sort sir history

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 1.8.2.9096
+Version: 1.8.2.9098
-Date: 2023-01-21
+Date: 2023-01-23
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 1.8.2.9096
+# AMR 1.8.2.9098
 
 *(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
 

R/aa_helper_functions.R

@@ -49,7 +49,8 @@ pm_left_join <- function(x, y, by = NULL, suffix = c(".x", ".y")) {
 
   merged <- cbind(
     x,
-    y[match(
+    y[
+      match(
         x[, by[1], drop = TRUE],
         y[, by[2], drop = TRUE]
       ),
@@ -190,7 +191,8 @@ addin_insert_like <- function() {
     )
   }
   replace_pos <- function(old, with) {
-    modifyRange(document_range(
+    modifyRange(
+      document_range(
         document_position(current_row, current_col - nchar(old)),
         document_position(current_row, current_col)
       ),
@@ -253,7 +255,8 @@ search_type_in_df <- function(x, type, info = TRUE) {
       # WHONET support
       found <- sort(colnames(x)[colnames_formatted %like_case% "^(specimen date|specimen_date|spec_date)"])
       if (!inherits(pm_pull(x, found), c("Date", "POSIXct"))) {
-        stop(font_red(paste0(
+        stop(
+          font_red(paste0(
             "Found column '", font_bold(found), "' to be used as input for `col_", type,
             "`, but this column contains no valid dates. Transform its values to valid dates first."
           )),
@@ -319,11 +322,13 @@ search_type_in_df <- function(x, type, info = TRUE) {
 }
 
 is_valid_regex <- function(x) {
-  regex_at_all <- tryCatch(vapply(
+  regex_at_all <- tryCatch(
+    vapply(
       FUN.VALUE = logical(1),
       X = strsplit(x, "", fixed = TRUE),
       FUN = function(y) {
-      any(y %in% c(
+        any(
+          y %in% c(
             "$", "(", ")", "*", "+", "-",
             ".", "?", "[", "]", "^", "{",
             "|", "}", "\\"
@@ -410,7 +415,8 @@ word_wrap <- function(...,
 
   if (msg %like% "\n") {
     # run word_wraps() over every line here, bind them and return again
-    return(paste0(vapply(
+    return(paste0(
+      vapply(
         FUN.VALUE = character(1),
         trimws(unlist(strsplit(msg, "\n", fixed = TRUE)), which = "right"),
         word_wrap,
@@ -429,7 +435,8 @@ word_wrap <- function(...,
   # we need to correct for already applied style, that adds text like "\033[31m\"
   msg_stripped <- font_stripstyle(msg)
   # where are the spaces now?
-  msg_stripped_wrapped <- paste0(strwrap(msg_stripped,
+  msg_stripped_wrapped <- paste0(
+    strwrap(msg_stripped,
       simplify = TRUE,
       width = width
     ),
@@ -487,7 +494,8 @@ message_ <- function(...,
                      appendLF = TRUE,
                      add_fn = list(font_blue),
                      as_note = TRUE) {
-  message(word_wrap(...,
+  message(
+    word_wrap(...,
       add_fn = add_fn,
       as_note = as_note
     ),
@@ -499,7 +507,8 @@ warning_ <- function(...,
                      add_fn = list(),
                      immediate = FALSE,
                      call = FALSE) {
-  warning(word_wrap(...,
+  warning(
+    word_wrap(...,
       add_fn = add_fn,
       as_note = FALSE
     ),
@@ -836,7 +845,8 @@ meet_criteria <- function(object,
     )
   }
   if (!is.null(contains_column_class)) {
-    stop_ifnot(any(vapply(
+    stop_ifnot(
+      any(vapply(
         FUN.VALUE = logical(1),
         object,
         function(col, columns_class = contains_column_class) {
@@ -1314,7 +1324,6 @@ round2 <- function(x, digits = 1, force_zero = TRUE) {
 
 # percentage from our other package: 'cleaner'
 percentage <- function(x, digits = NULL, ...) {
-
   # getdecimalplaces() function
   getdecimalplaces <- function(x, minimum = 0, maximum = 3) {
     if (maximum < minimum) {
@@ -1330,7 +1339,8 @@ percentage <- function(x, digits = NULL, ...) {
       ), ".", fixed = TRUE),
       function(y) ifelse(length(y) == 2, nchar(y[2]), 0)
     )), na.rm = TRUE)
-    max(min(max_places,
+    max(
+      min(max_places,
         maximum,
         na.rm = TRUE
       ),
@@ -1366,7 +1376,8 @@ percentage <- function(x, digits = NULL, ...) {
     # max one digit if undefined
     digits <- getdecimalplaces(x, minimum = 0, maximum = 1)
   }
-  format_percentage(structure(
+  format_percentage(
+    structure(
       .Data = as.double(x),
       class = c("percentage", "numeric")
     ),

R/ab.R

@@ -87,7 +87,6 @@
 #'
 #' \donttest{
 #' if (require("dplyr")) {
-#'
 #'   # you can quickly rename 'sir' columns using set_ab_names() with dplyr:
 #'   example_isolates %>%
 #'     set_ab_names(where(is.sir), property = "atc")
@@ -338,7 +337,8 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
       }
 
       # transform back from other languages and try again
-      x_translated <- paste(lapply(
+      x_translated <- paste(
+        lapply(
           strsplit(x[i], "[^A-Z0-9]"),
           function(y) {
             for (i in seq_len(length(y))) {
@@ -362,7 +362,8 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
       }
 
       # now also try to coerce brandname combinations like "Amoxy/clavulanic acid"
-      x_translated <- paste(lapply(
+      x_translated <- paste(
+        lapply(
           strsplit(x_translated, "[^A-Z0-9 ]"),
           function(y) {
             for (i in seq_len(length(y))) {
@@ -513,8 +514,10 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
     )
   }
   x_unknown <- x_unknown[!x_unknown %in% x_unknown_ATCs]
-  x_unknown <- c(x_unknown,
+  x_unknown <- c(
-                 AMR_env$ab_previously_coerced$x_bak[which(AMR_env$ab_previously_coerced$x %in% x & is.na(AMR_env$ab_previously_coerced$ab))])
+    x_unknown,
+    AMR_env$ab_previously_coerced$x_bak[which(AMR_env$ab_previously_coerced$x %in% x & is.na(AMR_env$ab_previously_coerced$ab))]
+  )
   if (length(x_unknown) > 0 && fast_mode == FALSE) {
     warning_(
       "in `as.ab()`: these values could not be coerced to a valid antimicrobial ID: ",

R/ab_selectors.R

@@ -95,20 +95,17 @@
 #' # dplyr -------------------------------------------------------------------
 #' \donttest{
 #' if (require("dplyr")) {
-#'
 #'   # get AMR for all aminoglycosides e.g., per ward:
 #'   example_isolates %>%
 #'     group_by(ward) %>%
 #'     summarise(across(aminoglycosides(), resistance))
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # You can combine selectors with '&' to be more specific:
 #'   example_isolates %>%
 #'     select(penicillins() & administrable_per_os())
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # get AMR for only drugs that matter - no intrinsic resistance:
 #'   example_isolates %>%
 #'     filter(mo_genus() %in% c("Escherichia", "Klebsiella")) %>%
@@ -116,7 +113,6 @@
 #'     summarise(across(not_intrinsic_resistant(), resistance))
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # get susceptibility for antibiotics whose name contains "trim":
 #'   example_isolates %>%
 #'     filter(first_isolate()) %>%
@@ -124,19 +120,16 @@
 #'     summarise(across(ab_selector(name %like% "trim"), susceptibility))
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
 #'   example_isolates %>%
 #'     select(carbapenems())
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
 #'   example_isolates %>%
 #'     select(mo, aminoglycosides())
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # any() and all() work in dplyr's filter() too:
 #'   example_isolates %>%
 #'     filter(
@@ -145,25 +138,21 @@
 #'     )
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # also works with c():
 #'   example_isolates %>%
 #'     filter(any(c(carbapenems(), aminoglycosides()) == "R"))
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # not setting any/all will automatically apply all():
 #'   example_isolates %>%
 #'     filter(aminoglycosides() == "R")
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
 #'   example_isolates %>%
 #'     select(mo, ab_class("mycobact"))
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # get bug/drug combinations for only glycopeptides in Gram-positives:
 #'   example_isolates %>%
 #'     filter(mo_is_gram_positive()) %>%
@@ -179,7 +168,6 @@
 #'     select(penicillins()) # only the 'J01CA01' column will be selected
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # with recent versions of dplyr this is all equal:
 #'   x <- example_isolates[carbapenems() == "R", ]
 #'   y <- example_isolates %>% filter(carbapenems() == "R")
@@ -433,7 +421,9 @@ administrable_per_os <- function(only_sir_columns = FALSE, ...) {
     ab_group = "administrable_per_os",
     examples = paste0(
       " (such as ",
-      vector_or(ab_name(sample(agents_all,
+      vector_or(
+        ab_name(
+          sample(agents_all,
             size = min(5, length(agents_all)),
             replace = FALSE
           ),
@@ -491,7 +481,8 @@ not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, ver
     sort = FALSE, fn = "not_intrinsic_resistant"
   )
   # intrinsic vars
-  vars_df_R <- tryCatch(sapply(
+  vars_df_R <- tryCatch(
+    sapply(
       eucast_rules(vars_df,
         col_mo = col_mo,
         version_expertrules = version_expertrules,
@@ -549,7 +540,8 @@ ab_select_exec <- function(function_name,
       if (message_not_thrown_before(function_name, "ab_class", "untreatable", entire_session = TRUE)) {
         warning_(
           "in `", function_name, "()`: some drugs were ignored since they cannot be used for treating patients: ",
-          vector_and(ab_name(names(ab_in_data)[names(ab_in_data) %in% untreatable],
+          vector_and(
+            ab_name(names(ab_in_data)[names(ab_in_data) %in% untreatable],
               language = NULL,
               tolower = TRUE
             ),
@@ -593,7 +585,8 @@ ab_select_exec <- function(function_name,
       }
       ab_group <- function_name
     }
-    examples <- paste0(" (such as ", vector_or(ab_name(sample(abx, size = min(2, length(abx)), replace = FALSE),
+    examples <- paste0(" (such as ", vector_or(
+      ab_name(sample(abx, size = min(2, length(abx)), replace = FALSE),
         tolower = TRUE,
         language = NULL
       ),
@@ -821,7 +814,8 @@ find_ab_names <- function(ab_group, n = 3) {
   if (length(drugs) == 0) {
     return("??")
   }
-  vector_or(ab_name(sample(drugs, size = min(n, length(drugs)), replace = FALSE),
+  vector_or(
+    ab_name(sample(drugs, size = min(n, length(drugs)), replace = FALSE),
       tolower = TRUE,
       language = NULL
     ),

