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# ---------------------------------------------------------------------------------
# Reproduction of the `microorganisms` data set
# ---------------------------------------------------------------------------------
# Data retrieved from:
#
# [1] Catalogue of Life (CoL) through the Encyclopaedia of Life
# https://opendata.eol.org/dataset/catalogue-of-life/
# * Download the resource file with a name like "Catalogue of Life yyyy-mm-dd"
# * Extract "taxon.tab"
#
# [2] Global Biodiversity Information Facility (GBIF)
# https://doi.org/10.15468/39omei
# * Extract "Taxon.tsv"
#
# [3] Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ)
# https://www.dsmz.de/support/bacterial-nomenclature-up-to-date-downloads.html
# * Download the latest "Complete List" as xlsx file (DSMZ_bactnames.xlsx)
# ---------------------------------------------------------------------------------
library(dplyr)
library(AMR)
data_col <- data.table::fread("Documents/taxon.tab")
data_gbif <- data.table::fread("Documents/Taxon.tsv")
# read the xlsx file from DSMZ (only around 2.5 MB):
data_dsmz <- readxl::read_xlsx("Downloads/DSMZ_bactnames.xlsx")
# the CoL data is over 3.7M rows:
data_col %>% freq(kingdom)
# Item Count Percent Cum. Count Cum. Percent
# --- ---------- ---------- -------- ----------- -------------
# 1 Animalia 2,225,627 59.1% 2,225,627 59.1%
# 2 Plantae 1,177,412 31.3% 3,403,039 90.4%
# 3 Fungi 290,145 7.7% 3,693,184 98.1%
# 4 Chromista 47,126 1.3% 3,740,310 99.3%
# 5 Bacteria 14,478 0.4% 3,754,788 99.7%
# 6 Protozoa 6,060 0.2% 3,760,848 99.9%
# 7 Viruses 3,827 0.1% 3,764,675 100.0%
# 8 Archaea 610 0.0% 3,765,285 100.0%
# the GBIF data is over 5.8M rows:
data_gbif %>% freq(kingdom)
# Item Count Percent Cum. Count Cum. Percent
# --- --------------- ---------- -------- ----------- -------------
# 1 Animalia 3,264,138 55.7% 3,264,138 55.7%
# 2 Plantae 1,814,962 31.0% 5,079,100 86.7%
# 3 Fungi 538,086 9.2% 5,617,186 95.9%
# 4 Chromista 181,374 3.1% 5,798,560 99.0%
# 5 Bacteria 24,048 0.4% 5,822,608 99.4%
# 6 Protozoa 15,138 0.3% 5,837,746 99.7%
# 7 incertae sedis 9,995 0.2% 5,847,741 99.8%
# 8 Viruses 9,630 0.2% 5,857,371 100.0%
# 9 Archaea 771 0.0% 5,858,142 100.0%
# Clean up helper function ------------------------------------------------
clean_new <- function(new) {
new %>%
# only the ones that have no new ID to refer to a newer name
filter(is.na(col_id_new)) %>%
filter(
(
# we only want all MICROorganisms and no viruses
!kingdom %in% c("Animalia", "Chromista", "Plantae", "Viruses")
# and not all fungi: Aspergillus, Candida, Trichphyton and Pneumocystis are the most important,
# so only keep these orders from the fungi:
& !(kingdom == "Fungi"
& !order %in% c("Eurotiales", "Saccharomycetales", "Schizosaccharomycetales", "Tremellales", "Onygenales", "Pneumocystales"))
)
# or the family has to contain a genus 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", "Chilomastix", "Chryseomonas", "Cladophialophora", "Cladosporium",
"Clonorchis", "Cordylobia", "Curvularia", "Demodex", "Dermatobia", "Diphyllobothrium", "Dracunculus", "Echinococcus",
"Enterobius", "Euascomycetes", "Exophiala", "Fasciola", "Fusarium", "Hendersonula", "Hymenolepis", "Kloeckera",
"Koserella", "Larva", "Leishmania", "Lelliottia", "Loa", "Lumbricus", "Malassezia", "Metagonimus", "Molonomonas",
"Mucor", "Nattrassia", "Necator", "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", "Trichomonas",
"Trichosporon", "Trichuris", "Trypanosoma", "Wuchereria")) %>%
mutate(
authors2 = iconv(ref, from = "UTF-8", to = "ASCII//TRANSLIT"),
# remove leading and trailing brackets
authors2 = gsub("^[(](.*)[)]$", "\\1", authors2),
# only take part after brackets if there's a name
authors2 = ifelse(grepl(".*[)] [a-zA-Z]+.*", authors2),
gsub(".*[)] (.*)", "\\1", authors2),
authors2),
