unit tests

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 1.8.2.9120
+Version: 1.8.2.9121
 Date: 2023-02-12
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 1.8.2.9120
+# AMR 1.8.2.9121
 
 *(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
 

R/ab.R

@@ -495,13 +495,15 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
   # save to package env to save time for next time
   if (isTRUE(initial_search)) {
     AMR_env$ab_previously_coerced <- AMR_env$ab_previously_coerced[which(!AMR_env$ab_previously_coerced$x %in% x), , drop = FALSE]
-    AMR_env$ab_previously_coerced <- unique(rbind2(AMR_env$ab_previously_coerced,
+    AMR_env$ab_previously_coerced <- unique(rbind2(
+      AMR_env$ab_previously_coerced,
       data.frame(
         x = x,
         ab = x_new,
         x_bak = x_bak[match(x, x_bak_clean)],
         stringsAsFactors = FALSE
-      )))
+      )
+    ))
   }
 
   # take failed ATC codes apart from rest

R/ab_property.R

@@ -363,7 +363,8 @@ set_ab_names <- function(data, ..., property = "name", language = get_AMR_locale
       df <- tryCatch(suppressWarnings(pm_select(data, ...)),
         error = function(e) {
           data[, c(...), drop = FALSE]
-                     })
+        }
+      )
     } else if (tryCatch(is.character(c(...)), error = function(e) FALSE)) {
       df <- data[, c(...), drop = FALSE]
     } else {

R/antibiogram.R

@@ -136,17 +136,20 @@
 #' # Traditional antibiogram ----------------------------------------------
 #'
 #' antibiogram(example_isolates,
-#'             antibiotics = c(aminoglycosides(), carbapenems()))
+#'   antibiotics = c(aminoglycosides(), carbapenems())
+#' )
 #'
 #' antibiogram(example_isolates,
 #'   antibiotics = aminoglycosides(),
 #'   ab_transform = "atc",
-#'             mo_transform = "gramstain")
+#'   mo_transform = "gramstain"
+#' )
 #'
 #' antibiogram(example_isolates,
 #'   antibiotics = carbapenems(),
 #'   ab_transform = "name",
-#'             mo_transform = "name")
+#'   mo_transform = "name"
+#' )
 #'
 #'
 #' # Combined antibiogram -------------------------------------------------
@@ -154,13 +157,15 @@
 #' # combined antibiotics yield higher empiric coverage
 #' antibiogram(example_isolates,
 #'   antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-#'             mo_transform = "gramstain")
+#'   mo_transform = "gramstain"
+#' )
 #'
 #' antibiogram(example_isolates,
 #'   antibiotics = c("TZP", "TZP+TOB"),
 #'   mo_transform = "gramstain",
 #'   ab_transform = "name",
-#'             sep = " & ")
+#'   sep = " & "
+#' )
 #'
 #'
 #' # Syndromic antibiogram ------------------------------------------------
@@ -168,7 +173,8 @@
 #' # the data set could contain a filter for e.g. respiratory specimens
 #' antibiogram(example_isolates,
 #'   antibiotics = c(aminoglycosides(), carbapenems()),
-#'             syndromic_group = "ward")
+#'   syndromic_group = "ward"
+#' )
 #'
 #' # now define a data set with only E. coli
 #' ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@@ -179,8 +185,10 @@
 #'   antibiotics = aminoglycosides(),
 #'   ab_transform = "name",
 #'   syndromic_group = ifelse(ex1$ward == "ICU",
-#'                                      "UCI", "No UCI"),
+#'     "UCI", "No UCI"
-#'             language = "es")
+#'   ),
+#'   language = "es"
+#' )
 #'
 #'
 #' # Weighted-incidence syndromic combination antibiogram (WISCA) ---------
@@ -192,18 +200,22 @@
 #'   minimum = 10, # this should be >= 30, but now just as example
 #'   syndromic_group = ifelse(example_isolates$age >= 65 &
 #'     example_isolates$gender == "M",
-#'                                      "WISCA Group 1", "WISCA Group 2"))
+#'   "WISCA Group 1", "WISCA Group 2"
+#'   )
+#' )
 #'
 #'
 #' # Generate plots with ggplot2 or base R --------------------------------
 #'
 #' ab1 <- antibiogram(example_isolates,
 #'   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-#'                    mo_transform = "gramstain")
+#'   mo_transform = "gramstain"
+#' )
 #' ab2 <- antibiogram(example_isolates,
 #'   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
 #'   mo_transform = "gramstain",
-#'                    syndromic_group = "ward")
+#'   syndromic_group = "ward"
+#' )
 #'
 #' plot(ab1)
 #'
@@ -307,8 +319,10 @@ antibiogram <- function(x,
         if (isTRUE(only_all_tested)) {
           x[new_colname] <- as.sir(vapply(FUN.VALUE = character(1), x_transposed, function(x) ifelse(anyNA(x), NA_character_, ifelse(any(x %in% S_values), "S", "R")), USE.NAMES = FALSE))
         } else {
-          x[new_colname] <- as.sir(vapply(FUN.VALUE = character(1), x_transposed, function(x) ifelse(any(x %in% S_values, na.rm = TRUE), "S", ifelse(anyNA(x), NA_character_, "R")),
+          x[new_colname] <- as.sir(vapply(
-                                          USE.NAMES = FALSE))
+            FUN.VALUE = character(1), x_transposed, function(x) ifelse(any(x %in% S_values, na.rm = TRUE), "S", ifelse(anyNA(x), NA_character_, "R")),
+            USE.NAMES = FALSE
+          ))
         }
       }
       antibiotics[[i]] <- new_colname
@@ -329,8 +343,10 @@ antibiogram <- function(x,
 
