case = FALSE, ignore.case = FALSE,
@ -852,6 +851,11 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
return(invisible()) return(invisible())
} }
if (identical(class(object), "list") && !"list" %in% allow_class) {
# coming from Python, possibly - turn lists (not data.frame) to the underlying data type
object <- unlist(object)
}
if (!is.null(allow_class) && !(suppressWarnings(all(is.na(object))) && allow_NA == TRUE)) { if (!is.null(allow_class) && !(suppressWarnings(all(is.na(object))) && allow_NA == TRUE)) {
stop_ifnot(inherits(object, allow_class), "argument `", obj_name, stop_ifnot(inherits(object, allow_class), "argument `", obj_name,
"` must be ", format_class(allow_class, plural = isTRUE(has_length > 1)), "` must be ", format_class(allow_class, plural = isTRUE(has_length > 1)),
@ -937,21 +941,6 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
call = call_depth call = call_depth
) )
} }
if (!is.null(contains_column_class)) {
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[1L], "'. ",
"See `?as.", contains_column_class[1L], "`.",
call = call_depth
)
}
if (!is.null(allow_arguments_from) && !is.null(names(object))) { if (!is.null(allow_arguments_from) && !is.null(names(object))) {
args_given <- names(object) args_given <- names(object)
if (is.function(allow_arguments_from)) { if (is.function(allow_arguments_from)) {
@ -973,6 +962,20 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
return(invisible()) return(invisible())
} }
ascertain_sir_classes <- function(x, obj_name) {
sirs <- vapply(FUN.VALUE = logical(1), x, is.sir)
if (!any(sirs, na.rm = TRUE)) {
warning_("the data provided in argument `", obj_name,
"` should contain at least one column of class 'sir'. Eligible SIR column were now guessed. ",
"See `?as.sir`.")
sirs_eligible <- is_sir_eligible(x)
for (col in colnames(x)[sirs_eligible]) {
x[[col]] <- as.sir(x[[col]])
}
}
x
}
get_current_data <- function(arg_name, call) { get_current_data <- function(arg_name, call) {
valid_df <- function(x) { valid_df <- function(x) {
!is.null(x) && is.data.frame(x) !is.null(x) && is.data.frame(x)

3
R/antibiogram.R
View File

@ -313,7 +313,8 @@ antibiogram <- function(x,
combine_SI = TRUE, combine_SI = TRUE,
sep = " + ", sep = " + ",
info = interactive()) { info = interactive()) {
meet_criteria(x, allow_class = "data.frame", contains_column_class = c("sir", "rsi")) meet_criteria(x, allow_class = "data.frame")
x <- ascertain_sir_classes(x, "x")
meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE) meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE)
meet_criteria(ab_transform, allow_class = "character", has_length = 1, is_in = colnames(AMR::antibiotics), allow_NULL = TRUE) meet_criteria(ab_transform, allow_class = "character", has_length = 1, is_in = colnames(AMR::antibiotics), allow_NULL = TRUE)
meet_criteria(syndromic_group, allow_class = "character", allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(syndromic_group, allow_class = "character", allow_NULL = TRUE, allow_NA = TRUE)

3
R/bug_drug_combinations.R
View File

@ -71,7 +71,8 @@ bug_drug_combinations <- function(x,
col_mo = NULL, col_mo = NULL,
FUN = mo_shortname, FUN = mo_shortname,
...) { ...) {
meet_criteria(x, allow_class = "data.frame", contains_column_class = c("sir", "rsi")) meet_criteria(x, allow_class = "data.frame")
x <- ascertain_sir_classes(x, "x")
meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), has_length = 1, allow_NULL = TRUE) meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), has_length = 1, allow_NULL = TRUE)
meet_criteria(FUN, allow_class = "function", has_length = 1) meet_criteria(FUN, allow_class = "function", has_length = 1)

8
R/disk.R
View File

@ -29,13 +29,13 @@
#' Transform Input to Disk Diffusion Diameters #' Transform Input to Disk Diffusion Diameters
#' #'
#' This transforms a vector to a new class [`disk`], which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 6 and 50. #' This transforms a vector to a new class [`disk`], which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 0 and 50.
#' @rdname as.disk #' @rdname as.disk
#' @param x vector #' @param x vector
#' @param na.rm a [logical] indicating whether missing values should be removed #' @param na.rm a [logical] indicating whether missing values should be removed
#' @details Interpret disk values as SIR values with [as.sir()]. It supports guidelines from EUCAST and CLSI. #' @details Interpret disk values as SIR values with [as.sir()]. It supports guidelines from EUCAST and CLSI.
#' #'
#' Disk diffusion growth zone sizes must be between 6 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 6-50 range will return `NA`. #' Disk diffusion growth zone sizes must be between 0 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 0-50 range will return `NA`.
#' @return An [integer] with additional class [`disk`] #' @return An [integer] with additional class [`disk`]
#' @aliases disk #' @aliases disk
#' @export #' @export
@ -108,8 +108,8 @@ as.disk <- function(x, na.rm = FALSE) {
# round up and make it an integer # round up and make it an integer
x <- as.integer(ceiling(clean_double2(x))) x <- as.integer(ceiling(clean_double2(x)))
# disks can never be less than 6 mm (size of smallest disk) or more than 50 mm # disks can never be less than 0 mm or more than 50 mm
x[x < 6 | x > 99] <- NA_integer_ x[x < 0 | x > 99] <- NA_integer_
x[x > 50] <- 50L x[x > 50] <- 50L
na_after <- length(x[is.na(x)]) na_after <- length(x[is.na(x)])

3
R/ggplot_sir.R
View File

@ -193,7 +193,8 @@ ggplot_sir <- function(data,
y.title = "Proportion", y.title = "Proportion",
...) { ...) {
stop_ifnot_installed("ggplot2") stop_ifnot_installed("ggplot2")
meet_criteria(data, allow_class = "data.frame", contains_column_class = c("sir", "rsi")) meet_criteria(data, allow_class = "data.frame")
data <- ascertain_sir_classes(data, "data")
meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE) meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE)
meet_criteria(x, allow_class = "character", has_length = 1) meet_criteria(x, allow_class = "character", has_length = 1)
meet_criteria(fill, allow_class = "character", has_length = 1) meet_criteria(fill, allow_class = "character", has_length = 1)

6
R/proportion.R
View File

@ -292,10 +292,10 @@ sir_confidence_interval <- function(...,
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5) error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
) )
# this applies the Clopper-Pearson method
if (x == 0) { if (x == 0) {
out <- c(NA_real_, NA_real_) out <- c(0, 0)
} else { } else {
# this applies the Clopper-Pearson method
out <- stats::binom.test(x = x, n = n, conf.level = confidence_level)$conf.int out <- stats::binom.test(x = x, n = n, conf.level = confidence_level)$conf.int
} }
out <- set_clean_class(out, "numeric") out <- set_clean_class(out, "numeric")
@ -312,7 +312,7 @@ sir_confidence_interval <- function(...,
if (is.numeric(out)) { if (is.numeric(out)) {
out <- round(out, digits = 3) out <- round(out, digits = 3)
} }
out[is.na(out)] <- "??" # out[is.na(out)] <- 0
out <- paste(out, collapse = ifelse(isTRUE(collapse), "-", collapse)) out <- paste(out, collapse = ifelse(isTRUE(collapse), "-", collapse))
} }

