new class bactid

This commit is contained in:
dr. M.S. (Matthijs) Berends 2018-07-23 14:14:03 +02:00
parent 40de1b4ac2
commit 8421638b60
21 changed files with 408 additions and 251 deletions

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@ -28,7 +28,6 @@ Depends:
R (>= 3.0.0) R (>= 3.0.0)
Imports: Imports:
backports, backports,
broom,
clipr, clipr,
curl, curl,
dplyr (>= 0.7.0), dplyr (>= 0.7.0),

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@ -1,5 +1,6 @@
# Generated by roxygen2: do not edit by hand # Generated by roxygen2: do not edit by hand
S3method(as.data.frame,bactid)
S3method(as.data.frame,frequency_tbl) S3method(as.data.frame,frequency_tbl)
S3method(as.double,mic) S3method(as.double,mic)
S3method(as.integer,mic) S3method(as.integer,mic)
@ -16,6 +17,7 @@ S3method(kurtosis,matrix)
S3method(plot,frequency_tbl) S3method(plot,frequency_tbl)
S3method(plot,mic) S3method(plot,mic)
S3method(plot,rsi) S3method(plot,rsi)
S3method(print,bactid)
S3method(print,frequency_tbl) S3method(print,frequency_tbl)
S3method(print,mic) S3method(print,mic)
S3method(print,rsi) S3method(print,rsi)
@ -32,6 +34,7 @@ export(MDRO)
export(MRGN) export(MRGN)
export(abname) export(abname)
export(anti_join_microorganisms) export(anti_join_microorganisms)
export(as.bactid)
export(as.mic) export(as.mic)
export(as.rsi) export(as.rsi)
export(atc_ddd) export(atc_ddd)
@ -48,6 +51,7 @@ export(guess_atc)
export(guess_bactid) export(guess_bactid)
export(inner_join_microorganisms) export(inner_join_microorganisms)
export(interpretive_reading) export(interpretive_reading)
export(is.bactid)
export(is.mic) export(is.mic)
export(is.rsi) export(is.rsi)
export(key_antibiotics) export(key_antibiotics)
@ -68,6 +72,7 @@ export(semi_join_microorganisms)
export(skewness) export(skewness)
export(susceptibility) export(susceptibility)
export(top_freq) export(top_freq)
exportMethods(as.data.frame.bactid)
exportMethods(as.data.frame.frequency_tbl) exportMethods(as.data.frame.frequency_tbl)
exportMethods(as.double.mic) exportMethods(as.double.mic)
exportMethods(as.integer.mic) exportMethods(as.integer.mic)
@ -85,6 +90,7 @@ exportMethods(kurtosis.matrix)
exportMethods(plot.frequency_tbl) exportMethods(plot.frequency_tbl)
exportMethods(plot.mic) exportMethods(plot.mic)
exportMethods(plot.rsi) exportMethods(plot.rsi)
exportMethods(print.bactid)
exportMethods(print.frequency_tbl) exportMethods(print.frequency_tbl)
exportMethods(print.mic) exportMethods(print.mic)
exportMethods(print.rsi) exportMethods(print.rsi)

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@ -4,6 +4,7 @@
* **BREAKING**: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call *key antibiotics*) to include more first isolates (afterwards called first *weighted* isolates) are now as follows: * **BREAKING**: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call *key antibiotics*) to include more first isolates (afterwards called first *weighted* isolates) are now as follows:
* Gram-positive: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole, vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin * Gram-positive: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole, vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
* Gram-negative: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole, gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem * Gram-negative: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole, gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
* Functions `as.bactid` and `is.bactid` to transform/look up microbial ID's; this replaces the function `guess_bactid` but it will remain available for backwards compatibility
* For convience, new descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices * For convience, new descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
* Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test), which use is the same as `chisq.test` * Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test), which use is the same as `chisq.test`
* Function `ratio` to transform a vector of values to a preset ratio * Function `ratio` to transform a vector of values to a preset ratio
@ -28,8 +29,9 @@
* Printing of class `mic` now shows all MIC values * Printing of class `mic` now shows all MIC values
* `%like%` now supports multiple patterns * `%like%` now supports multiple patterns
* Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the parameter `toConsole` is not longer needed. * Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the parameter `toConsole` is not longer needed.
* Small translational improvements to the `septic_patients` dataset * Fix for `freq` where the class of an item would be lost
* Small improvements to the `microorganisms` dataset, especially for *Salmonella* * Small translational improvements to the `septic_patients` dataset and the column `bactid` now has the new class `"bactid"`
* Small improvements to the `microorganisms` dataset (especially for *Salmonella*) and the column `bactid` now has the new class `"bactid"`
* Combined MIC/RSI values will now be coerced by the `rsi` and `mic` functions: * Combined MIC/RSI values will now be coerced by the `rsi` and `mic` functions:
* `as.rsi("<=0.002; S")` will return `S` * `as.rsi("<=0.002; S")` will return `S`
* `as.mic("<=0.002; S")` will return `<=0.002` * `as.mic("<=0.002; S")` will return `<=0.002`
@ -38,7 +40,8 @@
* Build-in host check for `atc_property` as it requires the host set by `url` to be responsive * Build-in host check for `atc_property` as it requires the host set by `url` to be responsive
* Improved `first_isolate` algorithm to exclude isolates where bacteria ID or genus is unavailable * Improved `first_isolate` algorithm to exclude isolates where bacteria ID or genus is unavailable
* Fix for warning *hybrid evaluation forced for row_number* ([`924b62`](https://github.com/tidyverse/dplyr/commit/924b62)) from the `dplyr` package v0.7.5 and above * Fix for warning *hybrid evaluation forced for row_number* ([`924b62`](https://github.com/tidyverse/dplyr/commit/924b62)) from the `dplyr` package v0.7.5 and above
* Support for 1 or 2 columns as input for `guess_bactid` * Support for empty values and for 1 or 2 columns as input for `guess_bactid` (now called `as.bactid`)
* So `yourdata %>% select(genus, species) %>% as.bactid()` now also works
#### Other #### Other
* Unit testing for R 3.0 and the latest available release: https://travis-ci.org/msberends/AMR * Unit testing for R 3.0 and the latest available release: https://travis-ci.org/msberends/AMR

