.github/workflows/publish-to-pypi.yml

@@ -59,9 +59,9 @@ jobs:
           bash _generate_python_wrapper.sh
       
       - name: Publish to PyPI
-        # env:
-        #   TWINE_USERNAME: "__token__"
-        #   TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+        env:
+          TWINE_USERNAME: "__token__"
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
         run: |
           cd data-raw/python_wrapper/AMR
           python -m twine upload dist/*

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 2.1.1.9100
+Version: 2.1.1.9099
 Date: 2024-10-17
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9100
+# AMR 2.1.1.9099
 
 *(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).)*
 

R/sir.R

@@ -319,23 +319,11 @@ as.sir <- function(x, ...) {
   UseMethod("as.sir")
 }
 
-as_sir_structure <- function(x,
-                             guideline = NULL,
-                             mo = NULL,
-                             ab = NULL,
-                             method = NULL,
-                             ref_tbl = NULL,
-                             ref_breakpoints = NULL) {
-  out <- structure(factor(as.character(unlist(unname(x))),
-                          levels = c("S", "SDD", "I", "R", "NI"),
-                          ordered = TRUE),
-                   guideline = guideline,
-                   mo = mo,
-                   ab = ab,
-                   method = method,
-                   ref_tbl = ref_tbl,
-                   ref_breakpoints = ref_breakpoints,
-                   class = c("sir", "ordered", "factor"))
+as_sir_structure <- function(x) {
+  structure(factor(as.character(unlist(unname(x))),
+                   levels = c("S", "SDD", "I", "R", "NI"),
+                   ordered = TRUE),
+            class = c("sir", "ordered", "factor"))
 }
 
 #' @rdname as.sir
@@ -1646,17 +1634,7 @@ get_skimmers.sir <- function(column) {
 #' @export
 #' @noRd
 print.sir <- function(x, ...) {
-  x_name <- deparse(substitute(x))
   cat("Class 'sir'\n")
-  if (!is.null(attributes(x)$guideline) && !all(is.na(attributes(x)$guideline))) {
-    cat(font_blue(word_wrap("These values were interpreted using ",
-                            font_bold(vector_and(attributes(x)$guideline, quotes = FALSE)),
-                            " based on ",
-                            vector_and(attributes(x)$method, quotes = FALSE),
-                            " values. ",
-                            "Use `sir_interpretation_history(", x_name, ")` to return a full logbook.")))
-    cat("\n")
-  }
   print(as.character(x), quote = FALSE)
 }
 
@@ -1737,27 +1715,7 @@ summary.sir <- function(object, ...) {
 #' @export
 #' @noRd
 c.sir <- function(...) {
-  lst <- list(...)
-  
-  guideline <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$guideline %||% NA_character_)
-  mo <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$mo %||% NA_character_)
-  ab <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ab %||% NA_character_)
-  method <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$method %||% NA_character_)
-  ref_tbl <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_tbl %||% NA_character_)
-  ref_breakpoints <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_breakpoints %||% NA_character_)
-  
-  out <- as.sir(unlist(lapply(list(...), as.character)))
-  
-  if (!all(is.na(guideline))) {
-    attributes(out)$guideline <- guideline
-    attributes(out)$mo <- mo
-    attributes(out)$ab <- ab
-    attributes(out)$method <- method
-    attributes(out)$ref_tbl <- ref_tbl
-    attributes(out)$ref_breakpoints <- ref_breakpoints
-  }
-  
-  out
+  as.sir(unlist(lapply(list(...), as.character)))
 }
 
