(v2.1.1.9148) scale fix, antibiogram fix

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9147
-Date: 2025-02-14
+Version: 2.1.1.9148
+Date: 2025-02-15
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9147
+# AMR 2.1.1.9148
 
 *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
 

PythonPackage/AMR/AMR.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.2
 Name: AMR
-Version: 2.1.1.9147
+Version: 2.1.1.9148
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends

PythonPackage/AMR/setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='AMR',
-    version='2.1.1.9147',
+    version='2.1.1.9148',
     packages=find_packages(),
     install_requires=[
         'rpy2',

R/aa_globals.R

@@ -179,12 +179,16 @@ globalVariables(c(
   "microorganisms.codes",
   "mo",
   "n",
+  "n_susceptible",
+  "n_tested",
+  "n_total",
   "name",
   "new",
   "numerator",
   "observations",
   "old",
   "old_name",
+  "p_susceptible",
   "pattern",
   "R",
   "rank_index",

R/antibiogram.R

@@ -441,7 +441,7 @@ antibiogram.default <- function(x,
   x <- ascertain_sir_classes(x, "x")
   meet_criteria(wisca, allow_class = "logical", has_length = 1)
   if (isTRUE(wisca)) {
-    if (!missing(mo_transform)) {
+    if (!is.null(mo_transform)) {
       warning_("WISCA must be based on the species level as WISCA parameters are based on this. For that reason, `mo_transform` will be ignored.")
     }
     mo_transform <- function(x) suppressMessages(suppressWarnings(paste(mo_genus(x, keep_synonyms = TRUE, language = NULL), mo_species(x, keep_synonyms = TRUE, language = NULL))))
@@ -469,7 +469,7 @@ antibiogram.default <- function(x,
   
   # try to find columns based on type
   if (is.null(col_mo)) {
-    col_mo <- search_type_in_df(x = x, type = "mo", info = interactive())
+    col_mo <- search_type_in_df(x = x, type = "mo", info = info)
     stop_if(is.null(col_mo), "`col_mo` must be set")
   }
   # transform MOs
@@ -594,7 +594,7 @@ antibiogram.default <- function(x,
   }
   if (all(out$n_tested < minimum, na.rm = TRUE) && wisca == FALSE) {
     warning_("All combinations had less than `minimum = ", minimum, "` results, returning an empty antibiogram")
-    return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
+    return(as_original_data_class(data.frame(), class(x), extra_class = "antibiogram"))
   } else if (any(out$n_tested < minimum, na.rm = TRUE)) {
     out <- out %pm>%
       # also for WISCA, refrain from anything below 15 isolates:
@@ -612,7 +612,7 @@ antibiogram.default <- function(x,
   }
   
   if (NROW(out) == 0) {
-    return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
+    return(as_original_data_class(data.frame(), class(x), extra_class = "antibiogram"))
   }
   
   out$p_susceptible <- out$n_susceptible / out$n_tested
@@ -927,7 +927,6 @@ antibiogram.default <- function(x,
   rownames(out) <- NULL
   rownames(wisca_parameters) <- NULL
   rownames(long_numeric) <- NULL
-  
   structure(out,
             has_syndromic_group = has_syndromic_group,
             combine_SI = combine_SI,
@@ -943,7 +942,7 @@ antibiogram.default <- function(x,
 #' @export
 antibiogram.grouped_df <- function(x,
                                    antibiotics = where(is.sir),
-                                   mo_transform = function (...) "no_mo",
+                                   mo_transform = NULL,
                                    ab_transform = "name",
                                    syndromic_group = NULL,
                                    add_total_n = FALSE,
@@ -960,6 +959,7 @@ antibiogram.grouped_df <- function(x,
                                    conf_interval = 0.95,
                                    interval_side = "two-tailed",
                                    info = interactive()) {
+  stop_ifnot(is.null(mo_transform), "`mo_transform` must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, which could include the pathogen information (though not necessary). Nonetheless, this makes `mo_transform` redundant.", call = FALSE)
   stop_ifnot(is.null(syndromic_group), "`syndromic_group` must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, making `syndromic_groups` redundant.", call = FALSE)
   groups <- attributes(x)$groups
   n_groups <- NROW(groups)
@@ -969,16 +969,19 @@ antibiogram.grouped_df <- function(x,
                               title = paste("Calculating AMR for", n_groups, "groups"))
   on.exit(close(progress))
   
+  out <- NULL
+  wisca_parameters <- NULL
+  long_numeric <- NULL
+  
   for (i in seq_len(n_groups)) {
-    if (i > 1) progress$tick()
+    progress$tick()
     rows <- unlist(groups[i, ]$.rows)
     if (length(rows) == 0) {
       next
     }
-    
     new_out <- antibiogram(as.data.frame(x)[rows, , drop = FALSE],
                            antibiotics = antibiotics,
-                           mo_transform = function(x) "no_mo",
+                           mo_transform = NULL,
                            ab_transform = ab_transform,
                            syndromic_group = NULL,
                            add_total_n = add_total_n,
@@ -994,17 +997,15 @@ antibiogram.grouped_df <- function(x,
                            simulations = simulations,
                            conf_interval = conf_interval,
                            interval_side = interval_side,
-                           info = i == 1 && info == TRUE)
+                           info = FALSE)
     new_wisca_parameters <- attributes(new_out)$wisca_parameters
     new_long_numeric <- attributes(new_out)$long_numeric
     
-    if (i == 1) progress$tick()
-    
     if (NROW(new_out) == 0) {
       next
     }
     
