.github/prehooks/pre-commit

@@ -33,7 +33,7 @@ echo "Running pre-commit hook..."
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 if command -v Rscript > /dev/null; then
-  if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
+  if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"', '"'styler'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
     Rscript -e "source('data-raw/_pre_commit_hook.R')"
     currentpkg=`Rscript -e "cat(pkgload::pkg_name())"`
     echo "-> Adding all files in 'data-raw' to this commit"

.github/workflows/check-current.yaml

@@ -58,9 +58,6 @@ jobs:
           - {os: ubuntu-latest,  r: 'release', allowfail: false}
           - {os: windows-latest, r: 'devel',   allowfail: false}
           - {os: windows-latest, r: 'release', allowfail: false}
-          - {os: macOS-latest,   r: '3.6',     allowfail: false}
-          - {os: ubuntu-latest,  r: '3.6',     allowfail: false}
-          - {os: windows-latest, r: '3.6',     allowfail: false}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/check-old.yaml

@@ -59,7 +59,6 @@ jobs:
           - {os: ubuntu-22.04,   r: '4.0',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.6',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           # R 3.5 returns a strange GC error when running examples, omit the checks for that
-          # - {os: ubuntu-22.04,   r: '3.5',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.4',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.3',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.2',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 1.8.2.9078
-Date: 2023-01-05
+Version: 1.8.2.9076
+Date: 2022-12-30
 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 1.8.2.9078
+#  1.8.2.9076
 
 *(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
 

R/aa_helper_functions.R

@@ -966,7 +966,7 @@ unique_call_id <- function(entire_session = FALSE, match_fn = NULL) {
   # and relevant system call (where 'match_fn' is being called in)
   calls <- sys.calls()
   in_test <- any(as.character(calls[[1]]) %like_case% "run_test_dir|run_test_file|test_all|tinytest|test_package|testthat", na.rm = TRUE)
-  if (!isTRUE(in_test) && !is.null(match_fn)) {
+  if (!isTRUE(in_test)) {
     for (i in seq_len(length(calls))) {
       call_clean <- gsub("[^a-zA-Z0-9_().-]", "", as.character(calls[[i]]), perl = TRUE)
       if (match_fn %in% call_clean || any(call_clean %like% paste0(match_fn, "\\("), na.rm = TRUE)) {
@@ -1262,7 +1262,6 @@ create_pillar_column <- function(x, ...) {
 
 as_original_data_class <- function(df, old_class = NULL) {
   if ("tbl_df" %in% old_class && pkg_is_available("tibble", also_load = FALSE)) {
-    # this will then also remove groups
     fn <- import_fn("as_tibble", "tibble")
   } else if ("tbl_ts" %in% old_class && pkg_is_available("tsibble", also_load = FALSE)) {
     fn <- import_fn("as_tsibble", "tsibble")
@@ -1271,7 +1270,7 @@ as_original_data_class <- function(df, old_class = NULL) {
   } else if ("tabyl" %in% old_class && pkg_is_available("janitor", also_load = FALSE)) {
     fn <- import_fn("as_tabyl", "janitor")
   } else {
-    fn <- function(x) base::as.data.frame(df, stringsAsFactors = FALSE)
+    fn <- base::as.data.frame
   }
   fn(df)
 }

R/ab_property.R

@@ -36,7 +36,7 @@
 #' @param language language of the returned text, defaults to system language (see [get_AMR_locale()]) and can also be set with `getOption("AMR_locale")`. Use `language = NULL` or `language = ""` to prevent translation.
 #' @param administration way of administration, either `"oral"` or `"iv"`
 #' @param open browse the URL using [utils::browseURL()]
-#' @param ... in case of [set_ab_names()] and `data` is a [data.frame]: columns to select (supports tidy selection such as `column1:column4`), otherwise other arguments passed on to [as.ab()]
+#' @param ... in case of [set_ab_names()] and `data` is a [data.frame]: variables to select (supports tidy selection such as `column1:column4`), otherwise other arguments passed on to [as.ab()]
 #' @param data a [data.frame] of which the columns need to be renamed, or a [character] vector of column names
 #' @param snake_case a [logical] to indicate whether the names should be in so-called [snake case](https://en.wikipedia.org/wiki/Snake_case): in lower case and all spaces/slashes replaced with an underscore (`_`)
 #' @param only_first a [logical] to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group)