   # get numbers of S, I, R (per group)
   out <- out %pm>%
-    bug_drug_combinations(col_mo = ".mo",
+    bug_drug_combinations(
-                          FUN = function(x) x)
+      col_mo = ".mo",
+      FUN = function(x) x
+    )
   counts <- out
 
   # regroup for summarising
@@ -404,8 +420,10 @@ antibiogram <- function(x,
       if (i == 1) {
         new_df <- long_to_wide(out[which(out$syndromic_group == grp), , drop = FALSE], digs = digits)
       } else {
-        new_df <- rbind2(new_df,
+        new_df <- rbind2(
-                         long_to_wide(out[which(out$syndromic_group == grp), , drop = FALSE], digs = digits))
+          new_df,
+          long_to_wide(out[which(out$syndromic_group == grp), , drop = FALSE], digs = digits)
+        )
       }
     }
     # sort rows
@@ -445,7 +463,8 @@ antibiogram <- function(x,
 
   structure(as_original_data_class(new_df, class(x), extra_class = "antibiogram"),
     long = long,
-            combine_SI = combine_SI)
+    combine_SI = combine_SI
+  )
 }
 
 #' @export
@@ -458,7 +477,7 @@ plot.antibiogram <- function(x, ...) {
     df$syndromic_group <- NULL
     df <- df[order(df$mo), , drop = FALSE]
   }
-  mo_levels = unique(df$mo)
+  mo_levels <- unique(df$mo)
   mfrow_old <- graphics::par()$mfrow
   sqrt_levels <- sqrt(length(mo_levels))
   graphics::par(mfrow = c(ceiling(sqrt_levels), floor(sqrt_levels)))
@@ -466,14 +485,16 @@ plot.antibiogram <- function(x, ...) {
     mo <- mo_levels[i]
     df_sub <- df[df$mo == mo, , drop = FALSE]
 
-    barplot(height = df_sub$SI * 100,
+    barplot(
+      height = df_sub$SI * 100,
       xlab = NULL,
       ylab = ifelse(isTRUE(attributes(x)$combine_SI), "%SI", "%S"),
       names.arg = df_sub$ab,
       col = "#aaaaaa",
       beside = TRUE,
       main = mo,
-            legend = NULL)
+      legend = NULL
+    )
   }
   graphics::par(mfrow = mfrow_old)
 }
@@ -490,22 +511,28 @@ barplot.antibiogram <- function(height, ...) {
 autoplot.antibiogram <- function(object, ...) {
   df <- attributes(object)$long
   ggplot2::ggplot(df) +
-    ggplot2::geom_col(ggplot2::aes(x = ab,
+    ggplot2::geom_col(
+      ggplot2::aes(
+        x = ab,
         y = SI * 100,
         fill = if ("syndromic_group" %in% colnames(df)) {
           syndromic_group
         } else {
           NULL
-                                   }),
+        }
-                      position = ggplot2::position_dodge2(preserve = "single")) +
+      ),
+      position = ggplot2::position_dodge2(preserve = "single")
+    ) +
     ggplot2::facet_wrap("mo") +
-    ggplot2::labs(y = ifelse(isTRUE(attributes(object)$combine_SI), "%SI", "%S"),
+    ggplot2::labs(
+      y = ifelse(isTRUE(attributes(object)$combine_SI), "%SI", "%S"),
       x = NULL,
       fill = if ("syndromic_group" %in% colnames(df)) {
         colnames(object)[1]
       } else {
         NULL
-                  })
+      }
+    )
 }
 
