P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-08-27 17:02:12 +02:00
Code Releases Activity

fix reference_df endless loop

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2023-06-26 13:52:02 +02:00
parent 1d9ee39cc7
commit 2d97cca6d9
29 changed files with 215 additions and 200 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
data-raw/_language_update.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/_pre_commit_hook.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/loinc.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

BIN
data-raw/microorganisms.dta
View File

Binary file not shown.

BIN
data-raw/microorganisms.feather
View File

Binary file not shown.

2
data-raw/microorganisms.md5
View File

@@ -1 +1 @@
63cc9e5166dc50c7b474bb809557c392
2d19b559a6c4b6f26449271ed1e37ea8

BIN
data-raw/microorganisms.parquet
View File

Binary file not shown.

BIN
data-raw/microorganisms.rds
View File

Binary file not shown.

BIN
data-raw/microorganisms.sav
View File

Binary file not shown.

7
data-raw/microorganisms.txt
View File

@@ -547,6 +547,7 @@
"B_ACNTB_AVST" "Acinetobacter avistercoris" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Pseudomonadales" "Moraxellaceae" "Acinetobacter" "avistercoris" "" "species" "likely facultative anaerobe" "GBIF" "11605583" "3223263" 1.5 ""
"B_ACNTB_BART" "Acinetobacter baretiae" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Pseudomonadales" "Moraxellaceae" "Acinetobacter" "baretiae" "" "species" "Alvarez-Perez et al., 2021" "likely facultative anaerobe" "LPSN" "19553" "515021" "11852261" "3223263" 1.5 ""
"B_ACNTB_BMNN" "Acinetobacter baumannii" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Pseudomonadales" "Moraxellaceae" "Acinetobacter" "baumannii" "" "species" "Bouvet et al., 1986" "aerobe" "LPSN" "772610" "515021" "3223274" "3223263" 1 "91288006,715174007,737526007,447891000124105,715353004"
"B_ACNTB_BMNN-C" "Acinetobacter baumannii complex" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Pseudomonadales" "Moraxellaceae" "Acinetobacter" "baumannii complex" "" "species group" "aerobe" "manually added" "515021" "3223263" 1 ""
"B_ACNTB_BYLY" "Acinetobacter baylyi" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Pseudomonadales" "Moraxellaceae" "Acinetobacter" "baylyi" "" "species" "Carr et al., 2003" "aerobe" "LPSN" "772611" "515021" "3223268" "3223263" 1 "423974000"
"B_ACNTB_BJRN" "Acinetobacter beijerinckii" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Pseudomonadales" "Moraxellaceae" "Acinetobacter" "beijerinckii" "" "species" "Nemec et al., 2009" "aerobe" "LPSN" "787668" "515021" "8317472" "3223263" 1 "771547006"
"B_ACNTB_BERZN" "Acinetobacter berezinae" "synonym" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Pseudomonadales" "Moraxellaceae" "Acinetobacter" "berezinae" "" "species" "Alexandr et al., 2009" "aerobe" "GBIF" "9467748" "3223263" "7798724" 1 ""
@@ -8476,6 +8477,7 @@
"B_BRKHL_CATD" "Burkholderia catudaia" "synonym" "Bacteria" "Pseudomonadota" "Betaproteobacteria" "Burkholderiales" "Burkholderiaceae" "Burkholderia" "catudaia" "" "species" "Peeters et al., 2016" "aerobe" "LPSN" "794437" "515281" "796021" "9576146" "3219916" 1.5 ""
"B_BRKHL_CNCP" "Burkholderia cenocepacia" "accepted" "Bacteria" "Pseudomonadota" "Betaproteobacteria" "Burkholderiales" "Burkholderiaceae" "Burkholderia" "cenocepacia" "" "species" "Vandamme et al., 2003" "aerobe" "LPSN" "774276" "515281" "10898083" "3219916" 1 "416941006"
"B_BRKHL_CEPC" "Burkholderia cepacia" "accepted" "Bacteria" "Pseudomonadota" "Betaproteobacteria" "Burkholderiales" "Burkholderiaceae" "Burkholderia" "cepacia" "" "species" "Yabuuchi et al., 1993" "aerobe" "LPSN" "774277" "515281" "10900882" "3219916" 1 "113669008"
"B_BRKHL_CEPC-C" "Burkholderia cepacia complex" "accepted" "Bacteria" "Pseudomonadota" "Betaproteobacteria" "Burkholderiales" "Burkholderiaceae" "Burkholderia" "cepacia complex" "" "species group" "aerobe" "manually added" "515281" "3219916" 1 ""
