styler dep

.github/prehooks/pre-commit

@@ -33,7 +33,7 @@ echo "Running pre-commit hook..."
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 if command -v Rscript > /dev/null; then
-  if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"', '"'styler'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
+  if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"') %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"

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 1.8.2.9077
+Version: 1.8.2.9078
 Date: 2023-01-05
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-#  1.8.2.9077
+# AMR 1.8.2.9078
 
 *(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
 

data-raw/_pre_commit_hook.R

@@ -486,14 +486,16 @@ suppressMessages(devtools::document(quiet = TRUE))
 
 
 # Style pkg ---------------------------------------------------------------
-# if (interactive()) {
-#   # only when sourcing this file ourselves
-#   usethis::ui_info("Styling package")
-#   styler::style_pkg(
-#     style = styler::tidyverse_style,
-#     filetype = c("R", "Rmd")
-#   )
-# }
+if (!"styler" %in% rownames(utils::installed.packages())) {
+  message("Package 'styler' not installed!")
+} else 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}: variables 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}: columns 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 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}:
+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}:
 
 \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.rsi]{rsi} or \link[=as.rsi]{rsi}, or a \link{data.frame} containing columns of any of these classes}
+\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{...}{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 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).
+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.
 
-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))}.
+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))}.
 
 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 variable, after which the mean of all columns will returned per row (using \code{\link[=rowMeans]{rowMeans()}}), see \emph{Examples}.
+For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, 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}.
 }
@@ -46,14 +46,24 @@ Isolates with distances less than 0.01 difference from each other should be cons
 }
 
 \examples{
-x <- random_mic(10)
-x
-mean_amr_distance(x)
+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)
 
 y <- data.frame(
   id = LETTERS[1:10],
-  amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
-  cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"),
+  amox = random_rsi(10, ab = "amox", mo = "Escherichia coli"),
+  cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
   gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
   tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
 )
@@ -65,7 +75,7 @@ y[order(y$amr_distance), ]
 if (require("dplyr")) {
   y \%>\%
     mutate(
-      amr_distance = mean_amr_distance(., where(is.mic)),
+      amr_distance = mean_amr_distance(y),
       check_id_C = amr_distance_from_row(amr_distance, id == "C")
     ) \%>\%
     arrange(check_id_C)
@@ -76,7 +86,7 @@ if (require("dplyr")) {
     filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\%
     select(mo, TCY, carbapenems()) \%>\%
     group_by(mo) \%>\%
-    mutate(d = mean_amr_distance(., where(is.rsi))) \%>\%
-    arrange(mo, d)
+    mutate(dist = mean_amr_distance(.)) \%>\%
+    arrange(mo, dist)
 }
 }