 #' @export

R/av.R

@@ -461,13 +461,15 @@ as.av <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
   # save to package env to save time for next time
   if (isTRUE(initial_search)) {
     AMR_env$av_previously_coerced <- AMR_env$av_previously_coerced[which(!AMR_env$av_previously_coerced$x %in% x), , drop = FALSE]
-    AMR_env$av_previously_coerced <- unique(rbind2(AMR_env$av_previously_coerced,
+    AMR_env$av_previously_coerced <- unique(rbind2(
+      AMR_env$av_previously_coerced,
       data.frame(
         x = x,
         av = x_new,
         x_bak = x_bak[match(x, x_bak_clean)],
         stringsAsFactors = FALSE
-      )))
+      )
+    ))
   }
 
   # take failed ATC codes apart from rest

R/eucast_rules.R

@@ -1161,8 +1161,10 @@ edit_sir <- function(x,
         )
         verbose_new <- verbose_new %pm>% pm_filter(old != new | is.na(old) | is.na(new) & !is.na(old))
         # save changes to data set 'verbose_info'
-        track_changes$verbose_info <- rbind2(track_changes$verbose_info,
+        track_changes$verbose_info <- rbind2(
-          verbose_new)
+          track_changes$verbose_info,
+          verbose_new
+        )
         # count adds and changes
         track_changes$added <- track_changes$added + verbose_new %pm>%
           pm_filter(is.na(old)) %pm>%

R/get_episode.R

@@ -68,9 +68,13 @@
 #' df[which(get_episode(df$date, 60) == 3), ]
 #'
 #' # the functions also work for less than a day, e.g. to include one per hour:
-#' get_episode(c(Sys.time(),
+#' get_episode(
-#'               Sys.time() + 60 * 60),
+#'   c(
-#'             episode_days = 1 / 24)
+#'     Sys.time(),
+#'     Sys.time() + 60 * 60
+#'   ),
+#'   episode_days = 1 / 24
+#' )
 #'
 #' \donttest{
 #' if (require("dplyr")) {
@@ -130,7 +134,6 @@
 #' # but is_new_episode() has a lot more flexibility than first_isolate(),
 #' # since you can now group on anything that seems relevant:
 #' if (require("dplyr")) {
-#'   
 #'   df %>%
 #'     group_by(patient, mo, ward) %>%
 #'     mutate(flag_episode = is_new_episode(date, 365)) %>%

R/mo.R

@@ -325,7 +325,8 @@ as.mo <- function(x,
         result_mo <- NA_character_
       } else {
         result_mo <- AMR_env$MO_lookup$mo[match(top_hits[1], AMR_env$MO_lookup$fullname)]
-        AMR_env$mo_uncertainties <- rbind2(AMR_env$mo_uncertainties,
+        AMR_env$mo_uncertainties <- rbind2(
+          AMR_env$mo_uncertainties,
           data.frame(
             original_input = x_search,
             input = x_search_cleaned,
@@ -335,14 +336,17 @@ as.mo <- function(x,
             minimum_matching_score = ifelse(is.null(minimum_matching_score), "NULL", minimum_matching_score),
             keep_synonyms = keep_synonyms,
             stringsAsFactors = FALSE
-          ))
+          )
+        )
         # save to package env to save time for next time
-        AMR_env$mo_previously_coerced <- unique(rbind2(AMR_env$mo_previously_coerced,
+        AMR_env$mo_previously_coerced <- unique(rbind2(
+          AMR_env$mo_previously_coerced,
           data.frame(
             x = paste(x_search, minimum_matching_score),
             mo = result_mo,
             stringsAsFactors = FALSE
-          )))
+          )
+        ))
       }
       # the actual result:
       as.character(result_mo)