R/age.R

@@ -83,7 +83,8 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
   # add decimals
   if (exact == TRUE) {
     # get dates of `x` when `x` would have the year of `reference`
-    x_in_reference_year <- as.POSIXlt(paste0(
+    x_in_reference_year <- as.POSIXlt(
+      paste0(
         format(as.Date(reference), "%Y"),
         format(as.Date(x), "-%m-%d")
       ),

R/av.R

@@ -308,7 +308,8 @@ as.av <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
       }
 
       # transform back from other languages and try again
-      x_translated <- paste(lapply(
+      x_translated <- paste(
+        lapply(
           strsplit(x[i], "[^A-Z0-9]"),
           function(y) {
             for (i in seq_len(length(y))) {
@@ -332,7 +333,8 @@ as.av <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
       }
 
       # now also try to coerce brandname combinations like "Amoxy/clavulanic acid"
-      x_translated <- paste(lapply(
+      x_translated <- paste(
+        lapply(
           strsplit(x_translated, "[^A-Z0-9 ]"),
           function(y) {
             for (i in seq_len(length(y))) {
@@ -478,8 +480,10 @@ as.av <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
     )
   }
   x_unknown <- x_unknown[!x_unknown %in% x_unknown_ATCs]
-  x_unknown <- c(x_unknown,
+  x_unknown <- c(
-                 AMR_env$av_previously_coerced$x_bak[which(AMR_env$av_previously_coerced$x %in% x & is.na(AMR_env$av_previously_coerced$av))])
+    x_unknown,
+    AMR_env$av_previously_coerced$x_bak[which(AMR_env$av_previously_coerced$x %in% x & is.na(AMR_env$av_previously_coerced$av))]
+  )
   if (length(x_unknown) > 0 && fast_mode == FALSE) {
     warning_(
       "in `as.av()`: these values could not be coerced to a valid antiviral drug ID: ",

R/custom_eucast_rules.R

@@ -240,7 +240,8 @@ print.custom_eucast_rules <- function(x, ...) {
       " (", rule$result_group, ")"
     )
     agents <- sort(agents)
-    rule_if <- word_wrap(paste0(
+    rule_if <- word_wrap(
+      paste0(
         i, ". ", font_bold("If "), font_blue(rule$query), font_bold(" then "),
         "set to {result}:"
       ),

R/custom_microorganisms.R

@@ -77,7 +77,8 @@
 #' # now add a custom entry - it will be considered by as.mo() and
 #' # all mo_*() functions
 #' add_custom_microorganisms(
-#'   data.frame(genus = "Enterobacter",
+#'   data.frame(
+#'     genus = "Enterobacter",
 #'     species = "asburiae/cloacae"
 #'   )
 #' )
@@ -100,8 +101,10 @@
 #'
 #' # the function tries to be forgiving:
 #' add_custom_microorganisms(
-#'   data.frame(GENUS = "BACTEROIDES / PARABACTEROIDES SLASHLINE",
+#'   data.frame(
-#'              SPECIES = "SPECIES")
+#'     GENUS = "BACTEROIDES / PARABACTEROIDES SLASHLINE",
+#'     SPECIES = "SPECIES"
+#'   )
 #' )
 #' mo_name("BACTEROIDES / PARABACTEROIDES")
 #' mo_rank("BACTEROIDES / PARABACTEROIDES")
@@ -112,9 +115,11 @@
 #'
 #' # for groups and complexes, set them as species or subspecies:
 #' add_custom_microorganisms(
-#'   data.frame(genus = "Citrobacter", 
+#'   data.frame(
+#'     genus = "Citrobacter",
 #'     species = c("freundii", "braakii complex"),
-#'              subspecies = c("complex", ""))
+#'     subspecies = c("complex", "")
+#'   )
 #' )
 #' mo_name(c("C. freundii complex", "C. braakii complex"))
 #' mo_species(c("C. freundii complex", "C. braakii complex"))
@@ -163,19 +168,27 @@ add_custom_microorganisms <- function(x) {
     x[, col] <- col_
   }
   # if subspecies is a group or complex, add it to the species and empty the subspecies
-  x$species[which(x$subspecies %in% c("group", "Group", "complex"))] <- paste(x$species[which(x$subspecies %in% c("group", "Group", "complex"))],
+  x$species[which(x$subspecies %in% c("group", "Group", "complex"))] <- paste(
-                                                                              x$subspecies[which(x$subspecies %in% c("group", "Group", "complex"))])
+    x$species[which(x$subspecies %in% c("group", "Group", "complex"))],
+    x$subspecies[which(x$subspecies %in% c("group", "Group", "complex"))]
+  )
   x$subspecies[which(x$subspecies %in% c("group", "Group", "complex"))] <- ""
 
   if ("rank" %in% colnames(x)) {
-    stop_ifnot(all(x$rank %in% AMR_env$MO_lookup$rank),
+    stop_ifnot(
-               "the 'rank' column can only contain these values: ", vector_or(AMR_env$MO_lookup$rank))
+      all(x$rank %in% AMR_env$MO_lookup$rank),
+      "the 'rank' column can only contain these values: ", vector_or(AMR_env$MO_lookup$rank)
+    )
   } else {
     x$rank <- ifelse(x$subspecies != "", "subspecies",
       ifelse(x$species != "", "species",
         ifelse(x$genus != "", "genus",
           stop("in add_custom_microorganisms(): only microorganisms up to the genus level can be added",
-                                        call. = FALSE))))
+            call. = FALSE
+          )
+        )
+      )
+    )
   }
   x$source <- "Added by user"
   if (!"fullname" %in% colnames(x)) {
@@ -230,13 +243,21 @@ add_custom_microorganisms <- function(x) {
   x$mo <- trimws2(as.character(x$mo))
   x$mo[x$mo == ""] <- NA_character_
   current <- sum(AMR_env$MO_lookup$source == "Added by user", na.rm = TRUE)
-  x$mo[is.na(x$mo)] <- paste0("CUSTOM",
+  x$mo[is.na(x$mo)] <- paste0(
+    "CUSTOM",
     seq.int(from = current + 1, to = current + nrow(x), by = 1),
     "_",
-                              toupper(unname(abbreviate(gsub(" +", " _ ",
+    toupper(unname(abbreviate(
-                                                             gsub("[^A-Za-z0-9-]", " ",
+      gsub(
-                                                                  trimws2(paste(x$genus, x$species, x$subspecies)))),
+        " +", " _ ",
-                                                        minlength = 10))))
+        gsub(
+          "[^A-Za-z0-9-]", " ",
+          trimws2(paste(x$genus, x$species, x$subspecies))
+        )
+      ),
+      minlength = 10
+    )))
+  )
   stop_if(anyDuplicated(c(as.character(AMR_env$MO_lookup$mo), x$mo)), "MO codes must be unique and not match existing MO codes of the AMR package")
 
   # add to package ----

R/episode.R

@@ -57,7 +57,8 @@
 #' 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(
+#' get_episode(
+#'   c(
 #'     Sys.time(),
 #'     Sys.time() + 60 * 60
 #'   ),
@@ -98,7 +99,6 @@
 #'     )
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # grouping on patients and microorganisms leads to the same
 #'   # results as first_isolate() when using 'episode-based':
 #'   x <- df %>%
@@ -115,7 +115,6 @@
 #'   identical(x, y)
 #' }
 #' 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:
 #'   df %>%