# get year from last 4 digits
lastyear = as.integer(gsub(".*([0-9]{4})$", "\\1", authors2)),
# can never be later than now
lastyear = ifelse(lastyear > as.integer(format(Sys.Date(), "%Y")),
NA,
lastyear),
# get authors without last year
authors = gsub("(.*)[0-9]{4}$", "\\1", authors2),
# remove nonsense characters from names
authors = gsub("[^a-zA-Z,'& -]", "", authors),
# remove trailing and leading spaces
authors = trimws(authors),
# only keep first author and replace all others by 'et al'
authors = gsub("(,| and| et| &| ex| emend\\.?) .*", " et al.", authors),
# et al. always with ending dot
authors = gsub(" et al\\.?", " et al.", authors),
authors = gsub(" ?,$", "", authors),
# don't start with 'sensu' or 'ehrenb'
authors = gsub("^(sensu|Ehrenb.?) ", "", authors, ignore.case = TRUE),
# no initials, only surname
authors = gsub("^([A-Z]+ )+", "", authors, ignore.case = FALSE),
# combine author and year if year is available
ref = ifelse(!is.na(lastyear),
paste0(authors, ", ", lastyear),
authors),
# fix beginning and ending
ref = gsub(", $", "", ref),
ref = gsub("^, ", "", ref)) %>%
# remove text if it contains 'Not assigned' like phylum in viruses
mutate_all(~gsub("Not assigned", "", .)) %>%
# Remove non-ASCII characters (these are not allowed by CRAN)
lapply(iconv, from = "UTF-8", to = "ASCII//TRANSLIT") %>%
as_tibble(stringsAsFactors = FALSE) %>%
mutate(fullname = trimws(case_when(rank == "family" ~ family,
rank == "order" ~ order,
rank == "class" ~ class,
rank == "phylum" ~ phylum,
rank == "kingdom" ~ kingdom,
TRUE ~ paste(genus, species, subspecies))))
}
clean_old <- function(old, new) {
old %>%
# only the ones that exist in the new data set
filter(col_id_new %in% new$col_id) %>%
mutate(
authors2 = iconv(ref, from = "UTF-8", to = "ASCII//TRANSLIT"),
# remove leading and trailing brackets
authors2 = gsub("^[(](.*)[)]$", "\\1", authors2),
# only take part after brackets if there's a name
authors2 = ifelse(grepl(".*[)] [a-zA-Z]+.*", authors2),
gsub(".*[)] (.*)", "\\1", authors2),
authors2),
# get year from last 4 digits
lastyear = as.integer(gsub(".*([0-9]{4})$", "\\1", authors2)),
# can never be later than now
lastyear = ifelse(lastyear > as.integer(format(Sys.Date(), "%Y")),
NA,
lastyear),
# get authors without last year
authors = gsub("(.*)[0-9]{4}$", "\\1", authors2),
# remove nonsense characters from names
authors = gsub("[^a-zA-Z,'& -]", "", authors),
# remove trailing and leading spaces
authors = trimws(authors),
# only keep first author and replace all others by 'et al'
authors = gsub("(,| and| et| &| ex| emend\\.?) .*", " et al.", authors),
# et al. always with ending dot
authors = gsub(" et al\\.?", " et al.", authors),
authors = gsub(" ?,$", "", authors),
# don't start with 'sensu' or 'ehrenb'
authors = gsub("^(sensu|Ehrenb.?) ", "", authors, ignore.case = TRUE),
# no initials, only surname
authors = gsub("^([A-Z]+ )+", "", authors, ignore.case = FALSE),
# combine author and year if year is available
ref = ifelse(!is.na(lastyear),
paste0(authors, ", ", lastyear),
authors),
# fix beginning and ending
ref = gsub(", $", "", ref),
ref = gsub("^, ", "", ref)) %>%
# remove text if it contains 'Not assigned' like phylum in viruses
mutate_all(~gsub("Not assigned", "", .)) %>%
# Remove non-ASCII characters (these are not allowed by CRAN)
lapply(iconv, from = "UTF-8", to = "ASCII//TRANSLIT") %>%
as_tibble(stringsAsFactors = FALSE) %>%
select(col_id_new, fullname, ref, authors2) %>%
left_join(new %>% select(col_id, fullname_new = fullname), by = c(col_id_new = "col_id")) %>%
mutate(fullname = trimws(
gsub("(.*)[(].*", "\\1",
stringr::str_replace(
string = fullname,
pattern = stringr::fixed(authors2),
replacement = "")) %>%
gsub(" (var|f|subsp)[.]", "", .))) %>%
select(-c("col_id_new", "authors2")) %>%
filter(!is.na(fullname), !is.na(fullname_new)) %>%
filter(fullname != fullname_new, !fullname %like% "^[?]")
