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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
library(dplyr)
library(readxl)
library(cleaner)
# URL:
# https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/Dosages_v_11.0_Breakpoint_Tables.pdf
# download the PDF file, open in Acrobat Pro and export as Excel workbook
breakpoints_version <- 11
dosage_source <- read_excel("data-raw/Dosages_v_11.0_Breakpoint_Tables.xlsx", skip = 5, na = "None") %>%
format_names(snake_case = TRUE, penicillins = "drug") %>%
filter(!tolower(standard_dosage) %in% c("standard dosage_source", "under review")) %>%
filter(!is.na(standard_dosage)) %>%
# keep only one drug in the table
arrange(desc(drug)) %>%
mutate(drug = gsub("(.*) ([(]|iv|oral).*", "\\1", drug)) %>%
#distinct(drug, .keep_all = TRUE) %>%
arrange(drug) %>%
mutate(ab = as.ab(drug),
ab_name = ab_name(ab, language = NULL))
dosage_source <- bind_rows(
# oral
dosage_source %>%
filter(standard_dosage %like% " oral") %>%
mutate(standard_dosage = gsub("oral.*", "oral", standard_dosage),
high_dosage = if_else(high_dosage %like% "oral",
gsub("oral.*", "oral", high_dosage),
NA_character_)),
# iv
dosage_source %>%
filter(standard_dosage %like% " iv") %>%
mutate(standard_dosage = gsub(".* or ", "", standard_dosage),
high_dosage = if_else(high_dosage %like% "( or | iv)",
gsub(".* or ", "", high_dosage),
NA_character_)),
# im
dosage_source %>%
filter(standard_dosage %like% " im")
) %>%
arrange(drug)
get_dosage_lst <- function(col_data) {
standard <- col_data %>%
# remove new lines
gsub(" ?(\n|\t)+ ?", " ", .) %>%
# keep only the first suggestion, replace all after 'or' and more informative texts
gsub("(.*?) (or|with|loading|depending|over|by) .*", "\\1", .) %>%
# remove (1 MU)
gsub(" [(][0-9] [A-Z]+[)]", "", .) %>%
# remove parentheses
gsub("[)(]", "", .) %>%
# remove drug names
gsub(" [a-z]{5,99}( |$)", " ", .) %>%
gsub(" [a-z]{5,99}( |$)", " ", .) %>%
gsub(" (acid|dose)", "", .)# %>%
# keep lowest value only (25-30 mg -> 25 mg)
# gsub("[-].*? ", " ", .)
dosage_lst <- lapply(strsplit(standard, " x "),
function(x) {
dose <- x[1]
if (dose %like% "under") {
dose <- NA_character_
}
admin <- x[2]
list(
dose = trimws(dose),
dose_times = gsub("^([0-9.]+).*", "\\1", admin),
administration = clean_character(admin),
notes = "",
original_txt = ""
)
})
for (i in seq_len(length(col_data))) {
dosage_lst[[i]]$original_txt <- gsub("\n", " ", col_data[i])
if (col_data[i] %like% " (or|with|loading|depending|over) ") {
dosage_lst[[i]]$notes <- gsub("\n", " ", gsub(".* ((or|with|loading|depending|over) .*)", "\\1", col_data[i]))
}
}
dosage_lst
}
standard <- get_dosage_lst(dosage_source$standard_dosage)
high <- get_dosage_lst(dosage_source$high_dosage)
uti <- get_dosage_lst(dosage_source$uncomplicated_uti)
dosage <- bind_rows(
# standard dose
data.frame(
ab = dosage_source$ab,
name = dosage_source$ab_name,
type = "standard_dosage",
dose = sapply(standard, function(x) x$dose),
dose_times = sapply(standard, function(x) x$dose_times),
administration = sapply(standard, function(x) x$administration),
notes = sapply(standard, function(x) x$notes),
original_txt = sapply(standard, function(x) x$original_txt),
stringsAsFactors = FALSE
),
# high dose
data.frame(
ab = dosage_source$ab,
name = dosage_source$ab_name,
type = "high_dosage",
dose = sapply(high, function(x) x$dose),
dose_times = sapply(high, function(x) x$dose_times),
administration = sapply(high, function(x) x$administration),
notes = sapply(high, function(x) x$notes),
original_txt = sapply(high, function(x) x$original_txt),
stringsAsFactors = FALSE
),
# UTIs
data.frame(
ab = dosage_source$ab,
name = dosage_source$ab_name,
type = "uncomplicated_uti",
dose = sapply(uti, function(x) x$dose),
dose_times = sapply(uti, function(x) x$dose_times),
administration = sapply(uti, function(x) x$administration),
notes = sapply(uti, function(x) x$notes),
original_txt = sapply(uti, function(x) x$original_txt),
stringsAsFactors = FALSE
)) %>%
mutate(eucast_version = breakpoints_version,
dose_times = as.integer(dose_times),
administration = gsub("([a-z]+) .*", "\\1", administration)) %>%
arrange(name, administration, type) %>%
filter(!is.na(dose), dose != ".") %>%
as.data.frame(stringsAsFactors = FALSE)
rownames(dosage) <- NULL
usethis::use_data(dosage, internal = FALSE, overwrite = TRUE, version = 2)