R/eucast_rules.R

@@ -702,7 +702,8 @@ eucast_rules <- function(x,
       # Print rule  -------------------------------------------------------------
       if (rule_current != rule_previous) {
         # is new rule within group, print its name
-        cat(italicise_taxonomy(word_wrap(rule_current,
+        cat(italicise_taxonomy(
+          word_wrap(rule_current,
             width = getOption("width") - 30,
             extra_indent = 6
           ),
@@ -721,7 +722,8 @@ eucast_rules <- function(x,
     if (mo_value %like% "coagulase" && any(x$genus == "Staphylococcus", na.rm = TRUE)) {
       if (mo_value %like% "negative") {
         eucast_rules_df[i, "this_value"] <- paste0(
-          "^(", paste0(all_staph[which(all_staph$CNS_CPS %like% "negative"),
+          "^(", paste0(
+            all_staph[which(all_staph$CNS_CPS %like% "negative"),
               "fullname",
               drop = TRUE
             ],
@@ -731,7 +733,8 @@ eucast_rules <- function(x,
         )
       } else {
         eucast_rules_df[i, "this_value"] <- paste0(
-          "^(", paste0(all_staph[which(all_staph$CNS_CPS %like% "positive"),
+          "^(", paste0(
+            all_staph[which(all_staph$CNS_CPS %like% "positive"),
               "fullname",
               drop = TRUE
             ],
@@ -745,7 +748,8 @@ eucast_rules <- function(x,
     # be sure to comprise all beta-haemolytic Streptococci (Lancefield groups A, B, C and G) when they are mentioned
     if (mo_value %like% "group [ABCG]" && any(x$genus == "Streptococcus", na.rm = TRUE)) {
       eucast_rules_df[i, "this_value"] <- paste0(
-        "^(", paste0(all_strep[which(all_strep$Lancefield %like% "group [ABCG]"),
+        "^(", paste0(
+          all_strep[which(all_strep$Lancefield %like% "group [ABCG]"),
             "fullname",
             drop = TRUE
           ],
@@ -789,12 +793,14 @@ eucast_rules <- function(x,
       if (length(source_antibiotics) == 0) {
         rows <- integer(0)
       } else if (length(source_antibiotics) == 1) {
-        rows <- tryCatch(which(x[, if_mo_property, drop = TRUE] %like% mo_value &
+        rows <- tryCatch(
+          which(x[, if_mo_property, drop = TRUE] %like% mo_value &
             as.sir_no_warning(x[, source_antibiotics[1L]]) == source_value[1L]),
           error = function(e) integer(0)
         )
       } else if (length(source_antibiotics) == 2) {
-        rows <- tryCatch(which(x[, if_mo_property, drop = TRUE] %like% mo_value &
+        rows <- tryCatch(
+          which(x[, if_mo_property, drop = TRUE] %like% mo_value &
             as.sir_no_warning(x[, source_antibiotics[1L]]) == source_value[1L] &
             as.sir_no_warning(x[, source_antibiotics[2L]]) == source_value[2L]),
           error = function(e) integer(0)
@@ -872,7 +878,8 @@ eucast_rules <- function(x,
       )
       if (isTRUE(info)) {
         # print rule
-        cat(italicise_taxonomy(word_wrap(format_custom_query_rule(rule$query, colours = FALSE),
+        cat(italicise_taxonomy(
+          word_wrap(format_custom_query_rule(rule$query, colours = FALSE),
             width = getOption("width") - 30,
             extra_indent = 6
           ),
@@ -1117,7 +1124,8 @@ edit_sir <- function(x,
       },
       error = function(e) {
         txt_error()
-        stop(paste0(
+        stop(
+          paste0(
             "In row(s) ", paste(rows[seq_len(min(length(rows), 10))], collapse = ","),
             ifelse(length(rows) > 10, "...", ""),
             " while writing value '", to,

R/first_isolate.R

@@ -144,13 +144,11 @@
 #'     filter(first_isolate())
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # short-hand version:
 #'   example_isolates %>%
 #'     filter_first_isolate(info = FALSE)
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # flag the first isolates per group:
 #'   example_isolates %>%
 #'     group_by(ward) %>%
@@ -244,7 +242,8 @@ first_isolate <- function(x = NULL,
     method <- "episode-based"
   }
   if (isTRUE(info) && message_not_thrown_before("first_isolate", "method")) {
-    message_(paste0(
+    message_(
+      paste0(
         "Determining first isolates ",
         ifelse(method %in% c("episode-based", "phenotype-based"),
           ifelse(is.infinite(episode_days),
@@ -469,7 +468,9 @@ first_isolate <- function(x = NULL,
   x$other_pat_or_mo <- !(x$newvar_patient_id == pm_lag(x$newvar_patient_id) & x$newvar_genus_species == pm_lag(x$newvar_genus_species))
 
   x$episode_group <- paste(x$newvar_patient_id, x$newvar_genus_species)
-  x$more_than_episode_ago <- unlist(lapply(split(
+  x$more_than_episode_ago <- unlist(
+    lapply(
+      split(
         x$newvar_date,
         x$episode_group
       ),
@@ -606,7 +607,8 @@ first_isolate <- function(x = NULL,
     }
     # mark up number of found
     n_found <- format(n_found, big.mark = big.mark, decimal.mark = decimal.mark)
-    message_(paste0(
+    message_(
+      paste0(
         "=> Found ",
         font_bold(paste0(
           n_found,

R/ggplot_pca.R

@@ -414,7 +414,8 @@ pca_calculations <- function(pca_model,
       sigma <- var(cbind(x$xvar, x$yvar))
       mu <- c(mean(x$xvar), mean(x$yvar))
       ed <- sqrt(qchisq(ellipse_prob, df = 2))
-      data.frame(sweep(circle %*% chol(sigma) * ed,
+      data.frame(
+        sweep(circle %*% chol(sigma) * ed,
           MARGIN = 2,
           STATS = mu,
           FUN = "+"

R/ggplot_sir.R

@@ -71,13 +71,11 @@
 #' @examples
 #' \donttest{
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # get antimicrobial results for drugs against a UTI:
 #'   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +
 #'     geom_sir()
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # prettify the plot using some additional functions:
 #'   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)
 #'   ggplot(df) +
@@ -88,21 +86,18 @@
 #'     theme_sir()
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # or better yet, simplify this using the wrapper function - a single command:
 #'   example_isolates %>%
 #'     select(AMX, NIT, FOS, TMP, CIP) %>%
 #'     ggplot_sir()
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # get only proportions and no counts:
 #'   example_isolates %>%
 #'     select(AMX, NIT, FOS, TMP, CIP) %>%
 #'     ggplot_sir(datalabels = FALSE)
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # add other ggplot2 arguments as you like:
 #'   example_isolates %>%
 #'     select(AMX, NIT, FOS, TMP, CIP) %>%
@@ -115,14 +110,12 @@
 #'     )
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # you can alter the colours with colour names:
 #'   example_isolates %>%
 #'     select(AMX) %>%
 #'     ggplot_sir(colours = c(SI = "yellow"))
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # but you can also use the built-in colour-blind friendly colours for
 #'   # your plots, where "S" is green, "I" is yellow and "R" is red:
 #'   data.frame(
@@ -135,7 +128,6 @@
 #'     scale_sir_colours(Value4 = "S", Value5 = "I", Value6 = "R")
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # resistance of ciprofloxacine per age group
 #'   example_isolates %>%
 #'     mutate(first_isolate = first_isolate()) %>%
@@ -149,14 +141,12 @@
 #'     ggplot_sir(x = "age_group")
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # a shorter version which also adjusts data label colours:
 #'   example_isolates %>%
 #'     select(AMX, NIT, FOS, TMP, CIP) %>%
 #'     ggplot_sir(colours = FALSE)
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
-#'
 #'   # it also supports groups (don't forget to use the group var on `x` or `facet`):
 #'   example_isolates %>%
 #'     filter(mo_is_gram_negative(), ward != "Outpatient") %>%

R/guess_ab_col.R

@@ -274,7 +274,8 @@ get_column_abx <- function(x,
       }
       if (names(out[i]) %in% names(duplicates)) {
         already_set_as <- out[unname(out) == unname(out[i])][1L]
-        warning_(paste0(
+        warning_(
+          paste0(
             "Column '", font_bold(out[i]), "' will not be used for ",
             names(out)[i], " (", ab_name(names(out)[i], tolower = TRUE, language = NULL), ")",
             ", as it is already set for ",
@@ -307,7 +308,8 @@ get_column_abx <- function(x,
     if (isTRUE(info) && !all(soft_dependencies %in% names(out))) {
       # missing a soft dependency may lower the reliability
       missing <- soft_dependencies[!soft_dependencies %in% names(out)]
-      missing_msg <- vector_and(paste0(
+      missing_msg <- vector_and(
+        paste0(
           ab_name(missing, tolower = TRUE, language = NULL),
           " (", font_bold(missing, collapse = NULL), ")"
         ),
@@ -355,7 +357,8 @@ generate_warning_abs_missing <- function(missing, any = FALSE) {
   } else {
     any_txt <- c("", "are")
   }
-  warning_(paste0(
+  warning_(
+    paste0(
       "Introducing NAs since", any_txt[1], " these antimicrobials ", any_txt[2], " required: ",
       vector_and(missing, quotes = FALSE)
     ),

R/italicise_taxonomy.R

@@ -73,7 +73,8 @@ italicise_taxonomy <- function(string, type = c("markdown", "ansi")) {
       search_strings <- gsub("[^a-zA-Z-]", "", s_split)
 
       ind_species <- search_strings != "" &
-        search_strings %in% AMR_env$MO_lookup[which(AMR_env$MO_lookup$rank %in% c(
+        search_strings %in% AMR_env$MO_lookup[
+          which(AMR_env$MO_lookup$rank %in% c(
             "family",
             "genus",
             "species",
@@ -87,7 +88,8 @@ italicise_taxonomy <- function(string, type = c("markdown", "ansi")) {
 
       ind_fullname <- search_strings != "" &
         search_strings %in% c(
-          AMR_env$MO_lookup[which(AMR_env$MO_lookup$rank %in% c(
+          AMR_env$MO_lookup[
+            which(AMR_env$MO_lookup$rank %in% c(
               "family",
               "genus",
               "species",
@@ -98,7 +100,8 @@ italicise_taxonomy <- function(string, type = c("markdown", "ansi")) {
             "fullname",
             drop = TRUE
           ],
-          AMR_env$MO_lookup[which(AMR_env$MO_lookup$rank %in% c(
+          AMR_env$MO_lookup[
+            which(AMR_env$MO_lookup$rank %in% c(
               "family",
               "genus",
               "species",

R/mdro.R

@@ -655,7 +655,8 @@ mdro <- function(x = NULL,
     cols <- cols[!ab_missing(cols)]
     cols <- cols[!is.na(cols)]
     if (length(rows) > 0 && length(cols) > 0) {
-      x[, cols] <- as.data.frame(lapply(
+      x[, cols] <- as.data.frame(
+        lapply(
           x[, cols, drop = FALSE],
           function(col) as.sir(col)
         ),
@@ -670,7 +671,8 @@ mdro <- function(x = NULL,
             x[row, group_vct, drop = FALSE],
             function(y) y %in% search_result
           )
-          paste(sort(c(
+          paste(
+            sort(c(
               unlist(strsplit(x[row, "columns_nonsusceptible", drop = TRUE], ", ", fixed = TRUE)),
               names(cols_nonsus)[cols_nonsus]
             )),
@@ -715,7 +717,8 @@ mdro <- function(x = NULL,
       # keep only unique ones:
       lst_vector <- lst_vector[!duplicated(paste(lst_vector, names(lst_vector)))]
 