R/translate.R

@@ -246,8 +246,8 @@ translate_into_language <- function(from,
   }
 
   lapply(
-    # starting from last row, since more general translations are on top, such as 'Group'
+    # starting with longest pattern, since more general translations are shorter, such as 'Group'
-    rev(seq_len(nrow(df_trans))),
+    order(nchar(df_trans$pattern), decreasing = TRUE),
     function(i) {
       from_unique_translated <<- gsub(
         pattern = df_trans$pattern[i],

data-raw/reproduction_of_poorman.R

@@ -56,7 +56,6 @@ for (use in has_usemethods) {
   }
   # add pm_ prefix
   contents[relevant_row - 1] <- paste0("pm_", contents[relevant_row - 1])
-  
 }
 # correct for NextMethod
 contents <- gsub("NextMethod\\(\"(.*)\"\\)", "\\1.data.frame(...)", contents)
@@ -92,7 +91,7 @@ contents <- contents[trimws(contents) != ""]
 contents <- gsub("if (!missing(.before))", "if (!missing(.before) && !is.null(.before))", contents, fixed = TRUE)
 contents <- gsub("if (!missing(.after))", "if (!missing(.after) && !is.null(.after))", contents, fixed = TRUE)
 contents[which(contents %like% "reshape\\($") + 1] <- gsub("data", "as.data.frame(data, stringsAsFactors = FALSE)", contents[which(contents %like% "reshape\\($") + 1])
-contents <- gsub('pm_relocate(.data = long, values_to, .after = -1)', 'pm_relocate(.data = long, "value", .after = -1)', contents, fixed = TRUE)
+contents <- gsub("pm_relocate(.data = long, values_to, .after = -1)", 'pm_relocate(.data = long, "value", .after = -1)', contents, fixed = TRUE)
 
 # who needs US spelling?
 contents <- contents[contents %unlike% "summarize"]

data-raw/salonella_fix.R

@@ -1,20 +1,24 @@
-
+snomed2 <- microorganisms %>%
-snomed2 <- microorganisms %>% filter(mo %in% c("B_SLMNL_TYPH", "B_SLMNL_HMRM", "B_SLMNL_PRTY")) %>% 
+  filter(mo %in% c("B_SLMNL_TYPH", "B_SLMNL_HMRM", "B_SLMNL_PRTY")) %>%
   pull(snomed)
 
 new_typhi <- microorganisms %>%
   filter(mo == "B_SLMNL_THSS") %>%
   slice(c(1, 1, 1)) %>%
-  mutate(mo = c("B_SLMNL_TYPH", "B_SLMNL_HMRM", "B_SLMNL_PRTY"),
+  mutate(
+    mo = c("B_SLMNL_TYPH", "B_SLMNL_HMRM", "B_SLMNL_PRTY"),
     fullname = c("Salmonella Typhi", "Salmonella Typhimurium", "Salmonella Paratyphi"),
     subspecies = c("Typhi", "Typhimurium", "Paratyphi"),
-         snomed = snomed2)
+    snomed = snomed2
+  )
 
 new_groupa <- microorganisms %>%
   filter(mo == "B_SLMNL_GRPB") %>%
-  mutate(mo = "B_SLMNL_GRPA",
+  mutate(
+    mo = "B_SLMNL_GRPA",
     fullname = gsub("roup B", "roup A", fullname),
-         species = gsub("roup B", "roup A", species))
+    species = gsub("roup B", "roup A", species)
+  )
 
 microorganisms$mo <- as.character(microorganisms$mo)
 

inst/tinytest/test-antibiogram.R

@@ -27,149 +27,105 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #
 
-expect_identical(as.mo("Enterobacter asburiae/cloacae"),
-                 as.mo("Enterobacter asburiae"))
 
-suppressMessages(
+# Traditional antibiogram ----------------------------------------------
-  add_custom_microorganisms(
-    data.frame(mo = "ENT_ASB_CLO",
-               genus = "Enterobacter",
-               species = "asburiae/cloacae")
-  )
-)
 
-expect_identical(as.character(as.mo("ENT_ASB_CLO")), "ENT_ASB_CLO")
+ab1 <- antibiogram(example_isolates,
-expect_identical(mo_name("ENT_ASB_CLO"), "Enterobacter asburiae/cloacae")
+                   antibiotics = c(aminoglycosides(), carbapenems()))
-expect_identical(mo_gramstain("ENT_ASB_CLO", language = NULL), "Gram-negative")
-# ==================================================================== #
-# TITLE                                                                #
-# AMR: An R Package for Working with Antimicrobial Resistance Data     #
-#                                                                      #
-# SOURCE                                                               #
-# https://github.com/msberends/AMR                                     #
-#                                                                      #
-# CITE AS                                                              #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
-# doi:10.18637/jss.v104.i03                                            #
-#                                                                      #
-# Developed at the University of Groningen and the University Medical  #
-# Center Groningen in The Netherlands, in collaboration with many      #
-# colleagues from around the world, see our website.                   #
-#                                                                      #
-# This R package is free software; you can freely use and distribute   #
-# it for both personal and commercial purposes under the terms of the  #
-# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
-# the Free Software Foundation.                                        #
-# We created this package for both routine data analysis and academic  #
-# research and it was publicly released in the hope that it will be    #
-# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
-#                                                                      #
-# Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
-# ==================================================================== #
 
