(v1.2.0.9037) survey on website

R/aa_helper_functions.R

@@ -72,6 +72,15 @@ addin_insert_like <- function() {
 }
 
 check_dataset_integrity <- function() {
+  # check if user overwrote our data sets in their global environment
+  data_in_pkg <- data(package = "AMR", envir = asNamespace("AMR"))$results[, "Item", drop = TRUE]
+  data_in_globalenv <- ls(envir = globalenv())
+  overwritten <- data_in_pkg[data_in_pkg %in% data_in_globalenv]
+  stop_if(length(overwritten) > 0,
+          "the following data set is overwritten by your global environment and prevents the AMR package from working correctly:\n",
+          paste0("'", overwritten, "'", collapse = ", "),
+          ".\nPlease rename your object before using this function.", call = FALSE)
+  # check if other packages did not overwrite our data sets
   tryCatch({
     check_microorganisms <- all(c("mo", "fullname", "kingdom", "phylum",
                                   "class", "order", "family", "genus", 
@@ -86,13 +95,6 @@ check_dataset_integrity <- function() {
   }, error = function(e)
     stop_('please use the command \'library("AMR")\' before using this function, to load the required reference data.', call = FALSE)
   )
-  data_in_pkg <- data(package = "AMR", envir = asNamespace("AMR"))$results[, "Item"]
-  data_in_globalenv <- ls(envir = globalenv())
-  overwritten <- data_in_pkg[data_in_pkg %in% data_in_globalenv]
-  stop_if(length(overwritten) > 0,
-          "the following data set is overwritten by your global environment and prevents the AMR package from working correctly:\n",
-          paste0("'", overwritten, "'", collapse = ", "),
-          ".\nPlease rename your object before using this function.", call = FALSE)
   invisible(TRUE)
 }
 

R/amr.R

@@ -23,12 +23,14 @@
 #'
 #' Welcome to the `AMR` package.
 #' @details
-#' `AMR` is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any table format, including WHONET/EARS-Net data.
-#'
-#' We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
+#' `AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
+#' 
+#' After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
+#' 
+#' This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
 #'
 #' This package can be used for:
-#' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the [Catalogue of Life](http://www.catalogueoflife.org)
+#' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature
 #' - Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
 #' - Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records
 #' - Determining first isolates to be used for AMR analysis
@@ -37,15 +39,16 @@
 #' - Calculating (empirical) susceptibility of both mono therapy and combination therapies
 #' - Predicting future antimicrobial resistance using regression models
 #' - Getting properties for any microorganism (like Gram stain, species, genus or family)
-#' - Getting properties for any antibiotic (like name, EARS-Net code, ATC code, PubChem code, defined daily dose or trade name)
+#' - Getting properties for any antibiotic (like name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)
 #' - Plotting antimicrobial resistance
-#' - Getting SNOMED codes of a microorganism, or get its name associated with a SNOMED code
-#' - Getting LOINC codes of an antibiotic, or get its name associated with a LOINC code
+#' - Applying EUCAST expert rules
+#' - Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code
+#' - Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code
 #' - Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to R/SI
 #' - Principal component analysis for AMR
-
+#' 
 #' @section Read more on our website!:
-#' On our website <https://msberends.github.io/AMR> you can find [a comprehensive tutorial](https://msberends.github.io/AMR/articles/AMR.html) about how to conduct AMR analysis, the [complete documentation of all functions](https://msberends.github.io/AMR/reference) (which reads a lot easier than here in R) and [an example analysis using WHONET data](https://msberends.github.io/AMR/articles/WHONET.html).
+#' On our website <https://msberends.github.io/AMR> you can find [a comprehensive tutorial](https://msberends.github.io/AMR/articles/AMR.html) about how to conduct AMR analysis, the [complete documentation of all functions](https://msberends.github.io/AMR/reference) (which reads a lot easier than here in R) and [an example analysis using WHONET data](https://msberends.github.io/AMR/articles/WHONET.html). As we would like to better understand the backgrounds and needs of our users, please [participate in our survey](https://msberends.github.io/AMR/survey.html)!
 #' @section Contact Us:
 #' For suggestions, comments or questions, please contact us at:
 #'