-      x[, lst_vector] <- as.data.frame(lapply(
+      x[, lst_vector] <- as.data.frame(
+        lapply(
           x[, lst_vector, drop = FALSE],
           function(col) as.sir(col)
         ),
@@ -748,7 +751,8 @@ mdro <- function(x = NULL,
         FUN.VALUE = double(1),
         rows,
         function(row, group_tbl = lst) {
-          sum(vapply(
+          sum(
+            vapply(
               FUN.VALUE = logical(1),
               group_tbl,
               function(group) {

R/mo.R

@@ -365,7 +365,8 @@ as.mo <- function(x,
           plural <- c("s", "these uncertainties")
         }
         if (length(AMR_env$mo_uncertainties$original_input) <= 3) {
-          examples <- vector_and(paste0(
+          examples <- vector_and(
+            paste0(
               '"', AMR_env$mo_uncertainties$original_input,
               '" (assumed ', italicise(AMR_env$mo_uncertainties$fullname), ")"
             ),
@@ -376,7 +377,7 @@ as.mo <- function(x,
         }
         msg <- c(msg, paste0(
           "Microorganism translation was uncertain for ", examples,
-          ". Run `mo_uncertainties()` to review ", plural[2], ", or use `add_custom_microorganisms()` to add own entries."
+          ". Run `mo_uncertainties()` to review ", plural[2], ", or use `add_custom_microorganisms()` to add custom entries."
         ))
 
         for (m in msg) {
@@ -577,7 +578,8 @@ pillar_shaft.mo <- function(x, ...) {
   if (!all(x %in% all_mos) ||
     (!is.null(df) && !all(unlist(df[, which(mo_cols), drop = FALSE]) %in% all_mos))) {
     # markup old mo codes
-    out[!x %in% all_mos] <- font_italic(font_na(x[!x %in% all_mos],
+    out[!x %in% all_mos] <- font_italic(
+      font_na(x[!x %in% all_mos],
         collapse = NULL
       ),
       collapse = NULL
@@ -835,9 +837,11 @@ print.mo_uncertainties <- function(x, ...) {
       candidates_formatted <- candidates_formatted[order(1 - scores)]
       scores_formatted <- scores_formatted[order(1 - scores)]
 
-      candidates <- word_wrap(paste0(
+      candidates <- word_wrap(
+        paste0(
           "Also matched: ",
-        vector_and(paste0(
+          vector_and(
+            paste0(
               candidates_formatted,
               font_blue(paste0(" (", scores_formatted, ")"), collapse = NULL)
             ),
@@ -999,10 +1003,14 @@ convert_colloquial_input <- function(x) {
 
 italicise <- function(x) {
   out <- font_italic(x, collapse = NULL)
-  out[x %like_case% "Salmonella [A-Z]"] <- paste(font_italic("Salmonella"), 
+  out[x %like_case% "Salmonella [A-Z]"] <- paste(
-                                                 gsub("Salmonella ", "", x[x %like_case% "Salmonella [A-Z]"]))
+    font_italic("Salmonella"),
-  out[x %like_case% "Streptococcus [A-Z]"] <- paste(font_italic("Streptococcus"), 
+    gsub("Salmonella ", "", x[x %like_case% "Salmonella [A-Z]"])
-                                                    gsub("Streptococcus ", "", x[x %like_case% "Streptococcus [A-Z]"]))
+  )
+  out[x %like_case% "Streptococcus [A-Z]"] <- paste(
+    font_italic("Streptococcus"),
+    gsub("Streptococcus ", "", x[x %like_case% "Streptococcus [A-Z]"])
+  )
   if (has_colour()) {
     out <- gsub("(Group|group|Complex|complex)(\033\\[23m)?", "\033[23m\\1", out, perl = TRUE)
   }

R/mo_property.R

@@ -133,7 +133,6 @@
 #' mo_fullname("K. pneu rh")
 #' mo_shortname("K. pneu rh")
 #'
-#'
 #' \donttest{
 #' # Becker classification, see ?as.mo ----------------------------------------
 #'
@@ -426,7 +425,8 @@ mo_pathogenicity <- function(x, language = get_AMR_locale(), keep_synonyms = get
   kngd <- AMR_env$MO_lookup$kingdom[match(x.mo, AMR_env$MO_lookup$mo)]
   rank <- AMR_env$MO_lookup$rank[match(x.mo, AMR_env$MO_lookup$mo)]
 
-  out <- factor(ifelse(prev == 1 & kngd == "Bacteria" & rank != "genus",
+  out <- factor(
+    ifelse(prev == 1 & kngd == "Bacteria" & rank != "genus",
       "Pathogenic",
       ifelse(prev < 2 & kngd == "Fungi",
         "Potentially pathogenic",
@@ -434,9 +434,14 @@ mo_pathogenicity <- function(x, language = get_AMR_locale(), keep_synonyms = get
           "Non-pathogenic",
           ifelse(kngd == "Bacteria",
             "Potentially pathogenic",
-                                            "Unknown")))),
+            "Unknown"
+          )
+        )
+      )
+    ),
     levels = c("Pathogenic", "Potentially pathogenic", "Non-pathogenic", "Unknown"),
-                ordered = TRUE)
+    ordered = TRUE
+  )
 
   load_mo_uncertainties(metadata)
   out

R/proportion.R

@@ -140,7 +140,6 @@
 #'     )
 #' }
 #' if (require("dplyr")) {
-#'
 #'   # scoped dplyr verbs with antibiotic selectors
 #'   # (you could also use across() of course)
 #'   example_isolates %>%

R/sir.R

@@ -64,7 +64,7 @@
 #'      ```
 #' 4. For **interpreting a complete data set**, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running `as.sir(your_data)`.
 #'
-#' For points 2, 3 and 4: Use [sir_interpretation_history()] to retrieve a [data.frame] (or [tibble][tibble::tibble()] if the `tibble` package is installed) with all results of the last [as.sir()] call.
+#' **For points 2, 3 and 4: Use [sir_interpretation_history()]** to retrieve a [data.frame] (or [tibble][tibble::tibble()] if the `tibble` package is installed) with all results of the last [as.sir()] call.
 #'
 #' ### Supported Guidelines
 #'
@@ -806,19 +806,23 @@ as_sir_method <- function(method_short,
     for (i in seq_len(length(messages))) {
       messages[i] <- word_wrap(extra_indent = 5, messages[i])
     }
-    message(font_green(font_bold(" Note:\n")),
+    message(
-            paste0("   ", font_black(AMR_env$bullet_icon)," ", font_black(messages, collapse = NULL) , collapse = "\n"))
+      font_green(font_bold(" Note:\n")),
+      paste0("   ", font_black(AMR_env$bullet_icon), " ", font_black(messages, collapse = NULL), collapse = "\n")
+    )
   }
 
   method <- method_short
 
   metadata_mo <- get_mo_uncertainties()
 
-  df <- data.frame(values = x,
+  df <- data.frame(
+    values = x,
     mo = mo,
     result = NA_sir_,
     uti = uti,
-                   stringsAsFactors = FALSE)
+    stringsAsFactors = FALSE
+  )
   if (method == "mic") {
     # when as.sir.mic is called directly
     df$values <- as.mic(df$values)
@@ -849,9 +853,11 @@ as_sir_method <- function(method_short,
   msgs <- character(0)
   if (nrow(breakpoints) == 0) {
     # apparently no breakpoints found
-    msg_note(paste0("No ", method_coerced, " breakpoints available for ",
+    msg_note(paste0(
+      "No ", method_coerced, " breakpoints available for ",
       suppressMessages(suppressWarnings(ab_name(ab_coerced, language = NULL, tolower = TRUE))),
-                    " (", ab_coerced, ")"))
+      " (", ab_coerced, ")"
+    ))
     load_mo_uncertainties(metadata_mo)
     return(rep(NA_sir_, nrow(df)))
   }
@@ -863,7 +869,6 @@ as_sir_method <- function(method_short,
 
   # run the rules
   for (mo_unique in unique(df$mo)) {
-    
     rows <- which(df$mo == mo_unique)
     values <- df[rows, "values", drop = TRUE]
     uti <- df[rows, "uti", drop = TRUE]
@@ -890,16 +895,20 @@ as_sir_method <- function(method_short,
     if (!mo_rank(mo_unique) %in% c("kingdom", "phylum", "class", "order")) {
       mo_formatted <- font_italic(mo_formatted)
     }
-    ab_formatted <- paste0(suppressMessages(suppressWarnings(ab_name(ab_coerced, language = NULL, tolower = TRUE))), 
+    ab_formatted <- paste0(
-                           " (", ab_coerced, ")")
+      suppressMessages(suppressWarnings(ab_name(ab_coerced, language = NULL, tolower = TRUE))),
+      " (", ab_coerced, ")"
+    )
 
     # gather all available breakpoints for current MO and sort on taxonomic rank
     # (this will prefer species breakpoints over order breakpoints)
     breakpoints_current <- breakpoints %pm>%
-      subset(mo %in% c(mo_current_genus, mo_current_family,
+      subset(mo %in% c(
+        mo_current_genus, mo_current_family,
         mo_current_order, mo_current_class,
         mo_current_becker, mo_current_lancefield,
-                       mo_current_other))
+        mo_current_other
+      ))
 
     if (any(df[rows, "uti", drop = TRUE], na.rm = TRUE)) {
       breakpoints_current <- breakpoints_current %pm>%
@@ -937,7 +946,6 @@ as_sir_method <- function(method_short,
     if (isTRUE(add_intrinsic_resistance) && guideline_coerced %like% "EUCAST" && paste(mo_unique, ab_coerced) %in% AMR_env$intrinsic_resistant) {
       msgs <- c(msgs, paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, ""))
       new_sir <- rep(as.sir("R"), length(rows))
-      
     } else {
       # then run the rules
       breakpoints_current <- breakpoints_current[1L, , drop = FALSE]
@@ -953,7 +961,6 @@ as_sir_method <- function(method_short,
           # and NA otherwise
           TRUE ~ NA_sir_
         )
-        
       } else if (method == "disk") {
         new_sir <- quick_case_when(
           is.na(values) ~ NA_sir_,
@@ -1027,6 +1034,9 @@ sir_interpretation_history <- function(clean = FALSE) {
     AMR_env$sir_interpretation_history <- out.bak
   }
 
