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(v0.7.1.9075) new microorganism codes

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dr. M.S. (Matthijs) Berends
2019-09-18 15:46:09 +02:00
parent f553a08a7b
commit e2aa4f996b
53 changed files with 636 additions and 475 deletions
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data-raw/internals.R
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@ -33,8 +33,12 @@ translations_file <- utils::read.delim(file = "data-raw/translations.tsv",
allowEscapes = TRUE, # else "\\1" will be imported as "\\\\1"
quote = "")
# Old microorganism codes -------------------------------------------------
microorganisms.translation <- readRDS("data-raw/microorganisms.translation.rds")
# Export to package as internal data ----
usethis::use_data(eucast_rules_file, translations_file,
usethis::use_data(eucast_rules_file, translations_file, microorganisms.translation,
internal = TRUE,
overwrite = TRUE,
version = 2)
@ -42,6 +46,7 @@ usethis::use_data(eucast_rules_file, translations_file,
# Remove from global environment ----
rm(eucast_rules_file)
rm(translations_file)
rm(microorganisms.translation)
# Clean mo history ----
mo_history_file <- file.path(file.path(system.file(package = "AMR"), "mo_history"), "mo_history.csv")

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259
data-raw/reproduction_of_microorganisms.R
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@ -102,22 +102,38 @@ MOs <- data_total %>%
& !order %in% c("Eurotiales", "Microascales", "Mucorales", "Saccharomycetales", "Schizosaccharomycetales", "Tremellales", "Onygenales", "Pneumocystales"))
)
# or the genus has to be one of the genera we found in our hospitals last decades (Northern Netherlands, 2002-2018)
| genus %in% c("Absidia", "Acremonium", "Actinotignum", "Alternaria", "Anaerosalibacter", "Ancylostoma", "Anisakis", "Apophysomyces",
"Arachnia", "Ascaris", "Aureobacterium", "Aureobasidium", "Balantidum", "Bilophilia", "Branhamella", "Brochontrix",
"Brugia", "Calymmatobacterium", "Catabacter", "Cdc", "Chilomastix", "Chryseomonas", "Cladophialophora", "Cladosporium",
"Clonorchis", "Cordylobia", "Curvularia", "Demodex", "Dermatobia", "Diphyllobothrium", "Dracunculus", "Echinococcus",
"Enterobius", "Euascomycetes", "Exophiala", "Fasciola", "Fusarium", "Hendersonula", "Hymenolepis", "Hypomyces", "Kloeckera",
"Koserella", "Larva", "Leishmania", "Lelliottia", "Loa", "Lumbricus", "Malassezia", "Metagonimus", "Molonomonas",
"Mucor", "Nattrassia", "Necator", "Nectria", "Novospingobium", "Onchocerca", "Opistorchis", "Paragonimus", "Paramyxovirus",
"Pediculus", "Phoma", "Phthirus", "Pityrosporum", "Pseudallescheria", "Pulex", "Rhizomucor", "Rhizopus", "Rhodotorula",
"Salinococcus", "Sanguibacteroides", "Schistosoma", "Scopulariopsis", "Scytalidium", "Sporobolomyces", "Stomatococcus",
"Strongyloides", "Syncephalastraceae", "Taenia", "Torulopsis", "Trichinella", "Trichobilharzia", "Trichoderma", "Trichomonas",
"Trichosporon", "Trichuris", "Trypanosoma", "Wuchereria")