 #' @method unique sir

data-raw/gpt_training_text.txt

@@ -21728,23 +21728,11 @@ as.sir <- function(x, ...) {
   UseMethod("as.sir")
 }
 
-as_sir_structure <- function(x,
-                             guideline = NULL,
-                             mo = NULL,
-                             ab = NULL,
-                             method = NULL,
-                             ref_tbl = NULL,
-                             ref_breakpoints = NULL) {
-  out <- structure(factor(as.character(unlist(unname(x))),
-                          levels = c("S", "SDD", "I", "R", "NI"),
-                          ordered = TRUE),
-                   guideline = guideline,
-                   mo = mo,
-                   ab = ab,
-                   method = method,
-                   ref_tbl = ref_tbl,
-                   ref_breakpoints = ref_breakpoints,
-                   class = c("sir", "ordered", "factor"))
+as_sir_structure <- function(x) {
+  structure(factor(as.character(unlist(unname(x))),
+                   levels = c("S", "SDD", "I", "R", "NI"),
+                   ordered = TRUE),
+            class = c("sir", "ordered", "factor"))
 }
 
 #' @rdname as.sir
@@ -23055,17 +23043,7 @@ get_skimmers.sir <- function(column) {
 #' @export
 #' @noRd
 print.sir <- function(x, ...) {
-  x_name <- deparse(substitute(x))
   cat("Class 'sir'\n")
-  if (!is.null(attributes(x)$guideline) && !all(is.na(attributes(x)$guideline))) {
-    cat(font_blue(word_wrap("These values were interpreted using ",
-                            font_bold(vector_and(attributes(x)$guideline, quotes = FALSE)),
-                            " based on ",
-                            vector_and(attributes(x)$method, quotes = FALSE),
-                            " values. ",
-                            "Use `sir_interpretation_history(", x_name, ")` to return a full logbook.")))
-    cat("\n")
-  }
   print(as.character(x), quote = FALSE)
 }
 
@@ -23146,27 +23124,7 @@ summary.sir <- function(object, ...) {
 #' @export
 #' @noRd
 c.sir <- function(...) {
-  lst <- list(...)
-  
-  guideline <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$guideline %||% NA_character_)
-  mo <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$mo %||% NA_character_)
-  ab <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ab %||% NA_character_)
-  method <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$method %||% NA_character_)
-  ref_tbl <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_tbl %||% NA_character_)
-  ref_breakpoints <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_breakpoints %||% NA_character_)
-  
-  out <- as.sir(unlist(lapply(list(...), as.character)))
-  
-  if (!all(is.na(guideline))) {
-    attributes(out)$guideline <- guideline
-    attributes(out)$mo <- mo
-    attributes(out)$ab <- ab
-    attributes(out)$method <- method
-    attributes(out)$ref_tbl <- ref_tbl
-    attributes(out)$ref_breakpoints <- ref_breakpoints
-  }
-  
-  out
+  as.sir(unlist(lapply(list(...), as.character)))
 }
 
 #' @method unique sir
@@ -24782,13 +24740,7 @@ This Python package is a wrapper round the `AMR` R package. It uses the `rpy2` p
 
 # Install
 
-1. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
-
-    ```bash
-    pip install AMR
-    ```
-
-2. Make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
+1. First make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
 
     For Linux:
 
@@ -24809,6 +24761,12 @@ This Python package is a wrapper round the `AMR` R package. It uses the `rpy2` p
     
     For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
 
+2. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
+
+    ```bash
+    pip install AMR
+    ```
+
 # Examples of Usage
 
 ## Cleaning Taxonomy
@@ -26024,7 +25982,7 @@ THE NEXT PART CONTAINS CONTENTS FROM FILE DESCRIPTION
 
 
 Package: AMR
-Version: 2.1.1.9099
+Version: 2.1.1.9098
 Date: 2024-10-17
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

vignettes/AMR_for_Python.Rmd

@@ -30,13 +30,7 @@ This Python package is a wrapper round the `AMR` R package. It uses the `rpy2` p
 
 # Install
 
-1. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
-
-    ```bash
-    pip install AMR
-    ```
-
-2. Make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
+1. First make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
 
     For Linux:
 
@@ -57,6 +51,12 @@ This Python package is a wrapper round the `AMR` R package. It uses the `rpy2` p
     
     For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
 
+2. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
+
+    ```bash
+    pip install AMR
+    ```
+
 # Examples of Usage
 
 ## Cleaning Taxonomy