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support for French and Italian, added quote to freq
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5
NEWS.md
5
NEWS.md
@ -10,7 +10,7 @@
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* Column names of datasets `microorganisms` and `septic_patients`
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* All old syntaxes will still work with this version, but will throw warnings
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* Functions `as.atc` and `is.atc` to transform/look up antibiotic ATC codes as defined by the WHO. The existing function `guess_atc` is now an alias of `as.atc`.
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* Aliases for existing function `mo_property`: `mo_family`, `mo_genus`, `mo_species`, `mo_subspecies`, `mo_fullname`, `mo_shortname`, `mo_aerobic`, `mo_type` and `mo_gramstain`. They also come with support for German, Dutch, Spanish and Portuguese, and it defaults to the systems locale:
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* Aliases for existing function `mo_property`: `mo_family`, `mo_genus`, `mo_species`, `mo_subspecies`, `mo_fullname`, `mo_shortname`, `mo_aerobic`, `mo_type` and `mo_gramstain`. They also come with support for German, Dutch, French, Italian, Spanish and Portuguese, and it defaults to the systems locale:
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```r
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mo_gramstain("E. coli")
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# [1] "Negative rods"
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@ -55,7 +55,8 @@
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* Fix for `ggplot_rsi` when the `ggplot2` package was not loaded
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* Added possibility to set any parameter to `geom_rsi` (and `ggplot_rsi`) so you can set your own preferences
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* Fix for joins, where predefined suffices would not be honoured
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* Support for types list and matrix for `freq`
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* Added parameter `quote` to the `freq` function
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* Support for types (classes) list and matrix for `freq`
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```r
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my_matrix = with(septic_patients, matrix(c(age, sex), ncol = 2))
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freq(my_matrix)
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6
R/freq.R
6
R/freq.R
@ -27,6 +27,7 @@
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#' @param row.names a logical value indicating whether row indices should be printed as \code{1:nrow(x)}
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#' @param markdown print table in markdown format (this forces \code{nmax = NA})
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#' @param digits how many significant digits are to be used for numeric values in the header (not for the items themselves, that depends on \code{\link{getOption}("digits")})
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#' @param quote a logical value indicating whether or not strings should be printed with surrounding quotes
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#' @param sep a character string to separate the terms when selecting multiple columns
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#' @param f a frequency table
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#' @param n number of top \emph{n} items to return, use -n for the bottom \emph{n} items. It will include more than \code{n} rows if there are ties.
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@ -148,6 +149,7 @@ frequency_tbl <- function(x,
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row.names = TRUE,
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markdown = FALSE,
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digits = 2,
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quote = FALSE,
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sep = " ") {
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mult.columns <- 0
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@ -429,6 +431,10 @@ frequency_tbl <- function(x,
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}
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}
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if (quote == TRUE) {
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df$item <- paste0('"', df$item, '"')
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}
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df <- as.data.frame(df, stringsAsFactors = FALSE)
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df$percent <- df$count / base::sum(df$count, na.rm = TRUE)
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2
R/mo.R
2
R/mo.R
@ -127,7 +127,7 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
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x_backup <- x
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# translate to English for supported languages of mo_property
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x <- gsub("(Gruppe|gruppe|groep|grupo)", "group", x)
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x <- gsub("(Gruppe|gruppe|groep|grupo|gruppo|groupe)", "group", x)
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# remove 'empty' genus and species values
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x <- gsub("(no MO)", "", x, fixed = TRUE)
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# remove dots and other non-text in case of "E. coli" except spaces
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@ -214,9 +214,9 @@ mo_translate <- function(x, language) {
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return(x)
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}
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supported <- c("en", "de", "nl", "es", "pt")
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supported <- c("en", "de", "nl", "es", "pt", "it", "fr")
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if (!language %in% supported) {
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stop("Unsupported language: '", language, "' - use one of ", paste0("'", sort(supported), "'", collapse = ", "), call. = FALSE)
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stop("Unsupported language: '", language, "' - use one of: ", paste0("'", sort(supported), "'", collapse = ", "), call. = FALSE)
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}
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case_when(
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@ -302,7 +302,50 @@ mo_translate <- function(x, language) {
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gsub("biotype", "bi\u00f3tipo", ., fixed = TRUE) %>%
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gsub("vegetative", "vegetativo", ., fixed = TRUE) %>%
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gsub("([([ ]*?)group", "\\1grupo", .) %>%
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gsub("([([ ]*?)Group", "\\1Grupo", .)