4
R/sir.R
View File

@ -1263,7 +1263,7 @@ as_sir_method <- function(method_short,
if (breakpoint_type == "animal" && !host_current %in% breakpoints_current$host) { if (breakpoint_type == "animal" && !host_current %in% breakpoints_current$host) {
if (guideline_coerced %like% "CLSI") { if (guideline_coerced %like% "CLSI") {
# VET09 says that staph/strep/enterococcus BP can be extrapolated to all Gr+ cocci except for intrinsic resistance, so take all Gr+ cocci: # VET09 says that staph/strep/enterococcus BP can be extrapolated to all Gr+ cocci except for intrinsic resistance, so take all Gr+ cocci:
all_gram_pos_genera <- c("B_STPHY", "B_STRPT", "B_ENTRC", "B_PPTST", "B_AERCC", "B_MCRCCC", "B_TRPRL") all_gram_pos_genera <- c("B_STPHY", "B_STRPT", "B_PPTST", "B_AERCC", "B_MCRCCC", "B_TRPRL")
# HUMAN SUBSTITUTES # HUMAN SUBSTITUTES
if (ab_current == "AZM" && mo_current_genus %in% all_gram_pos_genera && host_current %in% c("dogs", "cats", "horse")) { if (ab_current == "AZM" && mo_current_genus %in% all_gram_pos_genera && host_current %in% c("dogs", "cats", "horse")) {
@ -1312,7 +1312,7 @@ as_sir_method <- function(method_short,
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09.")) notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09."))
} else if (host_current %in% c("dogs", "cats") && (mo_current_genus %in% c("B_AMYCS", "B_NOCRD", "B_CMPYL", "B_CRYNB", "B_ENTRC", "B_MYCBC", "B_PSDMN", "B_AERMN") | mo_current_class == "B_[CLS]_BTPRTBCT" | mo_current == "B_LISTR_MNCY")) { } else if (host_current %in% c("dogs", "cats") && (mo_current_genus %in% c("B_AMYCS", "B_NOCRD", "B_CMPYL", "B_CRYNB", "B_ENTRC", "B_MYCBC", "B_PSDMN", "B_AERMN") | mo_current_class == "B_[CLS]_BTPRTBCT" | mo_current == "B_LISTR_MNCY")) {
# human breakpoints if no canine/feline # dog breakpoints if no canine/feline
breakpoints_current <- breakpoints_current %pm>% subset(host == "human") breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", mo_formatted, " based on CLSI VET09.")) notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", mo_formatted, " based on CLSI VET09."))

3
R/sir_calc.R
View File

@ -223,7 +223,8 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
combine_SI = TRUE, combine_SI = TRUE,
confidence_level = 0.95) { confidence_level = 0.95) {
meet_criteria(type, is_in = c("proportion", "count", "both"), has_length = 1) meet_criteria(type, is_in = c("proportion", "count", "both"), has_length = 1)
meet_criteria(data, allow_class = "data.frame", contains_column_class = "sir") meet_criteria(data, allow_class = "data.frame")
data <- ascertain_sir_classes(data, "data")
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE) meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
language <- validate_language(language) language <- validate_language(language)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE) meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)

122
data-raw/_generate_python_wrappers.sh → data-raw/_generate_python_wrapper.sh
View File

@ -29,16 +29,71 @@
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ # # how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== # # ==================================================================== #
# Output Python file # Clean up
output_file="python_wrapper/amr_python_wrapper.py" rm -rf python_wrapper/AMR/*
mkdir python_wrapper/AMR/AMR
# Write header to the output Python file, including the convert_to_python function # Output Python file
cat <<EOL > "$output_file" functions_file="python_wrapper/AMR/AMR/functions.py"
datasets_file="python_wrapper/AMR/AMR/datasets.py"
init_file="python_wrapper/AMR/AMR/__init__.py"
# Write header to the datasets Python file, including the convert_to_python function
cat <<EOL > "$datasets_file"
BLUE = '\033[94m'
GREEN = '\033[32m'
RESET = '\033[0m'
print(f"{BLUE}AMR:{RESET} Setting up R environment and AMR datasets...", flush=True)
from rpy2 import robjects
from rpy2.robjects import pandas2ri
from rpy2.robjects.packages import importr, isinstalled
import pandas as pd
# Check if the R package is installed
if not isinstalled('AMR'):
utils = importr('utils')
utils.install_packages('AMR')
# Activate the automatic conversion between R and pandas DataFrames
pandas2ri.activate()
# example_isolates
example_isolates = pandas2ri.rpy2py(robjects.r('''
df <- AMR::example_isolates
df[] <- lapply(df, function(x) {
if (inherits(x, c("Date", "POSIXt", "factor"))) {
as.character(x)
} else {
x
}
})
df
'''))
example_isolates['date'] = pd.to_datetime(example_isolates['date'])
# microorganisms
microorganisms = pandas2ri.rpy2py(robjects.r('AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]'))
antibiotics = pandas2ri.rpy2py(robjects.r('AMR::antibiotics[, !sapply(AMR::antibiotics, is.list)]'))
clinical_breakpoints = pandas2ri.rpy2py(robjects.r('AMR::clinical_breakpoints'))
print(f"{BLUE}AMR:{RESET} {GREEN}Done.{RESET}", flush=True)
EOL
echo "from .datasets import example_isolates" >> $init_file
echo "from .datasets import microorganisms" >> $init_file
echo "from .datasets import antibiotics" >> $init_file
echo "from .datasets import clinical_breakpoints" >> $init_file
# Write header to the functions Python file, including the convert_to_python function
cat <<EOL > "$functions_file"
import rpy2.robjects as robjects import rpy2.robjects as robjects
from rpy2.robjects.packages import importr from rpy2.robjects.packages import importr
from rpy2.robjects.vectors import StrVector, FactorVector, IntVector, FloatVector from rpy2.robjects.vectors import StrVector, FactorVector, IntVector, FloatVector, DataFrame
from rpy2.robjects import pandas2ri from rpy2.robjects import pandas2ri
import pandas as pd import pandas as pd
import numpy as np
# Activate automatic conversion between R data frames and pandas data frames # Activate automatic conversion between R data frames and pandas data frames
pandas2ri.activate() pandas2ri.activate()
@ -62,10 +117,15 @@ def convert_to_python(r_output):
# Check if it's a pandas-compatible R data frame # Check if it's a pandas-compatible R data frame
elif isinstance(r_output, pd.DataFrame): elif isinstance(r_output, pd.DataFrame):
return r_output # Return as pandas DataFrame (already converted by pandas2ri) return r_output # Return as pandas DataFrame (already converted by pandas2ri)
elif isinstance(r_output, DataFrame):
return pandas2ri.rpy2py(r_output) # Return as pandas DataFrame
# Fallback: return the raw rpy2 object if we don't know how to convert it # Check if the input is a NumPy array and has a string data type
if isinstance(r_output, np.ndarray) and np.issubdtype(r_output.dtype, np.str_):
return r_output.tolist() # Convert to a regular Python list
# Fall-back
return r_output return r_output
EOL EOL
# Directory where the .Rd files are stored (update path as needed) # Directory where the .Rd files are stored (update path as needed)
@ -110,21 +170,24 @@ for rd_file in "$rd_dir"/*.Rd; do
# Count the number of arguments # Count the number of arguments
arg_count = split(func_args, arg_array, ",") arg_count = split(func_args, arg_array, ",")
# Handle "..." arguments (convert them to *args in Python) # Handle "..." arguments (convert them to *args, **kwargs in Python)
gsub("\\.\\.\\.", "*args", func_args) gsub("\\.\\.\\.", "*args, **kwargs", func_args)
# Remove default values from arguments # Remove default values from arguments
gsub(/ = [^,]+/, "", func_args) gsub(/ = [^,]+/, "", func_args)
# If no arguments, skip the function (dont print it) # If no arguments, skip the function (dont print it)
if (arg_count == 0) { if (arg_count == 0) {
next func_args = "*args, **kwargs"
} }
# If more than 1 argument, replace the 2nd to nth arguments with *args # If more than 1 argument, replace the 2nd to nth arguments with *args, **kwargs
if (arg_count > 1) { if (arg_count > 1) {
first_arg = arg_array[1] first_arg = arg_array[1]
func_args = first_arg ", *args" func_args = first_arg ", *args, **kwargs"
}
if (arg_array[1] == "...") {
func_args = "*args, **kwargs"
} }
# Skip functions where func_name_py is identical to func_args # Skip functions where func_name_py is identical to func_args
@ -132,23 +195,31 @@ for rd_file in "$rd_dir"/*.Rd; do
next next
} }
# Skip functions matching the regex pattern ^(x |facet|scale|set|get|NA_) # Skip functions matching the regex pattern
if (func_name_py ~ /^(x |facet|scale|set|get|NA_)/) { if (func_name_py ~ /^(x |facet|scale|set|get|NA_|microorganisms|antibiotics|clinical_breakpoints|example_isolates)/) {
next next
} }
# Replace TRUE/FALSE/NULL
gsub("TRUE", "True", func_args)
gsub("FALSE", "False", func_args)
gsub("NULL", "None", func_args)
# Write the Python function definition to the output file # Write the Python function definition to the output file
print "def " func_name_py "(" func_args "):" >> "'"$output_file"'" print "def " func_name_py "(" func_args "):" >> "'"$functions_file"'"
print " \"\"\"See our website of the R package for the manual: https://msberends.github.io/AMR/index.html\"\"\"" >> "'"$output_file"'" print " \"\"\"See our website of the R package for the manual: https://msberends.github.io/AMR/index.html\"\"\"" >> "'"$functions_file"'"
print " return convert_to_python(amr_r." func_name_py "(" func_args "))" >> "'"$output_file"'" print " return convert_to_python(amr_r." func_name_py "(" func_args "))" >> "'"$functions_file"'"
print "from .functions import " func_name_py >> "'"$init_file"'"
} }
' "$rd_file" ' "$rd_file"
done done
# Output completion message # Output completion message
echo "Python wrapper functions generated in $output_file." echo "Python wrapper functions generated in $functions_file."
echo "Python wrapper functions listed in $init_file."
cp ../README.md python_wrapper/ cp ../vignettes/AMR_for_Python.Rmd python_wrapper/AMR/README.md
echo "README copied" echo "README copied"
@ -156,23 +227,22 @@ echo "README copied"
description_file="../DESCRIPTION" description_file="../DESCRIPTION"
# Output setup.py file # Output setup.py file
output_file="python_wrapper/setup.py" functions_file="python_wrapper/AMR/setup.py"
# Extract the relevant fields from DESCRIPTION # Extract the relevant fields from DESCRIPTION
version=$(grep "^Version:" "$description_file" | awk '{print $2}') version=$(grep "^Version:" "$description_file" | awk '{print $2}')
license=$(grep "^License:" "$description_file" | awk '{print $2}')
# Write the setup.py file # Write the setup.py file
cat <<EOL > "$output_file" cat <<EOL > "$functions_file"
from setuptools import setup, find_packages from setuptools import setup, find_packages
setup( setup(
name='AMR', name='AMR',
#version='$version', version='$version',
version='2.1.1.1',
packages=find_packages(), packages=find_packages(),
install_requires=[ install_requires=[
'rpy2', 'rpy2',
'numpy',
'pandas', 'pandas',
], ],
author='Matthijs Berends', author='Matthijs Berends',
@ -194,8 +264,8 @@ setup(
EOL EOL
# Output completion message # Output completion message
echo "setup.py has been generated in $output_file." echo "setup.py has been generated in $functions_file."
cd python_wrapper cd python_wrapper/AMR
python3 setup.py sdist bdist_wheel python3 setup.py sdist bdist_wheel