"B_BRKHL_CHOC" "Burkholderia choica" "synonym" "Bacteria" "Pseudomonadota" "Betaproteobacteria" "Burkholderiales" "Burkholderiaceae" "Burkholderia" "choica" "" "species" "Vandamme et al., 2013" "aerobe" "LPSN" "791329" "515281" "794372" 1.5 ""
"B_BRKHL_CCVN" "Burkholderia cocovenenans" "synonym" "Bacteria" "Pseudomonadota" "Betaproteobacteria" "Burkholderiales" "Burkholderiaceae" "Burkholderia" "cocovenenans" "" "species" "Gillis et al., 1995" "aerobe" "LPSN" "7156" "515281" "774282" "3219917" "3219916" "3219971" 1 ""
"B_BRKHL_CNCT" "Burkholderia concitans" "synonym" "Bacteria" "Pseudomonadota" "Betaproteobacteria" "Burkholderiales" "Burkholderiaceae" "Burkholderia" "concitans" "" "species" "Peeters et al., 2016" "aerobe" "LPSN" "794438" "515281" "796022" "9429304" "3219916" 1.5 ""
@@ -9058,6 +9060,7 @@
"F_CANDD_ALBC" "Candida albicans" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "albicans" "" "species" "Berkhout" "GBIF" "2599597" "2518121" 1.5 "53326005,243448008,714313009"
"F_CANDD_ALBC_ALBC" "Candida albicans albicans" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "albicans" "albicans" "subspecies" "GBIF" "11228679" "2599597" "2599597" 1.5 ""
"F_CANDD_ALBC_CLSS" "Candida albicans claussenii" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "albicans" "claussenii" "subspecies" "Lodder et al." "GBIF" "11281986" "2599597" "2599597" 1.5 ""
"F_CANDD_ALBC-C" "Candida albicans complex" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "albicans complex" "" "species group" "manually added" "2518121" 1.5 ""
"F_CANDD_ALBC_MTLN" "Candida albicans metalondinensis" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "albicans" "metalondinensis" "subspecies" "Castell et al." "GBIF" "11270252" "2599597" "2599597" 1.5 ""
"F_CANDD_ALBC_NNLQ" "Candida albicans non-liquefaciens" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "albicans" "non-liquefaciens" "subspecies" "Sasak" "GBIF" "11323433" "2599597" "2599597" 1.5 ""
"F_CANDD_ALBC_STLL" "Candida albicans stellatoidea" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "albicans" "stellatoidea" "subspecies" "Jones et al." "GBIF" "11340449" "2599597" "2599597" 1.5 ""
@@ -9216,6 +9219,7 @@
"F_CANDD_FNSS" "Candida gifuensis" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "gifuensis" "" "species" "Tanig" "GBIF" "5495793" "2518121" "2599597" 1.5 ""
"F_CANDD_GGNT" "Candida gigantensis" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "gigantensis" "" "species" "Suh et al." "GBIF" "3480169" "2518121" 1.5 ""
"F_CANDD_GLBR" "Candida glabrata" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "glabrata" "" "species" "Mey et al." "GBIF" "2599530" "2518121" 1.5 "444877006,782948004"
"F_CANDD_GLBR-C" "Candida glabrata complex" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "glabrata complex" "" "species group" "manually added" "2518121" 1.5 ""
"F_CANDD_BRTS" "Candida glabratus" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "glabratus" "" "species" "Anderson" "GBIF" "7407691" "2518121" "2599530" 1.5 ""
"F_CANDD_GLBS" "Candida glaebosa" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "glaebosa" "" "species" "Komag et al." "GBIF" "3480310" "2518121" 1.5 ""
"F_CANDD_LBSM" "Candida globosum" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "globosum" "" "species" "Zach" "GBIF" "3480508" "2518121" "2599539" 1.5 ""
@@ -9390,6 +9394,7 @@
"F_CANDD_PRPL" "Candida parapolymorpha" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "parapolymorpha" "" "species" "Suh et al." "GBIF" "8157046" "2518121" 1.5 ""
"F_CANDD_PRPSL" "Candida parapsilopsis" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "parapsilopsis" "" "species" "Ashford" "GBIF" "2599545" "2518121" "2599539" 1.5 ""
"F_CANDD_PRPS" "Candida parapsilosis" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "parapsilosis" "" "species" "Langeron et al." "GBIF" "2599539" "2518121" 1.5 "61302002"
"F_CANDD_PRPS-C" "Candida parapsilosis complex" "accepted" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "parapsilosis complex" "" "species group" "manually added" "2518121" 1.5 ""