R/filter_ab_class.R

@@ -33,8 +33,7 @@
 #' @seealso [antibiotic_class_selectors()] for the `select()` equivalent.
 #' @export
 #' @examples
-#' \dontrun{
-#' library(dplyr)
+#' if (require(dplyr)) {
 #'
 #' # filter on isolates that have any result for any aminoglycoside
 #' example_isolates %>% filter_ab_class("aminoglycoside")
@@ -64,6 +63,12 @@
 #'   filter_aminoglycosides("R", "all") %>%
 #'   filter_fluoroquinolones("R", "all")
 #' }
+#' 
+#' \dontrun{
+#' # with dplyr 1.0.0 and higher (that adds 'across()'), this is equal:
+#' example_isolates %>% filter_carbapenems("R", "all")
+#' example_isolates %>% filter(across(carbapenems(), ~. == "R"))
+#' }
 filter_ab_class <- function(x,
                             ab_class,
                             result = NULL,

R/ggplot_pca.R

@@ -40,6 +40,7 @@
 #' @param arrows_colour the colour of the arrow and their text
 #' @param arrows_size the size (thickness) of the arrow lines
 #' @param arrows_textsize the size of the text at the end of the arrows
+#' @param arrows_textangled a logical whether the text at the end of the arrows should be angled
 #' @param arrows_alpha the alpha (transparency) of the arrows and their text
 #' @param base_textsize the text size for all plot elements except the labels and arrows
 #' @param ... Parameters passed on to functions
@@ -58,20 +59,25 @@
 #' # `example_isolates` is a dataset available in the AMR package.
 #' # See ?example_isolates.
 #'
-#' \dontrun{
 #' # See ?pca for more info about Principal Component Analysis (PCA).
-#' library(dplyr)
-#' pca_model <- example_isolates %>% 
-#'   filter(mo_genus(mo) == "Staphylococcus") %>% 
-#'   group_by(species = mo_shortname(mo)) %>%
-#'   summarise_if (is.rsi, resistance) %>%
-#'   pca(FLC, AMC, CXM, GEN, TOB, TMP, SXT, CIP, TEC, TCY, ERY)
+#' if (require("dplyr")) {
+#'   pca_model <- example_isolates %>% 
+#'     filter(mo_genus(mo) == "Staphylococcus") %>% 
+#'     group_by(species = mo_shortname(mo)) %>%
+#'     summarise_if (is.rsi, resistance) %>%
+#'     pca(FLC, AMC, CXM, GEN, TOB, TMP, SXT, CIP, TEC, TCY, ERY)
+#'     
+#'   # old (base R)
+#'   biplot(pca_model)
 #'   
-#' # old
-#' biplot(pca_model)
-#' 
-#' # new 
-#' ggplot_pca(pca_model)
+#'   # new 
+#'   ggplot_pca(pca_model)
+#'   
+#'   if (require("ggplot2")) {
+#'     ggplot_pca(pca_model) +
+#'       scale_colour_viridis_d() +
+#'       labs(title = "Title here")
+#'   }
 #' }
 ggplot_pca <- function(x,
                        choices = 1:2,
@@ -91,26 +97,28 @@ ggplot_pca <- function(x,
                        arrows_colour = "darkblue",
                        arrows_size = 0.5,
                        arrows_textsize = 3,
+                       arrows_textangled = TRUE,
                        arrows_alpha = 0.75,
                        base_textsize = 10,
                        ...) {
   