-# 
+ab2 <- antibiogram(example_isolates,
-# 
+                   antibiotics = aminoglycosides(),
-# # Traditional antibiogram ----------------------------------------------
+                   ab_transform = "atc",
-# 
+                   mo_transform = "gramstain")
-# ab1 <- antibiogram(example_isolates,
+
-#                    antibiotics = c(aminoglycosides(), carbapenems()))
+ab3 <- antibiogram(example_isolates,
-# 
+                   antibiotics = carbapenems(),
-# ab2 <- antibiogram(example_isolates,
+                   ab_transform = "name",
-#                    antibiotics = aminoglycosides(),
+                   mo_transform = "name")
-#                    ab_transform = "atc",
+
-#                    mo_transform = "gramstain")
+expect_inherits(ab1, "antibiogram")
-# 
+expect_inherits(ab2, "antibiogram")
-# ab3 <- antibiogram(example_isolates,
+expect_inherits(ab3, "antibiogram")
-#                    antibiotics = carbapenems(),
+expect_equal(colnames(ab1), c("Pathogen (N min-max)", "AMK", "GEN", "IPM", "KAN", "MEM", "TOB"))
-#                    ab_transform = "name",
+expect_equal(colnames(ab2), c("Pathogen (N min-max)", "J01GB01", "J01GB03", "J01GB04", "J01GB06"))
-#                    mo_transform = "name")
+expect_equal(colnames(ab3), c("Pathogen (N min-max)", "Imipenem", "Meropenem"))
-# 
+expect_equal(ab3$Meropenem, c(52, NA, 100, 100, NA))
-# expect_inherits(ab1, "antibiogram")
+
-# expect_inherits(ab2, "antibiogram")
+# Combined antibiogram -------------------------------------------------
-# expect_inherits(ab3, "antibiogram")
+
-# expect_equal(colnames(ab1), c("Pathogen (N min-max)", "AMK", "GEN", "IPM", "KAN", "MEM", "TOB"))
+# combined antibiotics yield higher empiric coverage
-# expect_equal(colnames(ab2), c("Pathogen (N min-max)", "J01GB01", "J01GB03", "J01GB04", "J01GB06"))
+ab4 <- antibiogram(example_isolates,
-# expect_equal(colnames(ab3), c("Pathogen (N min-max)", "Imipenem", "Meropenem"))
+                   antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-# expect_equal(ab3$Meropenem, c(52, NA, 100, 100, NA))
+                   mo_transform = "gramstain")
-# 
+
-# # Combined antibiogram -------------------------------------------------
+ab5 <- antibiogram(example_isolates,
-# 
+                   antibiotics = c("TZP", "TZP+TOB"),
-# # combined antibiotics yield higher empiric coverage
+                   mo_transform = "gramstain",
-# ab4 <- antibiogram(example_isolates,
+                   ab_transform = "name",
-#                    antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
+                   sep = " & ",
-#                    mo_transform = "gramstain")
+                   add_total_n = FALSE)
-# 
+
-# ab5 <- antibiogram(example_isolates,
+expect_inherits(ab4, "antibiogram")
-#                    antibiotics = c("TZP", "TZP+TOB"),
+expect_inherits(ab5, "antibiogram")
-#                    mo_transform = "gramstain",
+expect_equal(colnames(ab4), c("Pathogen (N min-max)", "TZP", "TZP + GEN", "TZP + TOB"))
-#                    ab_transform = "name",
+expect_equal(colnames(ab5), c("Pathogen", "Piperacillin/tazobactam", "Piperacillin/tazobactam & Tobramycin"))
-#                    sep = " & ",
+
-#                    add_total_n = FALSE)
+# Syndromic antibiogram ------------------------------------------------
-# 
+
-# expect_inherits(ab4, "antibiogram")
+# the data set could contain a filter for e.g. respiratory specimens
-# expect_inherits(ab5, "antibiogram")
+ab6 <- antibiogram(example_isolates,
-# expect_equal(colnames(ab4), c("Pathogen (N min-max)", "TZP", "TZP + GEN", "TZP + TOB"))
+                   antibiotics = c(aminoglycosides(), carbapenems()),
-# expect_equal(colnames(ab5), c("Pathogen", "Piperacillin/tazobactam", "Piperacillin/tazobactam & Tobramycin"))
+                   syndromic_group = "ward")
-# 
+
-# # Syndromic antibiogram ------------------------------------------------
+# with a custom language, though this will be determined automatically
-# 
+# (i.e., this table will be in Spanish on Spanish systems)
-# # the data set could contain a filter for e.g. respiratory specimens
+ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
-# ab6 <- antibiogram(example_isolates,
+ab7 <- antibiogram(ex1,
-#                    antibiotics = c(aminoglycosides(), carbapenems()),
+                   antibiotics = aminoglycosides(),