| genus %in% c("Absidia", "Acremonium", "Actinotignum", "Aedes", "Alternaria", "Anaerosalibacter", "Ancylostoma", "Angiostrongylus",
"Anisakis", "Anopheles", "Apophysomyces", "Arachnia", "Ascaris", "Aureobacterium", "Aureobasidium", "Balantidum", "Basidiobolus",
"Beauveria", "Bilophilia", "Branhamella", "Brochontrix", "Brugia", "Calymmatobacterium", "Capillaria", "Catabacter", "Cdc", "Chaetomium",
"Chilomastix", "Chryseomonas", "Chrysonilia", "Cladophialophora", "Cladosporium", "Clonorchis", "Conidiobolus", "Contracaecum",
"Cordylobia", "Curvularia", "Demodex", "Dermatobia", "Dicrocoelium", "Dioctophyma", "Diphyllobothrium", "Dipylidium", "Dirofilaria",
"Dracunculus", "Echinococcus", "Echinostoma", "Enterobius", "Enteromonas", "Euascomycetes", "Exophiala", "Exserohilum", "Fasciola",
"Fasciolopsis", "Fonsecaea", "Fusarium", "Gnathostoma", "Hendersonula", "Heterophyes", "Hymenolepis", "Hypomyces", "Hysterothylacium",
"Kloeckera", "Koserella", "Larva", "Lecythophora", "Leishmania", "Lelliottia", "Leptomyxida", "Leptosphaeria", "Loa", "Lucilia",
"Lumbricus", "Malassezia", "Malbranchea", "Mansonella", "Mesocestoides", "Metagonimus", "Metarrhizium", "Molonomonas", "Mortierella",
"Mucor", "Multiceps", "Mycocentrospora", "Nanophetus", "Nattrassia", "Necator", "Nectria", "Novospingobium", "Ochroconis",
"Oesophagostomum", "Oidiodendron", "Onchocerca", "Opisthorchis", "Opistorchis", "Paragonimus", "Paramyxovirus", "Pediculus",
"Phlebotomus", "Phocanema", "Phoma", "Phthirus", "Piedraia", "Pithomyces", "Pityrosporum", "Pseudallescheria", "Pseudoterranova",
"Pulex", "Retortamonas", "Rhizomucor", "Rhizopus", "Rhodotorula", "Salinococcus", "Sanguibacteroides", "Sarcophagidae", "Sarcoptes",
"Schistosoma", "Scolecobasidium", "Scopulariopsis", "Scytalidium", "Spirometra", "Sporobolomyces", "Stachybotrys", "Stenotrophomononas",
"Stomatococcus", "Strongyloides", "Syncephalastraceae", "Syngamus", "Taenia", "Ternidens", "Torulopsis", "Toxocara", "Trichinella",
"Trichobilharzia", "Trichoderma", "Trichomonas", "Trichosporon", "Trichostrongylus", "Trichuris", "Tritirachium", "Trombicula",
"Trypanosoma", "Tunga", "Wuchereria")
# or the taxonomic entry is old - the species was renamed
| !is.na(col_id_new)
) %>%
# really no Plantae (e.g. Dracunculus exist both as worm and as plant)
filter(kingdom != "Plantae")
filter(kingdom != "Plantae") %>%
filter(!rank %in% c("kingdom", "phylum", "class", "order", "family", "genus"))
# include all ranks other than species for the included species
MOs <- MOs %>% bind_rows(data_total %>%
filter((kingdom %in% MOs$kingdom & rank == "kingdom")
| (phylum %in% MOs$phylum & rank == "phylum")
| (class %in% MOs$class & rank == "class")
| (order %in% MOs$order & rank == "order")
| (family %in% MOs$family & rank == "family")
| (genus %in% MOs$genus & rank == "genus")))
# filter old taxonomic names so only the ones with an existing reference will be kept
MOs <- MOs %>%
@ -193,6 +209,11 @@ MOs.old <- MOs %>%
distinct(fullname, .keep_all = TRUE) %>%
arrange(col_id)
MO.bak <- MOs
MOold.bak <- MOs.old
MOs <- MO.bak
MOs.old <- MOold.bak
MOs <- MOs %>%