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@ -16,37 +16,58 @@
# GNU General Public License for more details. # # GNU General Public License for more details. #
# ==================================================================== # # ==================================================================== #
#' Find bacteria ID based on genus/species #' Transform to bacteria ID
#' #'
#' Use this function to determine a valid ID based on a genus (and species). This input could be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples. #' Use this function to determine a valid ID based on a genus (and species). This input can be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
#' @param x character vector or a dataframe with one or two columns #' @param x a character vector or a dataframe with one or two columns
#' @rdname as.bactid
#' @details Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. For example:
#' \itemize{
#' \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
#' \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae}}
#' \item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}}
#' \item{Something like \code{"stau"} or \code{"staaur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}}
#' }
#' Moreover, this function also supports ID's based on only Gram stain, when the species is not known. \cr
#' For example, \code{"Gram negative rods"} and \code{"GNR"} will both return the ID of a Gram negative rod: \code{GNR}.
#' @export #' @export
#' @importFrom dplyr %>% filter pull #' @importFrom dplyr %>% filter pull
#' @return Character (vector). #' @return Character (vector) with class \code{"bactid"}. Unknown values will return \code{NA}.
#' @seealso \code{\link{microorganisms}} for the dataframe that is being used to determine ID's. #' @seealso \code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
#' @examples #' @examples
#' # These examples all return "STAAUR", the ID of S. aureus: #' # These examples all return "STAAUR", the ID of S. aureus:
#' guess_bactid("stau") #' as.bactid("stau")
#' guess_bactid("STAU") #' as.bactid("STAU")
#' guess_bactid("staaur") #' as.bactid("staaur")
#' guess_bactid("S. aureus") #' as.bactid("S. aureus")
#' guess_bactid("S aureus") #' as.bactid("S aureus")
#' guess_bactid("Staphylococcus aureus") #' as.bactid("Staphylococcus aureus")
#' guess_bactid("MRSA") # Methicillin-resistant S. aureus #' as.bactid("MRSA") # Methicillin Resistant S. aureus
#' guess_bactid("VISA") # Vancomycin Intermediate S. aureus #' as.bactid("VISA") # Vancomycin Intermediate S. aureus
#' as.bactid("VRSA") # Vancomycin Resistant S. aureus
#' #'
#' \dontrun{ #' \dontrun{
#' df$bactid <- guess_bactid(df$microorganism_name) #' df$bactid <- as.bactid(df$microorganism_name)
#' #'
#' # the select function of tidyverse is also supported: #' # the select function of tidyverse is also supported:
#' df$bactid <- df %>% select(microorganism_name) %>% guess_bactid() #' library(dplyr)
#' df$bactid <- df %>%
#' select(microorganism_name) %>%
#' as.bactid()
#' #'
#' # and can even contain 2 columns, which is convenient for genus/species combinations: #' # and can even contain 2 columns, which is convenient for genus/species combinations:
#' df$bactid <- df %>% select(genus, species) %>% guess_bactid() #' df$bactid <- df %>%
#' select(genus, species) %>%
#' as.bactid()
#'
#' # same result: #' # same result:
#' df <- df %>% mutate(bactid = paste(genus, species)) %>% guess_bactid()) #' df <- df %>%
#' mutate(bactid = paste(genus, species) %>%
#' as.bactid())
#' } #' }
guess_bactid <- function(x) { as.bactid <- function(x) {
failures <- character(0)
if (NCOL(x) == 2) { if (NCOL(x) == 2) {
# support tidyverse selection like: df %>% select(colA, colB) # support tidyverse selection like: df %>% select(colA, colB)
@ -60,17 +81,19 @@ guess_bactid <- function(x) {
if (NCOL(x) > 2) { if (NCOL(x) > 2) {
stop('`x` can be 2 columns at most', call. = FALSE) stop('`x` can be 2 columns at most', call. = FALSE)
} }
# support tidyverse selection like: df %>% select(colA) # support tidyverse selection like: df %>% select(colA)
if (!is.vector(x)) { if (!is.vector(x)) {
x <- pull(x, 1) x <- pull(x, 1)
} }
} }
x.fullbackup <- x
# remove dots and other non-text in case of "E. coli" except spaces # remove dots and other non-text in case of "E. coli" except spaces
x <- gsub("[^a-zA-Z ]+", "", x) x <- gsub("[^a-zA-Z0-9 ]+", "", x)
# but spaces before and after should be omitted # but spaces before and after should be omitted
x <- trimws(x, which = "both") x <- trimws(x, which = "both")
x.bak <- x x.backup <- x
# replace space by regex sign # replace space by regex sign
x <- gsub(" ", ".*", x, fixed = TRUE) x <- gsub(" ", ".*", x, fixed = TRUE)
# add start and stop # add start and stop
@ -96,42 +119,44 @@ guess_bactid <- function(x) {
# avoid detection of Pasteurella aerogenes in case of Pseudomonas aeruginosa # avoid detection of Pasteurella aerogenes in case of Pseudomonas aeruginosa
x[i] <- 'Pseudomonas aeruginosa' x[i] <- 'Pseudomonas aeruginosa'
} }
if (tolower(x[i]) %like% 'coagulase') { if (tolower(x[i]) %like% 'coagulase'
# coerce S. coagulase negative | tolower(x[i]) %like% 'cns'
| tolower(x[i]) %like% 'cons') {