   stop_ifnot_installed("ggplot2")
   stop_ifnot(length(choices) == 2, "`choices` must be of length 2")
-  stop_ifnot(is.logical(scale), "`scale` must be TRUE or FALSE")
-  stop_ifnot(is.logical(pc.biplot), "`pc.biplot` must be TRUE or FALSE")
-  stop_ifnot(is.numeric(choices), "`choices` must be numeric")
-  stop_ifnot(is.numeric(labels_textsize), "`labels_textsize` must be numeric")
-  stop_ifnot(is.numeric(labels_text_placement), "`labels_text_placement` must be numeric")
-  stop_ifnot(is.logical(ellipse), "`ellipse` must be TRUE or FALSE")
-  stop_ifnot(is.numeric(ellipse_prob), "`ellipse_prob` must be numeric")
-  stop_ifnot(is.numeric(ellipse_size), "`ellipse_size` must be numeric")
-  stop_ifnot(is.numeric(ellipse_alpha), "`ellipse_alpha` must be numeric")
   stop_ifnot(is.logical(arrows), "`arrows` must be TRUE or FALSE")
+  stop_ifnot(is.logical(arrows_textangled), "`arrows_textangled` must be TRUE or FALSE")
+  stop_ifnot(is.logical(ellipse), "`ellipse` must be TRUE or FALSE")
+  stop_ifnot(is.logical(pc.biplot), "`pc.biplot` must be TRUE or FALSE")
+  stop_ifnot(is.logical(scale), "`scale` must be TRUE or FALSE")
+  stop_ifnot(is.numeric(arrows_alpha), "`arrows_alpha` must be numeric")
   stop_ifnot(is.numeric(arrows_size), "`arrows_size` must be numeric")
   stop_ifnot(is.numeric(arrows_textsize), "`arrows_textsize` must be numeric")
-  stop_ifnot(is.numeric(arrows_alpha), "`arrows_alpha` must be numeric")
   stop_ifnot(is.numeric(base_textsize), "`base_textsize` must be numeric")
+  stop_ifnot(is.numeric(choices), "`choices` must be numeric")
+  stop_ifnot(is.numeric(ellipse_alpha), "`ellipse_alpha` must be numeric")
+  stop_ifnot(is.numeric(ellipse_prob), "`ellipse_prob` must be numeric")
+  stop_ifnot(is.numeric(ellipse_size), "`ellipse_size` must be numeric")
+  stop_ifnot(is.numeric(labels_text_placement), "`labels_text_placement` must be numeric")
+  stop_ifnot(is.numeric(labels_textsize), "`labels_textsize` must be numeric")
   
   calculations <- pca_calculations(pca_model = x,
                                    groups = groups, 
@@ -206,12 +214,20 @@ ggplot_pca <- function(x,
                                                           type = "open"), 
                                    colour = arrows_colour, 
                                    size = arrows_size,
-                                   alpha = arrows_alpha) +
-      ggplot2::geom_text(data = df.v, 
-                         ggplot2::aes(label = varname, x = xvar, y = yvar, angle = angle, hjust = hjust), 
-                         colour = arrows_colour,
-                         size = arrows_textsize,
-                         alpha = arrows_alpha)
+                                   alpha = arrows_alpha)
+    if (arrows_textangled == TRUE) {
+      g <- g + ggplot2::geom_text(data = df.v, 
+                                  ggplot2::aes(label = varname, x = xvar, y = yvar, angle = angle, hjust = hjust), 
+                                  colour = arrows_colour,
+                                  size = arrows_textsize,
+                                  alpha = arrows_alpha)
+    } else {
+      g <- g + ggplot2::geom_text(data = df.v, 
+                                  ggplot2::aes(label = varname, x = xvar, y = yvar, hjust = hjust), 
+                                  colour = arrows_colour,
+                                  size = arrows_textsize,
+                                  alpha = arrows_alpha)
+    }
   }
   
   # Add caption label about total explained variance

R/rsi.R

@@ -37,7 +37,7 @@
 #' 
 #' Supported guidelines to be used as input for the `guideline` parameter are: `r paste0('"', sort(unique(AMR::rsi_translation$guideline)), '"', collapse = ", ")`. Simply using `"CLSI"` or `"EUCAST"` for input will automatically select the latest version of that guideline. 
 #' 
-#' The repository of this package [contains a machine readable version](https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt) of all guidelines. This is a CSV file consisting of `r format(nrow(AMR::rsi_translation), big.mark = ",")` rows and `r ncol(AMR::rsi_translation)` columns. This file is machine readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial agent and the microorganism. This **allows for easy implementation of these rules in laboratory information systems (LIS)**.
+#' The repository of this package [contains a machine readable version](https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt) of all guidelines. This is a CSV file consisting of `r format(nrow(AMR::rsi_translation), big.mark = ",")` rows and `r ncol(AMR::rsi_translation)` columns. This file is machine readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial agent and the microorganism. **This allows for easy implementation of these rules in laboratory information systems (LIS)**.
 #'
 #' After using [as.rsi()], you can use [eucast_rules()] to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
 #'

R/rsi_calc.R

@@ -214,7 +214,7 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
     out <- data.frame(antibiotic = character(0),
                       interpretation = character(0),
                       value = double(0),
-                      isolates <- integer(0),
+                      isolates = integer(0),
                       stringsAsFactors = FALSE)
     if (data_has_groups) {
       group_values <- unique(.data[, which(colnames(.data) %in% groups), drop = FALSE])