-    # remove first column 'Pathogen' (in whatever language)
+    # remove first column 'Pathogen' (in whatever language), except WISCA since that never has Pathogen column
     if (isFALSE(wisca)) {
       new_out <- new_out[, -1, drop = FALSE]
       new_long_numeric <- new_long_numeric[, -1, drop = FALSE]
@@ -1037,17 +1038,17 @@ antibiogram.grouped_df <- function(x,
       long_numeric <- rbind_AMR(long_numeric, new_long_numeric)
     }
   }
-
+  
   close(progress)
   
-  out <- structure(as_original_data_class(out, class(x), extra_class = "antibiogram"),
-                   has_syndromic_group = FALSE,
-                   combine_SI = isTRUE(combine_SI),
-                   wisca = isTRUE(wisca),
-                   conf_interval = conf_interval,
-                   formatting_type = formatting_type,
-                   wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
-                   long_numeric = as_original_data_class(long_numeric, class(x)))
+  structure(as_original_data_class(out, class(x), extra_class = "antibiogram"),
+            has_syndromic_group = FALSE,
+            combine_SI = isTRUE(combine_SI),
+            wisca = isTRUE(wisca),
+            conf_interval = conf_interval,
+            formatting_type = formatting_type,
+            wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
+            long_numeric = as_original_data_class(long_numeric, class(x)))
 }
 
 #' @export
@@ -1072,6 +1073,7 @@ wisca <- function(x,
   antibiogram(x = x,
               antibiotics = antibiotics,
               ab_transform = ab_transform,
+              mo_transform = NULL,
               syndromic_group = syndromic_group,
               add_total_n = add_total_n,
               only_all_tested = only_all_tested,

R/plotting.R

@@ -227,10 +227,16 @@
 #' }
 NULL
 
-create_scale_mic <- function(aest, keep_operators, mic_range, ...) {
+create_scale_mic <- function(aest, keep_operators, mic_range = NULL, ...) {
   ggplot_fn <- getExportedValue(paste0("scale_", aest, "_continuous"),
                                 ns = asNamespace("ggplot2"))
   args <- list(...)
+  breaks_set <- args$breaks
+  if (!is.null(args$limits)) {
+    stop_ifnot(is.null(mic_range),
+               "In `scale_", aest, "_mic()`, `limits` cannot be combined with `mic_range`, as they working identically. Use `mic_range` OR `limits`.", call = FALSE)
+    mic_range <- args$limits
+  }
   # do not take these arguments into account, as they will be overwritten and seem to allow weird behaviour
   args[c("aesthetics", "trans", "transform", "transform_df", "breaks", "labels", "limits")] <- NULL
   scale <- do.call(ggplot_fn, args)
@@ -252,8 +258,30 @@ create_scale_mic <- function(aest, keep_operators, mic_range, ...) {
     df[[aest]] <- self$`.values_log`
     df
   }
-  scale$breaks <- function(..., self) log2(as.mic(self$`.values_levels`))
-  scale$labels <- function(..., self) self$`.values_levels`
+  scale$breaks <- function(..., self) {
+    if (!is.null(breaks_set)) {
+      if (is.function(breaks_set)) {
+        breaks_set(...)
+      } else {
+        log2(as.mic(breaks_set))
+      }
+    } else {
+      log2(as.mic(self$`.values_levels`))
+    }
+  }
+  scale$labels <- function(..., self) {
+    if (is.null(breaks_set)) {
+      self$`.values_levels`
+    } else {
+      breaks <- tryCatch(scale$breaks(), error = function(e) NULL)
+      if (!is.null(breaks)) {
+        # for when breaks are set by the user
+        2 ^ breaks
+      } else {
+        self$`.values_levels`
+      }
+    }
+  }
   scale$limits <- function(x, ..., self) {
     rng <- range(log2(as.mic(self$`.values_levels`)))
     # add 0.5 extra space

data-raw/gpt_training_text_v2.1.1.9148.txt

@@ -1,6 +1,6 @@
 This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
 
-First and foremost, you are trained on version 2.1.1.9147. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9148. Remember this whenever someone asks which AMR package version you’re at.
 
 Below are the contents of the  file, the  file, and all the  files (documentation) in the package. Every file content is split using 100 hypens.
 ----------------------------------------------------------------------------------------------------

tests/testthat/test-antibiogram.R

@@ -104,7 +104,7 @@ ab8 <- suppressWarnings(antibiogram(example_isolates,
                                     wisca = TRUE))
 
 expect_inherits(ab8, "antibiogram")
-expect_equal(colnames(ab8), c("Pathogen", "Piperacillin/tazobactam", "Piperacillin/tazobactam + Gentamicin", "Piperacillin/tazobactam + Tobramycin"))
+expect_equal(colnames(ab8), c("Piperacillin/tazobactam", "Piperacillin/tazobactam + Gentamicin", "Piperacillin/tazobactam + Tobramycin"))
 
 # grouped tibbles
 
@@ -128,7 +128,7 @@ expect_silent(plot(ab5))
 expect_silent(plot(ab6))
 expect_silent(plot(ab7))
 expect_silent(plot(ab8))
-expect_error(plot(ab9))
+expect_silent(plot(ab9))
 
 if (AMR:::pkg_is_available("ggplot2")) {
   expect_inherits(ggplot2::autoplot(ab1), "gg")
@@ -139,5 +139,5 @@ if (AMR:::pkg_is_available("ggplot2")) {
   expect_inherits(ggplot2::autoplot(ab6), "gg")
   expect_inherits(ggplot2::autoplot(ab7), "gg")
   expect_inherits(ggplot2::autoplot(ab8), "gg")
-  expect_error(ggplot2::autoplot(ab9))
+  expect_inherits(ggplot2::autoplot(ab9), "gg")
 }