-#                    syndromic_group = "ward")
+                   ab_transform = "name",
-# 
+                   syndromic_group = ifelse(ex1$ward == "ICU",
-# # with a custom language, though this will be determined automatically
+                                            "UCI", "No UCI"),
-# # (i.e., this table will be in Spanish on Spanish systems)
+                   language = "es")
-# ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
+
-# ab7 <- antibiogram(ex1,
+expect_inherits(ab6, "antibiogram")
-#                    antibiotics = aminoglycosides(),
+expect_inherits(ab7, "antibiogram")
-#                    ab_transform = "name",
+expect_equal(colnames(ab6), c("Syndromic Group", "Pathogen (N min-max)", "AMK", "GEN", "IPM", "KAN", "MEM", "TOB"))
-#                    syndromic_group = ifelse(ex1$ward == "ICU",
+expect_equal(colnames(ab7), c("Grupo sindrómico", "Patógeno (N min-max)", "Amikacina", "Gentamicina", "Tobramicina"))
-#                                             "UCI", "No UCI"),
+
-#                    language = "es")
+# Weighted-incidence syndromic combination antibiogram (WISCA) ---------
-# 
+
-# expect_inherits(ab6, "antibiogram")
+# the data set could contain a filter for e.g. respiratory specimens
-# expect_inherits(ab7, "antibiogram")
+ab8 <- antibiogram(example_isolates,
-# expect_equal(colnames(ab6), c("Syndromic Group", "Pathogen (N min-max)", "AMK", "GEN", "IPM", "KAN", "MEM", "TOB"))
+                   antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
-# expect_equal(colnames(ab7), c("Grupo sindrómico", "Patógeno (N min-max)", "Amikacina", "Gentamicina", "Tobramicina"))
+                   mo_transform = "gramstain",
-# 
+                   minimum = 10, # this should be >= 30, but now just as example
-# # Weighted-incidence syndromic combination antibiogram (WISCA) ---------
+                   syndromic_group = ifelse(example_isolates$age >= 65 &
-# 
+                                              example_isolates$gender == "M",
-# # the data set could contain a filter for e.g. respiratory specimens
+                                            "WISCA Group 1", "WISCA Group 2"))
-# ab8 <- antibiogram(example_isolates,
+
-#                    antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
+expect_inherits(ab8, "antibiogram")
-#                    mo_transform = "gramstain",
+expect_equal(colnames(ab8), c("Syndromic Group", "Pathogen (N min-max)", "AMC", "AMC + CIP", "TZP", "TZP + TOB"))
-#                    minimum = 10, # this should be >= 30, but now just as example
+
-#                    syndromic_group = ifelse(example_isolates$age >= 65 &
+# Generate plots with ggplot2 or base R --------------------------------
-#                                               example_isolates$gender == "M",
+
-#                                             "WISCA Group 1", "WISCA Group 2"))
+pdf(NULL) # prevent Rplots.pdf being created
-# 
+
-# expect_inherits(ab8, "antibiogram")
+expect_silent(plot(ab1))
-# expect_equal(colnames(ab8), c("Syndromic Group", "Pathogen (N min-max)", "AMC", "AMC + CIP", "TZP", "TZP + TOB"))
+expect_silent(plot(ab2))
-# 
+expect_silent(plot(ab3))
-# # Generate plots with ggplot2 or base R --------------------------------
+expect_silent(plot(ab4))
-# 
+expect_silent(plot(ab5))
-# pdf(NULL) # prevent Rplots.pdf being created
+expect_silent(plot(ab6))
-# 
+expect_silent(plot(ab7))
-# expect_silent(plot(ab1))
+expect_silent(plot(ab8))
-# expect_silent(plot(ab2))
+
-# expect_silent(plot(ab3))
+if (AMR:::pkg_is_available("ggplot2")) {
-# expect_silent(plot(ab4))
+  expect_inherits(autoplot(ab1), "gg")
-# expect_silent(plot(ab5))
+  expect_inherits(autoplot(ab2), "gg")
-# expect_silent(plot(ab6))
+  expect_inherits(autoplot(ab3), "gg")
-# expect_silent(plot(ab7))
+  expect_inherits(autoplot(ab4), "gg")
-# expect_silent(plot(ab8))
+  expect_inherits(autoplot(ab5), "gg")
-# 
+  expect_inherits(autoplot(ab6), "gg")
-# if (AMR:::pkg_is_available("ggplot2")) {
+  expect_inherits(autoplot(ab7), "gg")
-#   expect_inherits(autoplot(ab1), "gg")
+  expect_inherits(autoplot(ab8), "gg")
-#   expect_inherits(autoplot(ab2), "gg")
+}
-#   expect_inherits(autoplot(ab3), "gg")
-#   expect_inherits(autoplot(ab4), "gg")
-#   expect_inherits(autoplot(ab5), "gg")
-#   expect_inherits(autoplot(ab6), "gg")
-#   expect_inherits(autoplot(ab7), "gg")
-#   expect_inherits(autoplot(ab8), "gg")
-# }