filter(is.na(col_id_new) | source == "DSMZ") %>%
transmute(col_id,
@ -215,20 +236,93 @@ MOs <- MOs %>%
species_id = gsub(".*/([a-f0-9]+)", "\\1", species_id),
source) %>%
#distinct(fullname, .keep_all = TRUE) %>%
filter(!grepl("unassigned", fullname, ignore.case = TRUE))
filter(!grepl("unassigned", fullname, ignore.case = TRUE)) %>%
# prefer DSMZ over CoL, since that's more recent
arrange(desc(source)) %>%
distinct(kingdom, fullname, .keep_all = TRUE)
# Filter out the DSMZ records that were renamed and are now in MOs.old
MOs <- MOs %>%
filter(!(source == "DSMZ" & fullname %in% MOs.old$fullname),
!(source == "DSMZ" & fullname %in% (MOs %>% filter(source == "CoL") %>% pull(fullname)))) %>%
distinct(fullname, .keep_all = TRUE)
# # Filter out the DSMZ records that were renamed and are now in MOs.old
# MOs <- MOs %>%
# filter(!(source == "DSMZ" & fullname %in% MOs.old$fullname)) %>%
# distinct(kingdom, fullname, .keep_all = TRUE) %>%
# filter(fullname != "")
# remove all genera that have no species - they are irrelevant for microbiology and almost all from the kingdom of Animalia
to_remove <- MOs %>%
filter(!kingdom %in% c("Bacteria", "Protozoa")) %>%
group_by(kingdom, genus) %>%
count() %>%
filter(n == 1) %>%
ungroup() %>%
mutate(kingdom_genus = paste(kingdom, genus)) %>%
pull(kingdom_genus)
MOs <- MOs %>% filter(!(paste(kingdom, genus) %in% to_remove))
rm(to_remove)
# add CoL ID from MOs.bak, for the cases where DSMZ took preference
MOs <- MOs %>%
mutate(kingdom_fullname = paste(kingdom, fullname)) %>%
select(-col_id) %>%
left_join(MO.bak %>%
filter(is.na(col_id_new), !is.na(col_id)) %>%
transmute(col_id, kingdom_fullname = trimws(paste(kingdom, genus, species, subspecies))),
by = "kingdom_fullname") %>%
select(col_id, everything(), -kingdom_fullname)
MOs.old <- MOs.old %>%
# remove the ones that are in the MOs data set
filter(col_id_new %in% MOs$col_id) %>%
# and remove the ones that have the exact same fullname in the MOs data set, like Moraxella catarrhalis
left_join(MOs, by = "fullname") %>%
filter(col_id_new != col_id.y | is.na(col_id.y)) %>%
select(col_id = col_id.x, col_id_new, fullname, ref = ref.x)
# remove the records that are in MOs.old
MOs <- MOs %>% filter(!fullname %in% MOs.old$fullname)
# what characters are in the fullnames?
table(sort(unlist(strsplit(x = paste(MOs$fullname, collapse = ""), split = ""))))
table(MOs$kingdom, MOs$rank)
table(AMR::microorganisms$kingdom, AMR::microorganisms$rank)
# set prevalence per species
MOs <- MOs %>%
mutate(prevalence = case_when(
class == "Gammaproteobacteria"
| genus %in% c("Enterococcus", "Staphylococcus", "Streptococcus")
~ 1,
phylum %in% c("Proteobacteria",
"Firmicutes",
"Actinobacteria",
"Sarcomastigophora")
| genus %in% c("Aspergillus",
"Bacteroides",
"Candida",
"Capnocytophaga",
"Chryseobacterium",
"Cryptococcus",
"Elisabethkingia",
"Flavobacterium",
"Fusobacterium",
"Giardia",
"Leptotrichia",
"Mycoplasma",
"Prevotella",
"Rhodotorula",
"Treponema",
"Trichophyton",
"Ureaplasma")
| rank %in% c("kingdom", "phylum", "class", "order", "family")
~ 2,
TRUE ~ 3
))
