mirror of https://github.com/msberends/AMR.git
new class bactid
This commit is contained in:
parent
40de1b4ac2
commit
8421638b60
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@ -28,7 +28,6 @@ Depends:
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R (>= 3.0.0)
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Imports:
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backports,
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broom,
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clipr,
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curl,
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dplyr (>= 0.7.0),
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@ -1,5 +1,6 @@
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# Generated by roxygen2: do not edit by hand
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S3method(as.data.frame,bactid)
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S3method(as.data.frame,frequency_tbl)
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S3method(as.double,mic)
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S3method(as.integer,mic)
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@ -16,6 +17,7 @@ S3method(kurtosis,matrix)
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S3method(plot,frequency_tbl)
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S3method(plot,mic)
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S3method(plot,rsi)
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S3method(print,bactid)
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S3method(print,frequency_tbl)
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S3method(print,mic)
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S3method(print,rsi)
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@ -32,6 +34,7 @@ export(MDRO)
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export(MRGN)
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export(abname)
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export(anti_join_microorganisms)
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export(as.bactid)
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export(as.mic)
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export(as.rsi)
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export(atc_ddd)
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@ -48,6 +51,7 @@ export(guess_atc)
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export(guess_bactid)
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export(inner_join_microorganisms)
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export(interpretive_reading)
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export(is.bactid)
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export(is.mic)
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export(is.rsi)
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export(key_antibiotics)
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@ -68,6 +72,7 @@ export(semi_join_microorganisms)
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export(skewness)
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export(susceptibility)
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export(top_freq)
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exportMethods(as.data.frame.bactid)
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exportMethods(as.data.frame.frequency_tbl)
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exportMethods(as.double.mic)
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exportMethods(as.integer.mic)
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@ -85,6 +90,7 @@ exportMethods(kurtosis.matrix)
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exportMethods(plot.frequency_tbl)
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exportMethods(plot.mic)
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exportMethods(plot.rsi)
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exportMethods(print.bactid)
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exportMethods(print.frequency_tbl)
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exportMethods(print.mic)
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exportMethods(print.rsi)
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9
NEWS.md
9
NEWS.md
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@ -4,6 +4,7 @@
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* **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:
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* Gram-positive: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole, vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
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* Gram-negative: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole, gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
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* 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
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* 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
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* 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`
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* Function `ratio` to transform a vector of values to a preset ratio
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@ -28,8 +29,9 @@
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* Printing of class `mic` now shows all MIC values
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* `%like%` now supports multiple patterns
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* 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.
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* Small translational improvements to the `septic_patients` dataset
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* Small improvements to the `microorganisms` dataset, especially for *Salmonella*
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* Fix for `freq` where the class of an item would be lost
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* Small translational improvements to the `septic_patients` dataset and the column `bactid` now has the new class `"bactid"`
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* Small improvements to the `microorganisms` dataset (especially for *Salmonella*) and the column `bactid` now has the new class `"bactid"`
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* Combined MIC/RSI values will now be coerced by the `rsi` and `mic` functions:
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* `as.rsi("<=0.002; S")` will return `S`
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* `as.mic("<=0.002; S")` will return `<=0.002`
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@ -38,7 +40,8 @@
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* Build-in host check for `atc_property` as it requires the host set by `url` to be responsive
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* Improved `first_isolate` algorithm to exclude isolates where bacteria ID or genus is unavailable
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* 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
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* Support for 1 or 2 columns as input for `guess_bactid`
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* Support for empty values and for 1 or 2 columns as input for `guess_bactid` (now called `as.bactid`)
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* So `yourdata %>% select(genus, species) %>% as.bactid()` now also works
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#### Other
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* 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 @@
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# GNU General Public License for more details. #
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# ==================================================================== #
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#' Find bacteria ID based on genus/species
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#' Transform to bacteria ID
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#'
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#' 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.
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#' @param x character vector or a dataframe with one or two columns
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#' 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.
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#' @param x a character vector or a dataframe with one or two columns
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#' @rdname as.bactid
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#' @details Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. For example:
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#' \itemize{
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#' \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
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#' \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae}}
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#' \item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}}
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#' \item{Something like \code{"stau"} or \code{"staaur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}}
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#' }
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#' Moreover, this function also supports ID's based on only Gram stain, when the species is not known. \cr
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#' For example, \code{"Gram negative rods"} and \code{"GNR"} will both return the ID of a Gram negative rod: \code{GNR}.
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#' @export
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#' @importFrom dplyr %>% filter pull
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#' @return Character (vector).
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#' @return Character (vector) with class \code{"bactid"}. Unknown values will return \code{NA}.
