(v1.4.0.9026) docu update

R/first_isolate.R

@@ -27,7 +27,7 @@
 #'
 #' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use [is_new_episode()] that also supports grouping with the `dplyr` package.
 #' @inheritSection lifecycle Stable lifecycle
-#' @param x,.data a [data.frame] containing isolates.
+#' @param x a [data.frame] containing isolates.
 #' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column with a date class
 #' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
 #' @param col_mo column name of the IDs of the microorganisms (see [as.mo()]), defaults to the first column of class [`mo`]. Values will be coerced using [as.mo()].
@@ -44,7 +44,7 @@
 #' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when `type = "points"`, see Details
 #' @param info print progress
 #' @param include_unknown logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
-#' @param ... parameters passed on to the [first_isolate()] function
+#' @param ... parameters passed on to [first_isolate()] when using [filter_first_isolate()], or parameters passed on to [key_antibiotics()] when using [filter_first_weighted_isolate()]
 #' @details The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but more efficient for data sets containing microorganism codes or names.
 #' 
 #' All isolates with a microbial ID of `NA` will be excluded as first isolate.

R/ggplot_rsi.R

@@ -36,7 +36,7 @@
 #' @param facet variable to split plots by, either `"interpretation"` (default) or `"antibiotic"` or a grouping variable
 #' @inheritParams proportion
 #' @param nrow (when using `facet`) number of rows
-#' @param colours a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be `FALSE` to use default [ggplot2][[ggplot2::ggplot()] colours.
+#' @param colours a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be `FALSE` to use default [ggplot2][ggplot2::ggplot()] colours.
 #' @param datalabels show datalabels using [labels_rsi_count()]
 #' @param datalabels.size size of the datalabels
 #' @param datalabels.colour colour of the datalabels
@@ -107,13 +107,12 @@
 #' }
 #'   
 #' \donttest{
-#' 
 #' # resistance of ciprofloxacine per age group
 #' example_isolates %>%
 #'   mutate(first_isolate = first_isolate(.)) %>%
 #'   filter(first_isolate == TRUE,
 #'          mo == as.mo("E. coli")) %>%
-#'   # `age_groups` is also a function of this AMR package:
+#'   # age_groups() is also a function in this AMR package:
 #'   group_by(age_group = age_groups(age)) %>%
 #'   select(age_group,
 #'          CIP) %>%

R/globals.R

@@ -73,5 +73,6 @@ globalVariables(c(".rowid",
                   "value",
                   "varname",
                   "xvar",
+                  "y",
                   "year",
                   "yvar"))

R/is_new_episode.R

@@ -33,7 +33,7 @@
 #' @details 
 #' Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least `episode_days` days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least `episode_days` days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored.
 #' 
-#' The `dplyr` package is not required for this function to work, but this function works conveniently inside `dplyr` verbs such as [filter()], [mutate()] and [summarise()].
+#' The `dplyr` package is not required for this function to work, but this function works conveniently inside `dplyr` verbs such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
 #' @return a [logical] vector
 #' @export
 #' @inheritSection AMR Read more on our website!
@@ -86,23 +86,17 @@ is_new_episode <- function(x, episode_days = 365, ...) {
   x <- as.double(as.Date(x, ...)) # as.Date() for POSIX classes
   if (length(x) == 1) {
     return(TRUE)
-  }
-  if (length(x) == 2 && max(x) - min(x) >= episode_days) {
-    return(rep(TRUE, 2))
+  } else if (length(x) == 2) {
+    if (max(x) - min(x) >= episode_days) {
+      return(c(TRUE, TRUE))
+    } else {
+      return(c(TRUE, FALSE))
+    }
   }
   
   # I asked on StackOverflow:
   # https://stackoverflow.com/questions/42122245/filter-one-row-every-year
   exec <- function(x, episode_days) {
-    if (length(x) == 1) {
-      return(TRUE)
-    } else if (length(x) == 2) {
-      if (max(x) - min(x) >= episode_days) {
-        return(c(TRUE, TRUE))
-      } else {
-        return(c(TRUE, FALSE))
-      }
-    }
     indices <- integer()
     start <- x[1]
     ind <- 1