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https://github.com/msberends/AMR.git
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(v2.1.1.9129) unit test fix
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@ -42,10 +42,6 @@ description_file="../DESCRIPTION"
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# Write header to the datasets Python file, including the convert_to_python function
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cat <<EOL > "$datasets_file"
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BLUE = '\033[94m'
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GREEN = '\033[32m'
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RESET = '\033[0m'
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import os
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import sys
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from rpy2 import robjects
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@ -61,18 +57,22 @@ venv_path = sys.prefix
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r_lib_path = os.path.join(venv_path, "R_libs")
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# Ensure the R library path exists
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os.makedirs(r_lib_path, exist_ok=True)
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# Set the R library path in .libPaths
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base = importr('base')
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# Turn off warnings
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base.options(warn = -1)
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base._libPaths(r_lib_path)
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# Import base and utils
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base = importr('base')
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utils = importr('utils')
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# Override R library paths globally for the session
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robjects.r(f'.Library <- "{r_lib_path}"') # Replace default library
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robjects.r(f'.Library.site <- "{r_lib_path}"') # Replace site-specific library
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base._libPaths(r_lib_path) # Override .libPaths() as well
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# Get the effective library path
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r_amr_lib_path = base._libPaths()[0]
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# Check if the AMR package is installed in R
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if not isinstalled('AMR', lib_loc = r_amr_lib_path):
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utils = importr('utils')
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print(f"{BLUE}AMR:{RESET} Installing AMR package to {BLUE}{r_amr_lib_path}/{RESET}...", flush=True)
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if not isinstalled('AMR', lib_loc=r_amr_lib_path):
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print(f"AMR: Installing latest AMR R package to {r_amr_lib_path}...", flush=True)
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utils.install_packages('AMR', repos='https://msberends.r-universe.dev', quiet=True)
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# Python package version of AMR
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@ -87,16 +87,12 @@ r_amr_version = robjects.r(f'as.character(packageVersion("AMR", lib.loc = "{r_li
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# Compare R and Python package versions
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if r_amr_version != python_amr_version:
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try:
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print(f"{BLUE}AMR:{RESET} Updating AMR package in {BLUE}{r_amr_lib_path}/{RESET}...", flush=True)
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utils = importr('utils')
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print(f"AMR: Updating AMR package in {r_amr_lib_path}...", flush=True)
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utils.install_packages('AMR', repos='https://msberends.r-universe.dev', quiet=True)
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except Exception as e:
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print(f"{BLUE}AMR:{RESET} Could not update: {e}{RESET}", flush=True)
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print(f"AMR: Could not update: {e}", flush=True)
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# Restore warnings to default
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base.options(warn = 0)
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print(f"{BLUE}AMR:{RESET} Setting up R environment and AMR datasets...", flush=True)
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print(f"AMR: Setting up R environment and AMR datasets...", flush=True)
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# Activate the automatic conversion between R and pandas DataFrames
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pandas2ri.activate()
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@ -121,7 +117,7 @@ microorganisms = pandas2ri.rpy2py(robjects.r('AMR::microorganisms[, !sapply(AMR:
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antibiotics = pandas2ri.rpy2py(robjects.r('AMR::antibiotics[, !sapply(AMR::antibiotics, is.list)]'))
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clinical_breakpoints = pandas2ri.rpy2py(robjects.r('AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]'))
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print(f"{BLUE}AMR:{RESET} {GREEN}Done.{RESET}", flush=True)
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print(f"AMR: Done.", flush=True)
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EOL
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echo "from .datasets import example_isolates" >> $init_file
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@ -1,5 +1,5 @@
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This files contains all context you must know about the AMR package for R.
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First and foremost, you are trained on version 2.1.1.9128. Remember this whenever someone asks which AMR package version you’re at.
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First and foremost, you are trained on version 2.1.1.9129. Remember this whenever someone asks which AMR package version you’re at.
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--------------------------------
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THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'NAMESPACE':
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@ -1705,7 +1705,7 @@ This function returns a table with values between 0 and 100 for \emph{susceptibi
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For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top \emph{n} species encountered in the data. You can filter on this top \emph{n} using \code{\link[=top_n_microorganisms]{top_n_microorganisms()}}. For example, use \code{top_n_microorganisms(your_data, n = 10)} as a pre-processing step to only include the top 10 species in the data.
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The numeric values of an antibiogram are stored in a long format as the \link{attribute} \code{long_numeric}. You can retrieve them using \code{attributes(x)$long_numeric}, where \code{x} is the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}. This is ideal for e.g. advanced plotting.
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The numeric values of an antibiogram are stored in a long format as the \link[=attributes]{attribute} \code{long_numeric}. You can retrieve them using \code{attributes(x)$long_numeric}, where \code{x} is the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}. This is ideal for e.g. advanced plotting.
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\subsection{Formatting Type}{
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The formatting of the 'cells' of the table can be set with the argument \code{formatting_type}. In these examples, \code{5} is the susceptibility percentage (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
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@ -3900,6 +3900,8 @@ bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname,
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\item{FUN}{the function to call on the \code{mo} column to transform the microorganism codes - the default is \code{\link[=mo_shortname]{mo_shortname()}}}
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\item{include_n_rows}{a \link{logical} to indicate if the total number of rows must be included in the output}
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\item{...}{arguments passed on to \code{FUN}}
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\item{translate_ab}{a \link{character} of length 1 containing column names of the \link{antibiotics} data set}
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@ -8027,7 +8029,7 @@ top_n_microorganisms(x, n, property = "fullname", n_for_each = NULL,
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This function filters a data set to include only the top \emph{n} microorganisms based on a specified property, such as taxonomic family or genus. For example, it can filter a data set to the top 3 species, or to any species in the top 5 genera, or to the top 3 species in each of the top 5 genera.
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}
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\details{
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This function is useful for preprocessing data before creating \link[=antibiograms]{antibiograms} or other analyses that require focused subsets of microbial data. For example, it can filter a data set to only include isolates from the top 10 species.
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This function is useful for preprocessing data before creating \link[=antibiogram]{antibiograms} or other analyses that require focused subsets of microbial data. For example, it can filter a data set to only include isolates from the top 10 species.
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}
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\examples{
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# filter to the top 3 species:
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@ -2210,6 +2210,6 @@ devtools::load_all(".")
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# run the unit tests
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Sys.setenv(NOT_CRAN = "true")
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testthat::test_file("inst/tinytest/test-data.R")
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testthat::test_file("inst/tinytest/test-mo.R")
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testthat::test_file("inst/tinytest/test-mo_property.R")
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testthat::test_file("inst/tests/testthat/test-data.R")
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testthat::test_file("inst/tests/testthat/test-mo.R")
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testthat::test_file("inst/tests/testthat/test-mo_property.R")
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