(v1.0.1.9009) prepare for next release

R/first_isolate.R

@@ -90,18 +90,8 @@
 #' library(dplyr)
 #' # Filter on first isolates:
 #' example_isolates %>%
-#'   mutate(first_isolate = first_isolate(.,
-#'                                        col_date = "date",
-#'                                        col_patient_id = "patient_id",
-#'                                        col_mo = "mo")) %>%
+#'   mutate(first_isolate = first_isolate(.)) %>%
 #'   filter(first_isolate == TRUE)
-#'
-#' # Which can be shortened to:
-#' example_isolates %>%
-#'   filter_first_isolate()
-#' # or for first weighted isolates:
-#' example_isolates %>%
-#'   filter_first_weighted_isolate()
 #' 
 #' # Now let's see if first isolates matter:
 #' A <- example_isolates %>%
@@ -116,14 +106,22 @@
 #'             resistance = resistance(GEN))  # gentamicin resistance
 #'
 #' # Have a look at A and B.
-#' # B is more reliable because every isolate is only counted once.
-#' # Gentamicin resitance in hospital D appears to be 3.1% higher than
+#' # B is more reliable because every isolate is counted only once.
+#' # Gentamicin resitance in hospital D appears to be 3.7% higher than
 #' # when you (erroneously) would have used all isolates for analysis.
 #'
 #'
 #' ## OTHER EXAMPLES:
 #'
 #' \dontrun{
+#' 
+#' # Short-hand versions:
+#' example_isolates %>%
+#'   filter_first_isolate()
+#'   
+#' example_isolates %>%
+#'   filter_first_weighted_isolate()
+#'
 #'
 #' # set key antibiotics to a new variable
 #' x$keyab <- key_antibiotics(x)