# coerce S. coagulase negative, also as CNS and CoNS
x[i] <- 'Coagulase Negative Staphylococcus (CNS)' x[i] <- 'Coagulase Negative Staphylococcus (CNS)'
} }
# translate known trivial names to genus+species # translate known trivial names to genus+species
if (!is.na(x.bak[i])) { if (!is.na(x.backup[i])) {
if (toupper(x.bak[i]) == 'MRSA' if (toupper(x.backup[i]) == 'MRSA'
| toupper(x.bak[i]) == 'VISA' | toupper(x.backup[i]) == 'VISA'
| toupper(x.bak[i]) == 'VRSA') { | toupper(x.backup[i]) == 'VRSA') {
x[i] <- 'Staphylococcus aureus' x[i] <- 'Staphylococcus aureus'
} }
if (toupper(x.bak[i]) == 'MRSE') { if (toupper(x.backup[i]) == 'MRSE') {
x[i] <- 'Staphylococcus epidermidis' x[i] <- 'Staphylococcus epidermidis'
} }
if (toupper(x.bak[i]) == 'VRE') { if (toupper(x.backup[i]) == 'VRE') {
x[i] <- 'Enterococcus' x[i] <- 'Enterococcus'
} }
if (toupper(x.bak[i]) == 'MRPA') { if (toupper(x.backup[i]) == 'MRPA') {
# multi resistant P. aeruginosa # multi resistant P. aeruginosa
x[i] <- 'Pseudomonas aeruginosa' x[i] <- 'Pseudomonas aeruginosa'
} }
if (toupper(x.bak[i]) == 'PISP' if (toupper(x.backup[i]) == 'PISP'
| toupper(x.bak[i]) == 'PRSP') { | toupper(x.backup[i]) == 'PRSP') {
# peni resistant S. pneumoniae # peni resistant S. pneumoniae
x[i] <- 'Streptococcus pneumoniae' x[i] <- 'Streptococcus pneumoniae'
} }
if (toupper(x.bak[i]) == 'VISP' if (toupper(x.backup[i]) == 'VISP'
| toupper(x.bak[i]) == 'VRSP') { | toupper(x.backup[i]) == 'VRSP') {
# vanco resistant S. pneumoniae # vanco resistant S. pneumoniae
x[i] <- 'Streptococcus pneumoniae' x[i] <- 'Streptococcus pneumoniae'
} }
} }
# let's try the ID's first # let's try the ID's first
found <- AMR::microorganisms %>% filter(bactid == x.bak[i]) found <- AMR::microorganisms %>% filter(bactid == x.backup[i])
if (nrow(found) == 0) { if (nrow(found) == 0) {
# now try exact match # now try exact match
@ -152,38 +177,82 @@ guess_bactid <- function(x) {
} }
if (nrow(found) == 0) { if (nrow(found) == 0) {
# search for GLIMS code # search for GLIMS code
if (toupper(x.bak[i]) %in% toupper(AMR::microorganisms.umcg$mocode)) { if (toupper(x.backup[i]) %in% toupper(AMR::microorganisms.umcg$mocode)) {
found <- AMR::microorganisms.umcg %>% filter(toupper(mocode) == toupper(x.bak[i])) found <- AMR::microorganisms.umcg %>% filter(toupper(mocode) == toupper(x.backup[i]))
} }
} }
if (nrow(found) == 0) { if (nrow(found) == 0) {
# try splitting of characters and then find ID # try splitting of characters and then find ID
# like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus
x_split <- x x_split <- x
x_length <- nchar(x.bak[i]) x_length <- nchar(x.backup[i])
x_split[i] <- paste0(x.bak[i] %>% substr(1, x_length / 2) %>% trimws(), x_split[i] <- paste0(x.backup[i] %>% substr(1, x_length / 2) %>% trimws(),
'.* ', '.* ',
x.bak[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws()) x.backup[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws())
found <- AMR::microorganisms %>% filter(fullname %like% paste0('^', x_split[i])) found <- AMR::microorganisms %>% filter(fullname %like% paste0('^', x_split[i]))
} }
if (nrow(found) == 0) { if (nrow(found) == 0) {
# try any match with text before and after original search string # try any match with text before and after original search string
# so "negative rods" will be "GNR" # so "negative rods" will be "GNR"
if (x.bak[i] %like% "^Gram") { if (x.backup[i] %like% "^Gram") {
x.bak[i] <- gsub("^Gram", "", x.bak[i], ignore.case = TRUE) x.backup[i] <- gsub("^Gram", "", x.backup[i], ignore.case = TRUE)
# remove leading and trailing spaces again # remove leading and trailing spaces again
x.bak[i] <- trimws(x.bak[i], which = "both") x.backup[i] <- trimws(x.backup[i], which = "both")
} }
if (!is.na(x.bak[i])) { if (!is.na(x.backup[i])) {
found <- AMR::microorganisms %>% filter(fullname %like% x.bak[i]) found <- AMR::microorganisms %>% filter(fullname %like% x.backup[i])
} }
} }
if (nrow(found) != 0) { if (nrow(found) != 0 & x.backup[i] != "") {
x[i] <- as.character(found[1, 'bactid']) x[i] <- as.character(found[1, 'bactid'])
} else { } else {
x[i] <- "" x[i] <- NA_character_
failures <- c(failures, x.fullbackup[i])
} }
} }
failures <- failures[!failures %in% c(NA, NULL, NaN)]
if (length(failures) > 0) {
warning("These values could not be coerced to a valid bactid: ",
paste('"', unique(failures), '"', sep = "", collapse = ', '),
".",
call. = FALSE)
}
class(x) <- "bactid"
attr(x, 'package') <- 'AMR'
attr(x, 'package.version') <- packageDescription('AMR')$Version
x x
} }
#' @rdname as.bactid
#' @export
guess_bactid <- as.bactid
#' @rdname as.bactid
#' @export
is.bactid <- function(x) {
identical(class(x), "bactid")
}
#' @exportMethod print.bactid
#' @export
#' @noRd
print.bactid <- function(x, ...) {
cat("Class 'bactid'\n")
print.default(as.character(x), quote = FALSE)
}
#' @exportMethod as.data.frame.bactid
#' @export
#' @noRd
as.data.frame.bactid <- function (x, ...) {
# same as as.data.frame.character but with removed stringsAsFactors
nm <- paste(deparse(substitute(x), width.cutoff = 500L),
collapse = " ")
if (!"nm" %in% names(list(...))) {
as.data.frame.vector(x, ..., nm = nm)
} else {
as.data.frame.vector(x, ...)
}
}