426
data-raw/gpt_training_text.txt
View File

@ -1028,7 +1028,6 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
is_positive = NULL, is_positive = NULL,
is_positive_or_zero = NULL, is_positive_or_zero = NULL,
is_finite = NULL, is_finite = NULL,
contains_column_class = NULL,
allow_NULL = FALSE, allow_NULL = FALSE,
allow_NA = FALSE, allow_NA = FALSE,
ignore.case = FALSE, ignore.case = FALSE,
@ -1058,6 +1057,11 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
return(invisible()) return(invisible())
} }
if (identical(class(object), "list") && !"list" %in% allow_class) {
# coming from Python, possibly - turn lists (not data.frame) to the underlying data type
object <- unlist(object)
}
if (!is.null(allow_class) && !(suppressWarnings(all(is.na(object))) && allow_NA == TRUE)) { if (!is.null(allow_class) && !(suppressWarnings(all(is.na(object))) && allow_NA == TRUE)) {
stop_ifnot(inherits(object, allow_class), "argument `", obj_name, stop_ifnot(inherits(object, allow_class), "argument `", obj_name,
"` must be ", format_class(allow_class, plural = isTRUE(has_length > 1)), "` must be ", format_class(allow_class, plural = isTRUE(has_length > 1)),
@ -1143,21 +1147,6 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
call = call_depth call = call_depth
) )
} }
if (!is.null(contains_column_class)) {
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[1L], "'. ",
"See `?as.", contains_column_class[1L], "`.",
call = call_depth
)
}
if (!is.null(allow_arguments_from) && !is.null(names(object))) { if (!is.null(allow_arguments_from) && !is.null(names(object))) {
args_given <- names(object) args_given <- names(object)
if (is.function(allow_arguments_from)) { if (is.function(allow_arguments_from)) {
@ -1179,6 +1168,20 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
return(invisible()) return(invisible())
} }
ascertain_sir_classes <- function(x, obj_name) {
sirs <- vapply(FUN.VALUE = logical(1), x, is.sir)
if (!any(sirs, na.rm = TRUE)) {
warning_("the data provided in argument `", obj_name,
"` should contain at least one column of class 'sir'. Eligible SIR column were now guessed. ",
"See `?as.sir`.")
sirs_eligible <- is_sir_eligible(x)
for (col in colnames(x)[sirs_eligible]) {
x[[col]] <- as.sir(x[[col]])
}
}
x
}
get_current_data <- function(arg_name, call) { get_current_data <- function(arg_name, call) {
valid_df <- function(x) { valid_df <- function(x) {
!is.null(x) && is.data.frame(x) !is.null(x) && is.data.frame(x)
@ -5893,7 +5896,8 @@ antibiogram <- function(x,
combine_SI = TRUE, combine_SI = TRUE,
sep = " + ", sep = " + ",
info = interactive()) { info = interactive()) {
meet_criteria(x, allow_class = "data.frame", contains_column_class = c("sir", "rsi")) meet_criteria(x, allow_class = "data.frame")
x <- ascertain_sir_classes(x, "x")
meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE) meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE)
meet_criteria(ab_transform, allow_class = "character", has_length = 1, is_in = colnames(AMR::antibiotics), allow_NULL = TRUE) meet_criteria(ab_transform, allow_class = "character", has_length = 1, is_in = colnames(AMR::antibiotics), allow_NULL = TRUE)
meet_criteria(syndromic_group, allow_class = "character", allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(syndromic_group, allow_class = "character", allow_NULL = TRUE, allow_NA = TRUE)
@ -7659,7 +7663,8 @@ bug_drug_combinations <- function(x,
col_mo = NULL, col_mo = NULL,
FUN = mo_shortname, FUN = mo_shortname,
...) { ...) {
meet_criteria(x, allow_class = "data.frame", contains_column_class = c("sir", "rsi")) meet_criteria(x, allow_class = "data.frame")
x <- ascertain_sir_classes(x, "x")
meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), has_length = 1, allow_NULL = TRUE) meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), has_length = 1, allow_NULL = TRUE)
meet_criteria(FUN, allow_class = "function", has_length = 1) meet_criteria(FUN, allow_class = "function", has_length = 1)
@ -9273,13 +9278,13 @@ THE NEXT PART CONTAINS CONTENTS FROM FILE ../R/disk.R
#' Transform Input to Disk Diffusion Diameters #' Transform Input to Disk Diffusion Diameters
#' #'
#' This transforms a vector to a new class [`disk`], which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 6 and 50. #' This transforms a vector to a new class [`disk`], which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 0 and 50.
#' @rdname as.disk #' @rdname as.disk
#' @param x vector #' @param x vector
#' @param na.rm a [logical] indicating whether missing values should be removed #' @param na.rm a [logical] indicating whether missing values should be removed
#' @details Interpret disk values as SIR values with [as.sir()]. It supports guidelines from EUCAST and CLSI. #' @details Interpret disk values as SIR values with [as.sir()]. It supports guidelines from EUCAST and CLSI.
#' #'
#' Disk diffusion growth zone sizes must be between 6 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 6-50 range will return `NA`. #' Disk diffusion growth zone sizes must be between 0 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 0-50 range will return `NA`.
#' @return An [integer] with additional class [`disk`] #' @return An [integer] with additional class [`disk`]
#' @aliases disk #' @aliases disk
#' @export #' @export
@ -9352,8 +9357,8 @@ as.disk <- function(x, na.rm = FALSE) {
# round up and make it an integer # round up and make it an integer
x <- as.integer(ceiling(clean_double2(x))) x <- as.integer(ceiling(clean_double2(x)))
# disks can never be less than 6 mm (size of smallest disk) or more than 50 mm # disks can never be less than 0 mm or more than 50 mm
x[x < 6 | x > 99] <- NA_integer_ x[x < 0 | x > 99] <- NA_integer_
x[x > 50] <- 50L x[x > 50] <- 50L
na_after <- length(x[is.na(x)]) na_after <- length(x[is.na(x)])
@ -12478,7 +12483,8 @@ ggplot_sir <- function(data,
y.title = "Proportion", y.title = "Proportion",
...) { ...) {
stop_ifnot_installed("ggplot2") stop_ifnot_installed("ggplot2")
meet_criteria(data, allow_class = "data.frame", contains_column_class = c("sir", "rsi")) meet_criteria(data, allow_class = "data.frame")
data <- ascertain_sir_classes(data, "data")
meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE) meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE)
meet_criteria(x, allow_class = "character", has_length = 1) meet_criteria(x, allow_class = "character", has_length = 1)
meet_criteria(fill, allow_class = "character", has_length = 1) meet_criteria(fill, allow_class = "character", has_length = 1)
@ -20723,8 +20729,12 @@ sir_confidence_interval <- function(...,
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5) error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
) )
# this applies the Clopper-Pearson method if (x == 0) {
out <- stats::binom.test(x = x, n = n, conf.level = confidence_level)$conf.int out <- c(0, 0)
} else {
# this applies the Clopper-Pearson method
out <- stats::binom.test(x = x, n = n, conf.level = confidence_level)$conf.int
}
out <- set_clean_class(out, "numeric") out <- set_clean_class(out, "numeric")
if (side %in% c("left", "l", "lower", "lowest", "less", "min")) { if (side %in% c("left", "l", "lower", "lowest", "less", "min")) {
@ -20733,12 +20743,13 @@ sir_confidence_interval <- function(...,
out <- out[2] out <- out[2]
} }
if (isTRUE(as_percent)) { if (isTRUE(as_percent)) {
out <- percentage(out, digits = 1) out <- trimws(percentage(out, digits = 1))
} }
if (!isFALSE(collapse) && length(out) > 1) { if (!isFALSE(collapse) && length(out) > 1) {
if (is.numeric(out)) { if (is.numeric(out)) {
