mirror of https://github.com/msberends/AMR.git
104 lines
4.5 KiB
R
104 lines
4.5 KiB
R
# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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# last updated: 30 October 2022 - Loinc_2.73
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# Steps to reproduce:
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# 1. Create a fake account at https://loinc.org (sad you have to create one...)
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# 2. Download the CSV from https://loinc.org/download/loinc-complete/
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# 3. Read file LoincTable/Loinc.csv
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loinc_df <- read.csv("data-raw/Loinc.csv",
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row.names = NULL,
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stringsAsFactors = FALSE
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)
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# 4. Clean and add
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library(dplyr)
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library(cleaner)
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library(AMR)
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# to find the drugs:
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loinc_df %>%
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filter(COMPONENT %like% "ampicillin|fluconazol|meropenem") %>%
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count(CLASS, sort = TRUE)
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loinc_df <- loinc_df %>%
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filter(CLASS %in% c("DRUG/TOX", "ABXBACT")) %>%
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mutate(name = generalise_antibiotic_name(COMPONENT), .before = 1)
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# antibiotics
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antibiotics$loinc <- as.list(rep(NA_character_, nrow(antibiotics)))
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for (i in seq_len(nrow(antibiotics))) {
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message(i)
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loinc_ab <- loinc_df %>%
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filter(name %like% paste0("^", generalise_antibiotic_name(antibiotics$name[i]))) %>%
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pull(LOINC_NUM)
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if (length(loinc_ab) > 0) {
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antibiotics$loinc[i] <- list(loinc_ab)
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}
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}
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# antivirals
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antivirals$loinc <- as.list(rep(NA_character_, nrow(antivirals)))
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for (i in seq_len(nrow(antivirals))) {
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message(i)
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loinc_ab <- loinc_df %>%
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filter(name %like% paste0("^", generalise_antibiotic_name(antivirals$name[i]))) %>%
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pull(LOINC_NUM)
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if (length(loinc_ab) > 0) {
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antivirals$loinc[i] <- list(loinc_ab)
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}
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}
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# sort and fix for empty values
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for (i in 1:nrow(antibiotics)) {
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loinc <- as.character(sort(unique(tolower(antibiotics[i, "loinc", drop = TRUE][[1]]))))
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loinc <- loinc[loinc != ""]
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antibiotics[i, "loinc"][[1]] <- ifelse(length(loinc) == 0, list(""), list(loinc))
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}
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for (i in 1:nrow(antivirals)) {
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loinc <- as.character(sort(unique(tolower(antivirals[i, "loinc", drop = TRUE][[1]]))))
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loinc <- loinc[loinc != ""]
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antivirals[i, "loinc"][[1]] <- ifelse(length(loinc) == 0, list(""), list(loinc))
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}
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antibiotics <- dataset_UTF8_to_ASCII(as.data.frame(antibiotics, stringsAsFactors = FALSE))
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antibiotics <- dplyr::arrange(antibiotics, name)
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antivirals <- dataset_UTF8_to_ASCII(as.data.frame(antivirals, stringsAsFactors = FALSE))
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antivirals <- dplyr::arrange(antivirals, name)
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# remember to update R/aa_globals.R for the documentation
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dim(antibiotics) # for R/data.R
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usethis::use_data(antibiotics, internal = FALSE, overwrite = TRUE, compress = "xz", version = 2)
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rm(antibiotics)
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dim(antivirals) # for R/data.R
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usethis::use_data(antivirals, internal = FALSE, overwrite = TRUE, compress = "xz", version = 2)
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rm(antivirals)
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