R/bug_drug_combinations.R

@@ -161,7 +161,7 @@ bug_drug_combinations <- function(x,
     out <- run_it(x)
   }
   rownames(out) <- NULL
-  out <- as_original_data_class(out, class(x.bak)) # will remove tibble groups
+  out <- as_original_data_class(out, class(x.bak))
   structure(out, class = c("bug_drug_combinations", ifelse(data_has_groups, "grouped", character(0)), class(out)))
 }
 
@@ -322,7 +322,7 @@ format.bug_drug_combinations <- function(x,
   }
 
   rownames(y) <- NULL
-  as_original_data_class(y, class(x.bak)) # will remove tibble groups
+  as_original_data_class(y, class(x.bak))
 }
 
 #' @method print bug_drug_combinations

R/custom_eucast_rules.R

@@ -77,7 +77,7 @@
 #'
 #' ### Using taxonomic properties in rules
 #'
-#' There is one exception in columns used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
+#' There is one exception in variables used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
 #'
 #' ```r
 #' y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",

R/eucast_rules.R

@@ -1035,7 +1035,7 @@ eucast_rules <- function(x,
 
   # Return data set ---------------------------------------------------------
   if (isTRUE(verbose)) {
-    as_original_data_class(verbose_info, old_attributes$class) # will remove tibble groups
+    as_original_data_class(verbose_info, old_attributes$class)
   } else {
     # x was analysed with only unique rows, so join everything together again
     x <- x[, c(cols_ab, ".rowid"), drop = FALSE]
@@ -1043,9 +1043,8 @@ eucast_rules <- function(x,
     x.bak <- x.bak %pm>%
       pm_left_join(x, by = ".rowid")
     x.bak <- x.bak[, old_cols, drop = FALSE]
-    # reset original attributes
+    # reset original attributes, no need for as_original_data_class() here
     attributes(x.bak) <- old_attributes
-    x.bak <- as_original_data_class(x.bak, old_class = class(x.bak)) # will remove tibble groups
     x.bak
   }
 }

R/join_microorganisms.R

@@ -185,5 +185,5 @@ join_microorganisms <- function(type, x, by, suffix, ...) {
     warning_("in `", type, "_microorganisms()`: the newly joined data set contains ", nrow(joined) - nrow(x), " rows more than the number of rows of `x`.")
   }
 
-  as_original_data_class(joined, class(x.bak))  # will remove tibble groups
+  as_original_data_class(joined, class(x.bak))
 }