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#' @seealso \code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
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#' @examples
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#' # These examples all return "STAAUR", the ID of S. aureus:
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#' guess_bactid("stau")
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#' guess_bactid("STAU")
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#' guess_bactid("staaur")
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#' guess_bactid("S. aureus")
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#' guess_bactid("S aureus")
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#' guess_bactid("Staphylococcus aureus")
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#' guess_bactid("MRSA") # Methicillin-resistant S. aureus
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#' guess_bactid("VISA") # Vancomycin Intermediate S. aureus
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#' as.bactid("stau")
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#' as.bactid("STAU")
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#' as.bactid("staaur")
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#' as.bactid("S. aureus")
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#' as.bactid("S aureus")
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#' as.bactid("Staphylococcus aureus")
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#' as.bactid("MRSA") # Methicillin Resistant S. aureus
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#' as.bactid("VISA") # Vancomycin Intermediate S. aureus
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#' as.bactid("VRSA") # Vancomycin Resistant S. aureus
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#'
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#' \dontrun{
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#' df$bactid <- guess_bactid(df$microorganism_name)
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#' df$bactid <- as.bactid(df$microorganism_name)
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#'
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#' # the select function of tidyverse is also supported:
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#' df$bactid <- df %>% select(microorganism_name) %>% guess_bactid()
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#' library(dplyr)
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#' df$bactid <- df %>%
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#' select(microorganism_name) %>%
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#' as.bactid()
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#'
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#' # and can even contain 2 columns, which is convenient for genus/species combinations:
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#' df$bactid <- df %>% select(genus, species) %>% guess_bactid()
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#' df$bactid <- df %>%
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#' select(genus, species) %>%
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#' as.bactid()
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#'
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#' # same result:
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#' df <- df %>% mutate(bactid = paste(genus, species)) %>% guess_bactid())
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#' df <- df %>%
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#' mutate(bactid = paste(genus, species) %>%
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#' as.bactid())
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#' }
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guess_bactid <- function(x) {
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as.bactid <- function(x) {
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failures <- character(0)
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if (NCOL(x) == 2) {
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# support tidyverse selection like: df %>% select(colA, colB)
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@ -60,17 +81,19 @@ guess_bactid <- function(x) {
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if (NCOL(x) > 2) {
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stop('`x` can be 2 columns at most', call. = FALSE)
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}
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# support tidyverse selection like: df %>% select(colA)
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if (!is.vector(x)) {
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x <- pull(x, 1)
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}
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}
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x.fullbackup <- x
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# remove dots and other non-text in case of "E. coli" except spaces
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x <- gsub("[^a-zA-Z ]+", "", x)
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x <- gsub("[^a-zA-Z0-9 ]+", "", x)
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# but spaces before and after should be omitted
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x <- trimws(x, which = "both")
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x.bak <- x
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x.backup <- x
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# replace space by regex sign
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x <- gsub(" ", ".*", x, fixed = TRUE)
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# add start and stop
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@ -96,42 +119,44 @@ guess_bactid <- function(x) {
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# avoid detection of Pasteurella aerogenes in case of Pseudomonas aeruginosa
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x[i] <- 'Pseudomonas aeruginosa'
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}
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if (tolower(x[i]) %like% 'coagulase') {
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# coerce S. coagulase negative
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if (tolower(x[i]) %like% 'coagulase'
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| tolower(x[i]) %like% 'cns'
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| tolower(x[i]) %like% 'cons') {
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# coerce S. coagulase negative, also as CNS and CoNS
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x[i] <- 'Coagulase Negative Staphylococcus (CNS)'
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}
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# translate known trivial names to genus+species
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if (!is.na(x.bak[i])) {
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if (toupper(x.bak[i]) == 'MRSA'
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| toupper(x.bak[i]) == 'VISA'
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| toupper(x.bak[i]) == 'VRSA') {
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if (!is.na(x.backup[i])) {
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if (toupper(x.backup[i]) == 'MRSA'
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| toupper(x.backup[i]) == 'VISA'
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| toupper(x.backup[i]) == 'VRSA') {
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x[i] <- 'Staphylococcus aureus'
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}
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if (toupper(x.bak[i]) == 'MRSE') {
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if (toupper(x.backup[i]) == 'MRSE') {
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x[i] <- 'Staphylococcus epidermidis'
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}
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if (toupper(x.bak[i]) == 'VRE') {
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if (toupper(x.backup[i]) == 'VRE') {
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x[i] <- 'Enterococcus'
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}
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if (toupper(x.bak[i]) == 'MRPA') {
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if (toupper(x.backup[i]) == 'MRPA') {
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# multi resistant P. aeruginosa
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x[i] <- 'Pseudomonas aeruginosa'
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}
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if (toupper(x.bak[i]) == 'PISP'