+  # sort descending on time
+  out <- out[order(out$datetime, decreasing = TRUE), , drop = FALSE]
+
   if (pkg_is_available("tibble", also_load = FALSE)) {
     import_fn("as_tibble", "tibble")(out)
   } else {

R/translate.R

@@ -144,7 +144,8 @@ translate_AMR <- function(x, language = get_AMR_locale()) {
     language = language,
     only_unknown = FALSE,
     only_affect_ab_names = FALSE,
-                          only_affect_mo_names = FALSE)
+    only_affect_mo_names = FALSE
+  )
 }
 
 
@@ -170,7 +171,8 @@ find_language <- function(language, fallback = TRUE) {
   language <- Map(LANGUAGES_SUPPORTED_NAMES,
     LANGUAGES_SUPPORTED,
     f = function(l, n, check = language) {
-      grepl(paste0(
+      grepl(
+        paste0(
           "^(", l[1], "|", l[2], "|",
           n, "(_|$)|", toupper(n), "(_|$))"
         ),
@@ -196,7 +198,6 @@ translate_into_language <- function(from,
                                     only_unknown = FALSE,
                                     only_affect_ab_names = FALSE,
                                     only_affect_mo_names = FALSE) {
-
   # get ISO-639-1 of language
   lang <- validate_language(language)
   if (lang == "en") {

R/zz_deprecated.R

@@ -35,7 +35,8 @@
 #' @rdname AMR-deprecated
 #' @export
 NA_rsi_ <- set_clean_class(factor(NA_character_, levels = c("S", "I", "R"), ordered = TRUE),
-                           new_class = c("rsi", "ordered", "factor"))
+  new_class = c("rsi", "ordered", "factor")
+)
 #' @rdname AMR-deprecated
 #' @export
 as.rsi <- function(x, ...) {
@@ -197,14 +198,18 @@ deprecation_warning <- function(old = NULL, new = NULL, extra_msg = NULL) {
     env <- paste0("deprecated_", old)
     if (!env %in% names(AMR_env)) {
       AMR_env[[paste0("deprecated_", old)]] <- 1
-      warning_(ifelse(is.null(new), 
+      warning_(
+        ifelse(is.null(new),
           paste0("The `", old, "()` function is no longer in use"),
-                      paste0("The `", old, "()` function has been replaced with `", new, "()`")),
+          paste0("The `", old, "()` function has been replaced with `", new, "()`")
+        ),
         ", see `?AMR-deprecated`.",
         ifelse(!is.null(extra_msg),
           paste0(" ", extra_msg),
-                      ""),
+          ""
-               "\nThis warning will be shown once per session.")
+        ),
+        "\nThis warning will be shown once per session."
+      )
     }
   }
 }

R/zzz.R

@@ -192,18 +192,24 @@ if (utf8_supported && !is_latex) {
   if (!is.null(getOption("AMR_custom_ab")) && file.exists(getOption("AMR_custom_ab", default = ""))) {
     packageStartupMessage("Adding custom antimicrobials from '", getOption("AMR_custom_ab"), "'...", appendLF = FALSE)
     x <- readRDS2(getOption("AMR_custom_ab"))
-    tryCatch({
+    tryCatch(
+      {
         suppressWarnings(suppressMessages(add_custom_antimicrobials(x)))
         packageStartupMessage("OK.")
-    }, error = function(e) packageStartupMessage("Failed: ", e$message))
+      },
+      error = function(e) packageStartupMessage("Failed: ", e$message)
+    )
   }
   # if custom mo option is available, load it
   if (!is.null(getOption("AMR_custom_mo")) && file.exists(getOption("AMR_custom_mo", default = ""))) {
     packageStartupMessage("Adding custom microorganisms from '", getOption("AMR_custom_mo"), "'...", appendLF = FALSE)
     x <- readRDS2(getOption("AMR_custom_mo"))
-    tryCatch({
+    tryCatch(
+      {
         suppressWarnings(suppressMessages(add_custom_microorganisms(x)))
         packageStartupMessage("OK.")
-    }, error = function(e) packageStartupMessage("Failed: ", e$message))
+      },
+      error = function(e) packageStartupMessage("Failed: ", e$message)
+    )
   }
 }

data-raw/_pre_commit_hook.R

@@ -101,7 +101,8 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
   MO_staph <- AMR::microorganisms
   MO_staph <- MO_staph[which(MO_staph$genus == "Staphylococcus"), , drop = FALSE]
   if (type == "CoNS") {
-    MO_staph[which(MO_staph$species %in% c(
+    MO_staph[
+      which(MO_staph$species %in% c(
         "coagulase-negative", "argensis", "arlettae",
         "auricularis", "borealis", "caeli", "capitis", "caprae",
         "carnosus", "casei", "caseolyticus", "chromogenes", "cohnii", "condimenti",
@@ -126,7 +127,8 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
       drop = TRUE
     ]
   } else if (type == "CoPS") {
-    MO_staph[which(MO_staph$species %in% c(
+    MO_staph[
+      which(MO_staph$species %in% c(
         "coagulase-positive", "coagulans",
         "agnetis", "argenteus",
         "cornubiensis",
@@ -254,7 +256,8 @@ create_AB_AV_lookup <- function(df) {
   }
   new_df$generalised_loinc <- lapply(new_df$loinc, generalise_antibiotic_name)
   new_df$generalised_all <- unname(lapply(
-    as.list(as.data.frame(t(new_df[,
+    as.list(as.data.frame(
+      t(new_df[,
         c(
           colnames(new_df)[colnames(new_df) %in% c("ab", "av", "atc", "cid", "name")],
           colnames(new_df)[colnames(new_df) %like% "generalised"]
@@ -472,7 +475,7 @@ suppressMessages(devtools::document(quiet = TRUE))
 if (!"styler" %in% rownames(utils::installed.packages())) {
   message("Package 'styler' not installed!")
 } else if (interactive()) {
-  # # only when sourcing this file ourselves
+  # only when sourcing this file ourselves
   # usethis::ui_info("Styling package")
   # styler::style_pkg(
   #   style = styler::tidyverse_style,

data-raw/create_survey_page.R

@@ -1,4 +1,3 @@
-
 license_text <- readLines("docs/LICENSE-text.html")
 license_text <- paste(license_text, collapse = "|||")
 license_text <- gsub("licen(s|c)e", "Survey", license_text, ignore.case = TRUE)

data-raw/read_EUCAST.R

@@ -66,7 +66,8 @@ read_EUCAST <- function(sheet, file, guideline_name) {
 
   # in the info header in the Excel file, EUCAST mentions which genera are targeted
   if (sheet %like% "anaerob.*Gram.*posi") {
-    sheet <- paste0(c(
+    sheet <- paste0(
+      c(
         "Actinomyces", "Bifidobacterium", "Clostridioides",
         "Clostridium", "Cutibacterium", "Eggerthella",
         "Eubacterium", "Lactobacillus", "Propionibacterium",
@@ -75,7 +76,8 @@ read_EUCAST <- function(sheet, file, guideline_name) {
       collapse = "_"
     )
   } else if (sheet %like% "anaerob.*Gram.*nega") {
-    sheet <- paste0(c(
+    sheet <- paste0(
+      c(
         "Bacteroides",
         "Bilophila",
         "Fusobacterium",
@@ -87,7 +89,8 @@ read_EUCAST <- function(sheet, file, guideline_name) {
       collapse = "_"
     )
   } else if (sheet == "Streptococcus A,B,C,G") {
-    sheet <- paste0(microorganisms %>%
+    sheet <- paste0(
+      microorganisms %>%
         filter(genus == "Streptococcus") %>%
         mutate(lancefield = mo_name(mo, Lancefield = TRUE)) %>%
         filter(lancefield %like% "^Streptococcus group") %>%

data-raw/reproduction_of_antibiotics.R

@@ -142,14 +142,15 @@ abx2 <- bind_rows(abx_atc1, abx_atc2)
 rm(abx_atc1)
 rm(abx_atc2)
 
-abx2$ab[is.na(abx2$ab)] <- toupper(abbreviate(gsub(
+abx2$ab[is.na(abx2$ab)] <- toupper(abbreviate(
+  gsub(
     "[/0-9-]",
     " ",
     abx2$name[is.na(abx2$ab)]
-),
+  ),
-minlength = 3,
+  minlength = 3,
-method = "left.kept",
+  method = "left.kept",
-strict = TRUE
+  strict = TRUE
 ))
 
 n_distinct(abx2$ab)
@@ -197,7 +198,8 @@ get_CID <- function(ab) {
     p$tick()
 