out <- round(out, digits = 3) out <- round(out, digits = 3)
} }
# out[is.na(out)] <- 0
out <- paste(out, collapse = ifelse(isTRUE(collapse), "-", collapse)) out <- paste(out, collapse = ifelse(isTRUE(collapse), "-", collapse))
} }
@ -20754,7 +20765,6 @@ sir_confidence_interval <- function(...,
return(NA_real_) return(NA_real_)
} }
} }
out out
} }
@ -22241,29 +22251,31 @@ convert_host <- function(x, lang = get_AMR_locale()) {
x_out[trimws2(tolower(x)) == "human"] <- "human" x_out[trimws2(tolower(x)) == "human"] <- "human"
x_out[trimws2(tolower(x)) == "ecoff"] <- "ecoff" x_out[trimws2(tolower(x)) == "ecoff"] <- "ecoff"
# this order is based on: clinical_breakpoints |> filter(type == "animal") |> count(host, sort = TRUE) # this order is based on: clinical_breakpoints |> filter(type == "animal") |> count(host, sort = TRUE)
x_out[is.na(x_out) & (x %like% "dog|canine" | x %like% translate_AMR("dog|dogs|canine", lang))] <- "dogs" x_out[is.na(x_out) & (x %like% "dog|canine|Canis lupus" | x %like% translate_AMR("dog|dogs|canine", lang))] <- "dogs"
x_out[is.na(x_out) & (x %like% "cattle|bovine" | x %like% translate_AMR("cattle|bovine", lang))] <- "cattle" x_out[is.na(x_out) & (x %like% "cattle|bovine|Bos taurus" | x %like% translate_AMR("cattle|bovine", lang))] <- "cattle"
x_out[is.na(x_out) & (x %like% "swine|suida(e)?" | x %like% translate_AMR("swine|swines", lang))] <- "swine" x_out[is.na(x_out) & (x %like% "swine|suida(e)?|Sus scrofa" | x %like% translate_AMR("swine|swines", lang))] <- "swine"
x_out[is.na(x_out) & (x %like% "cat|feline" | x %like% translate_AMR("cat|cats|feline", lang))] <- "cats" x_out[is.na(x_out) & (x %like% "cat|feline|Felis catus" | x %like% translate_AMR("cat|cats|feline", lang))] <- "cats"
x_out[is.na(x_out) & (x %like% "horse|equine" | x %like% translate_AMR("horse|horses|equine", lang))] <- "horse" x_out[is.na(x_out) & (x %like% "horse|equine|Equus ferus" | x %like% translate_AMR("horse|horses|equine", lang))] <- "horse"
x_out[is.na(x_out) & (x %like% "aqua|fish" | x %like% translate_AMR("aquatic|fish", lang))] <- "aquatic" x_out[is.na(x_out) & (x %like% "aqua|fish|Pisces" | x %like% translate_AMR("aquatic|fish", lang))] <- "aquatic"
x_out[is.na(x_out) & (x %like% "bird|chicken|poultry|avia" | x %like% translate_AMR("bird|birds|poultry", lang))] <- "poultry" x_out[is.na(x_out) & (x %like% "bird|chicken|poultry|avia|Gallus gallus" | x %like% translate_AMR("bird|birds|poultry", lang))] <- "poultry"
# additional animals, not necessarily currently in breakpoint guidelines: # additional animals, not necessarily currently in breakpoint guidelines:
x_out[is.na(x_out) & (x %like% "camel|camelid" | x %like% translate_AMR("camel|camels|camelid", lang))] <- "camels" x_out[is.na(x_out) & (x %like% "camel|camelid|Camelus dromedarius" | x %like% translate_AMR("camel|camels|camelid", lang))] <- "camels"
x_out[is.na(x_out) & (x %like% "deer|cervine" | x %like% translate_AMR("deer|deers|cervine", lang))] <- "deer" x_out[is.na(x_out) & (x %like% "deer|cervine|Cervidae" | x %like% translate_AMR("deer|deers|cervine", lang))] <- "deer"
x_out[is.na(x_out) & (x %like% "donkey|asinine" | x %like% translate_AMR("donkey|donkeys|asinine", lang))] <- "donkeys" x_out[is.na(x_out) & (x %like% "donkey|asinine|Equus africanus" | x %like% translate_AMR("donkey|donkeys|asinine", lang))] <- "donkeys"
x_out[is.na(x_out) & (x %like% "ferret|musteline" | x %like% translate_AMR("ferret|ferrets|musteline", lang))] <- "ferrets" x_out[is.na(x_out) & (x %like% "ferret|musteline|Mustela putorius" | x %like% translate_AMR("ferret|ferrets|musteline", lang))] <- "ferrets"
x_out[is.na(x_out) & (x %like% "goat|caprine" | x %like% translate_AMR("goat|goats|caprine", lang))] <- "goats" x_out[is.na(x_out) & (x %like% "goat|caprine|Capra aegagrus" | x %like% translate_AMR("goat|goats|caprine", lang))] <- "goats"
x_out[is.na(x_out) & (x %like% "guinea pig|caviine" | x %like% translate_AMR("guinea pig|guinea pigs|caviine", lang))] <- "guinea pigs" x_out[is.na(x_out) & (x %like% "guinea pig|caviine|Cavia porcellus" | x %like% translate_AMR("guinea pig|guinea pigs|caviine", lang))] <- "guinea pigs"
x_out[is.na(x_out) & (x %like% "hamster|cricetine" | x %like% translate_AMR("hamster|hamsters|cricetine", lang))] <- "hamsters" x_out[is.na(x_out) & (x %like% "hamster|cricetine|Cricetinae" | x %like% translate_AMR("hamster|hamsters|cricetine", lang))] <- "hamsters"
x_out[is.na(x_out) & (x %like% "monkey|simian" | x %like% translate_AMR("monkey|monkeys|simian", lang))] <- "monkeys" x_out[is.na(x_out) & (x %like% "monkey|simian|Simia" | x %like% translate_AMR("monkey|monkeys|simian", lang))] <- "monkeys"
x_out[is.na(x_out) & (x %like% "mouse|murine" | x %like% translate_AMR("mouse|mice|murine", lang))] <- "mice" x_out[is.na(x_out) & (x %like% "mouse|murine|Mus musculus" | x %like% translate_AMR("mouse|mice|murine", lang))] <- "mice"
x_out[is.na(x_out) & (x %like% "pig|porcine" | x %like% translate_AMR("pig|pigs|porcine", lang))] <- "pigs" x_out[is.na(x_out) & (x %like% "pig|porcine|Sus scrofa" | x %like% translate_AMR("pig|pigs|porcine", lang))] <- "pigs"
x_out[is.na(x_out) & (x %like% "rabbit|leporine" | x %like% translate_AMR("rabbit|rabbits|leporine", lang))] <- "rabbits" x_out[is.na(x_out) & (x %like% "rabbit|leporine|Oryctolagus cuniculus" | x %like% translate_AMR("rabbit|rabbits|leporine", lang))] <- "rabbits"
x_out[is.na(x_out) & (x %like% "rat|ratine" | x %like% translate_AMR("rat|rats|ratine", lang))] <- "rats" x_out[is.na(x_out) & (x %like% "rat|ratine|Rattus" | x %like% translate_AMR("rat|rats|ratine", lang))] <- "rats"
x_out[is.na(x_out) & (x %like% "sheep|ovine" | x %like% translate_AMR("sheep|sheeps|ovine", lang))] <- "sheep" x_out[is.na(x_out) & (x %like% "sheep|ovine|Ovis aries" | x %like% translate_AMR("sheep|sheeps|ovine", lang))] <- "sheep"
x_out[is.na(x_out) & (x %like% "snake|serpentine" | x %like% translate_AMR("snake|snakes|serpentine", lang))] <- "snakes" x_out[is.na(x_out) & (x %like% "snake|serpentine|Serpentes" | x %like% translate_AMR("snake|snakes|serpentine", lang))] <- "snakes"
x_out[is.na(x_out) & (x %like% "turkey|meleagrine" | x %like% translate_AMR("turkey|turkeys|meleagrine", lang))] <- "turkey" x_out[is.na(x_out) & (x %like% "turkey|meleagrine|Meleagris gallopavo" | x %like% translate_AMR("turkey|turkeys|meleagrine", lang))] <- "turkey"
x_out[x_out == "ecoff"] <- "ECOFF" x_out[x_out == "ecoff"] <- "ECOFF"
x_out x_out
@ -22660,7 +22672,7 @@ as_sir_method <- function(method_short,
if (breakpoint_type == "animal" && !host_current %in% breakpoints_current$host) { if (breakpoint_type == "animal" && !host_current %in% breakpoints_current$host) {
if (guideline_coerced %like% "CLSI") { if (guideline_coerced %like% "CLSI") {
# VET09 says that staph/strep/enterococcus BP can be extrapolated to all Gr+ cocci except for intrinsic resistance, so take all Gr+ cocci: # VET09 says that staph/strep/enterococcus BP can be extrapolated to all Gr+ cocci except for intrinsic resistance, so take all Gr+ cocci:
all_gram_pos_genera <- c("B_STPHY", "B_STRPT", "B_ENTRC", "B_PPTST", "B_AERCC", "B_MCRCCC", "B_TRPRL") all_gram_pos_genera <- c("B_STPHY", "B_STRPT", "B_PPTST", "B_AERCC", "B_MCRCCC", "B_TRPRL")
# HUMAN SUBSTITUTES # HUMAN SUBSTITUTES
if (ab_current == "AZM" && mo_current_genus %in% all_gram_pos_genera && host_current %in% c("dogs", "cats", "horse")) { if (ab_current == "AZM" && mo_current_genus %in% all_gram_pos_genera && host_current %in% c("dogs", "cats", "horse")) {
@ -22709,7 +22721,7 @@ as_sir_method <- function(method_short,