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| toupper(x.bak[i]) == 'PRSP') {
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if (toupper(x.backup[i]) == 'PISP'
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| toupper(x.backup[i]) == 'PRSP') {
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# peni resistant S. pneumoniae
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x[i] <- 'Streptococcus pneumoniae'
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}
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if (toupper(x.bak[i]) == 'VISP'
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| toupper(x.bak[i]) == 'VRSP') {
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if (toupper(x.backup[i]) == 'VISP'
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| toupper(x.backup[i]) == 'VRSP') {
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# vanco resistant S. pneumoniae
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x[i] <- 'Streptococcus pneumoniae'
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}
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}
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# let's try the ID's first
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found <- AMR::microorganisms %>% filter(bactid == x.bak[i])
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found <- AMR::microorganisms %>% filter(bactid == x.backup[i])
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if (nrow(found) == 0) {
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# now try exact match
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@ -152,38 +177,82 @@ guess_bactid <- function(x) {
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}
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if (nrow(found) == 0) {
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# search for GLIMS code
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if (toupper(x.bak[i]) %in% toupper(AMR::microorganisms.umcg$mocode)) {
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found <- AMR::microorganisms.umcg %>% filter(toupper(mocode) == toupper(x.bak[i]))
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if (toupper(x.backup[i]) %in% toupper(AMR::microorganisms.umcg$mocode)) {
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found <- AMR::microorganisms.umcg %>% filter(toupper(mocode) == toupper(x.backup[i]))
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}
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}
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if (nrow(found) == 0) {
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# try splitting of characters and then find ID
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# like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus
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x_split <- x
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x_length <- nchar(x.bak[i])
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x_split[i] <- paste0(x.bak[i] %>% substr(1, x_length / 2) %>% trimws(),
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x_length <- nchar(x.backup[i])
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x_split[i] <- paste0(x.backup[i] %>% substr(1, x_length / 2) %>% trimws(),
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'.* ',
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x.bak[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws())
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x.backup[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws())
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found <- AMR::microorganisms %>% filter(fullname %like% paste0('^', x_split[i]))
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}
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if (nrow(found) == 0) {
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# try any match with text before and after original search string
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# so "negative rods" will be "GNR"
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if (x.bak[i] %like% "^Gram") {
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x.bak[i] <- gsub("^Gram", "", x.bak[i], ignore.case = TRUE)
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if (x.backup[i] %like% "^Gram") {
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x.backup[i] <- gsub("^Gram", "", x.backup[i], ignore.case = TRUE)
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# remove leading and trailing spaces again
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x.bak[i] <- trimws(x.bak[i], which = "both")
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x.backup[i] <- trimws(x.backup[i], which = "both")
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}
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if (!is.na(x.bak[i])) {
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found <- AMR::microorganisms %>% filter(fullname %like% x.bak[i])
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if (!is.na(x.backup[i])) {
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found <- AMR::microorganisms %>% filter(fullname %like% x.backup[i])
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}
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}
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if (nrow(found) != 0) {
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if (nrow(found) != 0 & x.backup[i] != "") {
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x[i] <- as.character(found[1, 'bactid'])
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} else {
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x[i] <- ""
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x[i] <- NA_character_
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failures <- c(failures, x.fullbackup[i])
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}
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}
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failures <- failures[!failures %in% c(NA, NULL, NaN)]
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if (length(failures) > 0) {
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warning("These values could not be coerced to a valid bactid: ",
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paste('"', unique(failures), '"', sep = "", collapse = ', '),
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".",
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call. = FALSE)
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}
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class(x) <- "bactid"
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attr(x, 'package') <- 'AMR'
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attr(x, 'package.version') <- packageDescription('AMR')$Version
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x
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}
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#' @rdname as.bactid
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#' @export
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guess_bactid <- as.bactid
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#' @rdname as.bactid
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#' @export
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is.bactid <- function(x) {
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identical(class(x), "bactid")
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}
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#' @exportMethod print.bactid
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#' @export
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#' @noRd
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print.bactid <- function(x, ...) {
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cat("Class 'bactid'\n")
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print.default(as.character(x), quote = FALSE)
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}
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#' @exportMethod as.data.frame.bactid
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#' @export
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#' @noRd
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as.data.frame.bactid <- function (x, ...) {
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# same as as.data.frame.character but with removed stringsAsFactors
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nm <- paste(deparse(substitute(x), width.cutoff = 500L),
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collapse = " ")
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if (!"nm" %in% names(list(...))) {
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as.data.frame.vector(x, ..., nm = nm)
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} else {
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as.data.frame.vector(x, ...)