# Add abbreviations so we can easily know which ones are which ones.
# These will become valid and unique microbial IDs for the AMR package.
MOs <- MOs %>%
arrange(prevalence, fullname) %>%
group_by(kingdom) %>%
mutate(abbr_other = case_when(
rank == "family" ~ paste0("[FAM]_",
@ -270,14 +364,14 @@ MOs <- MOs %>%
# species abbreviations may be the same between genera
# because the genus abbreviation is part of the abbreviation
mutate(abbr_species = abbreviate(species,
minlength = 3,
use.classes = FALSE,
minlength = 4,
use.classes = TRUE,
method = "both.sides")) %>%
ungroup() %>%
group_by(genus, species) %>%
mutate(abbr_subspecies = abbreviate(subspecies,
minlength = 3,
use.classes = FALSE,
minlength = 4,
use.classes = TRUE,
method = "both.sides")) %>%
ungroup() %>%
# remove trailing underscores
@ -302,9 +396,6 @@ MOs <- MOs %>%
# put `mo` in front, followed by the rest
select(mo, everything(), -abbr_other, -abbr_genus, -abbr_species, -abbr_subspecies)
# remove empty fullnames
MOs <- MOs %>% filter(fullname != "")
# add non-taxonomic entries
MOs <- MOs %>%
bind_rows(
@ -324,6 +415,7 @@ MOs <- MOs %>%
ref = NA_character_,
species_id = "",
source = "manually added",
prevalence = 1,
stringsAsFactors = FALSE),
data.frame(mo = "B_GRAMN",
col_id = NA_integer_,
@ -340,6 +432,7 @@ MOs <- MOs %>%
ref = NA_character_,
species_id = "",
source = "manually added",
prevalence = 1,
stringsAsFactors = FALSE),
data.frame(mo = "B_GRAMP",
col_id = NA_integer_,
@ -356,6 +449,7 @@ MOs <- MOs %>%
ref = NA_character_,
species_id = "",
source = "manually added",
prevalence = 1,
stringsAsFactors = FALSE),
data.frame(mo = "F_YEAST",
col_id = NA_integer_,
@ -372,6 +466,7 @@ MOs <- MOs %>%
ref = NA_character_,
species_id = "",
source = "manually added",
prevalence = 2,
stringsAsFactors = FALSE),
data.frame(mo = "F_FUNGUS",
col_id = NA_integer_,
@ -388,11 +483,12 @@ MOs <- MOs %>%
ref = NA_character_,
species_id = "",
source = "manually added",
prevalence = 2,
stringsAsFactors = FALSE),
# CoNS
MOs %>%
filter(genus == "Staphylococcus", species == "epidermidis") %>% .[1,] %>%
mutate(mo = gsub("EPI", "CNS", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_CONS", mo),
col_id = NA_integer_,
species = "coagulase-negative",
fullname = "Coagulase-negative Staphylococcus (CoNS)",
@ -402,7 +498,7 @@ MOs <- MOs %>%
# CoPS
MOs %>%
filter(genus == "Staphylococcus", species == "epidermidis") %>% .[1,] %>%
mutate(mo = gsub("EPI", "CPS", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_COPS", mo),
col_id = NA_integer_,
species = "coagulase-positive",
fullname = "Coagulase-positive Staphylococcus (CoPS)",
@ -413,18 +509,20 @@ MOs <- MOs %>%
MOs %>%
filter(genus == "Streptococcus", species == "pyogenes") %>% .[1,] %>%
# we can keep all other details, since S. pyogenes is the only member of group A
mutate(mo = gsub("PYO", "GRA", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_GRPA", mo),
species = "group A" ,
fullname = "Streptococcus group A"),
fullname = "Streptococcus group A",
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
# we can keep all other details, since S. agalactiae is the only member of group B
mutate(mo = gsub("AGA", "GRB", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_GRPB", mo),
species = "group B" ,
fullname = "Streptococcus group B"),
fullname = "Streptococcus group B",
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "dysgalactiae") %>% .[1,] %>%
mutate(mo = gsub("DYS", "GRC", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_GRPC", mo),
col_id = NA_integer_,
species = "group C" ,
fullname = "Streptococcus group C",
@ -433,7 +531,7 @@ MOs <- MOs %>%
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = gsub("AGA", "GRD", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_GRPD", mo),
col_id = NA_integer_,