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09.")) notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09."))
} else if (host_current %in% c("dogs", "cats") && (mo_current_genus %in% c("B_AMYCS", "B_NOCRD", "B_CMPYL", "B_CRYNB", "B_ENTRC", "B_MYCBC", "B_PSDMN", "B_AERMN") | mo_current_class == "B_[CLS]_BTPRTBCT" | mo_current == "B_LISTR_MNCY")) { } else if (host_current %in% c("dogs", "cats") && (mo_current_genus %in% c("B_AMYCS", "B_NOCRD", "B_CMPYL", "B_CRYNB", "B_ENTRC", "B_MYCBC", "B_PSDMN", "B_AERMN") | mo_current_class == "B_[CLS]_BTPRTBCT" | mo_current == "B_LISTR_MNCY")) {
# human breakpoints if no canine/feline # dog breakpoints if no canine/feline
breakpoints_current <- breakpoints_current %pm>% subset(host == "human") breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", mo_formatted, " based on CLSI VET09.")) notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", mo_formatted, " based on CLSI VET09."))
@ -23349,7 +23361,8 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
combine_SI = TRUE, combine_SI = TRUE,
confidence_level = 0.95) { confidence_level = 0.95) {
meet_criteria(type, is_in = c("proportion", "count", "both"), has_length = 1) meet_criteria(type, is_in = c("proportion", "count", "both"), has_length = 1)
meet_criteria(data, allow_class = "data.frame", contains_column_class = "sir") meet_criteria(data, allow_class = "data.frame")
data <- ascertain_sir_classes(data, "data")
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE) meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
language <- validate_language(language) language <- validate_language(language)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE) meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
@ -24721,166 +24734,183 @@ knitr::opts_chunk$set(
# Introduction # Introduction
The `AMR` package for R is an incredible tool for antimicrobial resistance (AMR) data analysis, providing extensive functionality for working with microbial and antimicrobial properties. But what if you're working in Python and still want to benefit from the robust features of `AMR`? The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the `AMR` Python package, which uses `rpy2` internally. This package allows Python users to access all the functions from the R `AMR` package without the need to set up `rpy2` themselves. Since this Python package is not a true 'port' (which would require all R functions to be rewritten into Python), R and the AMR R package are still required to be installed. Yet, Python users can now easily work with AMR data directly through Python code.
The best way is to access R directly from Python with the help of `rpy2`, a simple yet powerful Python package. You can easily call functions from the `AMR` package to process your own data in your own Python environment. This post will guide you through setting up `rpy2` and show you how to use R functions from `AMR` in Python to supercharge your antimicrobial resistance analysis. In this document, we explain how this works and provide simple examples of using the `AMR` Python package.
<a href="https://chatgpt.com/g/g-M4UNLwFi5-amr-for-r-assistant"><img src="../AMRforRGPT.svg" style="min-width: 300px; width: 10%;" /></a> ## How It Works
# What is `rpy2`? The `AMR` Python package acts as a wrapper around the functions in the `AMR` R package. The package simplifies the process of calling R functions in Python, eliminating the need to manually manage the `rpy2` setup, which Python uses internally to be able to work with the R package. By just using `import AMR`, Python users can directly use the functions from the `AMR` R package as if they were native Python functions.
`rpy2` is a Python library that allows Python users to run R code within their Python scripts. Essentially, it acts as a bridge between the two languages, allowing you to tap into the rich ecosystem of R libraries (like `AMR`) while maintaining the flexibility of Python. Internally, `rpy2` is still being used, but all complexity is hidden from the user. This approach keeps the Python code clean and Pythonic, while still leveraging the full power of the R `AMR` package.
## Key Features of `rpy2`: ## Example of Usage
- Seamlessly call R functions from Python.
- Convert R data structures into Python data structures like pandas DataFrames.
- Leverage the full power of R libraries without leaving your Python environment.
# Setting Up `rpy2` Here’s an example that demonstrates how to clean microorganism and drug names using the `AMR` Python package:
Before diving into the examples, you’ll need to install both R and `rpy2`. Here's a step-by-step guide on setting things up.
## Step 1: Install R
Ensure that R is installed on your system. R has minimal dependencies and is very simple to install:
* **Linux**
* Ubuntu / Debian:
`sudo apt install r-base`
* Fedora:
`sudo dnf install R`
* CentOS/RHEL:
`sudo yum install R`
* Arch Linux:
`sudo pacman -S r`
* **macOS** (with Homebrew):
`brew install r`
* **Other Systems:**
Visit the [CRAN download page](https://cran.r-project.org).
## Step 2: Install the `AMR` package in R
On Linux and macOS, open Terminal and run:
```bash
Rscript -e 'install.packages("AMR")'
```
For other systems, open your R console and install the `AMR` package by running:
```r
install.packages("AMR")
```
On any system, you can also install the latest development version of the `AMR` package by setting `repos` to our beta channel:
```r
install.packages("AMR", repos = "https://msberends.r-universe.dev")
```
## Step 3: Install `rpy2` in Python
To install `rpy2`, simply run the following command in your terminal:
```bash
pip install rpy2
```
## Step 4: Test `rpy2` Installation
To ensure everything is set up correctly, you can test your installation by running the following Python script, which essentially runs R in the background:
```python
import rpy2.robjects as ro
# Test a simple R function from Python
ro.r('1 + 1')
```
If this returns `2`, you're good to go!
# Working with `AMR` in Python
Now that we have `rpy2` set up, let’s walk through some practical examples of using the `AMR` package within Python.
## Example 1: Converting Taxonomic Data
Let’s start by converting taxonomic user input to valid taxonomy using the `AMR` package, from within Python:
```python ```python
import pandas as pd import pandas as pd
import rpy2.robjects as ro import AMR
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
# Load the AMR package from R # Sample data
amr = importr('AMR') data = {
"MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
"Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
}
df = pd.DataFrame(data)
# Example user dataset in Python # Use AMR functions to clean microorganism and drug names
data = pd.DataFrame({ df['MO_clean'] = AMR.mo_name(df['MOs'])
'microorganism': ['E. coli', 'S. aureus', 'P. aeruginosa', 'K. pneumoniae'] df['Drug_clean'] = AMR.ab_name(df['Drug'])
})
# Apply mo_name() from the AMR package to the 'microorganism' column # Display the results
ro.globalenv['r_data'] = data print(df)
ro.r('r_data$mo_name <- mo_name(r_data$microorganism)') ```
# Retrieve and print the modified R DataFrame in Python | MOs | Drug | MO_clean | Drug_clean |
result = ro.r('r_data') |-------------|-----------|--------------------|---------------|
result = pandas2ri.rpy2py(result) | E. coli | Cipro | Escherichia coli | Ciprofloxacin |
| ESCCOL | CIP | Escherichia coli | Ciprofloxacin |
| esco | J01MA02 | Escherichia coli | Ciprofloxacin |
| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