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@ -201,9 +201,10 @@ EUCAST_rules <- function(tbl,
} }
# join to microorganisms table # join to microorganisms table
joinby <- colnames(AMR::microorganisms)[1] if (!tbl %>% pull(col_bactid) %>% is.bactid()) {
names(joinby) <- col_bactid tbl[, col_bactid] <- tbl %>% pull(col_bactid) %>% as.bactid()
tbl <- tbl %>% left_join(y = AMR::microorganisms, by = joinby, suffix = c("_tempmicroorganisms", "")) }
tbl <- tbl %>% left_join_microorganisms(by = col_bactid, suffix = c("_tempmicroorganisms", ""))
# antibiotic classes # antibiotic classes
aminoglycosides <- c(tobr, gent, kana, neom, neti, siso) aminoglycosides <- c(tobr, gent, kana, neom, neti, siso)

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@ -22,7 +22,7 @@
#' @param tbl a \code{data.frame} containing isolates. #' @param tbl a \code{data.frame} containing isolates.
#' @param col_date column name of the result date (or date that is was received on the lab) #' @param col_date column name of the result date (or date that is was received on the lab)
#' @param col_patient_id column name of the unique IDs of the patients #' @param col_patient_id column name of the unique IDs of the patients
#' @param col_bactid column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset). Get your bactid's with the function \code{\link{guess_bactid}}, that takes microorganism names as input. #' @param col_bactid column name of the unique IDs of the microorganisms: \code{bactid}'s. If this column has another class than \code{"bactid"}, values will be coerced using \code{\link{as.bactid}}.
#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation. #' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.
#' @param col_specimen column name of the specimen type or group #' @param col_specimen column name of the specimen type or group
#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU) #' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
@ -126,7 +126,7 @@ first_isolate <- function(tbl,
# bactid OR genus+species must be available # bactid OR genus+species must be available
if (is.na(col_bactid) & (is.na(col_genus) | is.na(col_species))) { if (is.na(col_bactid) & (is.na(col_genus) | is.na(col_species))) {
stop('`col_bactid or both `col_genus` and `col_species` must be available.') stop('`col_bactid` or both `col_genus` and `col_species` must be available.')
} }
# check if columns exist # check if columns exist
@ -152,6 +152,9 @@ first_isolate <- function(tbl,
check_columns_existance(col_keyantibiotics) check_columns_existance(col_keyantibiotics)
if (!is.na(col_bactid)) { if (!is.na(col_bactid)) {
if (!tbl %>% pull(col_bactid) %>% is.bactid()) {
tbl[, col_bactid] <- tbl %>% pull(col_bactid) %>% as.bactid()
}
tbl <- tbl %>% left_join_microorganisms(by = col_bactid) tbl <- tbl %>% left_join_microorganisms(by = col_bactid)
col_genus <- "genus" col_genus <- "genus"
col_species <- "species" col_species <- "species"