species = "group D" ,
fullname = "Streptococcus group D",
@ -442,7 +540,7 @@ MOs <- MOs %>%
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = gsub("AGA", "GRF", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_GRPF", mo),
col_id = NA_integer_,
species = "group F" ,
fullname = "Streptococcus group F",
@ -451,7 +549,7 @@ MOs <- MOs %>%
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = gsub("AGA", "GRG", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_GRPG", mo),
col_id = NA_integer_,
species = "group G" ,
fullname = "Streptococcus group G",
@ -460,7 +558,7 @@ MOs <- MOs %>%
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = gsub("AGA", "GRH", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_GRPH", mo),
col_id = NA_integer_,
species = "group H" ,
fullname = "Streptococcus group H",
@ -469,7 +567,7 @@ MOs <- MOs %>%
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = gsub("AGA", "GRK", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_GRPK", mo),
col_id = NA_integer_,
species = "group K" ,
fullname = "Streptococcus group K",
@ -479,7 +577,7 @@ MOs <- MOs %>%
# Beta haemolytic Streptococci
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = gsub("AGA", "HAE", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_HAEM", mo),
col_id = NA_integer_,
species = "beta-haemolytic" ,
fullname = "Beta-haemolytic Streptococcus",
@ -489,7 +587,7 @@ MOs <- MOs %>%
# Viridans Streptococci
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = gsub("AGA", "VIR", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_VIRI", mo),
col_id = NA_integer_,
species = "viridans" ,
fullname = "Viridans Group Streptococcus (VGS)",
@ -499,7 +597,7 @@ MOs <- MOs %>%
# Milleri Streptococci
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = gsub("AGA", "MIL", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_MILL", mo),
col_id = NA_integer_,
species = "milleri" ,
fullname = "Milleri Group Streptococcus (MGS)",
@ -509,7 +607,7 @@ MOs <- MOs %>%
# Trichomonas vaginalis is missing, same order as Dientamoeba
MOs %>%
filter(fullname == "Dientamoeba") %>%
mutate(mo = gsub("DNTMB", "THMNS", mo),
mutate(mo = gsub("(.*?)_.*", "\\1_THMNS", mo),
col_id = NA,
fullname = "Trichomonas",
family = "Trichomonadidae",
@ -519,8 +617,7 @@ MOs <- MOs %>%
species_id = ""),
MOs %>%
filter(fullname == "Dientamoeba fragilis") %>%
mutate(mo = gsub("DNTMB", "THMNS", mo),
mo = gsub("FRA", "VAG", mo),
mutate(mo = gsub("(.*?)_.*", "\\1_THMNS_VAG", mo),
col_id = NA,
fullname = "Trichomonas vaginalis",
family = "Trichomonadidae",
@ -531,7 +628,7 @@ MOs <- MOs %>%
species_id = ""),
MOs %>% # add family as such too
filter(fullname == "Monocercomonadidae") %>%
mutate(mo = gsub("MNCRCMND", "TRCHMNDD", mo),
mutate(mo = gsub("(.*)_(.*)_.*", "\\1_\\2_TRCHMNDD", mo),
col_id = NA,
fullname = "Trichomonadidae",
family = "Trichomonadidae",
@ -541,9 +638,12 @@ MOs <- MOs %>%
source = "manually added",
ref = "",
species_id = ""),
)
MOs <- MOs %>%
group_by(kingdom) %>%
distinct(fullname, .keep_all = TRUE) %>%
ungroup()
# everything distinct?
sum(duplicated(MOs$mo))
@ -551,60 +651,44 @@ sum(duplicated(MOs$fullname))
colnames(MOs)
# here we welcome the new ones:
MOs %>% filter(!fullname %in% AMR::microorganisms$fullname) %>% View()
MOs %>% arrange(genus, species, subspecies) %>% filter(!fullname %in% AMR::microorganisms$fullname) %>% View()
# and the ones we lost:
AMR::microorganisms %>% filter(!fullname %in% MOs$fullname) %>% View()
# and these IDs have changed:
MOs %>%
filter(fullname %in% AMR::microorganisms$fullname) %>%
left_join(AMR::microorganisms %>% select(mo, fullname), by = "fullname", suffix = c("_new", "_old")) %>%
old_new <- MOs %>%
mutate(kingdom_fullname = paste(kingdom, fullname)) %>%
filter(kingdom_fullname %in% (AMR::microorganisms %>% mutate(kingdom_fullname = paste(kingdom, fullname)) %>% pull(kingdom_fullname))) %>%
