(v2.1.1.9129) unit test fix

R/aa_globals.R

@@ -165,6 +165,7 @@ globalVariables(c(
   "lang",
   "language",
   "lookup",
+  "lower",
   "method",
   "mic ",
   "mic",
@@ -198,6 +199,7 @@ globalVariables(c(
   "total",
   "txt",
   "type",
+  "upper",
   "uti_index",
   "value",
   "varname",

R/antibiogram.R

@@ -60,7 +60,7 @@
 #' 
 #' For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top *n* species encountered in the data. You can filter on this top *n* using [top_n_microorganisms()]. For example, use `top_n_microorganisms(your_data, n = 10)` as a pre-processing step to only include the top 10 species in the data.
 #' 
-#' The numeric values of an antibiogram are stored in a long format as the [attribute] `long_numeric`. You can retrieve them using `attributes(x)$long_numeric`, where `x` is the outcome of [antibiogram()] or [wisca()]. This is ideal for e.g. advanced plotting.
+#' The numeric values of an antibiogram are stored in a long format as the [attribute][attributes()] `long_numeric`. You can retrieve them using `attributes(x)$long_numeric`, where `x` is the outcome of [antibiogram()] or [wisca()]. This is ideal for e.g. advanced plotting.
 #' 
 #' ### Formatting Type
 #' 

R/bug_drug_combinations.R

@@ -36,6 +36,7 @@
 #' @param remove_intrinsic_resistant [logical] to indicate that rows and columns with 100% resistance for all tested antimicrobials must be removed from the table
 #' @param FUN the function to call on the `mo` column to transform the microorganism codes - the default is [mo_shortname()]
 #' @param translate_ab a [character] of length 1 containing column names of the [antibiotics] data set
+#' @param include_n_rows a [logical] to indicate if the total number of rows must be included in the output
 #' @param ... arguments passed on to `FUN`
 #' @inheritParams sir_df
 #' @inheritParams base::formatC
@@ -182,8 +183,8 @@ bug_drug_combinations <- function(x,
     out <- out[, colnames(out)[colnames(out) != "total_rows"], drop = FALSE]
   }
   
-  out <- out %pm>% pm_arrange(mo, ab)
   out <- as_original_data_class(out, class(x.bak)) # will remove tibble groups
+  out <- out %pm>% pm_arrange(mo, ab)
   rownames(out) <- NULL
   structure(out, class = c("bug_drug_combinations", if(data_has_groups) "grouped" else NULL, class(out)))
 }

R/ggplot_pca.R

@@ -261,7 +261,7 @@ ggplot_pca <- function(x,
         type = "open"
       ),
       colour = arrows_colour,
-      size = arrows_size,
+      linewidth = arrows_size,
       alpha = arrows_alpha
     )
     if (arrows_textangled == TRUE) {

R/top_n_microorganisms.R

@@ -36,7 +36,7 @@
 #' @param n_for_each an optional integer specifying the maximum number of rows to retain for each value of the selected property. If `NULL`, all rows within the top *n* groups will be included.
 #' @param col_mo A character string indicating the column in `x` that contains microorganism names or codes. Defaults to the first column of class [`mo`]. Values will be coerced using [as.mo()].
 #' @param ... Additional arguments passed on to [mo_property()] when `property` is not `NULL`.
-#' @details This function is useful for preprocessing data before creating [antibiograms][antibiograms()] or other analyses that require focused subsets of microbial data. For example, it can filter a data set to only include isolates from the top 10 species.
+#' @details This function is useful for preprocessing data before creating [antibiograms][antibiogram()] or other analyses that require focused subsets of microbial data. For example, it can filter a data set to only include isolates from the top 10 species.
 #' @export
 #' @seealso [mo_property()], [as.mo()], [antibiogram()]
 #' @examples