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@ -273,8 +273,11 @@ frequency_tbl <- function(x,
} else { } else {
NAs <- x[is.na(x)] NAs <- x[is.na(x)]
} }
if (na.rm == TRUE) { if (na.rm == TRUE) {
x_class <- class(x)
x <- x[!x %in% NAs] x <- x[!x %in% NAs]
class(x) <- x_class
} }
if (missing(sort.count) & 'factor' %in% class(x)) { if (missing(sort.count) & 'factor' %in% class(x)) {

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@ -26,8 +26,8 @@
#' df2 <- left_join_microorganisms(df, "bacteria_id") #' df2 <- left_join_microorganisms(df, "bacteria_id")
#' colnames(df2) #' colnames(df2)
inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) { inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
if (any(class(x) %in% c('character', 'factor'))) { if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
x <- data.frame(bactid = x, stringsAsFactors = FALSE) x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
} }
# no name set to `by` parameter # no name set to `by` parameter
if (is.null(names(by))) { if (is.null(names(by))) {
@ -36,7 +36,9 @@ inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...
} else { } else {
joinby <- by joinby <- by
} }
join <- dplyr::inner_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...) join <- suppressWarnings(
dplyr::inner_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
)
if (nrow(join) > nrow(x)) { if (nrow(join) > nrow(x)) {
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original') warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
} }
@ -46,8 +48,8 @@ inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...
#' @rdname join #' @rdname join
#' @export #' @export
left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) { left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
if (any(class(x) %in% c('character', 'factor'))) { if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
x <- data.frame(bactid = x, stringsAsFactors = FALSE) x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
} }
# no name set to `by` parameter # no name set to `by` parameter
if (is.null(names(by))) { if (is.null(names(by))) {
@ -56,7 +58,9 @@ left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...)
} else { } else {
joinby <- by joinby <- by
} }
join <- dplyr::left_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...) join <- suppressWarnings(
dplyr::left_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
)
if (nrow(join) > nrow(x)) { if (nrow(join) > nrow(x)) {
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original') warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
} }
@ -66,8 +70,8 @@ left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...)
#' @rdname join #' @rdname join
#' @export #' @export
right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) { right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
if (any(class(x) %in% c('character', 'factor'))) { if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
x <- data.frame(bactid = x, stringsAsFactors = FALSE) x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
} }
# no name set to `by` parameter # no name set to `by` parameter
if (is.null(names(by))) { if (is.null(names(by))) {
@ -76,7 +80,9 @@ right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...
} else { } else {
joinby <- by joinby <- by
} }
join <- dplyr::right_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...) join <- suppressWarnings(
dplyr::right_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
)
if (nrow(join) > nrow(x)) { if (nrow(join) > nrow(x)) {
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original') warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
} }
@ -86,8 +92,8 @@ right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...
#' @rdname join #' @rdname join
#' @export #' @export
full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) { full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
if (any(class(x) %in% c('character', 'factor'))) { if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
x <- data.frame(bactid = x, stringsAsFactors = FALSE) x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
} }
# no name set to `by` parameter # no name set to `by` parameter
if (is.null(names(by))) { if (is.null(names(by))) {
@ -96,7 +102,9 @@ full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...)
} else { } else {
joinby <- by joinby <- by
} }
join <- dplyr::full_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...) join <- suppressWarnings(
dplyr::full_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
)
if (nrow(join) > nrow(x)) { if (nrow(join) > nrow(x)) {
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original') warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
} }
@ -106,8 +114,8 @@ full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...)
#' @rdname join #' @rdname join
#' @export #' @export
semi_join_microorganisms <- function(x, by = 'bactid', ...) { semi_join_microorganisms <- function(x, by = 'bactid', ...) {
if (any(class(x) %in% c('character', 'factor'))) { if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
x <- data.frame(bactid = x, stringsAsFactors = FALSE) x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
} }
# no name set to `by` parameter # no name set to `by` parameter
if (is.null(names(by))) { if (is.null(names(by))) {
@ -116,14 +124,16 @@ semi_join_microorganisms <- function(x, by = 'bactid', ...) {
} else { } else {
joinby <- by joinby <- by
} }
dplyr::semi_join(x = x, y = AMR::microorganisms, by = joinby, ...) suppressWarnings(
dplyr::semi_join(x = x, y = AMR::microorganisms, by = joinby, ...)
)
} }
#' @rdname join #' @rdname join
#' @export #' @export
anti_join_microorganisms <- function(x, by = 'bactid', ...) { anti_join_microorganisms <- function(x, by = 'bactid', ...) {
if (any(class(x) %in% c('character', 'factor'))) { if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
x <- data.frame(bactid = x, stringsAsFactors = FALSE) x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
} }
# no name set to `by` parameter # no name set to `by` parameter
if (is.null(names(by))) { if (is.null(names(by))) {
@ -132,5 +142,7 @@ anti_join_microorganisms <- function(x, by = 'bactid', ...) {
} else { } else {
joinby <- by joinby <- by
} }
dplyr::anti_join(x = x, y = AMR::microorganisms, by = joinby, ...) suppressWarnings(
dplyr::anti_join(x = x, y = AMR::microorganisms, by = joinby, ...)
)
} }

