(v2.1.1.9152) MIC plot fix

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9151
-Date: 2025-02-15
+Version: 2.1.1.9152
+Date: 2025-02-18
 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 2.1.1.9151
+# AMR 2.1.1.9152
 
 *(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).)*
 
@@ -64,6 +64,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
   * Added Tigemonam (`TNM`), a monobactam
 * MICs
   * Added as valid levels: 4096, 6 powers of 0.0625, and 5 powers of 192 (192, 384, 576, 768, 960)
+  * Fixed a bug in `as.mic()` that failed translation of scientifically formatted numbers
   * Added new argument `keep_operators` to `as.mic()`. This can be `"all"` (default), `"none"`, or `"edges"`. This argument is also available in the new `rescale_mic()` and `scale_*_mic()` functions.
   * Comparisons of MIC values are now more strict. For example, `>32` is higher than (and never equal to) `32`. Thus, `as.mic(">32") == as.mic(32)` now returns `FALSE`, and `as.mic(">32") > as.mic(32)` now returns `TRUE`.
   * Sorting of MIC values (using `sort()`) was fixed in the same manner; `<0.001` now gets sorted before `0.001`, and `>0.001` gets sorted after `0.001`.

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

@@ -1,6 +1,6 @@
 Metadata-Version: 2.2
 Name: AMR
-Version: 2.1.1.9151
+Version: 2.1.1.9152
 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='2.1.1.9151',
+    version='2.1.1.9152',
     packages=find_packages(),
     install_requires=[
         'rpy2',

R/mic.R

@@ -43,9 +43,14 @@ VALID_MIC_LEVELS <- c(t(vapply(FUN.VALUE = character(length(VALID_MIC_LEVELS)),
                                c("<", "<=", "", ">=", ">"),
                                paste0,
                                VALID_MIC_LEVELS)))
-COMMON_MIC_VALUES <- c(0.001, 0.002, 0.004, 0.008, 0.016, 0.032, 0.064,
-                       0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32,
-                       64, 128, 256, 512, 1024)
+COMMON_MIC_VALUES <- c(0.0001, 0.0002, 0.0005,
+                       0.001, 0.002, 0.004, 0.008,
+                       0.016, 0.032, 0.064,
+                       0.125, 0.25, 0.5,
+                       1, 2, 4, 8,
+                       16, 32, 64,
+                       128, 256, 512,
+                       1024, 2048, 4096)
 
 #' Transform Input to Minimum Inhibitory Concentrations (MIC)
 #'
@@ -158,7 +163,7 @@ as.mic <- function(x, na.rm = FALSE, keep_operators = "all") {
   
   if (is.mic(x) && (keep_operators == "all" || !any(x %like% "[>=<]", na.rm = TRUE))) {
     if (!identical(levels(x), VALID_MIC_LEVELS)) {
-      # from an older AMR version - just update MIC factor levels
+      # might be from an older AMR version - just update MIC factor levels
       x <- set_clean_class(factor(as.character(x), levels = VALID_MIC_LEVELS, ordered = TRUE),
                            new_class = c("mic", "ordered", "factor"))
     }
@@ -184,20 +189,19 @@ as.mic <- function(x, na.rm = FALSE, keep_operators = "all") {
   
   # comma to period
   x <- gsub(",", ".", x, fixed = TRUE)
-  # transform scientific notation
-  x[x %like% "[-]?[0-9]+([.][0-9]+)?e[-]?[0-9]+"] <- as.double(x[x %like% "[-]?[0-9]+([.][0-9]+)?e[-]?[0-9]+"])
   # transform Unicode for >= and <=
   x <- gsub("\u2264", "<=", x, fixed = TRUE)
   x <- gsub("\u2265", ">=", x, fixed = TRUE)
   # remove other invalid characters
-  x <- gsub("[^a-zA-Z0-9.><= ]+", "", x, perl = TRUE)
-  # remove space between operator and number ("<= 0.002" -> "<=0.002")
-  x <- gsub("(<|=|>) +", "\\1", x, perl = TRUE)
+  x <- gsub("[^a-zA-Z0-9.><= -]+", "", x, perl = TRUE)
   # transform => to >= and =< to <=
   x <- gsub("=<", "<=", x, fixed = TRUE)
   x <- gsub("=>", ">=", x, fixed = TRUE)
   # Remove leading == and =
   x <- gsub("^=+", "", x)
+  # retrieve signs and remove them from input
+  x_signs <- trimws(gsub("[^>=<]", "", x))
+  x <- trimws(gsub("[>=<]", "", x))
   # dots without a leading zero must start with 0
   x <- gsub("([^0-9]|^)[.]", "\\10.", x, perl = TRUE)
   # values like "<=0.2560.512" should be 0.512
@@ -217,6 +221,12 @@ as.mic <- function(x, na.rm = FALSE, keep_operators = "all") {
   x[x %like% "[.]"] <- gsub("0+$", "", x[x %like% "[.]"])
   # never end with dot
   x <- gsub("[.]$", "", x, perl = TRUE)
+  # remove scientific notation
+  x[x %like% "[0-9]e[-]?[0-9]"] <- trimws(format(suppressWarnings(as.double(x[x %like% "[0-9]e[-]?[0-9]"])), scientific = FALSE))
+  # add signs again
+  x <- paste0(x_signs, x)
+  # remove NAs introduced by format()
+  x <- gsub("(NA)+", "", x)
   # trim it
   x <- trimws2(x)
   