R/mean_amr_distance.R

@@ -30,40 +30,30 @@
 #' Calculate the Mean AMR Distance
 #'
 #' Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
-#' @param x a vector of class [rsi][as.rsi()], [mic][as.mic()] or [disk][as.disk()], or a [data.frame] containing columns of any of these classes
+#' @param x a vector of class [rsi][as.rsi()], [rsi][as.rsi()] or [rsi][as.rsi()], or a [data.frame] containing columns of any of these classes
 #' @param ... variables to select (supports [tidyselect language][tidyselect::language] such as `column1:column4` and `where(is.mic)`, and can thus also be [antibiotic selectors][ab_selector()]
 #' @param combine_SI 	a [logical] to indicate whether all values of S and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant), defaults to `TRUE`
-#' @details The mean AMR distance is effectively [the Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
+#' @details The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. For common numeric data this distance is equal to [Z scores](https://en.wikipedia.org/wiki/Standard_score) (the number of standard deviations from the mean).
 #'
-#' MIC values (see [as.mic()]) are transformed with [log2()] first; their distance is thus calculated as `(log2(x) - mean(log2(x))) / sd(log2(x))`.
+#' MIC values (see [as.mic()]) are transformed with [log2()] first; their distance is calculated as `(log2(x) - mean(log2(x))) / sd(log2(x))`.
 #'
 #' R/SI values (see [as.rsi()]) are transformed using `"S"` = 1, `"I"` = 2, and `"R"` = 3. If `combine_SI` is `TRUE` (default), the `"I"` will be considered to be 1.
 #'
-#' For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, see *Examples*.
+#' For data sets, the mean AMR distance will be calculated per variable, after which the mean of all columns will returned per row (using [rowMeans()]), see *Examples*.
 #'
 #' Use [amr_distance_from_row()] to subtract distances from the distance of one row, see *Examples*.
 #' @section Interpretation:
 #' Isolates with distances less than 0.01 difference from each other should be considered similar. Differences lower than 0.025 should be considered suspicious.
 #' @export
 #' @examples
-#' rsi <- random_rsi(10)
-#' rsi
-#' mean_amr_distance(rsi)
-#' 
-#' mic <- random_mic(10)
-#' mic
-#' mean_amr_distance(mic)
-#' # equal to the Z-score of their log2:
-#' (log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#' 
-#' disk <- random_disk(10)
-#' disk
-#' mean_amr_distance(disk)
+#' x <- random_mic(10)
+#' x
+#' mean_amr_distance(x)
 #'
 #' y <- data.frame(
 #'   id = LETTERS[1:10],
-#'   amox = random_rsi(10, ab = "amox", mo = "Escherichia coli"),
-#'   cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
+#'   amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
+#'   cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"),
 #'   gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
 #'   tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
 #' )
@@ -75,7 +65,7 @@
 #' if (require("dplyr")) {
 #'   y %>%
 #'     mutate(
-#'       amr_distance = mean_amr_distance(y),
+#'       amr_distance = mean_amr_distance(., where(is.mic)),
 #'       check_id_C = amr_distance_from_row(amr_distance, id == "C")
 #'     ) %>%
 #'     arrange(check_id_C)
@@ -86,8 +76,8 @@
 #'     filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%
 #'     select(mo, TCY, carbapenems()) %>%
 #'     group_by(mo) %>%
-#'     mutate(dist = mean_amr_distance(.)) %>%
-#'     arrange(mo, dist)
+#'     mutate(d = mean_amr_distance(., where(is.rsi))) %>%
+#'     arrange(mo, d)
 #' }
 mean_amr_distance <- function(x, ...) {
   UseMethod("mean_amr_distance")
@@ -97,7 +87,6 @@ mean_amr_distance <- function(x, ...) {
 #' @export
 mean_amr_distance.default <- function(x, ...) {
   x <- as.double(x)
-  # calculate z-score
   (x - mean(x, na.rm = TRUE)) / stats::sd(x, na.rm = TRUE)
 }
 
@@ -131,7 +120,6 @@ mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
   if (is_null_or_grouped_tbl(df)) {
     df <- get_current_data("x", -2)
   }
-  df <- as.data.frame(df, stringsAsFactors = FALSE)
   if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
     out <- tryCatch(suppressWarnings(c(...)), error = function(e) NULL)
     if (!is.null(out)) {
@@ -140,18 +128,13 @@ mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
       df <- pm_select(df, ...)
     }
   }
-  df_classes <- colnames(df)[vapply(FUN.VALUE = logical(1), df, function(x) is.disk(x) | is.mic(x) | is.disk(x), USE.NAMES = FALSE)]
-  df_antibiotics <- unname(get_column_abx(df, info = FALSE))
-  df <- df[, colnames(df)[colnames(df) %in% union(df_classes, df_antibiotics)], drop = FALSE]
-  
   stop_if(ncol(df) < 2,
     "data set must contain at least two variables",
     call = -2
   )
   if (message_not_thrown_before("mean_amr_distance", "groups")) {
-    message_("Calculating mean AMR distance based on columns ", vector_and(colnames(df), sort = FALSE))
+    message_("Calculating mean AMR distance based on columns ", vector_and(colnames(df)))
   }
-  
   res <- vapply(
     FUN.VALUE = double(nrow(df)),
     df,
@@ -166,7 +149,7 @@ mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
     }
   }
   res <- rowMeans(res, na.rm = TRUE)
-  res[is.infinite(res) | is.nan(res)] <- 0
+  res[is.infinite(res)] <- 0
   res
 }
 

