new tibble export

R/ab_selectors.R

@@ -49,115 +49,147 @@
 #' @examples 
 #' # `example_isolates` is a data set available in the AMR package.
 #' # See ?example_isolates.
-#' df <- example_isolates[ , c("hospital_id", "mo",
-#'                             "AMP", "AMC", "TZP", "CXM", "CRO", "GEN",
-#'                             "TOB", "COL", "IPM", "MEM", "TEC", "VAN")]
+#' example_isolates
 #' 
 #' # base R ------------------------------------------------------------------
 #' 
 #' # select columns 'IPM' (imipenem) and 'MEM' (meropenem)
-#' df[, carbapenems()]
+#' example_isolates[, carbapenems()]
 #' 
 #' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
-#' df[, c("mo", aminoglycosides())]
+#' example_isolates[, c("mo", aminoglycosides())]
 #' 
 #' # select only antibiotic columns with DDDs for oral treatment
-#' df[, administrable_per_os()]
+#' example_isolates[, administrable_per_os()]
 #' 
 #' # filter using any() or all()
-#' df[any(carbapenems() == "R"), ]
-#' subset(df, any(carbapenems() == "R"))
+#' example_isolates[any(carbapenems() == "R"), ]
+#' subset(example_isolates, any(carbapenems() == "R"))
 #' 
 #' # filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
-#' df[any(carbapenems()), ]
-#' df[all(carbapenems()), ]
+#' example_isolates[any(carbapenems()), ]
+#' example_isolates[all(carbapenems()), ]
 #' 
 #' # filter with multiple antibiotic selectors using c()
-#' df[all(c(carbapenems(), aminoglycosides()) == "R"), ]
+#' example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
 #' 
 #' # filter + select in one go: get penicillins in carbapenems-resistant strains
-#' df[any(carbapenems() == "R"), penicillins()]
+#' example_isolates[any(carbapenems() == "R"), penicillins()]
 #' 
 #' # You can combine selectors with '&' to be more specific. For example,
 #' # penicillins() would select benzylpenicillin ('peni G') and
 #' # administrable_per_os() would select erythromycin. Yet, when combined these
 #' # drugs are both omitted since benzylpenicillin is not administrable per os
 #' # and erythromycin is not a penicillin:
-#' df[, penicillins() & administrable_per_os()]
+#' 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:
-#' df[, ab_selector(oral_ddd > 1 & oral_units == "g")]
+#' example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
 #' 
 #' # dplyr -------------------------------------------------------------------
 #' \donttest{
 #' if (require("dplyr")) {
 #' 
-#'   # get AMR for all aminoglycosides e.g., per hospital:
-#'   df %>%
-#'     group_by(hospital_id) %>% 
+#'   # get AMR for all aminoglycosides e.g., per ward:
+#'   example_isolates %>%
+#'     group_by(ward) %>% 
 #'     summarise(across(aminoglycosides(), resistance))
 #'     
+#' }
+#' if (require("dplyr")) {
+#'     
 #'   # You can combine selectors with '&' to be more specific:
-#'   df %>%
+#'   example_isolates %>%
 #'     select(penicillins() & administrable_per_os())
+#'     
+#' }
+#' if (require("dplyr")) {
 #'   
 #'   # get AMR for only drugs that matter - no intrinsic resistance:
-#'   df %>%
+#'   example_isolates %>%
 #'     filter(mo_genus() %in% c("Escherichia", "Klebsiella")) %>% 
-#'     group_by(hospital_id) %>% 
+#'     group_by(ward) %>% 
 #'     summarise(across(not_intrinsic_resistant(), resistance))
 #'     
+#' }
+#' if (require("dplyr")) {
+#'     
 #'   # get susceptibility for antibiotics whose name contains "trim":
-#'   df %>%
+#'   example_isolates %>%
 #'     filter(first_isolate()) %>% 
-#'     group_by(hospital_id) %>% 
+#'     group_by(ward) %>% 
 #'     summarise(across(ab_selector(name %like% "trim"), susceptibility))
+#'     
+#' }
+#' if (require("dplyr")) {
 #' 
 #'   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
-#'   df %>% 
+#'   example_isolates %>% 
 #'     select(carbapenems())
 #'     
+#' }
+#' if (require("dplyr")) {
+#'     
 #'   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
-#'   df %>% 
+#'   example_isolates %>% 
 #'     select(mo, aminoglycosides())
 #'     
+#' }
+#' if (require("dplyr")) {
+#'     
 #'  # any() and all() work in dplyr's filter() too:
-#'  df %>% 
+#'  example_isolates %>% 
 #'     filter(any(aminoglycosides() == "R"),
 #'            all(cephalosporins_2nd() == "R"))
 #'     
+#' }
+#' if (require("dplyr")) {
+#'     
 #'  # also works with c():
-#'  df %>% 
+#'  example_isolates %>% 
 #'     filter(any(c(carbapenems(), aminoglycosides()) == "R"))
 #'     
+#' }
+#' if (require("dplyr")) {
+#'     
 #'  # not setting any/all will automatically apply all():
-#'  df %>% 
+#'  example_isolates %>% 
 #'     filter(aminoglycosides() == "R")
 #'     
+#' }
+#' if (require("dplyr")) {
+#'     
 #'   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
-#'   df %>% 
+#'   example_isolates %>% 
 #'     select(mo, ab_class("mycobact"))
 #'     
+#' }
+#' if (require("dplyr")) {
+#'     
 #'   # get bug/drug combinations for only glycopeptides in Gram-positives:
-#'   df %>% 
+#'   example_isolates %>% 
 #'     filter(mo_is_gram_positive()) %>% 
 #'     select(mo, glycopeptides()) %>% 
 #'     bug_drug_combinations() %>%
 #'     format()
 #'     
+#' }
+#' if (require("dplyr")) {
+#' 
 #'   data.frame(some_column = "some_value",
 #'              J01CA01 = "S") %>%   # ATC code of ampicillin
 #'     select(penicillins())         # only the 'J01CA01' column will be selected
 #'     
-#'     
+#' }
+#' if (require("dplyr")) {
+#' 
 #'   # with recent versions of dplyr this is all equal:
-#'   x <- df[carbapenems() == "R", ]
-#'   y <- df %>% filter(carbapenems() == "R")
-#'   z <- df %>% filter(if_all(carbapenems(), ~.x == "R"))
-#'   identical(x, y)
-#'   identical(y, z)
+#'   x <- example_isolates[carbapenems() == "R", ]
+#'   y <- example_isolates %>% filter(carbapenems() == "R")
+#'   z <- example_isolates %>% filter(if_all(carbapenems(), ~.x == "R"))
+#'   identical(x, y) && identical(y, z)
 #' }
 #' }
 ab_class <- function(ab_class,