R/plotting.R

@@ -237,20 +237,33 @@ create_scale_mic <- function(aest, keep_operators, mic_range = NULL, ...) {
                "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
+  # do not take these arguments into account, as they will be overwritten and seem to allow weird behaviour if set anyway
   args[c("aesthetics", "trans", "transform", "transform_df", "breaks", "labels", "limits")] <- NULL
   scale <- do.call(ggplot_fn, args)
   
   scale$transform <- function(x) {
-    as.double(rescale_mic(x = as.double(x), keep_operators = , "labels", mic_range = mic_range, as.mic = TRUE))
+    as.double(rescale_mic(x = as.double(x), keep_operators = keep_operators, mic_range = mic_range, as.mic = TRUE))
   }
   scale$transform_df <- function(self, df) {
     self$`.values_rescaled` <- rescale_mic(x = as.double(df[[aest]]), keep_operators = keep_operators, mic_range = mic_range, as.mic = TRUE)
-    self$`.values_levels` <- levels(rescale_mic(x = as.double(df[[aest]]), keep_operators = keep_operators, mic_range = mic_range, as.mic = FALSE))
-    if (length(self$`.values_levels`) > 6 & "0.025" %in% self$`.values_levels`) {
-      # TODO weird levelling out leading to 0.025 being redundant
-      self$`.values_levels` <- self$`.values_levels`[self$`.values_levels` != "0.025"]
+    
+    # create new breaks and labels here
+    lims <- range(self$`.values_rescaled`)
+    if (!is.na(mic_range[1]) && mic_range[1] < lims[1]) {
+      lims[1] <- mic_range[1]
     }
+    if (!is.na(mic_range[2]) && mic_range[2] > lims[2]) {
+      lims[2] <- mic_range[2]
+    }
+    ind_min <- which(COMMON_MIC_VALUES <= lims[1])[which.min(abs(COMMON_MIC_VALUES[COMMON_MIC_VALUES <= lims[1]] - lims[1]))]  # Closest index where COMMON_MIC_VALUES <= lims[1]
+    ind_max <- which(COMMON_MIC_VALUES >= lims[2])[which.min(abs(COMMON_MIC_VALUES[COMMON_MIC_VALUES >= lims[2]] - lims[2]))]  # Closest index where COMMON_MIC_VALUES >= lims[2]
+    self$`.values_levels` <- as.mic(COMMON_MIC_VALUES[ind_min:ind_max])
+    
+    if (keep_operators %in% c("edges", "all") && length(self$`.values_levels`) > 1) {
+      self$`.values_levels`[1] <- paste0("<=", self$`.values_levels`[1])
+      self$`.values_levels`[length(self$`.values_levels`)] <- paste0(">=", self$`.values_levels`[length(self$`.values_levels`)])
+    }
+   
     self$`.values_log` <- log2(as.double(self$`.values_rescaled`))
     if (aest == "y" && "group" %in% colnames(df)) {
       df$group <- as.integer(factor(df$x))

data-raw/gpt_training_text_v2.1.1.9152.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 2.1.1.9151. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9152. 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-mic.R

@@ -27,12 +27,15 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #
 
+# used in multiple functions, also in plotting
+expect_true(all(as.mic(COMMON_MIC_VALUES) %in% VALID_MIC_LEVELS))
+expect_true(all(paste0("<=", as.mic(COMMON_MIC_VALUES)) %in% VALID_MIC_LEVELS))
+expect_true(all(paste0(">=", as.mic(COMMON_MIC_VALUES)) %in% VALID_MIC_LEVELS))
+
 expect_true(as.mic(8) == as.mic("8"))
 expect_true(as.mic("1") > as.mic("<=0.0625"))
 expect_true(as.mic("1") < as.mic(">=32"))
 expect_true(is.mic(as.mic(8)))
-# expect_true(as.mic(1024) < as.mic(">1024"))
-# expect_true(as.mic("<1024") > as.mic("1024"))
 
 
 expect_equal(as.double(as.mic(">=32")), 32)