149
README.md
View File

@ -126,6 +126,86 @@ after
# 5 PSEAER R R - - R # 5 PSEAER R R - - R
``` ```
Bacteria ID's can be retrieved with the `as.bactid` function. It uses any type of info about a microorganism as input. For example, all these will return value `STAAUR`, the ID of *S. aureus*:
```r
as.bactid("stau")
as.bactid("STAU")
as.bactid("staaur")
as.bactid("S. aureus")
as.bactid("S aureus")
as.bactid("Staphylococcus aureus")
as.bactid("MRSA") # Methicillin Resistant S. aureus
as.bactid("VISA") # Vancomycin Intermediate S. aureus
as.bactid("VRSA") # Vancomycin Resistant S. aureus
```
### New classes
This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`).
Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
```r
# Transform values to new classes
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
```
These functions also try to coerce valid values.
Quick overviews when just printing objects:
```r
mic_data
# Class 'mic': 7 isolates
#
# <NA> 0
#
# <=0.128 1 8 16 >=32
# 1 1 2 2 1
rsi_data
# Class 'rsi': 880 isolates
#
# <NA>: 0
# Sum of S: 474
# Sum of IR: 406
# - Sum of R: 370
# - Sum of I: 36
#
# %S %IR %I %R
# 53.9 46.1 4.1 42.0
```
A plot of `rsi_data`:
```r
plot(rsi_data)
```
![example1](man/figures/rsi_example.png)
A plot of `mic_data` (defaults to bar plot):
```r
plot(mic_data)
```
![example2](man/figures/mic_example.png)
Other epidemiological functions:
```r
# Determine key antibiotic based on bacteria ID
key_antibiotics(...)
# Selection of first isolates of any patient
first_isolate(...)
# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
rsi(...)
# Predict resistance levels of antibiotics
rsi_predict(...)
# Get name of antibiotic by ATC code
abname(...)
abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
```
### Frequency tables ### Frequency tables
Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`). It can be used in two ways: Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`). It can be used in two ways:
```r ```r
@ -235,79 +315,12 @@ Learn more about this function with:
?freq ?freq
``` ```
### New classes
This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`).
Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
```r
# Transform values to new classes
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
```
These functions also try to coerce valid values.
Quick overviews when just printing objects:
```r
mic_data
# Class 'mic': 7 isolates
#
# <NA> 0
#
# <=0.128 1 8 16 >=32
# 1 1 2 2 1
rsi_data
# Class 'rsi': 880 isolates
#
# <NA>: 0
# Sum of S: 474
# Sum of IR: 406
# - Sum of R: 370
# - Sum of I: 36
#
# %S %IR %I %R
# 53.9 46.1 4.1 42.0
```
A plot of `rsi_data`:
```r
plot(rsi_data)
```
![example1](man/figures/rsi_example.png)
A plot of `mic_data` (defaults to bar plot):
```r
plot(mic_data)
```
![example2](man/figures/mic_example.png)
Other epidemiological functions:
```r
# Determine key antibiotic based on bacteria ID
key_antibiotics(...)
# Selection of first isolates of any patient
first_isolate(...)
# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
rsi(...)
# Predict resistance levels of antibiotics
rsi_predict(...)
# Get name of antibiotic by ATC code
abname(...)
abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
```
### Databases included in package ### Databases included in package
Datasets to work with antibiotics and bacteria properties. Datasets to work with antibiotics and bacteria properties.
```r ```r
# Dataset with 2000 random blood culture isolates from anonymised # Dataset with 2000 random blood culture isolates from anonymised
# septic patients between 2001 and 2017 in 5 Dutch hospitals # septic patients between 2001 and 2017 in 5 Dutch hospitals
septic_patients # A tibble: 4,000 x 47 septic_patients # A tibble: 2,000 x 47
# Dataset with ATC antibiotics codes, official names, trade names # Dataset with ATC antibiotics codes, official names, trade names
# and DDD's (oral and parenteral) # and DDD's (oral and parenteral)

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69
man/as.bactid.Rd Normal file
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@ -0,0 +1,69 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bactid.R
\name{as.bactid}
\alias{as.bactid}
\alias{guess_bactid}
\alias{is.bactid}
\title{Transform to bacteria ID}
\usage{
as.bactid(x)
guess_bactid(x)
is.bactid(x)
}
\arguments{
\item{x}{a character vector or a dataframe with one or two columns}
}
\value{
Character (vector) with class \code{"bactid"}. Unknown values will return \code{NA}.
}
\description{
Use this function to determine a valid ID based on a genus (and species). This input can be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
}
\details{
Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. For example:
\itemize{
\item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
\item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae}}
\item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}}
\item{Something like \code{"stau"} or \code{"staaur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}}
}
Moreover, this function also supports ID's based on only Gram stain, when the species is not known. \cr
For example, \code{"Gram negative rods"} and \code{"GNR"} will both return the ID of a Gram negative rod: \code{GNR}.
}
\examples{
# These examples all return "STAAUR", the ID of S. aureus:
as.bactid("stau")
as.bactid("STAU")
as.bactid("staaur")
as.bactid("S. aureus")
as.bactid("S aureus")
as.bactid("Staphylococcus aureus")
as.bactid("MRSA") # Methicillin Resistant S. aureus
as.bactid("VISA") # Vancomycin Intermediate S. aureus
as.bactid("VRSA") # Vancomycin Resistant S. aureus
\dontrun{
df$bactid <- as.bactid(df$microorganism_name)
# the select function of tidyverse is also supported:
library(dplyr)
df$bactid <- df \%>\%
select(microorganism_name) \%>\%
as.bactid()
# and can even contain 2 columns, which is convenient for genus/species combinations:
df$bactid <- df \%>\%
select(genus, species) \%>\%
as.bactid()
# same result:
df <- df \%>\%
mutate(bactid = paste(genus, species) \%>\%
as.bactid())
}
}
\seealso{
\code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
}

