(v2.1.1.9118) move ggplot2 plotting functions to general 'plotting' man page

.github/workflows/website.yaml

@@ -46,6 +46,7 @@ jobs:
       - uses: actions/checkout@v3
         with:
           # this is to keep timestamps, the default fetch-depth: 1 gets the timestamps of the moment of cloning
+          # we need this for the download page on our website - dates must be of the files, not of the latest git push
           fetch-depth: 0
 
       - name: Preserve timestamps
@@ -69,7 +70,6 @@ jobs:
           extra-packages: |
             any::pkgdown
             any::tidymodels
-            any::data.table
 
       # Send updates to repo using GH Actions bot
       - name: Create website in separate branch

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9117
-Date: 2024-12-13
+Version: 2.1.1.9118
+Date: 2024-12-14
 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 2.1.1.9117
+# AMR 2.1.1.9118
 
 *(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

@@ -1,9 +1,9 @@
 Metadata-Version: 2.1
 Name: AMR
-Version: 2.1.1.9117
+Version: 2.1.1.9118
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
-Author: Dr. Matthijs Berends
+Author: Matthijs Berends
 Author-email: m.s.berends@umcg.nl
 License: GPL 2
 Project-URL: Bug Tracker, https://github.com/msberends/AMR/issues

PythonPackage/AMR/AMR/__init__.py

@@ -117,8 +117,6 @@ from .functions import is_new_episode
 from .functions import ggplot_pca
 from .functions import ggplot_sir
 from .functions import geom_sir
-from .functions import theme_sir
-from .functions import labels_sir_count
 from .functions import guess_ab_col
 from .functions import italicise_taxonomy
 from .functions import italicize_taxonomy
@@ -181,6 +179,8 @@ from .functions import mo_info
 from .functions import mo_url
 from .functions import mo_property
 from .functions import pca
+from .functions import theme_sir
+from .functions import labels_sir_count
 from .functions import resistance
 from .functions import susceptibility
 from .functions import sir_confidence_interval

PythonPackage/AMR/AMR/functions.py

@@ -381,12 +381,6 @@ def ggplot_sir(*args, **kwargs):
 def geom_sir(*args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
     return convert_to_python(amr_r.geom_sir(*args, **kwargs))
-def theme_sir(*args, **kwargs):
-    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
-    return convert_to_python(amr_r.theme_sir(*args, **kwargs))
-def labels_sir_count(*args, **kwargs):
-    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
-    return convert_to_python(amr_r.labels_sir_count(*args, **kwargs))
 def guess_ab_col(*args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
     return convert_to_python(amr_r.guess_ab_col(*args, **kwargs))
@@ -573,6 +567,12 @@ def mo_property(*args, **kwargs):
 def pca(*args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
     return convert_to_python(amr_r.pca(*args, **kwargs))
+def theme_sir(*args, **kwargs):
+    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
+    return convert_to_python(amr_r.theme_sir(*args, **kwargs))
+def labels_sir_count(*args, **kwargs):
+    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
+    return convert_to_python(amr_r.labels_sir_count(*args, **kwargs))
 def resistance(*args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
     return convert_to_python(amr_r.resistance(*args, **kwargs))

PythonPackage/AMR/setup.py

@@ -2,14 +2,14 @@ from setuptools import setup, find_packages
 
 setup(
     name='AMR',
-    version='2.1.1.9117',
+    version='2.1.1.9118',
     packages=find_packages(),
     install_requires=[
         'rpy2',
         'numpy',
         'pandas',
     ],
-    author='Dr. Matthijs Berends',
+    author='Matthijs Berends',
     author_email='m.s.berends@umcg.nl',
     description='A Python wrapper for the AMR R package',
     long_description=open('README.md').read(),