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}
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}
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@ -201,9 +201,10 @@ EUCAST_rules <- function(tbl,
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}
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# join to microorganisms table
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joinby <- colnames(AMR::microorganisms)[1]
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names(joinby) <- col_bactid
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tbl <- tbl %>% left_join(y = AMR::microorganisms, by = joinby, suffix = c("_tempmicroorganisms", ""))
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if (!tbl %>% pull(col_bactid) %>% is.bactid()) {
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tbl[, col_bactid] <- tbl %>% pull(col_bactid) %>% as.bactid()
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}
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tbl <- tbl %>% left_join_microorganisms(by = col_bactid, suffix = c("_tempmicroorganisms", ""))
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# antibiotic classes
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aminoglycosides <- c(tobr, gent, kana, neom, neti, siso)
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@ -22,7 +22,7 @@
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#' @param tbl a \code{data.frame} containing isolates.
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#' @param col_date column name of the result date (or date that is was received on the lab)
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#' @param col_patient_id column name of the unique IDs of the patients
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#' @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.
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#' @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_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)
|
||||
|
|
@ -126,7 +126,7 @@ first_isolate <- function(tbl,
|
|||
|
||||
# bactid OR genus+species must be available
|
||||
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
|
||||
|
|
@ -152,6 +152,9 @@ first_isolate <- function(tbl,
|
|||
check_columns_existance(col_keyantibiotics)
|
||||
|
||||
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)
|
||||
col_genus <- "genus"
|
||||
col_species <- "species"
|
||||
3
R/freq.R
3
R/freq.R
|
|
@ -273,8 +273,11 @@ frequency_tbl <- function(x,
|
|||
} else {
|
||||
NAs <- x[is.na(x)]
|
||||
}
|
||||
|
||||
if (na.rm == TRUE) {
|
||||
x_class <- class(x)
|
||||
x <- x[!x %in% NAs]
|
||||
class(x) <- x_class
|
||||
}
|
||||
|
||||
if (missing(sort.count) & 'factor' %in% class(x)) {
|
||||
|
|
|
|||
|
|
@ -26,8 +26,8 @@
|
|||
#' df2 <- left_join_microorganisms(df, "bacteria_id")
|
||||
#' colnames(df2)
|
||||
inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
|
||||
if (any(class(x) %in% c('character', 'factor'))) {
|
||||
x <- data.frame(bactid = x, stringsAsFactors = FALSE)
|
||||
if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
|
|
@ -36,7 +36,9 @@ inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...
|
|||
} else {
|
||||
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)) {
|
||||
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
|
||||
#' @export
|
||||
left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
|
||||
if (any(class(x) %in% c('character', 'factor'))) {
|
||||
x <- data.frame(bactid = x, stringsAsFactors = FALSE)
|
||||
if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
|
|
@ -56,7 +58,9 @@ left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...)
|
|||
} else {
|
||||
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)) {
|
||||
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
|
||||
#' @export
|
||||
right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
|
||||
if (any(class(x) %in% c('character', 'factor'))) {
|
||||
x <- data.frame(bactid = x, stringsAsFactors = FALSE)
|
||||
if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
|
|
@ -76,7 +80,9 @@ right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...
|
|||
} else {
|
||||
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)) {
|
||||
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
|
||||
#' @export
|
||||
full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
|
||||
if (any(class(x) %in% c('character', 'factor'))) {
|
||||
x <- data.frame(bactid = x, stringsAsFactors = FALSE)
|
||||
if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
|
|
@ -96,7 +102,9 @@ full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...)
|
|||
} else {
|
||||
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)) {
|
||||
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
|
||||
#' @export
|
||||
semi_join_microorganisms <- function(x, by = 'bactid', ...) {
|
||||
if (any(class(x) %in% c('character', 'factor'))) {
|
||||
x <- data.frame(bactid = x, stringsAsFactors = FALSE)
|
||||
if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
|
|
@ -116,14 +124,16 @@ semi_join_microorganisms <- function(x, by = 'bactid', ...) {
|
|||
} else {
|
||||
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
|
||||
#' @export
|
||||
anti_join_microorganisms <- function(x, by = 'bactid', ...) {
|
||||
if (any(class(x) %in% c('character', 'factor'))) {
|
||||
x <- data.frame(bactid = x, stringsAsFactors = FALSE)
|
||||
if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
|
|
@ -132,5 +142,7 @@ anti_join_microorganisms <- function(x, by = 'bactid', ...) {
|
|||
} else {
|
||||
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
149
README.md
|
|
@ -126,6 +126,86 @@ after
|
|||
# 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)
|
||||
```
|
||||
|
||||
|
||||
|
||||
A plot of `mic_data` (defaults to bar plot):
|
||||
```r
|
||||
plot(mic_data)
|
||||
```
|
||||
|
||||
|
||||
|
||||
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
|
||||
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
|
||||
|
|
@ -235,79 +315,12 @@ Learn more about this function with:
|
|||
?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)
|
||||
```
|
||||
|
||||
|
||||
|
||||
A plot of `mic_data` (defaults to bar plot):
|
||||
```r
|
||||
plot(mic_data)
|
||||
```
|
||||
|
||||
|
||||
|
||||
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
|
||||
Datasets to work with antibiotics and bacteria properties.