View File

@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/first_isolates.R % Please edit documentation in R/first_isolate.R
\name{first_isolate} \name{first_isolate}
\alias{first_isolate} \alias{first_isolate}
\title{Determine first (weighted) isolates} \title{Determine first (weighted) isolates}
@ -21,7 +21,7 @@ first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,
\item{col_patient_id}{column name of the unique IDs of the patients} \item{col_patient_id}{column name of the unique IDs of the patients}
\item{col_bactid}{column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset). Get your bactid's with the function \code{\link{guess_bactid}}, that takes microorganism names as input.} \item{col_bactid}{column name of the unique IDs of the microorganisms: \code{bactid}'s. If this column has another class than \code{"bactid"}, values will be coerced using \code{\link{as.bactid}}.}
\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.} \item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.}

View File

@ -1,43 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/guess_bactid.R
\name{guess_bactid}
\alias{guess_bactid}
\title{Find bacteria ID based on genus/species}
\usage{
guess_bactid(x)
}
\arguments{
\item{x}{character vector or a dataframe with one or two columns}
}
\value{
Character (vector).
}
\description{
Use this function to determine a valid ID based on a genus (and species). This input could be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
}
\examples{
# These examples all return "STAAUR", the ID of S. aureus:
guess_bactid("stau")
guess_bactid("STAU")
guess_bactid("staaur")
guess_bactid("S. aureus")
guess_bactid("S aureus")
guess_bactid("Staphylococcus aureus")
guess_bactid("MRSA") # Methicillin-resistant S. aureus
guess_bactid("VISA") # Vancomycin Intermediate S. aureus
\dontrun{
df$bactid <- guess_bactid(df$microorganism_name)
# the select function of tidyverse is also supported:
df$bactid <- df \%>\% select(microorganism_name) \%>\% guess_bactid()
# and can even contain 2 columns, which is convenient for genus/species combinations:
df$bactid <- df \%>\% select(genus, species) \%>\% guess_bactid()
# same result:
df <- df \%>\% mutate(bactid = paste(genus, species)) \%>\% guess_bactid())
}
}
\seealso{
\code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
}

View File

@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/join.R % Please edit documentation in R/join_microorganisms.R
\name{join} \name{join}
\alias{join} \alias{join}
\alias{inner_join_microorganisms} \alias{inner_join_microorganisms}

View File

@ -19,7 +19,7 @@ key_antibiotics_equal(x, y, type = c("keyantibiotics", "points"),
\arguments{ \arguments{
\item{tbl}{table with antibiotics coloms, like \code{amox} and \code{amcl}.} \item{tbl}{table with antibiotics coloms, like \code{amox} and \code{amcl}.}
\item{col_bactid}{column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset). Get your bactid's with the function \code{\link{guess_bactid}}, that takes microorganism names as input.} \item{col_bactid}{column name of the unique IDs of the microorganisms: \code{bactid}'s. If this column has another class than \code{"bactid"}, values will be coerced using \code{\link{as.bactid}}.}
\item{universal_1, universal_2, universal_3, universal_4, universal_5, universal_6}{column names of \strong{broad-spectrum} antibiotics, case-insensitive} \item{universal_1, universal_2, universal_3, universal_4, universal_5, universal_6}{column names of \strong{broad-spectrum} antibiotics, case-insensitive}

View File

@ -0,0 +1,64 @@
context("bactid.R")
test_that("as.bactid works", {
expect_identical(
as.character(as.bactid(c("E. coli", "H. influenzae"))),
c("ESCCOL", "HAEINF"))
expect_equal(as.character(as.bactid("Escherichia coli")), "ESCCOL")
expect_equal(as.character(as.bactid("P. aer")), "PSEAER") # not Pasteurella aerogenes
expect_equal(as.character(as.bactid("Negative rods")), "GNR")
expect_equal(as.character(as.bactid("MRSE")), "STAEPI")
expect_equal(as.character(as.bactid("VRE")), "ENC")
expect_equal(as.character(as.bactid("MRPA")), "PSEAER")
expect_equal(as.character(as.bactid("PISP")), "STCPNE")
expect_equal(as.character(as.bactid("PRSP")), "STCPNE")
expect_equal(as.character(as.bactid("VISP")), "STCPNE")
expect_equal(as.character(as.bactid("VRSP")), "STCPNE")
expect_identical(
as.character(
as.bactid(c("stau",
"STAU",
"staaur",
"S. aureus",
"S aureus",
"Staphylococcus aureus",
"MRSA",
"VISA"))),
rep("STAAUR", 8))
# select with one column
expect_identical(
septic_patients[1:10,] %>%
left_join_microorganisms() %>%
select(genus) %>%
as.bactid() %>%
as.character(),
c("STC", "STC", "NEI", "STA", "STA",
"NEI", "ENT", "ENT", "ESC", "KLE"))
# select with two columns
expect_identical(
septic_patients[1:10,] %>%
pull(bactid),
septic_patients[1:10,] %>%
left_join_microorganisms() %>%
select(genus, species) %>%
as.bactid() %>%
as.character())
# unknown results
expect_warning(as.bactid(c("INVALID", "Yeah, unknown")))
# print
expect_output(print(as.bactid(c("ESCCOL", NA))))
# helper function
expect_identical(as.bactid("ESCCOL"),
guess_bactid("ESCCOL"))
})