R/ggplot_sir.R

@@ -52,18 +52,15 @@
 #' @param ... other arguments passed on to [geom_sir()] or, in case of [scale_sir_colours()], named values to set colours. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red. See *Examples*.
 #' @details At default, the names of antibiotics will be shown on the plots using [ab_name()]. This can be set with the `translate_ab` argument. See [count_df()].
 #'
-#' ### The Functions
 #' [geom_sir()] will take any variable from the data that has an [`sir`] class (created with [as.sir()]) using [sir_df()] and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
+#' 
+#' Additional functions include:
 #'
-#' [facet_sir()] creates 2d plots (at default based on S/I/R) using [ggplot2::facet_wrap()].
-#'
-#' [scale_y_percent()] transforms the y axis to a 0 to 100% range using [ggplot2::scale_y_continuous()].
-#'
-#' [scale_sir_colours()] sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
-#'
-#' [theme_sir()] is a [ggplot2 theme][[ggplot2::theme()] with minimal distraction.
-#'
-#' [labels_sir_count()] print datalabels on the bars with percentage and amount of isolates using [ggplot2::geom_text()].
+#' * [facet_sir()] creates 2d plots (at default based on S/I/R) using [ggplot2::facet_wrap()].
+#' * [scale_y_percent()] transforms the y axis to a 0 to 100% range using [ggplot2::scale_y_continuous()].
+#' * [scale_sir_colours()] sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
+#' * [theme_sir()] is a [ggplot2 theme][[ggplot2::theme()] with minimal distraction.
+#' * [labels_sir_count()] print datalabels on the bars with percentage and amount of isolates using [ggplot2::geom_text()].
 #'
 #' [ggplot_sir()] is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (`%>%`). See *Examples*.
 #' @rdname ggplot_sir
@@ -344,187 +341,3 @@ geom_sir <- function(position = NULL,
     ...
   )
 }
-
-#' @rdname ggplot_sir
-#' @export
-facet_sir <- function(facet = c("interpretation", "antibiotic"), nrow = NULL) {
-  facet <- facet[1]
-  stop_ifnot_installed("ggplot2")
-  meet_criteria(facet, allow_class = "character", has_length = 1)
-  meet_criteria(nrow, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
-
-  # we work with aes_string later on
-  facet_deparse <- deparse(substitute(facet))
-  if (facet_deparse != "facet") {
-    facet <- facet_deparse
-  }
-  if (facet %like% '".*"') {
-    facet <- substr(facet, 2, nchar(facet) - 1)
-  }
-
-  if (tolower(facet) %in% tolower(c("SIR", "sir", "interpretations", "result"))) {
-    facet <- "interpretation"
-  } else if (tolower(facet) %in% tolower(c("ab", "abx", "antibiotics"))) {
-    facet <- "antibiotic"
-  }
-
-  ggplot2::facet_wrap(facets = facet, scales = "free_x", nrow = nrow)
-}
-
-#' @rdname ggplot_sir
-#' @export
-scale_y_percent <- function(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1), limits = NULL) {
-  stop_ifnot_installed("ggplot2")
-  meet_criteria(breaks, allow_class = c("numeric", "integer", "function"))
-  meet_criteria(limits, allow_class = c("numeric", "integer"), has_length = 2, allow_NULL = TRUE, allow_NA = TRUE)
-
-  if (!is.function(breaks) && all(breaks[breaks != 0] > 1)) {
-    breaks <- breaks / 100
-  }
-  ggplot2::scale_y_continuous(
-    breaks = breaks,
-    labels = if (is.function(breaks)) function(x) percentage(breaks(x)) else percentage(breaks),
-    limits = limits
-  )
-}
-
-#' @rdname ggplot_sir
-#' @export
-scale_sir_colours <- function(...,
-                              aesthetics = "fill") {
-  stop_ifnot_installed("ggplot2")
-  meet_criteria(aesthetics, allow_class = "character", is_in = c("alpha", "colour", "color", "fill", "linetype", "shape", "size"))
-  # behaviour until AMR pkg v1.5.0 and also when coming from ggplot_sir()
-  if ("colours" %in% names(list(...))) {
-    original_cols <- c(
-      S = "#3CAEA3",
-      SI = "#3CAEA3",
-      I = "#F6D55C",
-      IR = "#ED553B",
-      R = "#ED553B"
-    )
-    colours <- replace(original_cols, names(list(...)$colours), list(...)$colours)
-    # limits = force is needed in ggplot2 3.3.4 and 3.3.5, see here;
-    # https://github.com/tidyverse/ggplot2/issues/4511#issuecomment-866185530
-    return(ggplot2::scale_fill_manual(values = colours, limits = force))
-  }
-  if (identical(unlist(list(...)), FALSE)) {
-    return(invisible())
-  }
-
-  names_susceptible <- c(
-    "S", "SI", "IS", "S+I", "I+S", "susceptible", "Susceptible",
-    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Susceptible"),
-      "replacement",
-      drop = TRUE
-    ])
-  )
-  names_incr_exposure <- c(
-    "I", "intermediate", "increased exposure", "incr. exposure",
-    "Increased exposure", "Incr. exposure", "Susceptible, incr. exp.",
-    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Intermediate"),
-      "replacement",
-      drop = TRUE
-    ]),
-    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Susceptible, incr. exp."),
-      "replacement",
-      drop = TRUE
-    ])
-  )
-  names_resistant <- c(
-    "R", "IR", "RI", "R+I", "I+R", "resistant", "Resistant",
-    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Resistant"),
-      "replacement",
-      drop = TRUE
-    ])
-  )
-
-  susceptible <- rep("#3CAEA3", length(names_susceptible))
-  names(susceptible) <- names_susceptible
-  incr_exposure <- rep("#F6D55C", length(names_incr_exposure))
-  names(incr_exposure) <- names_incr_exposure
-  resistant <- rep("#ED553B", length(names_resistant))
-  names(resistant) <- names_resistant
-
-  original_cols <- c(susceptible, incr_exposure, resistant)
-  dots <- c(...)
-  # replace S, I, R as colours: scale_sir_colours(mydatavalue = "S")
-  dots[dots == "S"] <- "#3CAEA3"
-  dots[dots == "I"] <- "#F6D55C"
-  dots[dots == "R"] <- "#ED553B"
-  cols <- replace(original_cols, names(dots), dots)
-  # limits = force is needed in ggplot2 3.3.4 and 3.3.5, see here;
-  # https://github.com/tidyverse/ggplot2/issues/4511#issuecomment-866185530
-  ggplot2::scale_discrete_manual(aesthetics = aesthetics, values = cols, limits = force)
-}
-
-#' @rdname ggplot_sir
-#' @export
-theme_sir <- function() {
-  stop_ifnot_installed("ggplot2")
-  ggplot2::theme_minimal(base_size = 10) +
-    ggplot2::theme(
-      panel.grid.major.x = ggplot2::element_blank(),
-      panel.grid.minor = ggplot2::element_blank(),
-      panel.grid.major.y = ggplot2::element_line(colour = "grey75"),
-      # center title and subtitle
-      plot.title = ggplot2::element_text(hjust = 0.5),
-      plot.subtitle = ggplot2::element_text(hjust = 0.5)
-    )
-}
-
-#' @rdname ggplot_sir
-#' @export
-labels_sir_count <- function(position = NULL,
-                             x = "antibiotic",
-                             translate_ab = "name",
-                             minimum = 30,
-                             language = get_AMR_locale(),
-                             combine_SI = TRUE,
-                             datalabels.size = 3,
-                             datalabels.colour = "grey15") {
-  stop_ifnot_installed("ggplot2")
-  meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE)
-  meet_criteria(x, allow_class = "character", has_length = 1)
-  meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
-  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
-  language <- validate_language(language)
-  meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
-  meet_criteria(datalabels.size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
-  meet_criteria(datalabels.colour, allow_class = "character", has_length = 1)
-
-  if (is.null(position)) {
-    position <- "fill"
-  }
-  if (identical(position, "fill")) {
-    position <- ggplot2::position_fill(vjust = 0.5, reverse = TRUE)
-  }
-  x_name <- x
-  ggplot2::geom_text(
-    mapping = ggplot2::aes_string(
-      label = "lbl",
-      x = x,
-      y = "value"
-    ),
-    position = position,
-    inherit.aes = FALSE,
-    size = datalabels.size,
-    colour = datalabels.colour,
-    lineheight = 0.75,
-    data = function(x) {
-      transformed <- sir_df(
-        data = x,
-        translate_ab = translate_ab,
-        combine_SI = combine_SI,
-        minimum = minimum,
-        language = language
-      )
-      transformed$gr <- transformed[, x_name, drop = TRUE]
-      transformed %pm>%
-        pm_group_by(gr) %pm>%
-        pm_mutate(lbl = paste0("n=", isolates)) %pm>%
-        pm_ungroup() %pm>%
-        pm_select(-gr)
-    }
-  )
-}