"F_CANDD_PRPS_HOKK" "Candida parapsilosis hokkaii" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "parapsilosis" "hokkaii" "subspecies" "Goto et al." "GBIF" "3480002" "2599539" "3479947" 1.5 ""
"F_CANDD_PRPS_INTR" "Candida parapsilosis intermedia" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "parapsilosis" "intermedia" "subspecies" "Kreger-van Rij et al." "GBIF" "3480258" "2599539" "3479947" 1.5 ""
"F_CANDD_PRPS_PRPS" "Candida parapsilosis parapsilosis" "synonym" "Fungi" "Ascomycota" "Saccharomycetes" "Saccharomycetales" "Debaryomycetaceae" "Candida" "parapsilosis" "parapsilosis" "subspecies" "GBIF" "11331780" "2599539" "2599539" 1.5 ""
@@ -11190,6 +11195,7 @@
"B_CTRBC" "Citrobacter" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Citrobacter" "" "" "genus" "Werkman et al., 1932" "facultative anaerobe" "LPSN" "515376" "482" "11150190" "11158430" 1 "1089501000112101,75972000"
"B_CTRBC_AMLNT" "Citrobacter amalonatica" "synonym" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Citrobacter" "amalonatica" "" "species" "Young et al., 1971" "aerobe" "GBIF" "5427576" "11150190" "3221795" 1 ""
"B_CTRBC_AMLN" "Citrobacter amalonaticus" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Citrobacter" "amalonaticus" "" "species" "Brenner et al., 1982" "aerobe" "LPSN" "774743" "515376" "3221795" "11150190" 1 "1089101000112105,55744003"
"B_CTRBC_AMLN-C" "Citrobacter amalonaticus complex" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Citrobacter" "amalonaticus complex" "" "species group" "aerobe" "manually added" "515376" "11150190" 1 ""
"B_CTRBC_BTTR" "Citrobacter bitternis" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Citrobacter" "bitternis" "" "species" "Ko et al., 2015" "likely facultative anaerobe" "GBIF" "11113073" "11150190" 1.5 ""
"B_CTRBC_BRAK" "Citrobacter braakii" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Citrobacter" "braakii" "" "species" "Brenner et al., 1993" "aerobe" "LPSN" "774744" "515376" "3221791" "11150190" 1 "1089301000112107,114262000"
"B_CTRBC_CRON" "Citrobacter cronae" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Citrobacter" "cronae" "" "species" "Oberhettinger et al., 2020" "likely facultative anaerobe" "LPSN" "7473" "515376" "11248376" "11150190" "3221797" 1 ""
@@ -16134,6 +16140,7 @@
"B_ENTRBC_CHND" "Enterobacter chuandaensis" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Enterobacter" "chuandaensis" "" "species" "Wu et al., 2019" "facultative anaerobe" "LPSN" "800158" "515587" "10745032" "11204930" 1.5 ""
"B_ENTRBC_CLOC" "Enterobacter cloacae" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Enterobacter" "cloacae" "" "species" "Hormaeche et al., 1960" "aerobe" "LPSN" "775933" "515587" "11234351" "11204930" 1 "714007005,737529000,14385002,721910001,714317005"
"B_ENTRBC_CLOC_CLOC" "Enterobacter cloacae cloacae" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Enterobacter" "cloacae" "cloacae" "subspecies" "Hoffmann et al., 2005" "LPSN" "775934" "775933" "7234533" "11234351" 1 ""
"B_ENTRBC_CLOC-C" "Enterobacter cloacae complex" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Enterobacter" "cloacae complex" "" "species group" "aerobe" "manually added" "515587" "11204930" 1 ""
"B_ENTRBC_CLOC_DSSL" "Enterobacter cloacae dissolvens" "accepted" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Enterobacter" "cloacae" "dissolvens" "subspecies" "Hoffmann et al., 2005" "aerobe" "LPSN" "775935" "775933" "3221982" "11234351" 1 ""
"B_ENTRBC_COWN" "Enterobacter cowanii" "synonym" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Enterobacter" "cowanii" "" "species" "Inoue et al., 2001" "aerobe" "LPSN" "775936" "515587" "791223" 1 ""
"B_ENTRBC_DSSL" "Enterobacter dissolvens" "synonym" "Bacteria" "Pseudomonadota" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Enterobacter" "dissolvens" "" "species" "Brenner et al., 1988" "aerobe" "LPSN" "775937" "515587" "775935" "3221983" "11204930" "3221982" 1.5 ""