inst/tinytest/test-mo.R

@@ -123,7 +123,7 @@ expect_identical(as.character(as.mo("  ")), NA_character_)
 # too few characters
 expect_warning(as.mo("ab"))
 
-expect_equal(
+expect_identical(
   suppressWarnings(as.character(as.mo(c("Qq species", "", "MRSA", "K. pneu rhino", "esco")))),
   c("UNKNOWN", NA_character_, "B_STPHY_AURS", "B_KLBSL_PNMN_RHNS", "B_ESCHR_COLI")
 )
@@ -317,7 +317,7 @@ expect_warning(x[[1]] <- "invalid code")
 expect_warning(c(x[1], "test"))
 
 # ignoring patterns
-expect_equal(
+expect_identical(
   as.character(as.mo(c("E. coli", "E. coli ignorethis"), ignore_pattern = "this")),
   c("B_ESCHR_COLI", NA)
 )

inst/tinytest/test-translate.R

@@ -28,9 +28,26 @@
 # ==================================================================== #
 
 expect_identical(mo_genus("B_GRAMP", language = "pt"), "(Gram positivos desconhecidos)")
-expect_identical(mo_fullname("CoNS", "en"), "Coagulase-negative Staphylococcus (CoNS)")
+
+expect_identical(mo_fullname("CoNS", "cs"), "Koaguláza-negativní stafylokok (KNS)")
+expect_identical(mo_fullname("CoNS", "da"), "Koagulase-negative stafylokokker (KNS)")
 expect_identical(mo_fullname("CoNS", "de"), "Koagulase-negative Staphylococcus (KNS)")
-expect_identical(mo_fullname("CoNS", "nl"), "Coagulase-negatieve Staphylococcus (CNS)")
+expect_identical(mo_fullname("CoNS", "el"), "Σταφυλόκοκκος με αρνητική πηκτικότητα (CoNS)")
+expect_identical(mo_fullname("CoNS", "en"), "Coagulase-negative Staphylococcus (CoNS)")
 expect_identical(mo_fullname("CoNS", "es"), "Staphylococcus coagulasa negativo (SCN)")
+expect_identical(mo_fullname("CoNS", "fi"), "Koagulaasinegatiivinen stafylokokki (KNS)")
+expect_identical(mo_fullname("CoNS", "fr"), "Staphylococcus à coagulase négative (CoNS)")
 expect_identical(mo_fullname("CoNS", "it"), "Staphylococcus negativo coagulasi (CoNS)")
+expect_identical(mo_fullname("CoNS", "ja"), "コアグラーゼ陰性ブドウ球菌 (グラム陰性)")
+expect_identical(mo_fullname("CoNS", "nl"), "Coagulase-negatieve Staphylococcus (CNS)")
+expect_identical(mo_fullname("CoNS", "no"), "Koagulase-negative stafylokokker (KNS)")
+expect_identical(mo_fullname("CoNS", "pl"), "Staphylococcus koagulazoujemny (CoNS)")
 expect_identical(mo_fullname("CoNS", "pt"), "Staphylococcus coagulase negativo (CoNS)")
+expect_identical(mo_fullname("CoNS", "ro"), "Stafilococ coagulazo-negativ (SCN)")
+expect_identical(mo_fullname("CoNS", "ru"), "Коагулазоотрицательный стафилококк (КОС)")
+expect_identical(mo_fullname("CoNS", "sv"), "Koagulasnegativa stafylokocker (KNS)")
+expect_identical(mo_fullname("CoNS", "tr"), "Koagülaz-negatif Stafilokok (KNS)")
+expect_identical(mo_fullname("CoNS", "uk"), "Коагулазонегативний стафілокок (КНС)")
+expect_identical(mo_fullname("CoNS", "zh"), "凝固酶阴性葡萄球菌 (CoNS)")
+
+expect_error(mo_fullname("CoNS", "aa"))