### Explanation
* **mo_name:** This function standardises microorganism names. Here, different variations of *Escherichia coli* (such as "E. coli", "ESCCOL", "esco", and "Esche coli") are all converted into the correct, standardised form, "Escherichia coli".
* **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
### Taxonomic Data Sets Now in Python!
As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
```python
AMR.microorganisms
```
| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
```python
AMR.antibiotics
```
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
| AMA | 4649.0 | 4-aminosalicylic acid| Antimycobacterials | 12.00 | g | NaN | None |
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
| ... | ... | ... | ... | ... | ... | ... | ... |
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
# Installation
To be able to use the `AMR` Python package, it is required to install both R and the `AMR` R package.
### Preparation: Install R and `AMR` R package
For Linux and macOS, this is just:
```bash
# Ubuntu / Debian
sudo apt install r-base && Rscript -e 'install.packages("AMR")'
# Fedora:
sudo dnf install R && Rscript -e 'install.packages("AMR")'
# CentOS/RHEL
sudo yum install R && Rscript -e 'install.packages("AMR")'
# Arch Linux
sudo pacman -S r && Rscript -e 'install.packages("AMR")'
# macOS
brew install r && Rscript -e 'install.packages("AMR")'
```
For Windows, visit the [CRAN download page](https://cran.r-project.org) in install R, then afterwards install the 'AMR' package manually.
### Install `AMR` Python Package
Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
```bash
pip install AMR
```
# Working with `AMR` in Python
Now that we have everything set up, let’s walk through some practical examples of using the `AMR` package within Python.
## Example 1: Calculating AMR
```python
import AMR
import pandas as pd
df = AMR.example_isolates
result = AMR.resistance(df["AMX"])
print(result) print(result)
``` ```
In this example, a Python dataset with microorganism names like *E. coli* and *S. aureus* is passed to the R function `mo_name()`. The result is an updated `DataFrame` that includes the standardised microorganism names based on the `mo_name()` function from the `AMR` package. ```
[0.59555556]
```
## Example 2: Generating an Antibiogram ## Example 2: Generating Antibiograms
One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python: One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:
```python ```python
# Run an antibiogram in R from Python result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
ro.r('result <- antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()))') print(result2a)
# Retrieve the result in Python
result = ro.r('as.data.frame(result)')
print(result)
``` ```
In this example, we generate an antibiogram by selecting aminoglycosides and carbapenems, two classes of antibiotics, and then convert the resulting R data frame into a Python-readable format. | Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|-----------------|-----------------|-----------------|--------------------------|
| CoNS | 7% (10/142) | 73% (183/252) | 30% (10/33) |
| E. coli | 50% (196/392) | 88% (399/456) | 94% (393/416) |
| K. pneumoniae | 0% (0/58) | 96% (53/55) | 89% (47/53) |
| P. aeruginosa | 0% (0/30) | 100% (30/30) | None |
| P. mirabilis | None | 94% (34/36) | None |
| S. aureus | 6% (8/131) | 90% (171/191) | None |
| S. epidermidis | 1% (1/91) | 64% (87/136) | None |
| S. hominis | None | 80% (56/70) | None |
| S. pneumoniae | 100% (112/112) | None | 100% (112/112) |
## Example 3: Filtering Data Based on Gram-Negative Bacteria
Let’s say you want to filter the dataset for Gram-negative bacteria and display their resistance to certain antibiotics:
```python ```python
# Filter for Gram-negative bacteria with intrinsic resistance to cefotaxime result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
ro.r('result <- example_isolates[which(mo_is_gram_negative() & mo_is_intrinsic_resistant(ab = "cefotax")), c("bacteria", aminoglycosides(), carbapenems())]') print(result2b)
# Retrieve the filtered result in Python
result = ro.r('as.data.frame(result)')
print(result)
``` ```
This example uses the AMR functions `mo_is_gram_negative()` and `mo_is_intrinsic_resistant()` to filter the dataset and returns a subset of bacteria with resistance data. | Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|----------------|-----------------|------------------|--------------------------|
| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641) |
| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345) |
## Example 4: Customising the Antibiogram
You can easily customise the antibiogram by passing different antibiotics or microorganism transformations, as shown below: In this example, we generate an antibiogram by selecting various antibiotics.
```python
# Customise the antibiogram with different settings
ro.r('result <- antibiogram(example_isolates, antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), mo_transform = "gramstain")')
# Retrieve and print the result
result = ro.r('as.data.frame(result)')
print(result)
```
Here, we use piperacillin/tazobactam (TZP) in combination with tobramycin (TOB) and gentamicin (GEN) to see how they perform against various Gram-negative bacteria.
# Conclusion # Conclusion
Using `rpy2`, you can easily integrate the power of R's `AMR` package into your Python workflows. Whether you are generating antibiograms, analyzing resistance data, or performing complex filtering, `rpy2` gives you the flexibility to run R code without leaving the Python environment. This makes it a perfect solution for teams working across both R and Python. With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
By using `import AMR`, you can seamlessly integrate the robust features of the R `AMR` package into your Python workflows. Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.
@ -25743,38 +25773,6 @@ microorganisms.codes %>%
THE NEXT PART CONTAINS CONTENTS FROM FILE ../vignettes/other_pkg.Rmd
---
title: "Using AMR with other packages: AMR & dplyr/tidyverse"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 3
vignette: >
%\VignetteIndexEntry{How to conduct AMR data analysis}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
warning = FALSE,
collapse = TRUE,
comment = "#>",
fig.width = 7.5,
fig.height = 5
)
```
This page will be updated shortly, to give explicit examples of how to work ideally with the `AMR` package, for those who are used to working in `dplyr` or other tidyverse packages.
THE NEXT PART CONTAINS CONTENTS FROM FILE ../vignettes/resistance_predict.Rmd THE NEXT PART CONTAINS CONTENTS FROM FILE ../vignettes/resistance_predict.Rmd
@ -25991,8 +25989,8 @@ THE NEXT PART CONTAINS CONTENTS FROM FILE ../DESCRIPTION
Package: AMR Package: AMR
Version: 2.1.1.9087 Version: 2.1.1.9094
Date: 2024-10-02 Date: 2024-10-10
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by data analysis and to work with microbial and antimicrobial properties by
@ -26431,6 +26429,8 @@ THE NEXT PART CONTAINS CONTENTS FROM FILE ../index.md
* Integrates with **WHONET**, ATC, **EARS-Net**, PubChem, **LOINC**, **SNOMED CT**, and **NCBI** * Integrates with **WHONET**, ATC, **EARS-Net**, PubChem, **LOINC**, **SNOMED CT**, and **NCBI**
* 100% free of costs and dependencies, highly suitable for places with **limited resources** * 100% free of costs and dependencies, highly suitable for places with **limited resources**