|
||||
```r
|
||||
# Dataset with 2000 random blood culture isolates from anonymised
|
||||
# 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
|
||||
# and DDD's (oral and parenteral)
|
||||
|
|
|
|||
Binary file not shown.
Binary file not shown.
|
|
@ -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.
|
||||
}
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
% 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}
|
||||
\alias{first_isolate}
|
||||
\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_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.}
|
||||
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
}
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
% 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}
|
||||
\alias{join}
|
||||
\alias{inner_join_microorganisms}
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ key_antibiotics_equal(x, y, type = c("keyantibiotics", "points"),
|
|||
\arguments{
|
||||
\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}
|
||||
|
||||
|
|
|
|||
|
|
@ -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"))
|
||||
|
||||
|
||||
})
|
||||
|
|
@ -1,27 +1,34 @@
|
|||
context("eucast.R")
|
||||
|
||||
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
|
||||
"PSEAER", # Pseudomonas aeruginosa
|
||||
"ENTAER"), # Enterobacter aerogenes
|
||||
a <- data.frame(bactid =
|
||||
c("KLEPNE", # Klebsiella pneumoniae
|
||||
"PSEAER", # Pseudomonas aeruginosa
|
||||
"ENTAER"), # Enterobacter aerogenes
|
||||
amox = "-", # Amoxicillin
|
||||
stringsAsFactors = FALSE)
|
||||
b <- data.frame(bactid = c("KLEPNE", # Klebsiella pneumoniae
|
||||
"PSEAER", # Pseudomonas aeruginosa
|
||||
"ENTAER"), # Enterobacter aerogenes
|
||||
b <- data.frame(bactid =
|
||||
as.bactid(
|
||||
c("KLEPNE", # Klebsiella pneumoniae
|
||||
"PSEAER", # Pseudomonas aeruginosa
|
||||
"ENTAER")), # Enterobacter aerogenes
|
||||
amox = "R", # Amoxicillin
|
||||
stringsAsFactors = FALSE)
|
||||
expect_equal(EUCAST_rules(a, info = FALSE), b)
|
||||
expect_equal(suppressWarnings(interpretive_reading(a, info = TRUE)), b)
|
||||
expect_identical(EUCAST_rules(a, info = FALSE), b)
|
||||
expect_identical(suppressWarnings(interpretive_reading(a, info = TRUE)), b)
|
||||
|
||||
a <- data.frame(bactid = c("STAAUR", # Staphylococcus aureus
|
||||
"STCGRA"), # Streptococcus pyognenes (Lancefield Group A)
|
||||
a <- data.frame(bactid =
|
||||
c("STAAUR", # Staphylococcus aureus
|
||||
"STCGRA"), # Streptococcus pyognenes (Lancefield Group A)
|
||||
coli = "-", # Colistin
|
||||
stringsAsFactors = FALSE)
|
||||
b <- data.frame(bactid = c("STAAUR", # Staphylococcus aureus
|
||||
"STCGRA"), # Streptococcus pyognenes (Lancefield Group A)
|
||||
b <- data.frame(bactid =
|
||||
as.bactid(
|
||||
c("STAAUR", # Staphylococcus aureus
|
||||
"STCGRA")), # Streptococcus pyognenes (Lancefield Group A)
|
||||
coli = "R", # Colistin
|
||||
stringsAsFactors = FALSE)
|
||||
expect_equal(EUCAST_rules(a, info = FALSE), b)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
context("first_isolates.R")
|
||||
context("first_isolate.R")
|
||||
|
||||
test_that("first isolates work", {
|
||||
# septic_patients contains 1959 out of 2000 first isolates
|
||||
|
|
@ -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())
|
||||
})
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
context("joins.R")
|
||||
context("join_microorganisms.R")
|
||||
|
||||
test_that("joins work", {
|
||||
unjoined <- septic_patients
|
||||
Loading…
Reference in New Issue