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styler dep

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dr. M.S. (Matthijs) Berends 2023-01-05 14:46:44 +01:00
parent a0ee86536a
commit a98d0d75ea
8 changed files with 37 additions and 25 deletions
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2
.github/prehooks/pre-commit vendored
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@ -33,7 +33,7 @@ echo "Running pre-commit hook..."
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if command -v Rscript > /dev/null; then 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')" Rscript -e "source('data-raw/_pre_commit_hook.R')"
currentpkg=`Rscript -e "cat(pkgload::pkg_name())"` currentpkg=`Rscript -e "cat(pkgload::pkg_name())"`
echo "-> Adding all files in 'data-raw' to this commit" echo "-> Adding all files in 'data-raw' to this commit"

2
DESCRIPTION
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@ -1,5 +1,5 @@
Package: AMR Package: AMR
Version: 1.8.2.9077 Version: 1.8.2.9078
Date: 2023-01-05 Date: 2023-01-05
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)

2
NEWS.md
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@ -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!)* *(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*

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R/sysdata.rda
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18
data-raw/_pre_commit_hook.R
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@ -486,14 +486,16 @@ suppressMessages(devtools::document(quiet = TRUE))
# Style pkg --------------------------------------------------------------- # Style pkg ---------------------------------------------------------------
# if (interactive()) { if (!"styler" %in% rownames(utils::installed.packages())) {
# # only when sourcing this file ourselves message("Package 'styler' not installed!")
# usethis::ui_info("Styling package") } else if (interactive()) {
# styler::style_pkg( # # only when sourcing this file ourselves
# style = styler::tidyverse_style, # usethis::ui_info("Styling package")
# filetype = c("R", "Rmd") # styler::style_pkg(
# ) # style = styler::tidyverse_style,
# } # filetype = c("R", "Rmd")
# )
}
# Finished ---------------------------------------------------------------- # Finished ----------------------------------------------------------------

2
man/ab_property.Rd
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@ -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{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)} \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)}

2
man/custom_eucast_rules.Rd
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@ -60,7 +60,7 @@ eucast_rules(df, rules = "custom", custom_rules = x, info = FALSE)
\subsection{Using taxonomic properties in rules}{ \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", \if{html}{\out{<div class="sourceCode r">}}\preformatted{y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R") TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R")

34
man/mean_amr_distance.Rd
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@ -16,7 +16,7 @@ mean_amr_distance(x, ...)
amr_distance_from_row(amr_distance, row) amr_distance_from_row(amr_distance, row)
} }
\arguments{ \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}} \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. Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
} }
\details{ \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. 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}. 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{ \examples{
x <- random_mic(10) rsi <- random_rsi(10)
x rsi
mean_amr_distance(x) 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( y <- data.frame(
id = LETTERS[1:10], id = LETTERS[1:10],
amox = random_mic(10, ab = "amox", mo = "Escherichia coli"), amox = random_rsi(10, ab = "amox", mo = "Escherichia coli"),
cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"), cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
gent = random_mic(10, ab = "gent", mo = "Escherichia coli"), gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli") tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
) )
@ -65,7 +75,7 @@ y[order(y$amr_distance), ]
if (require("dplyr")) { if (require("dplyr")) {
y \%>\% y \%>\%
mutate( 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") check_id_C = amr_distance_from_row(amr_distance, id == "C")
) \%>\% ) \%>\%
arrange(check_id_C) arrange(check_id_C)
@ -76,7 +86,7 @@ if (require("dplyr")) {
filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\% filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\%
select(mo, TCY, carbapenems()) \%>\% select(mo, TCY, carbapenems()) \%>\%
group_by(mo) \%>\% group_by(mo) \%>\%
mutate(d = mean_amr_distance(., where(is.rsi))) \%>\% mutate(dist = mean_amr_distance(.)) \%>\%
arrange(mo, d) arrange(mo, dist)
} }
} }