left_join(AMR::microorganisms %>% mutate(kingdom_fullname = paste(kingdom, fullname)) %>% select(mo, kingdom_fullname), by = "kingdom_fullname", suffix = c("_new", "_old")) %>%
filter(mo_new != mo_old) %>%
select(mo_old, mo_new, everything()) %>%
select(mo_old, mo_new, everything())
old_new %>%
View()
# and these codes are now missing (which will throw a unit test error):
AMR::microorganisms.codes %>% filter(!mo %in% AMR::microorganisms$mo)
# set prevalence per species
MOs <- MOs %>%
mutate(prevalence = case_when(
class == "Gammaproteobacteria"
| genus %in% c("Enterococcus", "Staphylococcus", "Streptococcus")
| mo %in% c("UNKNOWN", "B_GRAMN", "B_GRAMP")
~ 1,
phylum %in% c("Proteobacteria",
"Firmicutes",
"Actinobacteria",
"Sarcomastigophora")
| genus %in% c("Aspergillus",
"Bacteroides",
"Candida",
"Capnocytophaga",
"Chryseobacterium",
"Cryptococcus",
"Elisabethkingia",
"Flavobacterium",
"Fusobacterium",
"Giardia",
"Leptotrichia",
"Mycoplasma",
"Prevotella",
"Rhodotorula",
"Treponema",
"Trichophyton",
"Ureaplasma")
| rank %in% c("kingdom", "phylum", "class", "order", "family")
~ 2,
TRUE ~ 3
))
AMR::microorganisms.codes %>% filter(!mo %in% MOs$mo)
# this is how to fix it
microorganisms.codes <- AMR::microorganisms.codes %>%
left_join(MOs %>%
mutate(kingdom_fullname = paste(kingdom, fullname)) %>%
left_join(AMR::microorganisms %>%
mutate(kingdom_fullname = paste(kingdom, fullname)) %>%
select(mo, kingdom_fullname), by = "kingdom_fullname", suffix = c("_new", "_old")) %>%
select(mo_old, mo_new),
by = c("mo" = "mo_old")) %>%
select(code, mo = mo_new) %>%
filter(!is.na(mo))
microorganisms.codes %>% filter(!mo %in% MOs$mo)
# arrange
MOs <- MOs %>% arrange(genus, species, subspecies)
MOs.old <- MOs.old %>% arrange(fullname)
microorganisms.codes <- microorganisms.codes %>% arrange(code)
# transform
MOs <- as.data.frame(MOs, stringsAsFactors = FALSE)
MOs.old <- as.data.frame(MOs.old, stringsAsFactors = FALSE)
microorganisms.codes <- as.data.frame(microorganisms.codes, stringsAsFactors = FALSE)
class(MOs$mo) <- "mo"
class(microorganisms.codes$mo) <- "mo"
MOs$col_id <- as.integer(MOs$col_id)
MOs.old$col_id <- as.integer(MOs.old$col_id)
MOs.old$col_id_new <- as.integer(MOs.old$col_id_new)
@ -616,10 +700,17 @@ saveRDS(MOs.old, "microorganisms.old.rds")
### for same server
microorganisms <- MOs
microorganisms.old <- MOs.old
microorganisms.translation <- old_new %>% select(mo_old, mo_new)
class(microorganisms.translation$mo_old) <- "mo"
class(microorganisms.translation$mo_new) <- "mo"
# on the server, do:
usethis::use_data(microorganisms, overwrite = TRUE, version = 2)
usethis::use_data(microorganisms.old, overwrite = TRUE, version = 2)
usethis::use_data(microorganisms.codes, overwrite = TRUE, version = 2)
saveRDS(AMR::microorganisms.translation, file = "microorganisms.translation.rds", version = 2) # this one will be covered in data-raw/internals.R
rm(microorganisms)
rm(microorganisms.old)
rm(microorganisms.codes)
rm(microorganisms.translation)
# and update the year and dimensions in R/data.R

4
data-raw/reproduction_of_rsi_translation.R
View File

@ -4,7 +4,7 @@ library(readxl)
# Installed WHONET 2019 software on Windows (http://www.whonet.org/software.html),
# opened C:\WHONET\Codes\WHONETCodes.mdb in MS Access
# and exported table 'DRGLST1' to MS Excel
DRGLST1 <- read_excel("DRGLST1.xlsx")
DRGLST1 <- read_excel("data-raw/DRGLST1.xlsx")
rsi_translation <- DRGLST1 %>%
# only keep CLSI and EUCAST guidelines:
filter(GUIDELINES %like% "^(CLSI|EUCST)") %>%
@ -22,6 +22,8 @@ rsi_translation <- DRGLST1 %>%
filter(!is.na(mo) & !is.na(ab)) %>%
arrange(desc(guideline), mo, ab)
print(mo_failures())
# create 2 tables: MIC and disk
tbl_mic <- rsi_translation %>%
filter(method == "MIC") %>%