     CID[i] <- tryCatch(
-      data.table::fread(paste0(
+      data.table::fread(
+        paste0(
           "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
           URLencode(ab[i], reserved = TRUE),
           "/cids/TXT?name_type=complete"
@@ -209,7 +211,8 @@ get_CID <- function(ab) {
     if (is.na(CID[i])) {
       # try with removing the text in brackets
       CID[i] <- tryCatch(
-        data.table::fread(paste0(
+        data.table::fread(
+          paste0(
             "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
             URLencode(trimws(gsub("[(].*[)]", "", ab[i])), reserved = TRUE),
             "/cids/TXT?name_type=complete"
@@ -223,7 +226,8 @@ get_CID <- function(ab) {
       # try match on word and take the lowest CID value (sorted)
       ab[i] <- gsub("[^a-z0-9]+", " ", ab[i], ignore.case = TRUE)
       CID[i] <- tryCatch(
-        data.table::fread(paste0(
+        data.table::fread(
+          paste0(
             "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
             URLencode(ab[i], reserved = TRUE),
             "/cids/TXT?name_type=word"
@@ -260,7 +264,8 @@ get_synonyms <- function(CID, clean = TRUE) {
     }
 
     synonyms_txt <- tryCatch(
-      data.table::fread(paste0(
+      data.table::fread(
+        paste0(
           "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/fastidentity/cid/",
           CID[i],
           "/synonyms/TXT"

data-raw/reproduction_of_antivirals.R

@@ -126,7 +126,8 @@ names_codes <- antivirals %>%
     into = paste0("name", c(1:7)),
     sep = "(, | and )",
     remove = FALSE,
-           fill = "right") %>%
+    fill = "right"
+  ) %>%
   # remove empty columns
   select(!where(function(x) all(is.na(x)))) %>%
   mutate_at(vars(matches("name[1-9]")), replace_with_av_code) %>%
@@ -144,7 +145,8 @@ antivirals <- antivirals %>% AMR:::dataset_UTF8_to_ASCII()
 # add loinc, see 'data-raw/loinc.R'
 loinc_df <- read.csv("data-raw/Loinc.csv",
   row.names = NULL,
-                     stringsAsFactors = FALSE)
+  stringsAsFactors = FALSE
+)
 
 loinc_df <- loinc_df %>% filter(CLASS == "DRUG/TOX")
 av_names <- antivirals %>%

data-raw/reproduction_of_microorganisms.R

@@ -39,8 +39,8 @@
 # 3. For data about human pathogens, we use Bartlett et al. (2022),
 #    https://doi.org/10.1099/mic.0.001269. Their latest supplementary material
 #    can be found here: https://github.com/padpadpadpad/bartlett_et_al_2022_human_pathogens.
-#.   Download their latest xlsx file in the `data` folder and save it to our
+# .   Download their latest xlsx file in the `data` folder and save it to our
-#.   `data-raw` folder.
+# .   `data-raw` folder.
 # 4. Set this folder_location to the path where these two files are:
 folder_location <- "~/Downloads/backbone/"
 file_gbif <- paste0(folder_location, "Taxon.tsv")
@@ -550,9 +550,11 @@ taxonomy <- taxonomy %>%
 taxonomy <- taxonomy %>%
   bind_rows(AMR::microorganisms %>%
     select(all_of(colnames(taxonomy))) %>%
-              filter(!paste(kingdom, fullname) %in% paste(taxonomy$kingdom, taxonomy$fullname),
+    filter(
+      !paste(kingdom, fullname) %in% paste(taxonomy$kingdom, taxonomy$fullname),
       # these will be added later:
-                     source != "manually added")) %>%
+      source != "manually added"
+    )) %>%
   arrange(fullname) %>%
   filter(fullname != "")
 
@@ -602,7 +604,8 @@ taxonomy <- taxonomy %>%
         source = "manually added"
       ) %>%
       filter(!paste(kingdom, rank) %in% paste(taxonomy$kingdom, taxonomy$rank)) %>%
-      left_join(current_gbif %>%
+      left_join(
+        current_gbif %>%
           select(kingdom, rank = taxonRank, ref = scientificNameAuthorship, gbif = taxonID, gbif_parent = parentNameUsageID),
         by = c("kingdom", "rank")
       ) %>%
@@ -626,7 +629,8 @@ for (i in 2:6) {
     ) %>%
     filter(!paste(kingdom, .[[ncol(.) - 4]], rank) %in% paste(taxonomy$kingdom, taxonomy[[i + 1]], taxonomy$rank)) %>%
     # get GBIF identifier where available
-  left_join(current_gbif %>%
+    left_join(
+      current_gbif %>%
         select(kingdom, all_of(i_name), rank = taxonRank, ref = scientificNameAuthorship, gbif = taxonID, gbif_parent = parentNameUsageID),
       by = c("kingdom", "rank", i_name)
     ) %>%
@@ -646,14 +650,18 @@ taxonomy <- taxonomy %>%
 
 # fix for duplicate fullnames within a kingdom (such as Nitrospira which is the name of the genus AND its class)
 taxonomy <- taxonomy %>%
-  mutate(rank_index = case_when(rank == "subspecies" ~ 1,
+  mutate(
+    rank_index = case_when(
+      rank == "subspecies" ~ 1,
       rank == "species" ~ 2,
       rank == "genus" ~ 3,
       rank == "family" ~ 4,
       rank == "order" ~ 5,
       rank == "class" ~ 6,
-                                TRUE ~ 7),
+      TRUE ~ 7
-         fullname_rank = paste0(fullname, " {", rank, "}")) %>% 
+    ),
+    fullname_rank = paste0(fullname, " {", rank, "}")
+  ) %>%
   arrange(kingdom, fullname, rank_index) %>%
   group_by(kingdom, fullname) %>%
   mutate(fullname = if_else(row_number() > 1, fullname_rank, fullname)) %>%
@@ -676,7 +684,8 @@ taxonomy <- taxonomy %>%
       ) %>%
       filter(!paste(kingdom, genus, species, rank) %in% paste(taxonomy$kingdom, taxonomy$genus, taxonomy$species, taxonomy$rank)) %>%
       # get GBIF identifier where available
-      left_join(current_gbif %>%
+      left_join(
+        current_gbif %>%
           select(kingdom, genus, species = specificEpithet, rank = taxonRank, ref = scientificNameAuthorship, gbif = taxonID, gbif_parent = parentNameUsageID),
         by = c("kingdom", "rank", "genus", "species")
       ) %>%
@@ -809,8 +818,10 @@ established <- pathogens %>%
   filter(status == "established") %>%
   mutate(fullname = paste(genus, species)) %>%
   pull(fullname) %>%
-  c(unlist(mo_current(.)),
+  c(
-    unlist(mo_synonyms(., keep_synonyms = FALSE))) %>% 
+    unlist(mo_current(.)),
+    unlist(mo_synonyms(., keep_synonyms = FALSE))
+  ) %>%
   strsplit(" ", fixed = TRUE) %>%
   sapply(function(x) ifelse(length(x) == 1, x, paste(x[1], x[2]))) %>%
   sort() %>%
@@ -821,8 +832,10 @@ putative <- pathogens %>%
   filter(status == "putative") %>%
   mutate(fullname = paste(genus, species)) %>%
   pull(fullname) %>%
-  c(unlist(mo_current(.)),
+  c(
-    unlist(mo_synonyms(., keep_synonyms = FALSE))) %>% 
+    unlist(mo_current(.)),
+    unlist(mo_synonyms(., keep_synonyms = FALSE))
+  ) %>%
   strsplit(" ", fixed = TRUE) %>%
   sapply(function(x) ifelse(length(x) == 1, x, paste(x[1], x[2]))) %>%
   sort() %>%
@@ -844,8 +857,10 @@ putative_genera <- putative %>%
   unique()
 
 nonbacterial_genera <- AMR:::MO_PREVALENT_GENERA %>%
-  c(unlist(mo_current(.)),
+  c(
-    unlist(mo_synonyms(., keep_synonyms = FALSE))) %>% 
+    unlist(mo_current(.)),
+    unlist(mo_synonyms(., keep_synonyms = FALSE))
+  ) %>%
   strsplit(" ", fixed = TRUE) %>%
   sapply(function(x) x[1]) %>%
   sort() %>%
@@ -874,7 +889,8 @@ taxonomy <- taxonomy %>%
     genus %in% AMR:::MO_PREVALENT_GENERA & kingdom != "Bacteria" & rank %in% c("genus", "species", "subspecies") ~ 1.5,
 
     # all others
-    TRUE ~ 2.0))
+    TRUE ~ 2.0
+  ))
 
 table(taxonomy$prevalence, useNA = "always")
 # (a lot will be removed further below)
@@ -909,7 +925,8 @@ mo_kingdom <- taxonomy %>%
 mo_phylum <- taxonomy %>%
   filter(rank == "phylum") %>%
   distinct(kingdom, phylum) %>%
-  left_join(AMR::microorganisms %>%
+  left_join(
+    AMR::microorganisms %>%
       filter(rank == "phylum") %>%
       transmute(kingdom,
         phylum = fullname,
@@ -935,7 +952,8 @@ mo_phylum <- mo_phylum %>%
 mo_class <- taxonomy %>%
   filter(rank == "class") %>%
   distinct(kingdom, class) %>%
-  left_join(AMR::microorganisms %>%
+  left_join(
+    AMR::microorganisms %>%
       filter(rank == "class") %>%
       transmute(kingdom,
         class = fullname,
@@ -961,7 +979,8 @@ mo_class <- mo_class %>%
 mo_order <- taxonomy %>%
   filter(rank == "order") %>%
   distinct(kingdom, order) %>%
-  left_join(AMR::microorganisms %>%
+  left_join(
+    AMR::microorganisms %>%
       filter(rank == "order") %>%
       transmute(kingdom,
         order = fullname,
@@ -987,7 +1006,8 @@ mo_order <- mo_order %>%
 mo_family <- taxonomy %>%
   filter(rank == "family") %>%
   distinct(kingdom, family) %>%
-  left_join(AMR::microorganisms %>%
+  left_join(
+    AMR::microorganisms %>%
       filter(rank == "family") %>%
       transmute(kingdom,
         family = fullname,
@@ -1014,7 +1034,8 @@ mo_genus <- taxonomy %>%
   filter(rank == "genus") %>%
   distinct(kingdom, genus) %>%
   # get available old MO codes
-  left_join(AMR::microorganisms %>%
+  left_join(
+    AMR::microorganisms %>%
       filter(rank == "genus") %>%
       transmute(mo_genus_old = gsub("^[A-Z]+_", "", as.character(mo)), kingdom, genus) %>%
       distinct(kingdom, genus, .keep_all = TRUE),
@@ -1060,7 +1081,8 @@ mo_genus <- mo_genus %>%
 mo_species <- taxonomy %>%
   filter(rank == "species") %>%
   distinct(kingdom, genus, species) %>%
-  left_join(AMR::microorganisms %>%
+  left_join(
+    AMR::microorganisms %>%
       filter(rank == "species") %>%
       transmute(mo_species_old = gsub("^[A-Z]+_[A-Z]+_", "", as.character(mo)), kingdom, genus, species) %>%
       filter(mo_species_old %unlike% "-") %>%
@@ -1108,7 +1130,8 @@ mo_species <- mo_species %>%
 mo_subspecies <- taxonomy %>%
   filter(rank == "subspecies") %>%
   distinct(kingdom, genus, species, subspecies) %>%
-  left_join(AMR::microorganisms %>%
+  left_join(
+    AMR::microorganisms %>%
       filter(rank %in% c("subspecies", "subsp.", "infraspecies")) %>%
       transmute(mo_subspecies_old = gsub("^[A-Z]+_[A-Z]+_[A-Z]+_", "", as.character(mo)), kingdom, genus, species, subspecies) %>%
       filter(mo_subspecies_old %unlike% "-") %>%
@@ -1187,20 +1210,26 @@ taxonomy <- taxonomy %>%
   arrange(fullname)
 
