(v1.3.0) skip more CRAN tests

man/ab_from_text.Rd

@@ -80,7 +80,7 @@ ab_from_text("500 mg amoxi po and 400mg cipro iv", collapse = ", ")
 abx <- ab_from_text("500 mg amoxi po and 400mg cipro iv")
 ab_group(abx[[1]])
 
-if (require(dplyr)) {
+if (require("dplyr")) {
   tibble(clinical_text = c("given 400mg cipro and 500 mg amox",
                            "started on doxy iv today")) \%>\% 
     mutate(abx_codes = ab_from_text(clinical_text),

man/antibiotic_class_selectors.Rd

@@ -58,7 +58,8 @@ All columns will be searched for known antibiotic names, abbreviations, brand na
 These functions only work if the \code{tidyselect} package is installed, that comes with the \code{dplyr} package. An error will be thrown if \code{tidyselect} package is not installed, or if the functions are used outside a function that allows Tidyverse selections like \code{select()} or \code{pivot_longer()}.
 }
 \examples{
-if (require("dplyr")) {
+\dontrun{
+  library(dplyr)
 
   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
   example_isolates \%>\% 

man/filter_ab_class.Rd

@@ -71,7 +71,8 @@ If the unlying code needs breaking changes, they will occur gradually. For examp
 }
 
 \examples{
-if (require(dplyr)) {
+\dontrun{
+library(dplyr)
 
 # filter on isolates that have any result for any aminoglycoside
 example_isolates \%>\% filter_ab_class("aminoglycoside")
@@ -100,9 +101,7 @@ example_isolates \%>\%
 example_isolates \%>\%
   filter_aminoglycosides("R", "all") \%>\%
   filter_fluoroquinolones("R", "all")
-}
 
-\dontrun{
 # with dplyr 1.0.0 and higher (that adds 'across()'), this is equal:
 example_isolates \%>\% filter_carbapenems("R", "all")
 example_isolates \%>\% filter(across(carbapenems(), ~. == "R"))

man/ggplot_pca.Rd

@@ -118,7 +118,8 @@ The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unly
 # See ?example_isolates.
 
 # See ?pca for more info about Principal Component Analysis (PCA).
-if (require("dplyr")) {
+\dontrun{
+  library(dplyr)
   pca_model <- example_isolates \%>\% 
     filter(mo_genus(mo) == "Staphylococcus") \%>\% 
     group_by(species = mo_shortname(mo)) \%>\%

man/resistance_predict.Rd

@@ -150,7 +150,9 @@ if (require("dplyr")) {
 }
 
 # create nice plots with ggplot2 yourself
-if (require(ggplot2) & require("dplyr")) {
+\dontrun{
+  library(dplyr)
+  library(ggplot2)
 
   data <- example_isolates \%>\%
     filter(mo == as.mo("E. coli")) \%>\%