BIN
data-raw/microorganisms.xlsx
View File

Binary file not shown.

BIN
data-raw/microorganisms.xpt
View File

Binary file not shown.

BIN
data-raw/organism_groups.rds Normal file
View File

Binary file not shown.

6
data-raw/poorman_prepend.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/read_EUCAST.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/reproduction_of_antibiotics.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/reproduction_of_antivirals.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

307
data-raw/reproduction_of_clinical_breakpoints.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -27,7 +27,7 @@
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
# This script runs in under a minute and renews all guidelines of CLSI and EUCAST!
# This script runs in 20-30 minutes and renews all guidelines of CLSI and EUCAST!
# Run it with source("data-raw/reproduction_of_clinical_breakpoints.R")
library(dplyr)
@@ -39,40 +39,55 @@ devtools::load_all()
# and copy the folder C:\WHONET\Resources to the data-raw/WHONET/ folder
# (for ASIARS-Net update, also copy C:\WHONET\Codes to the data-raw/WHONET/ folder)
# MICROORGANISMS WHONET CODES ----
# READ DATA ----
whonet_organisms <- read_tsv("data-raw/WHONET/Resources/Organisms.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>%
# remove old taxonomic names
filter(TAXONOMIC_STATUS == "C") %>%
transmute(ORGANISM_CODE = tolower(WHONET_ORG_CODE), ORGANISM) %>%
mutate(
# what's wrong here? all these are only in the table on subspecies level (where species == subspecies), not on species level
ORGANISM = if_else(ORGANISM_CODE == "sau", "Staphylococcus aureus", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "pam", "Pasteurella multocida", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "kpn", "Klebsiella pneumoniae", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "caj", "Campylobacter jejuni", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "mmo", "Morganella morganii", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "sap", "Staphylococcus saprophyticus", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "fne", "Fusobacterium necrophorum", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "fnu", "Fusobacterium nucleatum", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "sdy", "Streptococcus dysgalactiae", ORGANISM),
ORGANISM = if_else(ORGANISM_CODE == "axy", "Achromobacter xylosoxidans", ORGANISM),
# and this one was called Issatchenkia orientalis, but it should be:
ORGANISM = if_else(ORGANISM_CODE == "ckr", "Candida krusei", ORGANISM)
)
mutate(ORGANISM_CODE = toupper(WHONET_ORG_CODE))
whonet_breakpoints <- read_tsv("data-raw/WHONET/Resources/Breakpoints.txt", na = c("", "NA", "-"),
show_col_types = FALSE, guess_max = Inf) %>%
filter(GUIDELINES %in% c("CLSI", "EUCAST"))
whonet_antibiotics <- read_tsv("data-raw/WHONET/Resources/Antibiotics.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>%
arrange(WHONET_ABX_CODE) %>%
distinct(WHONET_ABX_CODE, .keep_all = TRUE)
# MICROORGANISMS WHONET CODES ----
# add some general codes
whonet_organisms <- whonet_organisms %>%
bind_rows(data.frame(
ORGANISM_CODE = c("ebc", "cof"),
ORGANISM = c("Enterobacterales", "Campylobacter")
))
select(ORGANISM_CODE, ORGANISM, SPECIES_GROUP, GBIF_TAXON_ID) %>%
mutate(
# this one was called Issatchenkia orientalis, but it should be:
ORGANISM = if_else(ORGANISM_CODE == "ckr", "Candida krusei", ORGANISM)