}
# clean CoL and GBIF ----
# clean data_col
data_col <- data_col %>%
as_tibble() %>%
select(col_id = taxonID,
col_id_new = acceptedNameUsageID,
fullname = scientificName,
kingdom,
phylum,
class,
order,
family,
genus,
species = specificEpithet,
subspecies = infraspecificEpithet,
rank = taxonRank,
ref = scientificNameAuthorship,
species_id = furtherInformationURL) %>%
mutate(source = "CoL")
# split into old and new
data_col.new <- data_col %>% clean_new()
data_col.old <- data_col %>% clean_old(new = data_col.new)
rm(data_col)
# clean data_gbif
data_gbif <- data_gbif %>%
as_tibble() %>%
filter(
# no uncertain taxonomic placements
taxonRemarks != "doubtful",
kingdom != "incertae sedis",
taxonRank != "unranked") %>%
transmute(col_id = taxonID,
col_id_new = acceptedNameUsageID,
fullname = scientificName,
kingdom,
phylum,
class,
order,
family,
genus,
species = specificEpithet,
subspecies = infraspecificEpithet,
rank = taxonRank,
ref = scientificNameAuthorship,
species_id = as.character(parentNameUsageID)) %>%
mutate(source = "GBIF")
# split into old and new
data_gbif.new <- data_gbif %>% clean_new()
data_gbif.old <- data_gbif %>% clean_old(new = data_gbif.new)
rm(data_gbif)
# put CoL and GBIF together ----
MOs.new <- bind_rows(data_col.new,
data_gbif.new) %>%
mutate(taxonomic_tree_length = nchar(trimws(paste(kingdom, phylum, class, order, family, genus, species, subspecies)))) %>%
arrange(desc(taxonomic_tree_length)) %>%
distinct(fullname, .keep_all = TRUE) %>%
select(-c("col_id_new", "authors2", "authors", "lastyear", "taxonomic_tree_length")) %>%
arrange(fullname)
MOs.old <- bind_rows(data_col.old,
data_gbif.old) %>%
distinct(fullname, .keep_all = TRUE) %>%
arrange(fullname)
# clean up DSMZ ---
data_dsmz <- data_dsmz %>%
as_tibble() %>%
transmute(col_id = NA_integer_,
col_id_new = NA_integer_,
fullname = "",
# kingdom = "",
# phylum = "",
# class = "",
# order = "",
# family = "",
genus = ifelse(is.na(GENUS), "", GENUS),
species = ifelse(is.na(SPECIES), "", SPECIES),
subspecies = ifelse(is.na(SUBSPECIES), "", SUBSPECIES),
rank = ifelse(species == "", "genus", "species"),
ref = AUTHORS,
species_id = as.character(RECORD_NO),
source = "DSMZ")
# DSMZ only contains genus/(sub)species, try to find taxonomic properties based on genus and data_col
ref_taxonomy <- MOs.new %>%
distinct(genus, .keep_all = TRUE) %>%
filter(family != "") %>%