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gsub("([([ ]*?)Group", "\\1Grupo", .),
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# Italian
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language == "it" ~ x %>%
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gsub("Coagulase Negative Staphylococcus","Staphylococcus negativo coagulasi", ., fixed = TRUE) %>%
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gsub("Coagulase Positive Staphylococcus","Staphylococcus positivo coagulasi", ., fixed = TRUE) %>%
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gsub("Beta-haemolytic Streptococcus", "Streptococcus Beta-emolitico", ., fixed = TRUE) %>%
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gsub("(no MO)", "(non MO)", ., fixed = TRUE) %>%
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gsub("Negative rods", "Bastoncini Gram-negativi", ., fixed = TRUE) %>%
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gsub("Negative cocci", "Cocchi Gram-negativi", ., fixed = TRUE) %>%
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gsub("Positive rods", "Bastoncini Gram-positivi", ., fixed = TRUE) %>%
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gsub("Positive cocci", "Cocchi Gram-positivi", ., fixed = TRUE) %>%
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gsub("Parasites", "Parassiti", ., fixed = TRUE) %>%
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gsub("Fungi and yeasts", "Funghi e lieviti", ., fixed = TRUE) %>%
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gsub("Bacteria", "Batterio", ., fixed = TRUE) %>%
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gsub("Fungus/yeast", "Fungo/lievito", ., fixed = TRUE) %>%
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gsub("Parasite", "Parassita", ., fixed = TRUE) %>%
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gsub("biogroup", "biogruppo", ., fixed = TRUE) %>%
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gsub("biotype", "biotipo", ., fixed = TRUE) %>%
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gsub("vegetative", "vegetativo", ., fixed = TRUE) %>%
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gsub("([([ ]*?)group", "\\1gruppo", .) %>%
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gsub("([([ ]*?)Group", "\\1Gruppo", .),
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# French
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language == "fr" ~ x %>%
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gsub("Coagulase Negative Staphylococcus","Staphylococcus \u00e0 coagulase n\u00e9gative", ., fixed = TRUE) %>%
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gsub("Coagulase Positive Staphylococcus","Staphylococcus \u00e0 coagulase positif", ., fixed = TRUE) %>%
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gsub("Beta-haemolytic Streptococcus", "Streptococcus B\u00eata-h\u00e9molytique", ., fixed = TRUE) %>%
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gsub("(no MO)", "(pas MO)", ., fixed = TRUE) %>%
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gsub("Negative rods", "Bacilles n\u00e9gatif", ., fixed = TRUE) %>%
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gsub("Negative cocci", "Cocci n\u00e9gatif", ., fixed = TRUE) %>%
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gsub("Positive rods", "Bacilles positif", ., fixed = TRUE) %>%
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gsub("Positive cocci", "Cocci positif", ., fixed = TRUE) %>%
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# gsub("Parasites", "Parasites", ., fixed = TRUE) %>%
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gsub("Fungi and yeasts", "Champignons et levures", ., fixed = TRUE) %>%
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gsub("Bacteria", "Bact\u00e9rie", ., fixed = TRUE) %>%
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gsub("Fungus/yeast", "Champignon/levure", ., fixed = TRUE) %>%
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# gsub("Parasite", "Parasite", ., fixed = TRUE) %>%
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gsub("biogroup", "biogroupe", ., fixed = TRUE) %>%
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# gsub("biotype", "biotype", ., fixed = TRUE) %>%
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gsub("vegetative", "v\u00e9g\u00e9tatif", ., fixed = TRUE) %>%
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gsub("([([ ]*?)group", "\\1groupe", .) %>%
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gsub("([([ ]*?)Group", "\\1Groupe", .)
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)
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}
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@ -314,7 +357,9 @@ mo_getlangcode <- function() {
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sys %like% '(Deutsch|German|de_)' ~ "de",
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sys %like% '(Nederlands|Dutch|nl_)' ~ "nl",
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sys %like% '(Espa.ol|Spanish|es_)' ~ "es",
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sys %like% '(Fran.ais|French|fr_)' ~ "fr",
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sys %like% '(Portugu.s|Portuguese|pt_)' ~ "pt",
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sys %like% '(Italiano|Italian|it_)' ~ "it",
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TRUE ~ "en"
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)
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}
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@ -55,7 +55,7 @@ This `AMR` package basically does four important things:
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* Use `first_isolate` to identify the first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute).
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* You can also identify first *weighted* isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
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* Use `MDRO` (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
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* The data set `microorganisms` contains the family, genus, species, subspecies, colloquial name and Gram stain of almost 3,000 potential human pathogenic microorganisms (bacteria, fungi/yeasts and parasites). This enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus`, `mo_family` or `mo_gramstain`. As they use `as.mo` internally, they also use artificial intelligence. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, Spanish and Portuguese. These functions can be used to add new variables to your data.
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* The data set `microorganisms` contains the family, genus, species, subspecies, colloquial name and Gram stain of almost 3,000 potential human pathogenic microorganisms (bacteria, fungi/yeasts and parasites). This enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus`, `mo_family` or `mo_gramstain`. As they use `as.mo` internally, they also use artificial intelligence. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, French, Italian, Spanish and Portuguese. These functions can be used to add new variables to your data.