 # now check these - e.g. Nitrospira is the name of a genus AND its class
-taxonomy %>% filter(fullname %in% .[duplicated(fullname), "fullname", drop = TRUE]) %>% View()
+taxonomy %>%
+  filter(fullname %in% .[duplicated(fullname), "fullname", drop = TRUE]) %>%
+  View()
 taxonomy <- taxonomy %>%
-  mutate(rank_index = case_when(kingdom == "Bacteria" ~ 1,
+  mutate(rank_index = case_when(
+    kingdom == "Bacteria" ~ 1,
     kingdom == "Fungi" ~ 2,
     kingdom == "Protozoa" ~ 3,
     kingdom == "Archaea" ~ 4,
-                                TRUE ~ 5)) %>% 
+    TRUE ~ 5
+  )) %>%
   arrange(fullname, rank_index) %>%
   distinct(fullname, .keep_all = TRUE) %>%
   select(-rank_index) %>%
   filter(mo != "")
 
 # this must not exist:
-taxonomy %>% filter(mo %like% "__") %>% View()
+taxonomy %>%
+  filter(mo %like% "__") %>%
+  View()
 taxonomy <- taxonomy %>% filter(mo %unlike% "__")
 
 
@@ -1214,14 +1243,20 @@ taxonomy <- taxonomy %>% distinct(mo, .keep_all = TRUE)
 taxonomy %>% filter(fullname %in% .[duplicated(fullname), "fullname", drop = TRUE])
 
 # are all GBIFs available?
-taxonomy %>% filter(!gbif_parent %in% gbif) %>% count(rank)
+taxonomy %>%
+  filter(!gbif_parent %in% gbif) %>%
+  count(rank)
 # try to find the right gbif IDs
 taxonomy$gbif_parent[which(!taxonomy$gbif_parent %in% taxonomy$gbif & taxonomy$rank == "species")] <- taxonomy$gbif[match(taxonomy$genus[which(!taxonomy$gbif_parent %in% taxonomy$gbif & taxonomy$rank == "species")], taxonomy$genus)]
 taxonomy$gbif_parent[which(!taxonomy$gbif_parent %in% taxonomy$gbif & taxonomy$rank == "class")] <- taxonomy$gbif[match(taxonomy$phylum[which(!taxonomy$gbif_parent %in% taxonomy$gbif & taxonomy$rank == "class")], taxonomy$phylum)]
-taxonomy %>% filter(!gbif_parent %in% gbif) %>% count(rank)
+taxonomy %>%
+  filter(!gbif_parent %in% gbif) %>%
+  count(rank)
 
 # are all LPSNs available?
-taxonomy %>% filter(!lpsn_parent %in% lpsn) %>% count(rank)
+taxonomy %>%
+  filter(!lpsn_parent %in% lpsn) %>%
+  count(rank)
 # make GBIF refer to newest renaming according to LPSN
 taxonomy$gbif_renamed_to[which(!is.na(taxonomy$gbif_renamed_to) & !is.na(taxonomy$lpsn_renamed_to))] <- taxonomy$gbif[match(taxonomy$lpsn_renamed_to[which(!is.na(taxonomy$gbif_renamed_to) & !is.na(taxonomy$lpsn_renamed_to))], taxonomy$lpsn)]
 
@@ -1251,21 +1286,33 @@ taxonomy <- taxonomy %>%
 
 # no ghost families, orders classes, phyla
 taxonomy <- taxonomy %>%
-  group_by(kingdom, family) %>% filter(n() > 1 |  fullname %like% "unknown" | rank == "kingdom") %>%
+  group_by(kingdom, family) %>%
-  group_by(kingdom, order) %>% filter(n() > 1 |  fullname %like% "unknown" | rank == "kingdom") %>%
+  filter(n() > 1 | fullname %like% "unknown" | rank == "kingdom") %>%
-  group_by(kingdom, class) %>% filter(n() > 1 |  fullname %like% "unknown" | rank == "kingdom") %>%
+  group_by(kingdom, order) %>%
-  group_by(kingdom, phylum) %>% filter(n() > 1 |  fullname %like% "unknown" | rank == "kingdom") %>% 
+  filter(n() > 1 | fullname %like% "unknown" | rank == "kingdom") %>%
+  group_by(kingdom, class) %>%
+  filter(n() > 1 | fullname %like% "unknown" | rank == "kingdom") %>%
+  group_by(kingdom, phylum) %>%
+  filter(n() > 1 | fullname %like% "unknown" | rank == "kingdom") %>%
   ungroup()
 
 
-message("\nCongratulations! The new taxonomic table will contain ", format(nrow(taxonomy), big.mark = ","), " rows.\n",
+message(
-        "This was ", format(nrow(AMR::microorganisms), big.mark = ","), " rows.\n")
+  "\nCongratulations! The new taxonomic table will contain ", format(nrow(taxonomy), big.mark = ","), " rows.\n",
+  "This was ", format(nrow(AMR::microorganisms), big.mark = ","), " rows.\n"
+)
 
 # these are the new ones:
-taxonomy %>% filter(!paste(kingdom, fullname) %in% paste(AMR::microorganisms$kingdom, AMR::microorganisms$fullname)) %>% View()
+taxonomy %>%
+  filter(!paste(kingdom, fullname) %in% paste(AMR::microorganisms$kingdom, AMR::microorganisms$fullname)) %>%
+  View()
 # these were removed:
-AMR::microorganisms %>% filter(!paste(kingdom, fullname) %in% paste(taxonomy$kingdom, taxonomy$fullname)) %>% View()
+AMR::microorganisms %>%
-AMR::microorganisms %>% filter(!fullname %in% taxonomy$fullname) %>% View()
+  filter(!paste(kingdom, fullname) %in% paste(taxonomy$kingdom, taxonomy$fullname)) %>%
+  View()
+AMR::microorganisms %>%
+  filter(!fullname %in% taxonomy$fullname) %>%
+  View()
 
 
 # Add SNOMED CT -----------------------------------------------------------

data-raw/salmonellae.R

@@ -35,7 +35,8 @@
 # WHO Collaborating Centre for Reference and Research on Salmonella
 # https://www.researchgate.net/publication/283428414
 
-serovars <- c("Aachen",
+serovars <- c(
+  "Aachen",
   "Aarhus",
   "Aba",
   "Abadina",
@@ -1550,7 +1551,8 @@ serovars <- c("Aachen",
   "Zinder",
   "Zongo",
   "Zuilen",
-              "Zwickau")
+  "Zwickau"
+)
 
 library(dplyr)
 salmonellae <- tibble(
@@ -1569,12 +1571,14 @@ salmonellae <- salmonellae %>%
   # remove e.g. Salmonella Enteritidis if Salmonella enteritidis already existed
   filter(!tolower(fullname) %in% tolower(AMR::microorganisms$fullname))
 
-groups <- c("Paratyphi A",
+groups <- c(
+  "Paratyphi A",
   "Paratyphi B",
   "Paratyphi C",
   "Group B",
   "Group C",
-            "Group D")
+  "Group D"
+)
 salmonellae <- salmonellae %>%
   bind_rows(tibble(
     genus = "Salmonella",

man/add_custom_microorganisms.Rd

@@ -58,7 +58,8 @@ mo_name("Enterobacter asburiae/cloacae")
 # now add a custom entry - it will be considered by as.mo() and
 # all mo_*() functions
 add_custom_microorganisms(
-  data.frame(genus = "Enterobacter",
+  data.frame(
+    genus = "Enterobacter",
     species = "asburiae/cloacae"
   )
 )
@@ -81,8 +82,10 @@ mo_info("Enterobacter asburiae/cloacae")
 
 # the function tries to be forgiving:
 add_custom_microorganisms(
-  data.frame(GENUS = "BACTEROIDES / PARABACTEROIDES SLASHLINE",
+  data.frame(
-             SPECIES = "SPECIES")
+    GENUS = "BACTEROIDES / PARABACTEROIDES SLASHLINE",
+    SPECIES = "SPECIES"
+  )
 )
 mo_name("BACTEROIDES / PARABACTEROIDES")
 mo_rank("BACTEROIDES / PARABACTEROIDES")
@@ -93,9 +96,11 @@ mo_family("Bacteroides/Parabacteroides")
 