R/plotting.R

@@ -27,7 +27,7 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #
 
-#' Plotting for Classes `sir`, `mic` and `disk`
+#' Plotting Helpers for AMR Data Analysis
 #'
 #' @description
 #' Functions to plot classes `sir`, `mic` and `disk`, with support for base \R and `ggplot2`.
@@ -49,6 +49,16 @@
 #' For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the `guideline` argument are: `r vector_and(AMR::clinical_breakpoints$guideline, quotes = TRUE, reverse = TRUE)`.
 #'
 #' Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline.
+#' 
+#' ### Additional `ggplot2` Functions
+#' 
+#' This package contains several functions that extend the `ggplot2` package, to help in visualising AMR data results. All these functions are internally used by [ggplot_sir()] too.
+#' 
+#' * [facet_sir()] creates 2d plots (at default based on S/I/R) using [ggplot2::facet_wrap()].
+#' * [scale_y_percent()] transforms the y axis to a 0 to 100% range using [ggplot2::scale_y_continuous()].
+#' * [scale_sir_colours()] sets colours to the bars (green for S, yellow for I, and red for R). Has multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
+#' * [theme_sir()] is a [ggplot2 theme][[ggplot2::theme()] with minimal distraction.
+#' * [labels_sir_count()] print datalabels on the bars with percentage and number of isolates, using [ggplot2::geom_text()].
 #' @name plot
 #' @rdname plot
 #' @return The `autoplot()` functions return a [`ggplot`][ggplot2::ggplot()] model that is extendible with any `ggplot2` function.
@@ -915,3 +925,192 @@ plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_SIR, f
   