filter(genus %in% data_dsmz$genus) %>%
distinct(genus, .keep_all = TRUE) %>%
select(kingdom, phylum, class, order, family, genus)
data_dsmz <- data_dsmz %>%
left_join(ref_taxonomy, by = "genus") %>%
mutate(kingdom = "Bacteria")
data_dsmz.new <- data_dsmz %>%
clean_new() %>%
distinct(fullname, .keep_all = TRUE) %>%
select(colnames(MOs.new)) %>%
arrange(fullname)
# combine everything ----
MOs <- bind_rows(MOs.new,
data_dsmz.new) %>%
distinct(fullname, .keep_all = TRUE) %>%
# not the ones that are old
filter(!fullname %in% MOs.old$fullname) %>%
arrange(fullname) %>%
mutate(col_id = ifelse(source != "CoL", NA_integer_, col_id)) %>%
filter(fullname != "")
rm(data_col.new)
rm(data_col.old)
rm(data_gbif.new)
rm(data_gbif.old)
rm(data_dsmz)
rm(data_dsmz.new)
rm(ref_taxonomy)
rm(MOs.new)
MOs.bak <- MOs
# Trichomonas trick ----
# for species in Trypanosoma and Trichomonas we observe al lot of taxonomic info missing
MOs %>% filter(genus %in% c("Trypanosoma", "Trichomonas")) %>% View()
MOs[which(MOs$genus == "Trypanosoma"), "kingdom"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$kingdom
MOs[which(MOs$genus == "Trypanosoma"), "phylum"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$phylum
MOs[which(MOs$genus == "Trypanosoma"), "class"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$class
MOs[which(MOs$genus == "Trypanosoma"), "order"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$order
MOs[which(MOs$genus == "Trypanosoma"), "family"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$family
MOs[which(MOs$genus == "Trichomonas"), "kingdom"] <- MOs[which(MOs$fullname == "Trichomonas"),]$kingdom
MOs[which(MOs$genus == "Trichomonas"), "phylum"] <- MOs[which(MOs$fullname == "Trichomonas"),]$phylum
MOs[which(MOs$genus == "Trichomonas"), "class"] <- MOs[which(MOs$fullname == "Trichomonas"),]$class
MOs[which(MOs$genus == "Trichomonas"), "order"] <- MOs[which(MOs$fullname == "Trichomonas"),]$order
MOs[which(MOs$genus == "Trichomonas"), "family"] <- MOs[which(MOs$fullname == "Trichomonas"),]$family
# fill taxonomic properties that are missing
MOs <- MOs %>%
mutate(phylum = ifelse(phylum %in% c(NA, ""), "(unknown phylum)", phylum),
class = ifelse(class %in% c(NA, ""), "(unknown class)", class),
order = ifelse(order %in% c(NA, ""), "(unknown order)", order),
family = ifelse(family %in% c(NA, ""), "(unknown family)", family))
# Abbreviations ----
# Add abbreviations so we can easily know which ones are which ones.
# These will become valid and unique microbial IDs for the AMR package.
MOs <- MOs %>%
arrange(kingdom, fullname) %>%
group_by(kingdom) %>%
mutate(abbr_other = case_when(
rank == "family" ~ paste0("[FAM]_",
abbreviate(family,
minlength = 8,
use.classes = TRUE,
method = "both.sides",
strict = FALSE)),
rank == "order" ~ paste0("[ORD]_",
abbreviate(order,
minlength = 8,
use.classes = TRUE,
method = "both.sides",
strict = FALSE)),
rank == "class" ~ paste0("[CLS]_",
abbreviate(class,
minlength = 8,
use.classes = TRUE,
method = "both.sides",
strict = FALSE)),
rank == "phylum" ~ paste0("[PHL]_",
abbreviate(phylum,
minlength = 8,
use.classes = TRUE,
method = "both.sides",
strict = FALSE)),
rank == "kingdom" ~ paste0("[KNG]_", kingdom),
TRUE ~ NA_character_
)) %>%
# abbreviations determined per kingdom and family
# becuase they are part of the abbreviation
mutate(abbr_genus = abbreviate(genus,
minlength = 7,
use.classes = TRUE,
method = "both.sides",
strict = FALSE)) %>%
ungroup() %>%
group_by(genus) %>%
# species abbreviations may be the same between genera
# because the genus abbreviation is part of the abbreviation
mutate(abbr_species = abbreviate(stringr::str_to_title(species),
minlength = 3,
use.classes = FALSE,
method = "both.sides")) %>%
ungroup() %>%
group_by(genus, species) %>%
mutate(abbr_subspecies = abbreviate(stringr::str_to_title(subspecies),
minlength = 3,
use.classes = FALSE,
method = "both.sides")) %>%
ungroup() %>%
# remove trailing underscores
mutate(mo = gsub("_+$", "",
toupper(paste(
# first character: kingdom
ifelse(kingdom %in% c("Animalia", "Plantae"),
substr(kingdom, 1, 2),
substr(kingdom, 1, 1)),
# next: genus, species, subspecies
ifelse(is.na(abbr_other),
paste(abbr_genus,
abbr_species,
abbr_subspecies,
sep = "_"),
abbr_other),
sep = "_")))) %>%
mutate(mo = ifelse(duplicated(.$mo),
# these one or two must be unique too
paste0(mo, "1"),
mo),
fullname = ifelse(fullname == "",
trimws(paste(genus, species, subspecies)),
fullname)) %>%
# put `mo` in front, followed by the rest
select(mo, everything(), -abbr_other, -abbr_genus, -abbr_species, -abbr_subspecies)
# add non-taxonomic entries
MOs <- MOs %>%
bind_rows(
# Unknowns
data.frame(mo = "UNKNOWN",
col_id = NA_integer_,
fullname = "(unknown name)",
kingdom = "(unknown kingdom)",
phylum = "(unknown phylum)",
class = "(unknown class)",
order = "(unknown order)",
family = "(unknown family)",
genus = "(unknown genus)",
species = "(unknown species)",
subspecies = "(unknown subspecies)",
rank = "(unknown rank)",
ref = NA_character_,
species_id = "",
source = "manually added",
stringsAsFactors = FALSE),
data.frame(mo = "B_GRAMN",
col_id = NA_integer_,
fullname = "(unknown Gram-negatives)",
kingdom = "Bacteria",
phylum = "(unknown phylum)",
class = "(unknown class)",
order = "(unknown order)",
family = "(unknown family)",
genus = "(unknown Gram-negatives)",
species = "(unknown species)",
subspecies = "(unknown subspecies)",
rank = "species",
ref = NA_character_,
species_id = "",
source = "manually added",
stringsAsFactors = FALSE),
data.frame(mo = "B_GRAMP",
col_id = NA_integer_,
fullname = "(unknown Gram-positives)",
kingdom = "Bacteria",
phylum = "(unknown phylum)",
class = "(unknown class)",
order = "(unknown order)",
family = "(unknown family)",
genus = "(unknown Gram-positives)",
species = "(unknown species)",
subspecies = "(unknown subspecies)",
rank = "species",
ref = NA_character_,
species_id = "",
source = "manually added",
stringsAsFactors = FALSE),
# CoNS
MOs %>%
filter(genus == "Staphylococcus", species == "") %>% .[1,] %>%
mutate(mo = paste(mo, "CNS", sep = "_"),
rank = "species",
col_id = NA_integer_,
species = "coagulase-negative",
fullname = "Coagulase-negative Staphylococcus (CoNS)",
ref = NA_character_,
species_id = "",
source = "manually added"),
# CoPS
MOs %>%
filter(genus == "Staphylococcus", species == "") %>% .[1,] %>%
mutate(mo = paste(mo, "CPS", sep = "_"),
rank = "species",
col_id = NA_integer_,
species = "coagulase-positive",
fullname = "Coagulase-positive Staphylococcus (CoPS)",
ref = NA_character_,
species_id = "",