R/resistance_predict.R

@@ -274,7 +274,7 @@ resistance_predict <- function(x,
   df_prediction$value <- ifelse(df_prediction$value > 1, 1, pmax(df_prediction$value, 0))
   df_prediction <- df_prediction[order(df_prediction$year), , drop = FALSE]
 
-  out <- as_original_data_class(df_prediction, class(x.bak))  # will remove tibble groups
+  out <- as_original_data_class(df_prediction, class(x.bak))
   structure(out,
     class = c("resistance_predict", class(out)),
     I_as_S = I_as_S,

R/rsi_calc.R

@@ -371,6 +371,6 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
   }
 
   rownames(out) <- NULL
-  out <- as_original_data_class(out, class(data.bak)) # will remove tibble groups
+  out <- as_original_data_class(out, class(data.bak))
   structure(out, class = c("rsi_df", class(out)))
 }

data-raw/_pre_commit_hook.R

@@ -486,16 +486,14 @@ suppressMessages(devtools::document(quiet = TRUE))
 
 
 # Style pkg ---------------------------------------------------------------
-if (!"styler" %in% rownames(utils::installed.packages())) {
-  message("Package 'styler' not installed!")
-} else if (interactive()) {
+# if (interactive()) {
 #   # only when sourcing this file ourselves
 #   usethis::ui_info("Styling package")
 #   styler::style_pkg(
 #     style = styler::tidyverse_style,
 #     filetype = c("R", "Rmd")
 #   )
-}
+# }
 
 
 # Finished ----------------------------------------------------------------

man/ab_property.Rd

@@ -62,7 +62,7 @@ set_ab_names(
 
 \item{tolower}{a \link{logical} to indicate whether the first \link{character} of every output should be transformed to a lower case \link{character}. This will lead to e.g. "polymyxin B" and not "polymyxin b".}
 
-\item{...}{in case of \code{\link[=set_ab_names]{set_ab_names()}} and \code{data} is a \link{data.frame}: columns to select (supports tidy selection such as \code{column1:column4}), otherwise other arguments passed on to \code{\link[=as.ab]{as.ab()}}}
+\item{...}{in case of \code{\link[=set_ab_names]{set_ab_names()}} and \code{data} is a \link{data.frame}: variables to select (supports tidy selection such as \code{column1:column4}), otherwise other arguments passed on to \code{\link[=as.ab]{as.ab()}}}
 
 \item{only_first}{a \link{logical} to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group)}
 

man/custom_eucast_rules.Rd

@@ -60,7 +60,7 @@ eucast_rules(df, rules = "custom", custom_rules = x, info = FALSE)
 
