DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 1.8.2.9029
+Version: 1.8.2.9027
 Date: 2022-10-21
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NAMESPACE

@@ -189,7 +189,6 @@ export(age_groups)
 export(all_antimicrobials)
 export(aminoglycosides)
 export(aminopenicillins)
-export(amr_distance_from_row)
 export(anti_join_microorganisms)
 export(antifungals)
 export(antimicrobials_equal)
@@ -226,6 +225,7 @@ export(count_resistant)
 export(count_susceptible)
 export(custom_eucast_rules)
 export(custom_mdro_guideline)
+export(distance_from_row)
 export(eucast_dosage)
 export(eucast_exceptional_phenotypes)
 export(eucast_rules)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 1.8.2.9029
+# AMR 1.8.2.9027
 
 This version will eventually become v2.0! We're happy to reach a new major milestone soon!
 
@@ -27,7 +27,7 @@ This version will eventually become v2.0! We're happy to reach a new major miles
 * Support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. We are very grateful for the valuable input by our colleagues from other countries. The `AMR` package is now available in 16 languages. The automatic language determination will give a note at start-up on systems in supported languages.
 * Our data sets are now also continually exported to Apache Feather and Apache Parquet formats. You can find more info [in this article on our website](https://msberends.github.io/AMR/articles/datasets.html).
 * Added confidence intervals in AMR calculation. This is now included in `rsi_df()` and `proportion_df()` and manually available as `rsi_confidence_interval()`
-* Support for using antibiotic selectors in scoped `dplyr` verbs (with or without `vars()`), such as in: `... %>% summarise_at(aminoglycosides(), resistance)`, see `resistance()`
+* Support for using antibiotic selectors in scoped `dplyr` verbs (worh or without `vars()`), such as in: `... %>% summarise_at(aminoglycosides(), resistance)`
 
 ### Changed
 * Fix for using `as.rsi()` on certain EUCAST breakpoints for MIC values

R/mean_amr_distance.R

@@ -41,7 +41,7 @@
 #'
 #' 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*.
+#' Use [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
@@ -66,7 +66,7 @@
 #'   y %>%
 #'     mutate(
 #'       amr_distance = mean_amr_distance(., where(is.mic)),
-#'       check_id_C = amr_distance_from_row(amr_distance, id == "C")
+#'       check_id_C = distance_from_row(amr_distance, id == "C")
 #'     ) %>%
 #'     arrange(check_id_C)
 #' }
@@ -104,7 +104,7 @@ mean_amr_distance.disk <- function(x, ...) {
 
 #' @rdname mean_amr_distance
 #' @export
-mean_amr_distance.rsi <- function(x, ..., combine_SI = TRUE) {
+mean_amr_distance.rsi <- function(x, combine_SI = TRUE, ...) {
   meet_criteria(combine_SI, allow_class = "logical", has_length = 1, .call_depth = -1)
   if (isTRUE(combine_SI)) {
     x[x == "I"] <- "S"
@@ -154,14 +154,14 @@ mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
 }
 
 #' @rdname mean_amr_distance
-#' @param amr_distance the outcome of [mean_amr_distance()]
+#' @param mean_distance the outcome of [mean_amr_distance()]
 #' @param row an index, such as a row number
 #' @export
-amr_distance_from_row <- function(amr_distance, row) {
+distance_from_row <- function(mean_distance, row) {
-  meet_criteria(amr_distance, allow_class = c("double", "numeric"), is_finite = TRUE)
+  meet_criteria(mean_distance, allow_class = c("double", "numeric"), is_finite = TRUE)
   meet_criteria(row, allow_class = c("logical", "double", "numeric"))
   if (is.logical(row)) {
     row <- which(row)
   }
-  abs(amr_distance[row] - amr_distance)
+  abs(mean_distance[row] - mean_distance)
 }

man/mean_amr_distance.Rd

@@ -7,7 +7,7 @@
 \alias{mean_amr_distance.disk}
 \alias{mean_amr_distance.rsi}
 \alias{mean_amr_distance.data.frame}
-\alias{amr_distance_from_row}
+\alias{distance_from_row}
 \title{Mean AMR Distance}
 \usage{
 mean_amr_distance(x, ...)
@@ -18,11 +18,11 @@ mean_amr_distance(x, ...)
 
 \method{mean_amr_distance}{disk}(x, ...)
 
-\method{mean_amr_distance}{rsi}(x, ..., combine_SI = TRUE)
+\method{mean_amr_distance}{rsi}(x, combine_SI = TRUE, ...)
 
 \method{mean_amr_distance}{data.frame}(x, ..., combine_SI = TRUE)
 
-amr_distance_from_row(amr_distance, row)
+distance_from_row(mean_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}
@@ -31,7 +31,7 @@ amr_distance_from_row(amr_distance, row)
 
 \item{combine_SI}{a \link{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 \code{TRUE}}
 
-\item{amr_distance}{the outcome of \code{\link[=mean_amr_distance]{mean_amr_distance()}}}
+\item{mean_distance}{the outcome of \code{\link[=mean_amr_distance]{mean_amr_distance()}}}
 
 \item{row}{an index, such as a row number}
 }
@@ -47,7 +47,7 @@ R/SI values (see \code{\link[=as.rsi]{as.rsi()}}) are transformed using \code{"S
 
 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}.
+Use \code{\link[=distance_from_row]{distance_from_row()}} to subtract distances from the distance of one row, see \emph{Examples}.
 }
 \section{Interpretation}{
 
@@ -75,7 +75,7 @@ if (require("dplyr")) {
   y \%>\%
     mutate(
       amr_distance = mean_amr_distance(., where(is.mic)),
-      check_id_C = amr_distance_from_row(amr_distance, id == "C")
+      check_id_C = distance_from_row(amr_distance, id == "C")
     ) \%>\%
     arrange(check_id_C)
 }