) %>%
# try to match on GBIF identifier
left_join(microorganisms %>% distinct(mo, gbif, status) %>% filter(!is.na(gbif)), by = c("GBIF_TAXON_ID" = "gbif")) %>%
# remove duplicates
arrange(ORGANISM_CODE, GBIF_TAXON_ID, status) %>%
distinct(ORGANISM_CODE, .keep_all = TRUE) %>%
# add Enterobacterales, which is a subkingdom code in their data
bind_rows(data.frame(ORGANISM_CODE = "ebc", ORGANISM = "Enterobacterales", mo = as.mo("Enterobacterales"))) %>%
arrange(ORGANISM)
# check non-existing species groups in the microorganisms table
complexes <- whonet_organisms %>%
filter(ORGANISM %like% " (group|complex)$" & toupper(SPECIES_GROUP) == toupper(ORGANISM_CODE)) %>%
mutate(mo = as.mo(ORGANISM, minimum_matching_score = 0.6, keep_synonyms = TRUE)) %>%
mutate(mo_new = paste0(mo, "-C"))
complexes[which(!complexes$mo_new %in% AMR::microorganisms$mo), ]
## Add new WHO codes to microorganisms.codes ----
matched <- whonet_organisms %>% filter(!is.na(mo))
unmatched <- whonet_organisms %>% filter(is.na(mo))
whonet_organisms.bak <- whonet_organisms
# generate the mo codes and add their names
whonet_organisms <- whonet_organisms.bak %>%
mutate(mo = as.mo(gsub("(sero[a-z]*| complex| nontypable| non[-][a-zA-Z]+|var[.]| not .*|sp[.],.*|, .*variant.*|, .*toxin.*|, microaer.*| beta-haem[.])", "", ORGANISM),
message("Getting MO codes for WHONET input...")
unmatched <- unmatched %>%
mutate(mo = as.mo(gsub("(sero[a-z]*| complex| group| nontypable| non[-][a-zA-Z]+|var[.]| not .*|sp[.],.*|, .*variant.*|, .*toxin.*|, microaer.*| beta-haem[.])", "", ORGANISM),
minimum_matching_score = 0.6,
keep_synonyms = TRUE,
language = "en"),
@@ -84,19 +99,48 @@ whonet_organisms <- whonet_organisms.bak %>%
keep_synonyms = TRUE,
language = "en"))
# check if coercion at least resembles the first part (genus)
new_mo_codes <- whonet_organisms %>%
unmatched <- unmatched %>%
mutate(
first_part = sapply(ORGANISM, function(x) strsplit(gsub("[^a-zA-Z _-]+", "", x), " ")[[1]][1], USE.NAMES = FALSE),
keep = mo_name %like_case% first_part | ORGANISM %like% "Gram " | ORGANISM == "Other" | ORGANISM %like% "anaerobic") %>%
filter(keep == TRUE) %>%
transmute(code = toupper(ORGANISM_CODE),
mo = mo)
filter(keep == TRUE)
organisms <- matched %>% transmute(code = toupper(ORGANISM_CODE), group = SPECIES_GROUP, mo) %>%
bind_rows(unmatched %>% transmute(code = toupper(ORGANISM_CODE), group = SPECIES_GROUP, mo)) %>%
mutate(name = mo_name(mo, keep_synonyms = TRUE)) %>%
# remove the species groups themselves, we'll look the species within these groups later
filter(is.na(group) | code != group) %>%
arrange(code)
# some subspecies exist, while their upper species do not, add them as the species level:
subspp <- organisms %>%
filter(mo_species(mo, keep_synonyms = TRUE) == mo_subspecies(mo, keep_synonyms = TRUE) &
mo_species(mo, keep_synonyms = TRUE) != "" &
mo_genus(mo, keep_synonyms = TRUE) != "Salmonella") %>%
mutate(mo = as.mo(paste(mo_genus(mo, keep_synonyms = TRUE),
mo_species(mo, keep_synonyms = TRUE)),
keep_synonyms = TRUE),
name = mo_name(mo, keep_synonyms = TRUE))
organisms <- organisms %>%
filter(!code %in% subspp$code) %>%
bind_rows(subspp) %>%
arrange(code)
organism_groups <- organisms %>%
filter(!is.na(group)) %>%
arrange(group, name)
saveRDS(organism_groups, "data-raw/organism_groups.rds", version = 2)
#---
# AT THIS POINT, `organisms` is clean and all entries have an mo code
#---