View File

@ -1,27 +1,34 @@
context("eucast.R") context("eucast.R")
test_that("EUCAST rules work", { test_that("EUCAST rules work", {
a <- suppressWarnings(EUCAST_rules(septic_patients)) expect_identical(colnames(septic_patients),
colnames(suppressWarnings(EUCAST_rules(septic_patients))))
a <- data.frame(bactid = c("KLEPNE", # Klebsiella pneumoniae a <- data.frame(bactid =
"PSEAER", # Pseudomonas aeruginosa c("KLEPNE", # Klebsiella pneumoniae
"ENTAER"), # Enterobacter aerogenes "PSEAER", # Pseudomonas aeruginosa
"ENTAER"), # Enterobacter aerogenes
amox = "-", # Amoxicillin amox = "-", # Amoxicillin
stringsAsFactors = FALSE) stringsAsFactors = FALSE)
b <- data.frame(bactid = c("KLEPNE", # Klebsiella pneumoniae b <- data.frame(bactid =
"PSEAER", # Pseudomonas aeruginosa as.bactid(
"ENTAER"), # Enterobacter aerogenes c("KLEPNE", # Klebsiella pneumoniae
"PSEAER", # Pseudomonas aeruginosa
"ENTAER")), # Enterobacter aerogenes
amox = "R", # Amoxicillin amox = "R", # Amoxicillin
stringsAsFactors = FALSE) stringsAsFactors = FALSE)
expect_equal(EUCAST_rules(a, info = FALSE), b) expect_identical(EUCAST_rules(a, info = FALSE), b)
expect_equal(suppressWarnings(interpretive_reading(a, info = TRUE)), b) expect_identical(suppressWarnings(interpretive_reading(a, info = TRUE)), b)
a <- data.frame(bactid = c("STAAUR", # Staphylococcus aureus a <- data.frame(bactid =
"STCGRA"), # Streptococcus pyognenes (Lancefield Group A) c("STAAUR", # Staphylococcus aureus
"STCGRA"), # Streptococcus pyognenes (Lancefield Group A)
coli = "-", # Colistin coli = "-", # Colistin
stringsAsFactors = FALSE) stringsAsFactors = FALSE)
b <- data.frame(bactid = c("STAAUR", # Staphylococcus aureus b <- data.frame(bactid =
"STCGRA"), # Streptococcus pyognenes (Lancefield Group A) as.bactid(
c("STAAUR", # Staphylococcus aureus
"STCGRA")), # Streptococcus pyognenes (Lancefield Group A)
coli = "R", # Colistin coli = "R", # Colistin
stringsAsFactors = FALSE) stringsAsFactors = FALSE)
expect_equal(EUCAST_rules(a, info = FALSE), b) expect_equal(EUCAST_rules(a, info = FALSE), b)

View File

@ -1,4 +1,4 @@
context("first_isolates.R") context("first_isolate.R")
test_that("first isolates work", { test_that("first isolates work", {
# septic_patients contains 1959 out of 2000 first isolates # septic_patients contains 1959 out of 2000 first isolates

View File

@ -1,49 +0,0 @@
context("guess_bactid.R")
test_that("guess_bactid works", {
expect_identical(
guess_bactid(c("E. coli", "H. influenzae")),
c("ESCCOL", "HAEINF"))
expect_equal(guess_bactid("Escherichia coli"), "ESCCOL")
expect_equal(guess_bactid("P. aer"), "PSEAER") # not Pasteurella aerogenes
expect_equal(guess_bactid("Negative rods"), "GNR")
expect_equal(guess_bactid("MRSE"), "STAEPI")
expect_equal(guess_bactid("VRE"), "ENC")
expect_equal(guess_bactid("MRPA"), "PSEAER")
expect_equal(guess_bactid("PISP"), "STCPNE")
expect_equal(guess_bactid("PRSP"), "STCPNE")
expect_equal(guess_bactid("VISP"), "STCPNE")
expect_equal(guess_bactid("VRSP"), "STCPNE")
expect_identical(
guess_bactid(c("stau",
"STAU",
"staaur",
"S. aureus",
"S aureus",
"Staphylococcus aureus",
"MRSA",
"VISA")),
rep("STAAUR", 8))
# select with one column
expect_identical(
septic_patients[1:10,] %>%
left_join_microorganisms() %>%
select(genus) %>%
guess_bactid(),
c("STC", "STC", "NEI", "STA", "STA",
"NEI", "ENT", "ENT", "ESC", "KLE"))
# select with two columns
expect_identical(
septic_patients[1:10,] %>%
pull(bactid),
septic_patients[1:10,] %>%
left_join_microorganisms() %>%
select(genus, species) %>%
guess_bactid())
})

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@ -1,4 +1,4 @@
context("joins.R") context("join_microorganisms.R")
test_that("joins work", { test_that("joins work", {
unjoined <- septic_patients unjoined <- septic_patients