(v1.2.0.9008) ab_class improvement

man/antibiotic_class_selectors.Rd

@@ -2,6 +2,7 @@
 % Please edit documentation in R/ab_class_selectors.R
 \name{antibiotic_class_selectors}
 \alias{antibiotic_class_selectors}
+\alias{ab_class}
 \alias{aminoglycosides}
 \alias{carbapenems}
 \alias{cephalosporins}
@@ -17,6 +18,8 @@
 \alias{tetracyclines}
 \title{Antibiotic class selectors}
 \usage{
+ab_class(ab_class)
+
 aminoglycosides()
 
 carbapenems()
@@ -43,6 +46,9 @@ penicillins()
 
 tetracyclines()
 }
+\arguments{
+\item{ab_class}{an antimicrobial class, like \code{"carbapenems"}. The columns \code{group}, \code{atc_group1} and \code{atc_group2} of the \link{antibiotics} data set will be searched (case-insensitive) for this value.}
+}
 \description{
 Use these selection helpers inside any function that allows \href{https://tidyselect.r-lib.org/reference/language.html}{Tidyverse selections}, like \code{dplyr::select()} or \code{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.
 }
@@ -58,16 +64,27 @@ if (require("dplyr")) {
   example_isolates \%>\% 
     select(carbapenems())
     
-    
   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
   example_isolates \%>\% 
     select(mo, aminoglycosides())
     
+  # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
+  example_isolates \%>\% 
+    select(mo, ab_class("mycobact"))
+    
+    
+  # get bug/drug combinations for only macrolides in Gram-positives:
+  example_isolates \%>\% 
+    filter(mo_gramstain(mo) \%like\% "pos") \%>\% 
+    select(mo, macrolides()) \%>\% 
+    bug_drug_combinations() \%>\%
+    format()
+    
     
   data.frame(irrelevant = "value",
              J01CA01 = "S") \%>\%   # ATC code of ampicillin
     select(penicillins())         # so the 'J01CA01' column is selected
-    
+
 }
 }
 \seealso{

man/as.mo.Rd

@@ -192,7 +192,7 @@ mo_gramstain("E. coli")       # returns "Gram negative"
 \dontrun{
 df$mo <- as.mo(df$microorganism_name)
 
-# the select function of tidyverse is also supported:
+# the select function of the Tidyverse is also supported:
 library(dplyr)
 df$mo <- df \%>\%
   select(microorganism_name) \%>\%

man/lifecycle.Rd

@@ -4,10 +4,10 @@
 \alias{lifecycle}
 \title{Lifecycles of functions in the \code{AMR} package}
 \description{
-Functions in this \code{AMR} package are categorised using \href{https://www.tidyverse.org/lifecycle}{the lifecycle circle of the \code{tidyverse} as found on www.tidyverse.org/lifecycle}.
+Functions in this \code{AMR} package are categorised using \href{https://www.Tidyverse.org/lifecycle}{the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle}.
 
-\if{html}{\figure{lifecycle_tidyverse.svg}{options: height=200px style=margin-bottom:5px} \cr}
-This page contains a section for every lifecycle (with text borrowed from the aforementioned \code{tidyverse} website), so they can be used in the manual pages of the functions.
+\if{html}{\figure{lifecycle_Tidyverse.svg}{options: height=200px style=margin-bottom:5px} \cr}
+This page contains a section for every lifecycle (with text borrowed from the aforementioned Tidyverse website), so they can be used in the manual pages of the functions.
 }
 \section{Experimental lifecycle}{
 

man/proportion.Rd

@@ -83,7 +83,7 @@ The function \code{\link[=resistance]{resistance()}} is equal to the function \c
 
 \strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set.
 
-These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{count()}][AMR::count()] functions to count isolates. The function \code{\link[=susceptibility]{susceptibility()}} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can influence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} parameter).}
+These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[AMR:count]{count()}} functions to count isolates. The function \code{\link[=susceptibility]{susceptibility()}} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can influence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} parameter).}
 
 The function \code{\link[=proportion_df]{proportion_df()}} takes any variable from \code{data} that has an \code{\link{rsi}} class (created with \code{\link[=as.rsi]{as.rsi()}}) and calculates the proportions R, I and S. It also supports grouped variables. The function \code{\link[=rsi_df]{rsi_df()}} works exactly like \code{\link[=proportion_df]{proportion_df()}}, but adds the number of isolates.
 }