# update microorganisms.codes with the latest WHONET codes
microorganisms.codes2 <- microorganisms.codes %>%
# remove all old WHONET codes, whether we (in the end) keep them or not
filter(!toupper(code) %in% toupper(new_mo_codes$code)) %>%
filter(!toupper(code) %in% toupper(organisms$code) | toupper(code) %in% complexes$SPECIES_GROUP) %>%
# and add the new ones
bind_rows(new_mo_codes) %>%
bind_rows(organisms %>% filter(code != group) %>% select(code, mo)) %>%
arrange(code)
# new codes:
microorganisms.codes2$code[which(!microorganisms.codes2$code %in% microorganisms.codes$code)]
@@ -104,31 +148,31 @@ mo_name(microorganisms.codes2$mo[which(!microorganisms.codes2$code %in% microorg
microorganisms.codes <- microorganisms.codes2
# Run this part to update ASIARS-Net:
# start
asiarsnet <- read_tsv("data-raw/WHONET/Codes/ASIARS_Net_Organisms_ForwardLookup.txt")
asiarsnet <- asiarsnet %>%
mutate(WHONET_Code = toupper(WHONET_Code)) %>%
left_join(whonet_organisms %>% mutate(WHONET_Code = toupper(ORGANISM_CODE))) %>%
mutate(
mo1 = as.mo(ORGANISM_CODE),
mo2 = as.mo(ORGANISM)
) %>%
mutate(mo = if_else(mo2 == "UNKNOWN" | is.na(mo2), mo1, mo2)) %>%
filter(!is.na(mo))
insert1 <- asiarsnet %>% transmute(code = WHONET_Code, mo)
insert2 <- asiarsnet %>% transmute(code = as.character(ASIARS_Net_Code), mo)
# these will be updated
bind_rows(insert1, insert2) %>%
rename(mo_new = mo) %>%
left_join(microorganisms.codes) %>%
filter(mo != mo_new)
microorganisms.codes <- microorganisms.codes %>%
filter(!code %in% c(insert1$code, insert2$code)) %>%
bind_rows(insert1, insert2) %>%
arrange(code)
# end
# # start
# asiarsnet <- read_tsv("data-raw/WHONET/Codes/ASIARS_Net_Organisms_ForwardLookup.txt")
# asiarsnet <- asiarsnet %>%
# mutate(WHONET_Code = toupper(WHONET_Code)) %>%
# left_join(whonet_organisms %>% mutate(WHONET_Code = toupper(ORGANISM_CODE))) %>%
# mutate(
# mo1 = as.mo(ORGANISM_CODE),
# mo2 = as.mo(ORGANISM)
# ) %>%
# mutate(mo = if_else(mo2 == "UNKNOWN" | is.na(mo2), mo1, mo2)) %>%
# filter(!is.na(mo))
# insert1 <- asiarsnet %>% transmute(code = WHONET_Code, mo)
# insert2 <- asiarsnet %>% transmute(code = as.character(ASIARS_Net_Code), mo)
# # these will be updated
# bind_rows(insert1, insert2) %>%
# rename(mo_new = mo) %>%
# left_join(microorganisms.codes) %>%
# filter(mo != mo_new)
# microorganisms.codes <- microorganisms.codes %>%
# filter(!code %in% c(insert1$code, insert2$code)) %>%
# bind_rows(insert1, insert2) %>%
# arrange(code)
# # end
# save to package
## Save to package ----
usethis::use_data(microorganisms.codes, overwrite = TRUE, compress = "xz", version = 2)
rm(microorganisms.codes)
devtools::load_all()
@@ -137,11 +181,11 @@ devtools::load_all()
# BREAKPOINTS ----
# now that we have the right MO codes, get the breakpoints and convert them
whonet_breakpoints <- read_tsv("data-raw/WHONET/Resources/Breakpoints.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>%
filter(BREAKPOINT_TYPE == "Human", GUIDELINES %in% c("CLSI", "EUCAST"))
whonet_antibiotics <- read_tsv("data-raw/WHONET/Resources/Antibiotics.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>%
arrange(WHONET_ABX_CODE) %>%
distinct(WHONET_ABX_CODE, .keep_all = TRUE)
whonet_breakpoints %>%
count(GUIDELINES, BREAKPOINT_TYPE) %>%
pivot_wider(names_from = BREAKPOINT_TYPE, values_from = n) %>%
janitor::adorn_totals(where = c("row", "col"))
breakpoints <- whonet_breakpoints %>%
mutate(code = toupper(ORGANISM_CODE)) %>%
@@ -151,13 +195,14 @@ breakpoints <- whonet_breakpoints %>%
