(v1.4.0.9050) ab selectors base R

R/ab_class_selectors.R

@@ -25,11 +25,9 @@
 
 #' Antibiotic class selectors
 #' 
-#' Use these selection helpers inside any function that allows [Tidyverse selection helpers](https://tidyselect.r-lib.org/reference/language.html), such as [`select()`][dplyr::select()] and [`pivot_longer()`][tidyr::pivot_longer()]. They help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations.
+#' These functions help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations.
 #' @inheritParams filter_ab_class 
 #' @details All columns will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.) in the [antibiotics] data set. This means that a selector like e.g. [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
-#' 
-#' **N.B. These functions require the `tidyselect` package to be installed**, that comes with the `dplyr` package. An error will be thrown if the `tidyselect` package is not installed, or if the functions are used outside a function that allows [Tidyverse selection helpers](https://tidyselect.r-lib.org/reference/language.html) such as [`select()`][dplyr::select()] and [`pivot_longer()`][tidyr::pivot_longer()]`.
 #' @rdname antibiotic_class_selectors
 #' @seealso [filter_ab_class()] for the `filter()` equivalent.
 #' @name antibiotic_class_selectors
@@ -37,6 +35,14 @@
 #' @inheritSection AMR Reference data publicly available
 #' @inheritSection AMR Read more on our website!
 #' @examples 
+#' # `example_isolates` is a dataset available in the AMR package.
+#' # See ?example_isolates.
+#' 
+#' # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
+#' example_isolates[, c(carbapenems())]
+#' # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
+#' example_isolates[, c("mo", aminoglycosides())]
+#' 
 #' if (require("dplyr")) {
 #' 
 #'   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
@@ -151,10 +157,13 @@ ab_selector <- function(ab_class, function_name) {
   meet_criteria(ab_class, allow_class = "character", has_length = 1, .call_depth = 1)
   meet_criteria(function_name, allow_class = "character", has_length = 1, .call_depth = 1)
 
-  peek_vars_tidyselect <- import_fn("peek_vars", "tidyselect")
-  vars_vct <- peek_vars_tidyselect(fn = function_name)
-  vars_df <- data.frame(as.list(vars_vct), stringsAsFactors = FALSE)[1, , drop = FALSE]
-  colnames(vars_df) <- vars_vct
+  for (i in seq_len(length(sys.frames()))) {
+    vars_df <- sys.frames()[[i]]$x
+    if (!is.null(vars_df) && is.data.frame(vars_df)) {
+      break
+    }
+  }
+  stop_ifnot(is.data.frame(vars_df), "the ", function_name, "() function must be used inside dplyr verbs or a data.frame call.")
   ab_in_data <- get_column_abx(vars_df, info = FALSE)
   
   if (length(ab_in_data) == 0) {

R/first_isolate.R

@@ -27,7 +27,7 @@
 #'
 #' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use [is_new_episode()] that also supports grouping with the `dplyr` package.
 #' @inheritSection lifecycle Stable lifecycle
-#' @param x a [data.frame] containing isolates. Can be omitted when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
+#' @param x a [data.frame] containing isolates. Can be left blank when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
 #' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column with a date class
 #' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
 #' @param col_mo column name of the IDs of the microorganisms (see [as.mo()]), defaults to the first column of class [`mo`]. Values will be coerced using [as.mo()].
@@ -46,7 +46,7 @@
 #' @param include_unknown logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
 #' @param ... arguments passed on to [first_isolate()] when using [filter_first_isolate()], or arguments passed on to [key_antibiotics()] when using [filter_first_weighted_isolate()]
 #' @details 
-#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be omitted, please see *Examples*.
+#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be left blank, please see *Examples*.
 #' 
 #' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but more efficient for data sets containing microorganism codes or names.
 #' 

