(v0.7.1.9009) note for WHOCC

This commit is contained in:
dr. M.S. (Matthijs) Berends 2019-07-09 11:22:46 +02:00
parent d9e257f8db
commit b15d59cf03
34 changed files with 711 additions and 41 deletions

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@ -1,6 +1,6 @@
Package: AMR
Version: 0.7.1.9008
Date: 2019-07-04
Version: 0.7.1.9009
Date: 2019-07-09
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(

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@ -1,4 +1,4 @@
# AMR 0.7.1.9008
# AMR 0.7.1.9009
### New
* Additional way to calculate co-resistance, i.e. when using multiple antibiotics as input for `portion_*` functions or `count_*` functions. This can be used to determine the empiric susceptibily of a combination therapy. A new parameter `only_all_tested` (**which defaults to `FALSE`**) replaces the old `also_single_tested` and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the `portion` and `count` help pages), where the %SI is being determined:

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@ -496,8 +496,8 @@ mdro <- function(x,
#' @rdname mdro
#' @export
brmo <- function(..., guideline = "BRMO") {
mdro(..., guideline = "BRMO")
brmo <- function(x, guideline = "BRMO", ...) {
mdro(x, guideline = "BRMO", ...)
}
#' @rdname mdro

2
R/mo.R
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@ -483,7 +483,7 @@ exec_as.mo <- function(x,
x <- gsub("(alpha|beta|gamma).?ha?emoly", "\\1-haemoly", x)
# remove genus as first word
x <- gsub("^Genus ", "", x)
# allow characters that resemble others
# allow characters that resemble others, but not on first try
if (initial_search == FALSE) {
x <- tolower(x)
x <- gsub("[iy]+", "[iy]+", x)

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@ -157,7 +157,7 @@ mo_shortname <- function(x, language = get_locale(), ...) {
# exceptions for Staphylococci
shortnames[shortnames == "S. coagulase-negative" ] <- "CoNS"
shortnames[shortnames == "S. coagulase-positive" ] <- "CoPS"
# exceptions for Streptococci
# exceptions for Streptococci: Streptococcus Group A -> GAS
shortnames[shortnames %like% "S. group [ABCDFGHK]"] <- paste0("G", gsub("S. group ([ABCDFGHK])", "\\1", shortnames[shortnames %like% "S. group [ABCDFGHK]"]), "S")
load_mo_failures_uncertainties_renamed(metadata)

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@ -24,7 +24,7 @@
#' All antimicrobial drugs and their official names, ATC codes, ATC groups and defined daily dose (DDD) are included in this package, using the WHO Collaborating Centre for Drug Statistics Methodology.
#' @section WHOCC:
#' \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
#' This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
#' This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}). \strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.}
#'
#' These have become the gold standard for international drug utilisation monitoring and research.
#'

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@ -0,0 +1,657 @@
# ---------------------------------------------------------------------------------
# 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)
# and update the year in R/data.R

View File

@ -78,7 +78,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -78,7 +78,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -78,7 +78,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -187,8 +187,10 @@ table a:not(.btn):hover, .table a:not(.btn):hover {
/* text below header in manual overview */
.template-reference-index h2 ~ p {
font-size: 110%;
/* font-weight: bold; */
font-size: 16px;
}
.template-reference-topic h2 {
font-size: 24px;
}
/* logos on index page */

View File

@ -46,6 +46,9 @@ $( document ).ready(function() {
window.location.replace(url_new);
}
// Replace 'Value' in manual to 'Returned value'
$(".template-reference-topic h2#value").text("Returned value");
// PR for 'R for Data Science' on How To pages
if ($(".template-article").length > 0) {
$('#sidebar').prepend(

View File

@ -42,7 +42,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>
@ -296,6 +296,7 @@
<img src="reference/figures/logo_who.png" height="75px" class="logo_img"><p class="logo_txt">WHO Collaborating Centre for Drug Statistics Methodology</p>
</div>
<p>This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href="https://www.whocc.no" class="uri">https://www.whocc.no</a>) and the <a href="http://ec.europa.eu/health/documents/community-register/html/atc.htm">Pharmaceuticals Community Register of the European Commission</a>.</p>
<p><strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{<a href="https://www.whocc.no/copyright_disclaimer/" class="uri">https://www.whocc.no/copyright_disclaimer/</a>}.</strong></p>
<p>Read more about the data from WHOCC <a href="./reference/WHOCC.html">in our manual</a>.</p>
</div>
<div id="whonet--ears-net" class="section level4">

View File

@ -78,7 +78,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>
@ -232,9 +232,9 @@
</div>
<div id="amr-0719008" class="section level1">
<div id="amr-0719009" class="section level1">
<h1 class="page-header">
<a href="#amr-0719008" class="anchor"></a>AMR 0.7.1.9008<small> Unreleased </small>
<a href="#amr-0719009" class="anchor"></a>AMR 0.7.1.9009<small> Unreleased </small>
</h1>
<div id="new" class="section level3">
<h3 class="hasAnchor">
@ -1196,7 +1196,7 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<div id="tocnav">
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#amr-0719008">0.7.1.9008</a></li>
<li><a href="#amr-0719009">0.7.1.9009</a></li>
<li><a href="#amr-071">0.7.1</a></li>
<li><a href="#amr-070">0.7.0</a></li>
<li><a href="#amr-061">0.6.1</a></li>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>
@ -246,7 +246,7 @@
<p><img src='figures/logo_who.png' height=60px style=margin-bottom:5px /> <br />
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>). <strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href='https://www.whocc.no/copyright_disclaimer/'>https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>
<p>These have become the gold standard for international drug utilisation monitoring and research.</p>
<p>The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.</p>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>
@ -276,7 +276,7 @@
<p><img src='figures/logo_who.png' height=60px style=margin-bottom:5px /> <br />
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>). <strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href='https://www.whocc.no/copyright_disclaimer/'>https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>
<p>These have become the gold standard for international drug utilisation monitoring and research.</p>
<p>The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.</p>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>
@ -274,7 +274,7 @@
<p><img src='figures/logo_who.png' height=60px style=margin-bottom:5px /> <br />
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>).</p>
This package contains <strong>all ~450 antimicrobial drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href='https://www.whocc.no'>https://www.whocc.no</a>) and the Pharmaceuticals Community Register of the European Commission (<a href='http://ec.europa.eu/health/documents/community-register/html/atc.htm'>http://ec.europa.eu/health/documents/community-register/html/atc.htm</a>). <strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href='https://www.whocc.no/copyright_disclaimer/'>https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>
<p>These have become the gold standard for international drug utilisation monitoring and research.</p>
<p>The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.</p>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -81,7 +81,7 @@ top_freq can be used to get the top/bottom n items of a frequency table, with co
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -78,7 +78,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>
@ -244,7 +244,7 @@
<pre class="usage"><span class='fu'>mdro</span>(<span class='no'>x</span>, <span class='kw'>guideline</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>col_mo</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='no'>...</span>)
<span class='fu'>brmo</span>(<span class='no'>...</span>, <span class='kw'>guideline</span> <span class='kw'>=</span> <span class='st'>"BRMO"</span>)
<span class='fu'>brmo</span>(<span class='no'>x</span>, <span class='kw'>guideline</span> <span class='kw'>=</span> <span class='st'>"BRMO"</span>, <span class='no'>...</span>)
<span class='fu'>mrgn</span>(<span class='no'>x</span>, <span class='kw'>guideline</span> <span class='kw'>=</span> <span class='st'>"MRGN"</span>, <span class='no'>...</span>)

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -80,7 +80,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9009</span>
</span>
</div>

View File

@ -117,6 +117,8 @@ Read more about the data from the Catalogue of Life [in our manual](./reference/
This package contains **all ~450 antimicrobial drugs** and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the [Pharmaceuticals Community Register of the European Commission](http://ec.europa.eu/health/documents/community-register/html/atc.htm).
**NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.**
Read more about the data from WHOCC [in our manual](./reference/WHOCC.html).
#### WHONET / EARS-Net

View File

@ -9,7 +9,7 @@ All antimicrobial drugs and their official names, ATC codes, ATC groups and defi
\section{WHOCC}{
\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}). \strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.}
These have become the gold standard for international drug utilisation monitoring and research.

View File

@ -41,7 +41,7 @@ Synonyms (i.e. trade names) are derived from the Compound ID (\code{cid}) and co
\section{WHOCC}{
\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}). \strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.}
These have become the gold standard for international drug utilisation monitoring and research.

View File

@ -35,7 +35,7 @@ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{htt
\section{WHOCC}{
\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}). \strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.}
These have become the gold standard for international drug utilisation monitoring and research.

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@ -18,7 +18,7 @@ Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Res
mdro(x, guideline = NULL, col_mo = NULL, info = TRUE,
verbose = FALSE, ...)
brmo(..., guideline = "BRMO")
brmo(x, guideline = "BRMO", ...)
mrgn(x, guideline = "MRGN", ...)

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@ -187,8 +187,10 @@ table a:not(.btn):hover, .table a:not(.btn):hover {
/* text below header in manual overview */
.template-reference-index h2 ~ p {
font-size: 110%;
/* font-weight: bold; */
font-size: 16px;
}
.template-reference-topic h2 {
font-size: 24px;
}
/* logos on index page */

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@ -46,6 +46,9 @@ $( document ).ready(function() {
window.location.replace(url_new);
}
// Replace 'Value' in manual to 'Returned value'
$(".template-reference-topic h2#value").text("Returned value");
// PR for 'R for Data Science' on How To pages
if ($(".template-article").length > 0) {
$('#sidebar').prepend(