mo = rep(as.mo("UNKNOWN"), 2))))
# these ones lack an MO name, they cannot be used:
unknown <- breakpoints %>%
filter(is.na(mo)) %>%
filter(is.na(mo) & !ORGANISM_CODE %in% organism_groups$group) %>%
pull(code) %>%
unique()
breakpoints %>%
filter(code %in% unknown)
filter(code %in% unknown) %>%
count(GUIDELINES, YEAR, ORGANISM_CODE, BREAKPOINT_TYPE, sort = TRUE)
breakpoints <- breakpoints %>%
filter(!is.na(mo))
filter(!is.na(mo) | ORGANISM_CODE %in% organism_groups$group)
# and these ones have unknown antibiotics according to WHONET itself:
breakpoints %>%
@@ -168,21 +213,48 @@ breakpoints %>%
filter(!WHONET_ABX_CODE %in% whonet_antibiotics$WHONET_ABX_CODE) %>%
pull(WHONET_ABX_CODE) %>%
unique()
# we cannot use them
# breakpoints <- breakpoints %>%
# filter(WHONET_ABX_CODE %in% whonet_antibiotics$WHONET_ABX_CODE)
# now check with our own antibiotics
breakpoints %>%
filter(!toupper(WHONET_ABX_CODE) %in% antibiotics$ab) %>%
pull(WHONET_ABX_CODE) %>%
unique()
# they are at the moment all old codes that have right replacements in `antibiotics`, so we can use as.ab()
## Extend species groups ----
message("Extending breakpoints table on species groups...")
# get the species groups, they must be converted to rules per-species and added
breakpoints.bak <- breakpoints
spp_groups <- breakpoints %>% filter(ORGANISM_CODE_TYPE == "SPECIES_GROUP")
p <- progress_ticker(nrow(spp_groups))
for (i in seq_len(nrow(spp_groups))) {
p$tick()
mos <- organism_groups %>% filter(group == spp_groups[i, "ORGANISM_CODE", drop = TRUE]) %>% pull(mo)
for (m in mos) {
breakpoints <- breakpoints %>%
bind_rows(spp_groups %>%
slice(i) %>%
mutate(mo = m))
}
}
close(p)
# extend all group A and B streptococci
breakpoints_new <- breakpoints_new %>%
bind_rows(breakpoints_new %>%
filter(mo == as.mo("Streptococcus Group A")) %>%
mutate(mo = as.mo("Streptococcus pyogenes"))) %>%
bind_rows(breakpoints_new %>%
filter(mo == as.mo("Streptococcus Group B")) %>%
mutate(mo = as.mo("Streptococcus agalactiae")))
# remove duplicates again for CoNS/CoPS/GBS and arrange
breakpoints_new <- breakpoints_new %>%
mutate(mo = as.mo(mo, keep_synonyms = TRUE)) %>%
distinct(guideline, ab, mo, method, site, .keep_all = TRUE) %>%
arrange(desc(guideline), ab, mo, method)
## Build new breakpoints table ----
breakpoints_new <- breakpoints %>%
# only last available 10 years
# filter(YEAR > max(YEAR) - 10) %>%
filter(!is.na(WHONET_ABX_CODE)) %>%
transmute(
guideline = paste(GUIDELINES, YEAR),
type = ifelse(BREAKPOINT_TYPE == "ECOFF", "ECOFF", tolower(BREAKPOINT_TYPE)),
method = TEST_METHOD,
site = SITE_OF_INFECTION,
mo,
@@ -196,10 +268,10 @@ breakpoints_new <- breakpoints %>%
TRUE ~ 6
),
ab = as.ab(WHONET_ABX_CODE),
ref_tbl = REFERENCE_TABLE,
ref_tbl = ifelse(type == "ECOFF" & is.na(REFERENCE_TABLE), "ECOFF", REFERENCE_TABLE),
disk_dose = POTENCY,
breakpoint_S = S,
breakpoint_R = R,
breakpoint_S = ifelse(type == "ECOFF" & is.na(S) & !is.na(ECV_ECOFF), ECV_ECOFF, S),
breakpoint_R = ifelse(type == "ECOFF" & is.na(R) & !is.na(ECV_ECOFF), ECV_ECOFF, R),
uti = ifelse(is.na(site), FALSE, gsub(".*(UTI|urinary|urine).*", "UTI", site) == "UTI")
) %>%
# Greek symbols and EM dash symbols are not allowed by CRAN, so replace them with ASCII:
@@ -213,7 +285,7 @@ breakpoints_new <- breakpoints %>%
# check the strange duplicates
breakpoints_new %>%
mutate(id = paste(guideline, ab, mo, method, site)) %>%
mutate(id = paste(guideline, type, ab, mo, method, site)) %>%