> Now available for Python too! [Click here](./articles/AMR_for_Python.html) to read more.
<div style="display: flex; font-size: 0.8em;"> <div style="display: flex; font-size: 0.8em;">
<p style="text-align:left; width: 50%;"><small><a href="https://msberends.github.io/AMR/">https://msberends.github.io/AMR</a></small></p> <p style="text-align:left; width: 50%;"><small><a href="https://msberends.github.io/AMR/">https://msberends.github.io/AMR</a></small></p>
<p style="text-align:right; width: 50%;"><small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small></p> <p style="text-align:right; width: 50%;"><small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small></p>

2
index.md
View File

@ -9,6 +9,8 @@
* Integrates with **WHONET**, ATC, **EARS-Net**, PubChem, **LOINC**, **SNOMED CT**, and **NCBI** * Integrates with **WHONET**, ATC, **EARS-Net**, PubChem, **LOINC**, **SNOMED CT**, and **NCBI**
* 100% free of costs and dependencies, highly suitable for places with **limited resources** * 100% free of costs and dependencies, highly suitable for places with **limited resources**
> Now available for Python too! [Click here](./articles/AMR_for_Python.html) to read more.
<div style="display: flex; font-size: 0.8em;"> <div style="display: flex; font-size: 0.8em;">
<p style="text-align:left; width: 50%;"><small><a href="https://msberends.github.io/AMR/">https://msberends.github.io/AMR</a></small></p> <p style="text-align:left; width: 50%;"><small><a href="https://msberends.github.io/AMR/">https://msberends.github.io/AMR</a></small></p>
<p style="text-align:right; width: 50%;"><small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small></p> <p style="text-align:right; width: 50%;"><small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small></p>

4
man/as.disk.Rd
View File

@ -26,12 +26,12 @@ is.disk(x)
An \link{integer} with additional class \code{\link{disk}} An \link{integer} with additional class \code{\link{disk}}
} }
\description{ \description{
This transforms a vector to a new class \code{\link{disk}}, which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 6 and 50. This transforms a vector to a new class \code{\link{disk}}, which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 0 and 50.
} }
\details{ \details{
Interpret disk values as SIR values with \code{\link[=as.sir]{as.sir()}}. It supports guidelines from EUCAST and CLSI. Interpret disk values as SIR values with \code{\link[=as.sir]{as.sir()}}. It supports guidelines from EUCAST and CLSI.
Disk diffusion growth zone sizes must be between 6 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 6-50 range will return \code{NA}. Disk diffusion growth zone sizes must be between 0 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 0-50 range will return \code{NA}.
\code{NA_disk_} is a missing value of the new \code{disk} class. \code{NA_disk_} is a missing value of the new \code{disk} class.
} }

269
vignettes/AMR_for_Python.Rmd
View File

@ -24,161 +24,180 @@ knitr::opts_chunk$set(
# Introduction # Introduction
The `AMR` package for R is an incredible tool for antimicrobial resistance (AMR) data analysis, providing extensive functionality for working with microbial and antimicrobial properties. But what if you're working in Python and still want to benefit from the robust features of `AMR`? The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the `AMR` Python package, which uses `rpy2` internally. This package allows Python users to access all the functions from the R `AMR` package without the need to set up `rpy2` themselves. Since this Python package is not a true 'port' (which would require all R functions to be rewritten into Python), R and the AMR R package are still required to be installed. Yet, Python users can now easily work with AMR data directly through Python code.
The best way is to access R directly from Python with the help of `rpy2`, a simple yet powerful Python package. You can easily call functions from the `AMR` package to process your own data in your own Python environment. This post will guide you through setting up `rpy2` and show you how to use R functions from `AMR` in Python to supercharge your antimicrobial resistance analysis. In this document, we explain how this works and provide simple examples of using the `AMR` Python package.
# What is `rpy2`? ## How It Works
`rpy2` is a Python library that allows Python users to run R code within their Python scripts. Essentially, it acts as a bridge between the two languages, allowing you to tap into the rich ecosystem of R libraries (like `AMR`) while maintaining the flexibility of Python. The `AMR` Python package acts as a wrapper around the functions in the `AMR` R package. The package simplifies the process of calling R functions in Python, eliminating the need to manually manage the `rpy2` setup, which Python uses internally to be able to work with the R package. By just using `import AMR`, Python users can directly use the functions from the `AMR` R package as if they were native Python functions.
## Key Features of `rpy2`: Internally, `rpy2` is still being used, but all complexity is hidden from the user. This approach keeps the Python code clean and Pythonic, while still leveraging the full power of the R `AMR` package.
- Seamlessly call R functions from Python.
- Convert R data structures into Python data structures like pandas DataFrames.
- Leverage the full power of R libraries without leaving your Python environment.
# Setting Up `rpy2` ## Example of Usage
Before diving into the examples, you’ll need to install both R and `rpy2`. Here's a step-by-step guide on setting things up. Here’s an example that demonstrates how to clean microorganism and drug names using the `AMR` Python package:
## Step 1: Install R
Ensure that R is installed on your system. R has minimal dependencies and is very simple to install:
* **Linux**
* Ubuntu / Debian:
`sudo apt install r-base`
* Fedora:
`sudo dnf install R`
* CentOS/RHEL:
`sudo yum install R`
* Arch Linux:
`sudo pacman -S r`
* **macOS** (with Homebrew):
`brew install r`
* **Other Systems:**
Visit the [CRAN download page](https://cran.r-project.org).
## Step 2: Install the `AMR` package in R
On Linux and macOS, open Terminal and run:
```bash
Rscript -e 'install.packages("AMR")'
```
For other systems, open your R console and install the `AMR` package by running:
```r
install.packages("AMR")
```
On any system, you can also install the latest development version of the `AMR` package by setting `repos` to our beta channel:
```r
install.packages("AMR", repos = "https://msberends.r-universe.dev")
```
## Step 3: Install `rpy2` in Python
To install `rpy2`, simply run the following command in your terminal:
```bash
pip install rpy2
```
## Step 4: Test `rpy2` Installation
To ensure everything is set up correctly, you can test your installation by running the following Python script, which essentially runs R in the background:
```python
import rpy2.robjects as ro
# Test a simple R function from Python
ro.r('1 + 1')
```
If this returns `2`, you're good to go!
# Working with `AMR` in Python
Now that we have `rpy2` set up, let’s walk through some practical examples of using the `AMR` package within Python.
## Example 1: Converting Taxonomic Data
Let’s start by converting taxonomic user input to valid taxonomy using the `AMR` package, from within Python:
```python ```python
import pandas as pd import pandas as pd
import rpy2.robjects as ro import AMR
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
# Load the AMR package from R # Sample data
amr = importr('AMR') data = {
"MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
"Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
}
df = pd.DataFrame(data)
# Example user dataset in Python # Use AMR functions to clean microorganism and drug names
data = pd.DataFrame({ df['MO_clean'] = AMR.mo_name(df['MOs'])
'microorganism': ['E. coli', 'S. aureus', 'P. aeruginosa', 'K. pneumoniae'] df['Drug_clean'] = AMR.ab_name(df['Drug'])
})
# Apply mo_name() from the AMR package to the 'microorganism' column # Display the results
ro.globalenv['r_data'] = data print(df)
ro.r('r_data$mo_name <- mo_name(r_data$microorganism)') ```
# Retrieve and print the modified R DataFrame in Python | MOs | Drug | MO_clean | Drug_clean |
result = ro.r('r_data') |-------------|-----------|--------------------|---------------|
result = pandas2ri.rpy2py(result) | E. coli | Cipro | Escherichia coli | Ciprofloxacin |
| ESCCOL | CIP | Escherichia coli | Ciprofloxacin |
| esco | J01MA02 | Escherichia coli | Ciprofloxacin |
| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
### Explanation
* **mo_name:** This function standardises microorganism names. Here, different variations of *Escherichia coli* (such as "E. coli", "ESCCOL", "esco", and "Esche coli") are all converted into the correct, standardised form, "Escherichia coli".
* **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
### Taxonomic Data Sets Now in Python!
As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
```python
AMR.microorganisms
```
| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
```python
AMR.antibiotics
```
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
| AMA | 4649.0 | 4-aminosalicylic acid| Antimycobacterials | 12.00 | g | NaN | None |
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
| ... | ... | ... | ... | ... | ... | ... | ... |
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
# Installation
To be able to use the `AMR` Python package, it is required to install both R and the `AMR` R package.
### Preparation: Install R and `AMR` R package
For Linux and macOS, this is just:
```bash
# Ubuntu / Debian
sudo apt install r-base && Rscript -e 'install.packages("AMR")'
# Fedora:
sudo dnf install R && Rscript -e 'install.packages("AMR")'
# CentOS/RHEL
sudo yum install R && Rscript -e 'install.packages("AMR")'
# Arch Linux
sudo pacman -S r && Rscript -e 'install.packages("AMR")'
# macOS
brew install r && Rscript -e 'install.packages("AMR")'
```
For Windows, visit the [CRAN download page](https://cran.r-project.org) in install R, then afterwards install the 'AMR' package manually.
### Install `AMR` Python Package
Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
```bash
pip install AMR
```
# Working with `AMR` in Python
Now that we have everything set up, let’s walk through some practical examples of using the `AMR` package within Python.
## Example 1: Calculating AMR
```python
import AMR
import pandas as pd
df = AMR.example_isolates
result = AMR.resistance(df["AMX"])
print(result) print(result)
``` ```
In this example, a Python dataset with microorganism names like *E. coli* and *S. aureus* is passed to the R function `mo_name()`. The result is an updated `DataFrame` that includes the standardised microorganism names based on the `mo_name()` function from the `AMR` package. ```
[0.59555556]
```
## Example 2: Generating an Antibiogram ## Example 2: Generating Antibiograms
One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python: One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:
```python ```python
# Run an antibiogram in R from Python result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
ro.r('result <- antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()))') print(result2a)
# Retrieve the result in Python
result = ro.r('as.data.frame(result)')
print(result)
``` ```
In this example, we generate an antibiogram by selecting aminoglycosides and carbapenems, two classes of antibiotics, and then convert the resulting R data frame into a Python-readable format. | Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|-----------------|-----------------|-----------------|--------------------------|
| CoNS | 7% (10/142) | 73% (183/252) | 30% (10/33) |
| E. coli | 50% (196/392) | 88% (399/456) | 94% (393/416) |
| K. pneumoniae | 0% (0/58) | 96% (53/55) | 89% (47/53) |
| P. aeruginosa | 0% (0/30) | 100% (30/30) | None |
| P. mirabilis | None | 94% (34/36) | None |
| S. aureus | 6% (8/131) | 90% (171/191) | None |
| S. epidermidis | 1% (1/91) | 64% (87/136) | None |
| S. hominis | None | 80% (56/70) | None |
| S. pneumoniae | 100% (112/112) | None | 100% (112/112) |
## Example 3: Filtering Data Based on Gram-Negative Bacteria
Let’s say you want to filter the dataset for Gram-negative bacteria and display their resistance to certain antibiotics:
```python ```python
# Filter for Gram-negative bacteria with intrinsic resistance to cefotaxime result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
ro.r('result <- example_isolates[which(mo_is_gram_negative() & mo_is_intrinsic_resistant(ab = "cefotax")), c("bacteria", aminoglycosides(), carbapenems())]') print(result2b)
# Retrieve the filtered result in Python
result = ro.r('as.data.frame(result)')
print(result)
``` ```
This example uses the AMR functions `mo_is_gram_negative()` and `mo_is_intrinsic_resistant()` to filter the dataset and returns a subset of bacteria with resistance data. | Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|----------------|-----------------|------------------|--------------------------|
| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641) |
| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345) |
## Example 4: Customising the Antibiogram
You can easily customise the antibiogram by passing different antibiotics or microorganism transformations, as shown below: In this example, we generate an antibiogram by selecting various antibiotics.
```python
# Customise the antibiogram with different settings
ro.r('result <- antibiogram(example_isolates, antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), mo_transform = "gramstain")')
# Retrieve and print the result
result = ro.r('as.data.frame(result)')
print(result)
```
Here, we use piperacillin/tazobactam (TZP) in combination with tobramycin (TOB) and gentamicin (GEN) to see how they perform against various Gram-negative bacteria.
# Conclusion # Conclusion
Using `rpy2`, you can easily integrate the power of R's `AMR` package into your Python workflows. Whether you are generating antibiograms, analyzing resistance data, or performing complex filtering, `rpy2` gives you the flexibility to run R code without leaving the Python environment. This makes it a perfect solution for teams working across both R and Python. With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
By using `import AMR`, you can seamlessly integrate the robust features of the R `AMR` package into your Python workflows. Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.

25
vignettes/other_pkg.Rmd
View File

@ -1,25 +0,0 @@
---
title: "Using AMR with other packages: AMR & dplyr/tidyverse"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 3
vignette: >
%\VignetteIndexEntry{How to conduct AMR data analysis}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
warning = FALSE,
collapse = TRUE,
comment = "#>",
fig.width = 7.5,
fig.height = 5
)
```
This page will be updated shortly, to give explicit examples of how to work ideally with the `AMR` package, for those who are used to working in `dplyr` or other tidyverse packages.