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* The data set `antibiotics` contains the ATC code, LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains a total of 298 trade names. Use functions like `ab_official` and `ab_tradenames` to look up values. As the `mo_*` functions use `as.mo` internally, the `ab_*` functions use `as.atc` internally so it uses AI to guess your expected result. For example, `ab_official("Fluclox")`, `ab_official("Floxapen")` and `ab_official("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
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3. It **analyses the data** with convenient functions that use well-known methods.
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@ -9,11 +9,11 @@
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\usage{
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frequency_tbl(x, ..., sort.count = TRUE,
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nmax = getOption("max.print.freq"), na.rm = TRUE, row.names = TRUE,
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markdown = FALSE, digits = 2, sep = " ")
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markdown = FALSE, digits = 2, quote = FALSE, sep = " ")
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freq(x, ..., sort.count = TRUE, nmax = getOption("max.print.freq"),
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na.rm = TRUE, row.names = TRUE, markdown = FALSE, digits = 2,
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sep = " ")
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quote = FALSE, sep = " ")
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top_freq(f, n)
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@ -37,6 +37,8 @@ top_freq(f, n)
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\item{digits}{how many significant digits are to be used for numeric values in the header (not for the items themselves, that depends on \code{\link{getOption}("digits")})}
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\item{quote}{a logical value indicating whether or not strings should be printed with surrounding quotes}
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\item{sep}{a character string to separate the terms when selecting multiple columns}
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\item{f}{a frequency table}
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@ -20,6 +20,7 @@ test_that("frequency table works", {
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expect_output(print(freq(septic_patients$age, markdown = TRUE), markdown = FALSE))
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expect_output(print(freq(septic_patients$age, markdown = TRUE), markdown = TRUE))
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expect_output(print(freq(septic_patients$age[0])))
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expect_output(print(freq(septic_patients$age, quote = TRUE)))
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# character
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expect_output(print(freq(septic_patients$mo)))
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@ -16,13 +16,6 @@ test_that("mo_property works", {
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expect_equal(mo_shortname("S. aga"), "S. agalactiae")
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expect_equal(mo_shortname("S. aga", Lancefield = TRUE), "GBS")
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expect_equal(mo_type("E. coli", language = "de"), "Bakterium")
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expect_equal(mo_type("E. coli", language = "nl"), "Bacterie")
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expect_equal(mo_gramstain("E. coli", language = "nl"), "Negatieve staven")
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expect_error(mo_type("E. coli", language = "INVALID"))
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# test integrity
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library(dplyr)
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MOs <- AMR::microorganisms %>% filter(!is.na(mo))
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@ -45,4 +38,19 @@ test_that("mo_property works", {
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expect_gt(sum(tb$c) / nrow(tb), 0.9) # more than 90% of MO code should be identical
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expect_identical(sum(tb$f), nrow(tb)) # all shortnames should be identical
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# check languages
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expect_equal(mo_type("E. coli", language = "de"), "Bakterium")
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expect_equal(mo_type("E. coli", language = "nl"), "Bacterie")
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expect_equal(mo_gramstain("E. coli", language = "nl"), "Negatieve staven")
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expect_output(print(mo_gramstain("E. coli", language = "en")))
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expect_output(print(mo_gramstain("E. coli", language = "de")))
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expect_output(print(mo_gramstain("E. coli", language = "nl")))
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expect_output(print(mo_gramstain("E. coli", language = "es")))
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expect_output(print(mo_gramstain("E. coli", language = "pt")))
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expect_output(print(mo_gramstain("E. coli", language = "it")))
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expect_output(print(mo_gramstain("E. coli", language = "fr")))
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expect_error(mo_gramstain("E. coli", language = "UNKNOWN"))
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})
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@ -34,7 +34,7 @@ This `AMR` package basically does four important things:
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* Use `first_isolate` to identify the first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute).
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* You can also identify first *weighted* isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
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* Use `MDRO` (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
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* The data set `microorganisms` contains the family, genus, species, subspecies, colloquial name and Gram stain of almost 3,000 potential human pathogenic microorganisms (bacteria, fungi/yeasts and parasites). This enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus`, `mo_family` or `mo_gramstain`. As they use `as.mo` internally, they also use artificial intelligence. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, Spanish and Portuguese. These functions can be used to add new variables to your data.
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* The data set `microorganisms` contains the family, genus, species, subspecies, colloquial name and Gram stain of almost 3,000 potential human pathogenic microorganisms (bacteria, fungi/yeasts and parasites). This enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus`, `mo_family` or `mo_gramstain`. As they use `as.mo` internally, they also use artificial intelligence. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, French, Italian, Spanish and Portuguese. These functions can be used to add new variables to your data.
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* The data set `antibiotics` contains the ATC code, LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains a total of 298 trade names. Use functions like `ab_official` and `ab_tradenames` to look up values. As the `mo_*` functions use `as.mo` internally, the `ab_*` functions use `as.atc` internally so it uses AI to guess your expected result. For example, `ab_official("Fluclox")`, `ab_official("Floxapen")` and `ab_official("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
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3. It **analyses the data** with convenient functions that use well-known methods.
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