filter(id %in% .$id[which(duplicated(id))])
# remove duplicates
breakpoints_new <- breakpoints_new %>%
@@ -272,74 +344,7 @@ clinical_breakpoints %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "
dim(breakpoints_new)
dim(clinical_breakpoints)
# ECOFFs ----
# ECOFF = Epidemiological Cut-Off
whonet_ecoff <- read_tsv("data-raw/WHONET/Resources/Breakpoints.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>%
filter(BREAKPOINT_TYPE == "ECOFF", GUIDELINES %in% c("CLSI", "EUCAST"))
ecoff <- whonet_ecoff %>%
filter(!ORGANISM_CODE %in% c("clu", "BFX", "PFX", "kma", "cdh")) %>%
transmute(guideline = paste(GUIDELINES, YEAR),
mo = as.mo(ORGANISM_CODE, keep_synonyms = TRUE),
ab = as.ab(WHONET_ABX_CODE),
method = TEST_METHOD,
ecoff = as.double(ECV_ECOFF)) %>%
filter(!is.na(ecoff)) %>%
distinct()
# join to breakpoints
breakpoints_new <- breakpoints_new %>%
bind_rows(breakpoints_new %>%
right_join(ecoff, by = c("guideline", "mo", "ab", "method"))) %>%
mutate(ref_tbl = ifelse(is.na(ref_tbl), "ECOFF", ref_tbl)) %>%
distinct(guideline, ab, mo, method, site, .keep_all = TRUE) %>%
arrange(desc(guideline), ab, mo, method) %>%
mutate(rank_index = case_when(
is.na(mo_rank(mo, keep_synonyms = TRUE)) ~ 6, # for UNKNOWN, B_GRAMN, B_ANAER, B_ANAER-NEG, etc.
mo_rank(mo, keep_synonyms = TRUE) %like% "(infra|sub)" ~ 1,
mo_rank(mo, keep_synonyms = TRUE) == "species" ~ 2,
mo_rank(mo, keep_synonyms = TRUE) == "genus" ~ 3,
mo_rank(mo, keep_synonyms = TRUE) == "family" ~ 4,
mo_rank(mo, keep_synonyms = TRUE) == "order" ~ 5,
TRUE ~ 6
)) %>%
mutate(uti = ifelse(is.na(uti), FALSE, uti)) %>%
relocate(ecoff, .after = breakpoint_R)
breakpoints_new.bak <- mutate(uti = ifelse(is.na(uti), FALSE, uti), .after = ecoff)
# EXTEND CoNS/CoPS/GAS/GBS ----
# extend all coagulase-postive/-negative staphylococci
CoNS <- breakpoints_new %>% filter(mo == as.mo("CoNS"))
for (m in MO_CONS[mo_subspecies(MO_CONS, keep_synonyms = TRUE) == ""]) {
breakpoints_new <- breakpoints_new %>%
bind_rows(CoNS %>%
mutate(mo = m))
}
CoPS <- breakpoints_new %>% filter(mo == as.mo("CoPS"))
for (m in MO_COPS[mo_subspecies(MO_COPS, keep_synonyms = TRUE) == ""]) {
breakpoints_new <- breakpoints_new %>%
bind_rows(CoPS %>%
mutate(mo = m))
}
# do the same for group A and B streptococci
breakpoints_new <- breakpoints_new %>%
bind_rows(breakpoints_new %>%
filter(mo == as.mo("Streptococcus Group A")) %>%
mutate(mo = as.mo("Streptococcus pyogenes"))) %>%
bind_rows(breakpoints_new %>%
filter(mo == as.mo("Streptococcus Group B")) %>%
mutate(mo = as.mo("Streptococcus agalactiae")))
# remove duplicates again for CoNS/CoPS/GBS and arrange
breakpoints_new <- breakpoints_new %>%
mutate(mo = as.mo(mo, keep_synonyms = TRUE)) %>%
distinct(guideline, ab, mo, method, site, .keep_all = TRUE) %>%
arrange(desc(guideline), ab, mo, method)
# Save to package ----
# SAVE TO PACKAGE ----
clinical_breakpoints <- breakpoints_new
usethis::use_data(clinical_breakpoints, overwrite = TRUE, compress = "xz", version = 2)

6
data-raw/reproduction_of_dosage.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/reproduction_of_example_isolates_unclean.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/reproduction_of_intrinsic_resistant.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/reproduction_of_microorganisms.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
data-raw/salmonellae.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #