AMR/data-raw/reproduction_of_rsi_transla...

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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
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# (c) 2018-2022 Berends MS, Luz CF et al. #
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# 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/ #
# ==================================================================== #
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# This script runs in under a minute and renews all guidelines of CLSI and EUCAST!
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library(dplyr)
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library(readr)
library(tidyr)
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library(AMR)
# Install the WHONET software on Windows (http://www.whonet.org/software.html),
# and copy the folder C:\WHONET\Codes to data-raw/WHONET/Codes
DRGLST <- readr::read_tsv("data-raw/WHONET/Codes/DRGLST.txt", na = c("", "NA", "-"))
DRGLST1 <- readr::read_tsv("data-raw/WHONET/Codes/DRGLST1.txt", na = c("", "NA", "-"))
ORGLIST <- readr::read_tsv("data-raw/WHONET/Codes/ORGLIST.txt", na = c("", "NA", "-"))
# create data set for generic rules (i.e., AB-specific but not MO-specific)
rsi_generic <- DRGLST %>%
filter(CLSI == "X" | EUCST == "X") %>%
select(ab = ANTIBIOTIC, disk_dose = POTENCY, matches("^(CLSI|EUCST)[0-9]")) %>%
mutate(ab = as.ab(ab),
across(matches("(CLSI|EUCST)"), as.double)) %>%
pivot_longer(-c(ab, disk_dose), names_to = "method") %>%
separate(method, into = c("guideline", "method"), sep = "_") %>%
mutate(method = ifelse(method %like% "D",
gsub("D", "DISK_", method, fixed = TRUE),
gsub("M", "MIC_", method, fixed = TRUE))) %>%
separate(method, into = c("method", "rsi"), sep = "_") %>%
# I is in the middle, so we only need R and S (saves data)
filter(rsi %in% c("R", "S")) %>%
pivot_wider(names_from = rsi, values_from = value) %>%
transmute(guideline = gsub("([0-9]+)$", " 20\\1", gsub("EUCST", "EUCAST", guideline)),
method,
site = NA_character_,
mo = as.mo("UNKNOWN"),
ab,
ref_tbl = "Generic rules",
disk_dose,
breakpoint_S = S,
breakpoint_R = R,
uti = FALSE) %>%
filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)), !is.na(mo), !is.na(ab))
rsi_generic
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# create data set for AB-specific and MO-specific rules
rsi_specific <- DRGLST1 %>%
# only support guidelines for humans (for now)
filter(HOST == "Human" & SITE_INF %unlike% "canine|feline",
# only CLSI and EUCAST
GUIDELINES %like% "(CLSI|EUCST)") %>%
# get microorganism names from another WHONET table
mutate(ORG_CODE = tolower(ORG_CODE)) %>%
left_join(ORGLIST %>%
transmute(ORG_CODE = tolower(ORG),
SCT_TEXT = case_when(is.na(SCT_TEXT) & is.na(ORGANISM) ~ ORG_CODE,
is.na(SCT_TEXT) ~ ORGANISM,
TRUE ~ SCT_TEXT)) %>%
# WHO for 'Generic'
bind_rows(tibble(ORG_CODE = "gen", SCT_TEXT = "Unknown")) %>%
# WHO for 'Enterobacterales'
bind_rows(tibble(ORG_CODE = "ebc", SCT_TEXT = "Enterobacterales"))
) %>%
# still some manual cleaning required
filter(!SCT_TEXT %in% c("Anaerobic Actinomycetes")) %>%
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transmute(guideline = gsub("([0-9]+)$", " 20\\1", gsub("EUCST", "EUCAST", GUIDELINES)),
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method = toupper(TESTMETHOD),
site = SITE_INF,
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mo = as.mo(SCT_TEXT),
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ab = as.ab(WHON5_CODE),
ref_tbl = REF_TABLE,
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disk_dose = POTENCY,
breakpoint_S = as.double(ifelse(method == "DISK", DISK_S, MIC_S)),
breakpoint_R = as.double(ifelse(method == "DISK", DISK_R, MIC_R)),
uti = site %like% "(UTI|urinary|urine)") %>%
filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)), !is.na(mo), !is.na(ab))
rsi_specific
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rsi_translation <- rsi_generic %>%
bind_rows(rsi_specific) %>%
# add the taxonomic rank index, used for sorting (so subspecies match first, order matches last)
mutate(rank_index = case_when(mo_rank(mo) %like% "(infra|sub)" ~ 1,
mo_rank(mo) == "species" ~ 2,
mo_rank(mo) == "genus" ~ 3,
mo_rank(mo) == "family" ~ 4,
mo_rank(mo) == "order" ~ 5,
TRUE ~ 6),
.after = mo) %>%
arrange(desc(guideline), ab, mo, method) %>%
distinct(guideline, ab, mo, method, site, .keep_all = TRUE) %>%
as.data.frame(stringsAsFactors = FALSE)
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# disks MUST be 6-50 mm, so correct where that is wrong:
rsi_translation[which(rsi_translation$method == "DISK" &
(is.na(rsi_translation$breakpoint_S) | rsi_translation$breakpoint_S > 50)), "breakpoint_S"] <- 50
rsi_translation[which(rsi_translation$method == "DISK" &
(is.na(rsi_translation$breakpoint_R) | rsi_translation$breakpoint_R < 6)), "breakpoint_R"] <- 6
m <- unique(as.double(as.mic(levels(as.mic(1)))))
rsi_translation[which(rsi_translation$method == "MIC" &
is.na(rsi_translation$breakpoint_S)), "breakpoint_S"] <- min(m)
rsi_translation[which(rsi_translation$method == "MIC" &
is.na(rsi_translation$breakpoint_R)), "breakpoint_R"] <- max(m)
# WHONET has no >1024 but instead uses 1025, 513, etc, so raise these one higher valid MIC factor level:
rsi_translation[which(rsi_translation$breakpoint_R == 129), "breakpoint_R"] <- m[which(m == 128) + 1]
rsi_translation[which(rsi_translation$breakpoint_R == 257), "breakpoint_R"] <- m[which(m == 256) + 1]
rsi_translation[which(rsi_translation$breakpoint_R == 513), "breakpoint_R"] <- m[which(m == 512) + 1]
rsi_translation[which(rsi_translation$breakpoint_R == 1025), "breakpoint_R"] <- m[which(m == 1024) + 1]
# Greek symbols and EM dash symbols are not allowed by CRAN, so replace them with ASCII:
rsi_translation$disk_dose <- gsub("μ", "u", rsi_translation$disk_dose, fixed = TRUE)
rsi_translation$disk_dose <- gsub("", "-", rsi_translation$disk_dose, fixed = TRUE)
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# save to package
usethis::use_data(rsi_translation, overwrite = TRUE)
rm(rsi_translation)
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devtools::load_all(".")