(v0.7.1.9006) new rsi calculations, atc class removal

R/freq.R

@@ -318,7 +318,7 @@ freq <- function(x,
       df <- df %>%
         ungroup() %>%
         # do not repeat group labels
-        mutate_at(vars(x.group), funs(ifelse(lag(.) == ., "", .)))
+        mutate_at(vars(x.group), ~(ifelse(lag(.) == ., "", .)))
       df[1, 1] <- df.topleft
       colnames(df)[1:2] <- c("group", "item")
 

R/globals.R

@@ -30,7 +30,7 @@ globalVariables(c(".",
                   "count.x",
                   "date_lab",
                   "diff.percent",
-                  "First",
+                  "First name",
                   "first_isolate_row_index",
                   "fullname",
                   "fullname_lower",
@@ -46,7 +46,7 @@ globalVariables(c(".",
                   "key_ab_other",
                   "kingdom",
                   "lang",
-                  "Last",
+                  "Last name",
                   "lookup",
                   "mdr",
                   "median",
@@ -56,8 +56,6 @@ globalVariables(c(".",
                   "mono_count",
                   "more_than_episode_ago",
                   "name",
-                  "name",
-                  "name",
                   "new",
                   "observations",
                   "observed",

R/mo.R

@@ -135,6 +135,7 @@
 #' @inheritSection AMR Read more on our website!
 #' @importFrom dplyr %>% pull left_join
 #' @examples
+#' \donttest{
 #' # These examples all return "B_STPHY_AUR", the ID of S. aureus:
 #' as.mo("sau") # WHONET code
 #' as.mo("stau")
@@ -169,7 +170,7 @@
 #' # All mo_* functions use as.mo() internally too (see ?mo_property):
 #' mo_genus("E. coli")           # returns "Escherichia"
 #' mo_gramstain("E. coli")       # returns "Gram negative"#'
-#'
+#' }
 #' \dontrun{
 #' df$mo <- as.mo(df$microorganism_name)
 #'

R/portion.R

@@ -36,7 +36,7 @@
 #' @inheritSection as.rsi Interpretation of S, I and R
 #' @details \strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link{first_isolate}} to determine them in your data set.
 #'
-#' These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. \emph{Low counts can infuence the outcome - these \code{portion} functions may camouflage this, since they only return the portion albeit being dependent on the \code{minimum} parameter.}
+#' These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. The function \code{portion_SI()} is essentially equal to \code{count_SI() / count_all()}. \emph{Low counts can infuence the outcome - the \code{portion} functions may camouflage this, since they only return the portion (albeit being dependent on the \code{minimum} parameter).}
 #'
 #' The function \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each group and each variable with class \code{"rsi"}.
 #'
@@ -70,12 +70,12 @@
 #' -------------------------------------------------------------------------
 #' }
 #'
-#' Please note that for \code{only_all_tested = TRUE} applies that:
+#' Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that:
 #' \preformatted{
 #'    count_S()  +  count_I()  +  count_R()  == count_all()
 #'   portion_S() + portion_I() + portion_R() == 1
 #' }
-#' and that for \code{only_all_tested = FALSE} applies that:
+#' and that, in combination therapies, for \code{only_all_tested = FALSE} applies that:
 #' \preformatted{
 #'    count_S()  +  count_I()  +  count_R()  >= count_all()
 #'   portion_S() + portion_I() + portion_R() >= 1

R/resistance_predict.R

@@ -166,7 +166,7 @@ resistance_predict <- function(x,
   df <- x %>%
     mutate_at(col_ab, as.rsi) %>%
     mutate_at(col_ab, droplevels) %>%
-    mutate_at(col_ab, funs(
+    mutate_at(col_ab, ~(
       if (I_as_S == TRUE) {
         gsub("I", "S", .)
       } else {

R/rsi_calc.R

@@ -120,9 +120,15 @@ rsi_calc <- function(...,
     #numerator <- x %>% filter_all(any_vars(. %in% ab_result)) %>% nrow()
     if (only_all_tested == TRUE) {
       # THE NUMBER OF ISOLATES WHERE *ALL* ABx ARE S/I/R
-      x_filtered <- x %>% filter_all(all_vars(!is.na(.)))
-      numerator <- x_filtered %>% filter_all(any_vars(. %in% ab_result)) %>% nrow()
-      denominator <- x_filtered %>% nrow()
+      # x_filtered <- x %>% filter_all(all_vars(!is.na(.)))
+      # numerator <- x_filtered %>% filter_all(any_vars(. %in% ab_result)) %>% nrow()
+      # denominator <- x_filtered %>% nrow()
+      x <- apply(X = x %>% mutate_all(as.integer),
+                 MARGIN = 1,
+                 FUN = base::min)
+      numerator <- sum(as.integer(x) %in% as.integer(ab_result), na.rm = TRUE)
+      denominator <- length(x) - sum(is.na(x))
+
     } else {
       # THE NUMBER OF ISOLATES WHERE *ANY* ABx IS S/I/R
       other_values <- base::setdiff(c(NA, levels(ab_result)), ab_result)