source = "manually added"),
# Streptococci groups A, B, C, F, H, K
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 = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRA", sep = "_"),
species = "group A" ,
fullname = "Streptococcus group A"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
# we can keep all other details, since S. agalactiae is the only member of group B
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRB", sep = "_"),
species = "group B" ,
fullname = "Streptococcus group B"),
MOs %>%
filter(genus == "Streptococcus", species == "dysgalactiae") %>% .[1,] %>%
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRC", sep = "_"),
col_id = NA_integer_,
species = "group C" ,
fullname = "Streptococcus group C",
ref = NA_character_,
species_id = "",
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRD", sep = "_"),
col_id = NA_integer_,
species = "group D" ,
fullname = "Streptococcus group D",
ref = NA_character_,
species_id = "",
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRF", sep = "_"),
col_id = NA_integer_,
species = "group F" ,
fullname = "Streptococcus group F",
ref = NA_character_,
species_id = "",
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRG", sep = "_"),
col_id = NA_integer_,
species = "group G" ,
fullname = "Streptococcus group G",
ref = NA_character_,
species_id = "",
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRH", sep = "_"),
col_id = NA_integer_,
species = "group H" ,
fullname = "Streptococcus group H",
ref = NA_character_,
species_id = "",
source = "manually added"),
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRK", sep = "_"),
col_id = NA_integer_,
species = "group K" ,
fullname = "Streptococcus group K",
ref = NA_character_,
species_id = "",
source = "manually added"),
# Beta-haemolytic Streptococci
MOs %>%
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "HAE", sep = "_"),
col_id = NA_integer_,
species = "beta-haemolytic" ,
fullname = "Beta-haemolytic Streptococcus",
ref = NA_character_,
species_id = "",
source = "manually added")
)
# everything distinct?
sum(duplicated(MOs$mo))
colnames(MOs)
# 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",
"Trichomonas",
"Ureaplasma")
| rank %in% c("kingdom", "phylum", "class", "order", "family")
~ 2,
TRUE ~ 3
))
# arrange
MOs <- MOs %>% arrange(fullname)
# transform
MOs <- as.data.frame(MOs, stringsAsFactors = FALSE)
MOs.old <- as.data.frame(MOs.old, stringsAsFactors = FALSE)
class(MOs$mo) <- "mo"
MOs$col_id <- as.integer(MOs$col_id)
# get differences in MO codes between this data and the package version
MO_diff <- AMR::microorganisms %>%
mutate(pastedtext = paste(mo, fullname)) %>%
filter(!pastedtext %in% (MOs %>% mutate(pastedtext = paste(mo, fullname)) %>% pull(pastedtext))) %>%
select(mo_old = mo, fullname, pastedtext) %>%
left_join(MOs %>%
transmute(mo_new = mo, fullname_new = fullname, pastedtext = paste(mo, fullname)), "pastedtext") %>%
select(mo_old, mo_new, fullname_new)
mo_diff2 <- AMR::microorganisms %>%
select(mo, fullname) %>%
left_join(MOs %>%
select(mo, fullname),
by = "fullname",
suffix = c("_old", "_new")) %>%
filter(mo_old != mo_new,
#!mo_new %in% mo_old,
!mo_old %like% "\\[")
mo_diff3 <- tibble(previous_old = names(AMR:::make_trans_tbl()),
previous_new = AMR:::make_trans_tbl()) %>%
left_join(AMR::microorganisms %>% select(mo, fullname), by = c(previous_new = "mo")) %>%
left_join(MOs %>% select(mo_new = mo, fullname), by = "fullname")
# what did we win most?
MOs %>% filter(!fullname %in% AMR::microorganisms$fullname) %>% freq(genus)
# what did we lose most?
AMR::microorganisms %>%
filter(kingdom != "Chromista" & !fullname %in% MOs$fullname & !fullname %in% MOs.old$fullname) %>%
freq(genus)
# save
saveRDS(MOs, "microorganisms.rds")
saveRDS(MOs.old, "microorganisms.old.rds")
# on the server, do:
usethis::use_data(microorganisms, overwrite = TRUE, version = 2)
usethis::use_data(microorganisms.old, overwrite = TRUE, version = 2)
rm(microorganisms)
rm(microorganisms.old)
# TO DO AFTER THIS
# * Update the year and dim()s in R/data.R
# * Rerun data-raw/reproduction_of_rsi_translation.R
# * Run unit tests
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