documentation for 'data.table' AB selectors

R/ab_selectors.R

@@ -29,14 +29,16 @@
 
 #' Antibiotic Selectors
 #'
-#' These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group, without the need to define the columns or antibiotic abbreviations. In short, if you have a column name that resembles an antimicrobial drug, it will be picked up by any of these functions that matches its pharmaceutical class: "cefazolin", "CZO" and "J01DB04" will all be picked up by [cephalosporins()].
+#' @description These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group (according to the [antibiotics] data set), without the need to define the columns or antibiotic abbreviations.
+#' 
+#' In short, if you have a column name that resembles an antimicrobial drug, it will be picked up by any of these functions that matches its pharmaceutical class: "cefazolin", "kefzol", "CZO" and "J01DB04" will all be picked up by [cephalosporins()].
 #' @param ab_class an antimicrobial class or a part of it, such as `"carba"` and `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
 #' @param filter an [expression] to be evaluated in the [antibiotics] data set, such as `name %like% "trim"`
 #' @param only_sir_columns a [logical] to indicate whether only columns of class `sir` must be selected (default is `FALSE`), see [as.sir()]
 #' @param only_treatable a [logical] to indicate whether antimicrobial drugs should be excluded that are only for laboratory tests (default is `TRUE`), such as gentamicin-high (`GEH`) and imipenem/EDTA (`IPE`)
 #' @param ... ignored, only in place to allow future extensions
 #' @details
-#' These functions can be used in data set calls for selecting columns and filtering rows. They are heavily inspired by the [Tidyverse selection helpers][tidyselect::language] such as [`everything()`][tidyselect::everything()], but also work in base \R and not only in `dplyr` verbs. Nonetheless, they are very convenient to use with `dplyr` functions such as [`select()`][dplyr::select()], [`filter()`][dplyr::filter()] and [`summarise()`][dplyr::summarise()], see *Examples*.
+#' These functions can be used in data set calls for selecting columns and filtering rows. They work with base \R, the Tidyverse, and `data.table`. They are heavily inspired by the [Tidyverse selection helpers][tidyselect::language] such as [`everything()`][tidyselect::everything()], but are not limited to `dplyr` verbs. Nonetheless, they are very convenient to use with `dplyr` functions such as [`select()`][dplyr::select()], [`filter()`][dplyr::filter()] and [`summarise()`][dplyr::summarise()], see *Examples*.
 #'
 #' All columns in the data in which these functions are called will be searched for known antibiotic names, abbreviations, brand names, and codes (ATC, EARS-Net, WHO, etc.) according to the [antibiotics] data set. This means that a selector such as [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
 #'
@@ -53,6 +55,10 @@
 #' # `example_isolates` is a data set available in the AMR package.
 #' # See ?example_isolates.
 #' example_isolates
+#' 
+#' 
+#' # Examples sections below are split into 'base R', 'dplyr', and 'data.table':
+#'
 #'
 #' # base R ------------------------------------------------------------------
 #'
@@ -76,7 +82,7 @@
 #' # filter with multiple antibiotic selectors using c()
 #' example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
 #'
-#' # filter + select in one go: get penicillins in carbapenems-resistant strains
+#' # filter + select in one go: get penicillins in carbapenem-resistant strains
 #' example_isolates[any(carbapenems() == "R"), penicillins()]
 #'
 #' # You can combine selectors with '&' to be more specific. For example,
@@ -86,13 +92,19 @@
 #' # and erythromycin is not a penicillin:
 #' example_isolates[, penicillins() & administrable_per_os()]
 #'
-#' # ab_selector() applies a filter in the `antibiotics` data set and is thus very
-#' # flexible. For instance, to select antibiotic columns with an oral DDD of at
-#' # least 1 gram:
+#' # ab_selector() applies a filter in the `antibiotics` data set and is thus
+#' # very flexible. For instance, to select antibiotic columns with an oral DDD
+#' # of at least 1 gram:
 #' example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
-#'
-#' # dplyr -------------------------------------------------------------------
+#' 
 #' \donttest{
+#' # dplyr -------------------------------------------------------------------
+#' 
+#' if (require("dplyr")) {
+#'   tibble(kefzol = random_sir(5)) %>%
+#'     select(cephalosporins())
+#' }
+#' 
 #' if (require("dplyr")) {
 #'   # get AMR for all aminoglycosides e.g., per ward:
 #'   example_isolates %>%
@@ -173,6 +185,35 @@
 #'   z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == "R"))
 #'   identical(x, y) && identical(y, z)
 #' }
+#' 
+#' 
+#' # data.table --------------------------------------------------------------
+#' 
+#' # data.table is supported as well, just use it in the same way as with
+#' # base R, but add `with = FALSE` if using a single AB selector:
+#' 
+#' if (require("data.table")) {
+#'   dt <- as.data.table(example_isolates)
+#' 
+#'   print(
+#'     dt[, carbapenems()] # incorrect, returns column *names*
+#'   )
+#'   print(
+#'     dt[, carbapenems(), with = FALSE] # so `with = FALSE` is required
+#'   )
+#'   
+#'   # for multiple selections or AB selectors, `with = FALSE` is not needed:
+#'   print(
+#'     dt[, c("mo", aminoglycosides())]
+#'   )
+#'   print(
+#'     dt[, c(carbapenems(), aminoglycosides())]
+#'   )
+#'   
+#'   # row filters are also supported:
+#'   print(dt[any(carbapenems() == "S"), ])
+#'   print(dt[any(carbapenems() == "S"), penicillins(), with = FALSE])
+#' }
 #' }
 ab_class <- function(ab_class,
                      only_sir_columns = FALSE,