P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-07-09 00:02:38 +02:00
Code Releases Activity

(v1.4.0.9042) auto dark theme website

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2020-12-21 22:46:29 +01:00
parent 81af41da3a
commit ee70883246
105 changed files with 4069 additions and 1785 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
R/aa_helper_functions.R
View File

@ -514,7 +514,7 @@ meet_criteria <- function(object,
}
get_current_data <- function(arg_name, call) {
# this mimics dplyr::cur_data_all for users that use our content-aware functions in dplyr verbs
# this mimics dplyr::cur_data_all for users that use our context-aware functions in dplyr verbs
cur_data_all_dplyr <- import_fn("cur_data_all", "dplyr", error_on_fail = FALSE)
if (is.null(cur_data_all_dplyr)) {
# dplyr not installed

6
R/amr.R
View File

@ -27,11 +27,11 @@
#'
#' Welcome to the `AMR` package.
#' @details
#' `AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
#' `AMR` is a free, open-source and independent \R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
#'
#' After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
#' After installing this package, \R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
#'
#' This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
#' This package is fully independent of any other \R package and works on Windows, macOS and Linux with all versions of \R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This \R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
#'
#' This package can be used for:
#' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature

2
R/catalogue_of_life.R
View File

@ -58,7 +58,7 @@ format_included_data_number <- function(data) {
#'
#' The Catalogue of Life (<http://www.catalogueoflife.org>) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.9 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation.
#'
#' The syntax used to transform the original data to a cleansed R format, can be found here: <https://github.com/msberends/AMR/blob/master/data-raw/reproduction_of_microorganisms.R>.
#' The syntax used to transform the original data to a cleansed \R format, can be found here: <https://github.com/msberends/AMR/blob/master/data-raw/reproduction_of_microorganisms.R>.
#' @inheritSection AMR Read more on our website!
#' @name catalogue_of_life
#' @rdname catalogue_of_life

8
R/data.R
View File

@ -58,12 +58,12 @@
#' Synonyms (i.e. trade names) are derived from the Compound ID (`cid`) and consequently only available where a CID is available.
#'
#' ### Direct download
#' These data sets are available as 'flat files' for use even without R - you can find the files here:
#' These data sets are available as 'flat files' for use even without \R - you can find the files here:
#'
#' * <https://github.com/msberends/AMR/raw/master/data-raw/antibiotics.txt>
#' * <https://github.com/msberends/AMR/raw/master/data-raw/antivirals.txt>
#'
#' Files in R format (with preserved data structure) can be found here:
#' Files in \R format (with preserved data structure) can be found here:
#'
#' * <https://github.com/msberends/AMR/raw/master/data/antibiotics.rda>
#' * <https://github.com/msberends/AMR/raw/master/data/antivirals.rda>
@ -106,11 +106,11 @@
#' - `r format(nrow(subset(microorganisms, source == "DSMZ")), big.mark = ",")` species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) since the DSMZ contain the latest taxonomic information based on recent publications
#'
#' ### Direct download
#' This data set is available as 'flat file' for use even without R - you can find the file here:
#' This data set is available as 'flat file' for use even without \R - you can find the file here:
#'
#' * <https://github.com/msberends/AMR/raw/master/data-raw/microorganisms.txt>
#'
#' The file in R format (with preserved data structure) can be found here:
#' The file in \R format (with preserved data structure) can be found here:
#'
#' * <https://github.com/msberends/AMR/raw/master/data/microorganisms.rda>
#' @section About the records from DSMZ (see source):

4
R/mo_source.R
View File

@ -34,9 +34,9 @@
#' @rdname mo_source
#' @name mo_source
#' @aliases set_mo_source get_mo_source
#' @details The reference file can be a text file separated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you will need to have the `readxl` package installed.
#' @details The reference file can be a text file separated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an \R object file (extension '.rds'). To use an Excel file, you will need to have the `readxl` package installed.
#'
#' [set_mo_source()] will check the file for validity: it must be a [data.frame], must have a column named `"mo"` which contains values from [`microorganisms$mo`][microorganisms] and must have a reference column with your own defined values. If all tests pass, [set_mo_source()] will read the file into R and will ask to export it to `"~/mo_source.rds"`. The CRAN policy disallows packages to write to the file system, although '*exceptions may be allowed in interactive sessions if the package obtains confirmation from the user*'. For this reason, this function only works in interactive sessions so that the user can **specifically confirm and allow** that this file will be created. The destination of this file can be set with the `destination` parameter and defaults to the user's home directory. It can also be set as an \R option, using `options(AMR_mo_source = "my/location/file.rds)`.
#' [set_mo_source()] will check the file for validity: it must be a [data.frame], must have a column named `"mo"` which contains values from [`microorganisms$mo`][microorganisms] and must have a reference column with your own defined values. If all tests pass, [set_mo_source()] will read the file into \R and will ask to export it to `"~/mo_source.rds"`. The CRAN policy disallows packages to write to the file system, although '*exceptions may be allowed in interactive sessions if the package obtains confirmation from the user*'. For this reason, this function only works in interactive sessions so that the user can **specifically confirm and allow** that this file will be created. The destination of this file can be set with the `destination` parameter and defaults to the user's home directory. It can also be set as an \R option, using `options(AMR_mo_source = "my/location/file.rds")`.
#'
#' The created compressed data file `"mo_source.rds"` will be used at default for MO determination (function [as.mo()] and consequently all `mo_*` functions like [mo_genus()] and [mo_gramstain()]). The location and timestamp of the original file will be saved as an attribute to the compressed data file.
#'