 # for groups and complexes, set them as species or subspecies:
 add_custom_microorganisms(
-  data.frame(genus = "Citrobacter", 
+  data.frame(
+    genus = "Citrobacter",
     species = c("freundii", "braakii complex"),
-             subspecies = c("complex", ""))
+    subspecies = c("complex", "")
+  )
 )
 mo_name(c("C. freundii complex", "C. braakii complex"))
 mo_species(c("C. freundii complex", "C. braakii complex"))

man/antibiotic_class_selectors.Rd

@@ -214,20 +214,17 @@ example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
 # dplyr -------------------------------------------------------------------
 \donttest{
 if (require("dplyr")) {
-
   # get AMR for all aminoglycosides e.g., per ward:
   example_isolates \%>\%
     group_by(ward) \%>\%
     summarise(across(aminoglycosides(), resistance))
 }
 if (require("dplyr")) {
-
   # You can combine selectors with '&' to be more specific:
   example_isolates \%>\%
     select(penicillins() & administrable_per_os())
 }
 if (require("dplyr")) {
-
   # get AMR for only drugs that matter - no intrinsic resistance:
   example_isolates \%>\%
     filter(mo_genus() \%in\% c("Escherichia", "Klebsiella")) \%>\%
@@ -235,7 +232,6 @@ if (require("dplyr")) {
     summarise(across(not_intrinsic_resistant(), resistance))
 }
 if (require("dplyr")) {
-
   # get susceptibility for antibiotics whose name contains "trim":
   example_isolates \%>\%
     filter(first_isolate()) \%>\%
@@ -243,19 +239,16 @@ if (require("dplyr")) {
     summarise(across(ab_selector(name \%like\% "trim"), susceptibility))
 }
 if (require("dplyr")) {
-
   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
   example_isolates \%>\%
     select(carbapenems())
 }
 if (require("dplyr")) {
-
   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
   example_isolates \%>\%
     select(mo, aminoglycosides())
 }
 if (require("dplyr")) {
-
   # any() and all() work in dplyr's filter() too:
   example_isolates \%>\%
     filter(
@@ -264,25 +257,21 @@ if (require("dplyr")) {
     )
 }
 if (require("dplyr")) {
-
   # also works with c():
   example_isolates \%>\%
     filter(any(c(carbapenems(), aminoglycosides()) == "R"))
 }
 if (require("dplyr")) {
-
   # not setting any/all will automatically apply all():
   example_isolates \%>\%
     filter(aminoglycosides() == "R")
 }
 if (require("dplyr")) {
-
   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
   example_isolates \%>\%
     select(mo, ab_class("mycobact"))
 }
 if (require("dplyr")) {
-
   # get bug/drug combinations for only glycopeptides in Gram-positives:
   example_isolates \%>\%
     filter(mo_is_gram_positive()) \%>\%
@@ -298,7 +287,6 @@ if (require("dplyr")) {
     select(penicillins()) # only the 'J01CA01' column will be selected
 }
 if (require("dplyr")) {
-
   # with recent versions of dplyr this is all equal:
   x <- example_isolates[carbapenems() == "R", ]
   y <- example_isolates \%>\% filter(carbapenems() == "R")

man/as.ab.Rd

@@ -91,7 +91,6 @@ ab_name("eryt")
 
 \donttest{
 if (require("dplyr")) {
-
   # you can quickly rename 'sir' columns using set_ab_names() with dplyr:
   example_isolates \%>\%
     set_ab_names(where(is.sir), property = "atc")

man/as.sir.Rd

@@ -125,7 +125,7 @@ your_data \%>\% mutate(across(where(is.disk), as.sir))
 \item For \strong{interpreting a complete data set}, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running \code{as.sir(your_data)}.
 }
 
-For points 2, 3 and 4: Use \code{\link[=sir_interpretation_history]{sir_interpretation_history()}} to retrieve a \link{data.frame} (or \link[tibble:tibble]{tibble} if the \code{tibble} package is installed) with all results of the last \code{\link[=as.sir]{as.sir()}} call.
+\strong{For points 2, 3 and 4: Use \code{\link[=sir_interpretation_history]{sir_interpretation_history()}}} to retrieve a \link{data.frame} (or \link[tibble:tibble]{tibble} if the \code{tibble} package is installed) with all results of the last \code{\link[=as.sir]{as.sir()}} call.
 }
 
 \subsection{Supported Guidelines}{

man/first_isolate.Rd

@@ -179,13 +179,11 @@ if (require("dplyr")) {
     filter(first_isolate())
 }
 if (require("dplyr")) {
-
   # short-hand version:
   example_isolates \%>\%
     filter_first_isolate(info = FALSE)
 }
 if (require("dplyr")) {
-
   # flag the first isolates per group:
   example_isolates \%>\%
     group_by(ward) \%>\%

man/get_episode.Rd

@@ -44,11 +44,12 @@ 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(
+get_episode(
+  c(
     Sys.time(),
     Sys.time() + 60 * 60
-),
+  ),
-episode_days = 1 / 24
+  episode_days = 1 / 24
 )
 
 \donttest{
@@ -85,7 +86,6 @@ if (require("dplyr")) {
     )
 }
 if (require("dplyr")) {
-
   # grouping on patients and microorganisms leads to the same
   # results as first_isolate() when using 'episode-based':
   x <- df \%>\%
@@ -102,7 +102,6 @@ if (require("dplyr")) {
   identical(x, y)
 }
 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:
   df \%>\%

man/ggplot_sir.Rd

@@ -138,13 +138,11 @@ At default, the names of antibiotics will be shown on the plots using \code{\lin
 \examples{
 \donttest{
 if (require("ggplot2") && require("dplyr")) {
-
   # get antimicrobial results for drugs against a UTI:
   ggplot(example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
     geom_sir()
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # prettify the plot using some additional functions:
   df <- example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)
   ggplot(df) +
@@ -155,21 +153,18 @@ if (require("ggplot2") && require("dplyr")) {
     theme_sir()
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # or better yet, simplify this using the wrapper function - a single command:
   example_isolates \%>\%
     select(AMX, NIT, FOS, TMP, CIP) \%>\%
     ggplot_sir()
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # get only proportions and no counts:
   example_isolates \%>\%
     select(AMX, NIT, FOS, TMP, CIP) \%>\%
     ggplot_sir(datalabels = FALSE)
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # add other ggplot2 arguments as you like:
   example_isolates \%>\%
     select(AMX, NIT, FOS, TMP, CIP) \%>\%
@@ -182,14 +177,12 @@ if (require("ggplot2") && require("dplyr")) {
     )
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # you can alter the colours with colour names:
   example_isolates \%>\%
     select(AMX) \%>\%
     ggplot_sir(colours = c(SI = "yellow"))
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # but you can also use the built-in colour-blind friendly colours for
   # your plots, where "S" is green, "I" is yellow and "R" is red:
   data.frame(
@@ -202,7 +195,6 @@ if (require("ggplot2") && require("dplyr")) {
     scale_sir_colours(Value4 = "S", Value5 = "I", Value6 = "R")
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # resistance of ciprofloxacine per age group
   example_isolates \%>\%
     mutate(first_isolate = first_isolate()) \%>\%
@@ -216,14 +208,12 @@ if (require("ggplot2") && require("dplyr")) {
     ggplot_sir(x = "age_group")
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # a shorter version which also adjusts data label colours:
   example_isolates \%>\%
     select(AMX, NIT, FOS, TMP, CIP) \%>\%
     ggplot_sir(colours = FALSE)
 }
 if (require("ggplot2") && require("dplyr")) {
-
   # it also supports groups (don't forget to use the group var on `x` or `facet`):
   example_isolates \%>\%
     filter(mo_is_gram_negative(), ward != "Outpatient") \%>\%

man/mo_property.Rd

@@ -405,7 +405,6 @@ mo_species("EHEC")
 mo_fullname("K. pneu rh")
 mo_shortname("K. pneu rh")
 
-
 \donttest{
 # Becker classification, see ?as.mo ----------------------------------------
 

man/proportion.Rd

@@ -204,7 +204,6 @@ if (require("dplyr")) {
     )
 }
 if (require("dplyr")) {
-
   # scoped dplyr verbs with antibiotic selectors
   # (you could also use across() of course)
   example_isolates \%>\%

vignettes/AMR.Rmd

@@ -48,15 +48,16 @@ For this tutorial, we will create fake demonstration data to work with.
 You can skip to [Cleaning the data](#cleaning-the-data) if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:
 
 ```{r example table, echo = FALSE, results = 'asis'}
-knitr::kable(data.frame(
+knitr::kable(
+  data.frame(
     date = Sys.Date(),
     patient_id = c("abcd", "abcd", "efgh"),
     mo = "Escherichia coli",
     AMX = c("S", "S", "R"),
     CIP = c("S", "R", "S"),
     stringsAsFactors = FALSE
-),
+  ),
-align = "c"
+  align = "c"
 )
 ``` 
 
@@ -129,7 +130,8 @@ sample_size <- 20000
 data <- data.frame(
   date = sample(dates, size = sample_size, replace = TRUE),
   patient_id = sample(patients, size = sample_size, replace = TRUE),
-  hospital = sample(c(
+  hospital = sample(
+    c(
       "Hospital A",
       "Hospital B",
       "Hospital C",
@@ -293,10 +295,11 @@ data_1st %>%
 ```
 
 ```{r bug_drg 2b, echo = FALSE, results = 'asis'}
-knitr::kable(data_1st %>%
+knitr::kable(
+  data_1st %>%
     filter(any(aminoglycosides() == "R")) %>%
     head(),
-align = "c"
+  align = "c"
 )
 ```
 
@@ -309,10 +312,11 @@ data_1st %>%
 ```
 
 ```{r bug_drg 1b, echo = FALSE, results = 'asis'}
-knitr::kable(data_1st %>%
+knitr::kable(
+  data_1st %>%
     bug_drug_combinations() %>%
     head(),
-align = "c"
+  align = "c"
 )
 ```
 
@@ -325,10 +329,11 @@ data_1st %>%
 
 
 ```{r bug_drg 3b, echo = FALSE, results = 'asis'}
-knitr::kable(data_1st %>%
+knitr::kable(
+  data_1st %>%
     select(bacteria, aminoglycosides()) %>%
     bug_drug_combinations(),
-align = "c"
+  align = "c"
 )
 ```
 

vignettes/WHONET.Rmd

@@ -88,11 +88,12 @@ data %>%
 
 ```{r, echo = FALSE}
 # on very old and some new releases of R, this may lead to an error
-tryCatch(data %>%
+tryCatch(
+  data %>%
     group_by(Country) %>%
     select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
     ggplot_sir(translate_ab = "ab", facet = "Country", datalabels = FALSE) %>%
     print(),
-error = function(e) base::invisible()
+  error = function(e) base::invisible()
 )
 ```