 \subsection{Using taxonomic properties in rules}{
 
-There is one exception in columns used for the rules: all column names of the \link{microorganisms} data set can also be used, but do not have to exist in the data set. These column names are: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" and "snomed". Thus, this next example will work as well, despite the fact that the \code{df} data set does not contain a column \code{genus}:
+There is one exception in variables used for the rules: all column names of the \link{microorganisms} data set can also be used, but do not have to exist in the data set. These column names are: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" and "snomed". Thus, this next example will work as well, despite the fact that the \code{df} data set does not contain a column \code{genus}:
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
                          TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R")

man/mean_amr_distance.Rd

@@ -16,7 +16,7 @@ mean_amr_distance(x, ...)
 amr_distance_from_row(amr_distance, row)
 }
 \arguments{
-\item{x}{a vector of class \link[=as.rsi]{rsi}, \link[=as.mic]{mic} or \link[=as.disk]{disk}, or a \link{data.frame} containing columns of any of these classes}
+\item{x}{a vector of class \link[=as.rsi]{rsi}, \link[=as.rsi]{rsi} or \link[=as.rsi]{rsi}, or a \link{data.frame} containing columns of any of these classes}
 
 \item{...}{variables to select (supports \link[tidyselect:language]{tidyselect language} such as \code{column1:column4} and \code{where(is.mic)}, and can thus also be \link[=ab_selector]{antibiotic selectors}}
 
@@ -30,13 +30,13 @@ amr_distance_from_row(amr_distance, row)
 Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
 }
 \details{
-The mean AMR distance is effectively \href{https://en.wikipedia.org/wiki/Standard_score}{the Z-score}; a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
+The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. For common numeric data this distance is equal to \href{https://en.wikipedia.org/wiki/Standard_score}{Z scores} (the number of standard deviations from the mean).
 
-MIC values (see \code{\link[=as.mic]{as.mic()}}) are transformed with \code{\link[=log2]{log2()}} first; their distance is thus calculated as \code{(log2(x) - mean(log2(x))) / sd(log2(x))}.
+MIC values (see \code{\link[=as.mic]{as.mic()}}) are transformed with \code{\link[=log2]{log2()}} first; their distance is calculated as \code{(log2(x) - mean(log2(x))) / sd(log2(x))}.
 
 R/SI values (see \code{\link[=as.rsi]{as.rsi()}}) are transformed using \code{"S"} = 1, \code{"I"} = 2, and \code{"R"} = 3. If \code{combine_SI} is \code{TRUE} (default), the \code{"I"} will be considered to be 1.
 
-For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, see \emph{Examples}.
+For data sets, the mean AMR distance will be calculated per variable, after which the mean of all columns will returned per row (using \code{\link[=rowMeans]{rowMeans()}}), see \emph{Examples}.
 
 Use \code{\link[=amr_distance_from_row]{amr_distance_from_row()}} to subtract distances from the distance of one row, see \emph{Examples}.
 }
@@ -46,24 +46,14 @@ Isolates with distances less than 0.01 difference from each other should be cons
 }
 
 \examples{
-rsi <- random_rsi(10)
-rsi
-mean_amr_distance(rsi)
-
-mic <- random_mic(10)
-mic
-mean_amr_distance(mic)
-# equal to the Z-score of their log2:
-(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-
-disk <- random_disk(10)
-disk
-mean_amr_distance(disk)
+x <- random_mic(10)
+x
+mean_amr_distance(x)
 
 y <- data.frame(
   id = LETTERS[1:10],
-  amox = random_rsi(10, ab = "amox", mo = "Escherichia coli"),
-  cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
+  amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
+  cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"),
   gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
   tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
 )
@@ -75,7 +65,7 @@ y[order(y$amr_distance), ]
 if (require("dplyr")) {
   y \%>\%
     mutate(
-      amr_distance = mean_amr_distance(y),
+      amr_distance = mean_amr_distance(., where(is.mic)),
       check_id_C = amr_distance_from_row(amr_distance, id == "C")
     ) \%>\%
     arrange(check_id_C)
@@ -86,7 +76,7 @@ if (require("dplyr")) {
     filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\%
     select(mo, TCY, carbapenems()) \%>\%
     group_by(mo) \%>\%
-    mutate(dist = mean_amr_distance(.)) \%>\%
-    arrange(mo, dist)
+    mutate(d = mean_amr_distance(., where(is.rsi))) \%>\%
+    arrange(mo, d)
 }
 }