R/key_antibiotics.R

@@ -27,7 +27,7 @@
 #'
 #' These function can be used to determine first isolates (see [first_isolate()]). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates can then be called first *weighted* isolates.
 #' @inheritSection lifecycle Stable lifecycle
-#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be omitted when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`.
+#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`.
 #' @param y,z character vectors to compare
 #' @inheritParams first_isolate
 #' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of **broad-spectrum** antibiotics, case-insensitive. See details for which antibiotics will be used at default (which are guessed with [guess_ab_col()]).
@@ -36,7 +36,7 @@
 #' @param warnings give a warning about missing antibiotic columns (they will be ignored)
 #' @param ... other arguments passed on to functions
 #' @details 
-#' The [key_antibiotics()] function is context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be omitted, please see *Examples*.
+#' The [key_antibiotics()] function is context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be left blank, please see *Examples*.
 #' 
 #' The function [key_antibiotics()] returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using [key_antibiotics_equal()], to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (`"."`) by [key_antibiotics()] and ignored by [key_antibiotics_equal()].
 #' 

R/mdro.R

@@ -27,7 +27,7 @@
 #'
 #' Determine which isolates are multidrug-resistant organisms (MDRO) according to international and national guidelines.
 #' @inheritSection lifecycle Stable lifecycle
-#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be omitted when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
+#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
 #' @param guideline a specific guideline to follow. When left empty, the publication by Magiorakos *et al.* (2012, Clinical Microbiology and Infection) will be followed, please see *Details*.
 #' @inheritParams eucast_rules
 #' @param pct_required_classes minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for *S. aureus*. Setting this `pct_required_classes` argument to `0.5` (default) means that for every *S. aureus* isolate at least 8 different classes must be available. Any lower number of available classes will return `NA` for that isolate.
@@ -35,7 +35,7 @@
 #' @param verbose a logical to turn Verbose mode on and off (default is off). In Verbose mode, the function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.
 #' @inheritSection eucast_rules Antibiotics
 #' @details 
-#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be omitted, please see *Examples*.
+#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be left blank, please see *Examples*.
 #' 
 #' For the `pct_required_classes` argument, values above 1 will be divided by 100. This is to support both fractions (`0.75` or `3/4`) and percentages (`75`).
 #' 

R/mo.R

@@ -438,7 +438,7 @@ exec_as.mo <- function(x,
 
     strip_whitespace <- function(x, dyslexia_mode) {
       # all whitespaces (tab, new lines, etc.) should be one space
-      # and spaces before and after should be omitted
+      # and spaces before and after should be left blank
       trimmed <- trimws2(x)
       # also, make sure the trailing and leading characters are a-z or 0-9
       # in case of non-regex

R/mo_property.R

@@ -27,7 +27,7 @@
 #'
 #' Use these functions to return a specific property of a microorganism based on the latest accepted taxonomy. All input values will be evaluated internally with [as.mo()], which makes it possible to use microbial abbreviations, codes and names as input. Please see *Examples*.
 #' @inheritSection lifecycle Stable lifecycle
-#' @param x any character (vector) that can be coerced to a valid microorganism code with [as.mo()]. Can be omitted for auto-guessing the column containing microorganism codes when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()], please see *Examples*.
+#' @param x any character (vector) that can be coerced to a valid microorganism code with [as.mo()]. Can be left blank for auto-guessing the column containing microorganism codes when used inside `dplyr` verbs, such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()], please see *Examples*.
 #' @param property one of the column names of the [microorganisms] data set: `r paste0('"``', colnames(microorganisms), '\``"', collapse = ", ")`, or must be `"shortname"`
 #' @param language language of the returned text, defaults to system language (see [get_locale()]) and can be overwritten by setting the option `AMR_locale`, e.g. `options(AMR_locale = "de")`, see [translate]. Also used to translate text like "no growth". Use `language = NULL` or `language = ""` to prevent translation.
 #' @param ... other arguments passed on to [as.mo()], such as 'allow_uncertain' and 'ignore_pattern'

R/mo_source.R

@@ -149,8 +149,8 @@ set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_s
     
   } else if (path %like% "[.]xlsx?$") {
     # is Excel file (old or new)
-    read_excel <- import_fn("read_excel", "readxl")
-    df <- read_excel(path)
+    stop_ifnot_installed("readxl")
+    df <- readxl::read_excel(path)
     
   } else if (path %like% "[.]tsv$") {
     df <- utils::read.table(header = TRUE, sep = "\t", stringsAsFactors = FALSE)