man/antibiogram.Rd

@@ -153,17 +153,20 @@ example_isolates
 # Traditional antibiogram ----------------------------------------------
 
 antibiogram(example_isolates,
-            antibiotics = c(aminoglycosides(), carbapenems()))
+  antibiotics = c(aminoglycosides(), carbapenems())
+)
 
 antibiogram(example_isolates,
   antibiotics = aminoglycosides(),
   ab_transform = "atc",
-            mo_transform = "gramstain")
+  mo_transform = "gramstain"
+)
 
 antibiogram(example_isolates,
   antibiotics = carbapenems(),
   ab_transform = "name",
-            mo_transform = "name")
+  mo_transform = "name"
+)
 
 
 # Combined antibiogram -------------------------------------------------
@@ -171,13 +174,15 @@ antibiogram(example_isolates,
 # combined antibiotics yield higher empiric coverage
 antibiogram(example_isolates,
   antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-            mo_transform = "gramstain")
+  mo_transform = "gramstain"
+)
 
 antibiogram(example_isolates,
   antibiotics = c("TZP", "TZP+TOB"),
   mo_transform = "gramstain",
   ab_transform = "name",
-            sep = " & ")
+  sep = " & "
+)
 
 
 # Syndromic antibiogram ------------------------------------------------
@@ -185,7 +190,8 @@ antibiogram(example_isolates,
 # the data set could contain a filter for e.g. respiratory specimens
 antibiogram(example_isolates,
   antibiotics = c(aminoglycosides(), carbapenems()),
-            syndromic_group = "ward")
+  syndromic_group = "ward"
+)
 
 # now define a data set with only E. coli
 ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@@ -196,8 +202,10 @@ antibiogram(ex1,
   antibiotics = aminoglycosides(),
   ab_transform = "name",
   syndromic_group = ifelse(ex1$ward == "ICU",
-                                     "UCI", "No UCI"),
+    "UCI", "No UCI"
-            language = "es")
+  ),
+  language = "es"
+)
 
 
 # Weighted-incidence syndromic combination antibiogram (WISCA) ---------
@@ -209,18 +217,22 @@ antibiogram(example_isolates,
   minimum = 10, # this should be >= 30, but now just as example
   syndromic_group = ifelse(example_isolates$age >= 65 &
     example_isolates$gender == "M",
-                                     "WISCA Group 1", "WISCA Group 2"))
+  "WISCA Group 1", "WISCA Group 2"
+  )
+)
 
 
 # Generate plots with ggplot2 or base R --------------------------------
 
 ab1 <- antibiogram(example_isolates,
   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-                   mo_transform = "gramstain")
+  mo_transform = "gramstain"
+)
 ab2 <- antibiogram(example_isolates,
   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
   mo_transform = "gramstain",
-                   syndromic_group = "ward")
+  syndromic_group = "ward"
+)
 
 plot(ab1)
 

man/get_episode.Rd

@@ -55,9 +55,13 @@ is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
 df[which(get_episode(df$date, 60) == 3), ]
 
 # the functions also work for less than a day, e.g. to include one per hour:
-get_episode(c(Sys.time(),
+get_episode(
-              Sys.time() + 60 * 60),
+  c(
-            episode_days = 1 / 24)
+    Sys.time(),
+    Sys.time() + 60 * 60
+  ),
+  episode_days = 1 / 24
+)
 
 \donttest{
 if (require("dplyr")) {
@@ -117,7 +121,6 @@ if (require("dplyr")) {
 # but is_new_episode() has a lot more flexibility than first_isolate(),
 # since you can now group on anything that seems relevant:
 if (require("dplyr")) {
-  
   df \%>\%
     group_by(patient, mo, ward) \%>\%
     mutate(flag_episode = is_new_episode(date, 365)) \%>\%