   list(cols = cols, count = as.double(x), sub = sub, guideline = guideline)
 }
+
+#' @rdname plot
+#' @export
+facet_sir <- function(facet = c("interpretation", "antibiotic"), nrow = NULL) {
+  facet <- facet[1]
+  stop_ifnot_installed("ggplot2")
+  meet_criteria(facet, allow_class = "character", has_length = 1)
+  meet_criteria(nrow, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
+  
+  # we work with aes_string later on
+  facet_deparse <- deparse(substitute(facet))
+  if (facet_deparse != "facet") {
+    facet <- facet_deparse
+  }
+  if (facet %like% '".*"') {
+    facet <- substr(facet, 2, nchar(facet) - 1)
+  }
+  
+  if (tolower(facet) %in% tolower(c("SIR", "sir", "interpretations", "result"))) {
+    facet <- "interpretation"
+  } else if (tolower(facet) %in% tolower(c("ab", "abx", "antibiotics"))) {
+    facet <- "antibiotic"
+  }
+  
+  ggplot2::facet_wrap(facets = facet, scales = "free_x", nrow = nrow)
+}
+
+#' @rdname plot
+#' @export
+scale_y_percent <- function(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1), limits = NULL) {
+  stop_ifnot_installed("ggplot2")
+  meet_criteria(breaks, allow_class = c("numeric", "integer", "function"))
+  meet_criteria(limits, allow_class = c("numeric", "integer"), has_length = 2, allow_NULL = TRUE, allow_NA = TRUE)
+  
+  if (!is.function(breaks) && all(breaks[breaks != 0] > 1)) {
+    breaks <- breaks / 100
+  }
+  ggplot2::scale_y_continuous(
+    breaks = breaks,
+    labels = if (is.function(breaks)) function(x) percentage(breaks(x)) else percentage(breaks),
+    limits = limits
+  )
+}
+
+#' @rdname plot
+#' @export
+scale_sir_colours <- function(...,
+                              aesthetics = "fill",
+                              colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B")) {
+  stop_ifnot_installed("ggplot2")
+  meet_criteria(aesthetics, allow_class = "character", is_in = c("alpha", "colour", "color", "fill", "linetype", "shape", "size"))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  if (length(colours_SIR) == 1) {
+    colours_SIR <- rep(colours_SIR, 3)
+  }
+  # behaviour until AMR pkg v1.5.0 and also when coming from ggplot_sir()
+  if ("colours" %in% names(list(...))) {
+    original_cols <- c(
+      S = colours_SIR[1],
+      SI = colours_SIR[1],
+      I = colours_SIR[2],
+      IR = colours_SIR[3],
+      R = colours_SIR[3]
+    )
+    colours <- replace(original_cols, names(list(...)$colours), list(...)$colours)
+    # limits = force is needed in ggplot2 3.3.4 and 3.3.5, see here;
+    # https://github.com/tidyverse/ggplot2/issues/4511#issuecomment-866185530
+    return(ggplot2::scale_fill_manual(values = colours, limits = force))
+  }
+  if (identical(unlist(list(...)), FALSE)) {
+    return(invisible())
+  }
+  
+  names_susceptible <- c(
+    "S", "SI", "IS", "S+I", "I+S", "susceptible", "Susceptible",
+    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Susceptible"),
+                        "replacement",
+                        drop = TRUE
+    ])
+  )
+  names_incr_exposure <- c(
+    "I", "intermediate", "increased exposure", "incr. exposure",
+    "Increased exposure", "Incr. exposure", "Susceptible, incr. exp.",
+    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Intermediate"),
+                        "replacement",
+                        drop = TRUE
+    ]),
+    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Susceptible, incr. exp."),
+                        "replacement",
+                        drop = TRUE
+    ])
+  )
+  names_resistant <- c(
+    "R", "IR", "RI", "R+I", "I+R", "resistant", "Resistant",
+    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Resistant"),
+                        "replacement",
+                        drop = TRUE
+    ])
+  )
+  
+  susceptible <- rep(colours_SIR[1], length(names_susceptible))
+  names(susceptible) <- names_susceptible
+  incr_exposure <- rep(colours_SIR[2], length(names_incr_exposure))
+  names(incr_exposure) <- names_incr_exposure
+  resistant <- rep(colours_SIR[3], length(names_resistant))
+  names(resistant) <- names_resistant
+  
+  original_cols <- c(susceptible, incr_exposure, resistant)
+  dots <- c(...)
+  # replace S, I, R as colours: scale_sir_colours(mydatavalue = "S")
+  dots[dots == "S"] <- colours_SIR[1]
+  dots[dots == "I"] <- colours_SIR[2]
+  dots[dots == "R"] <- colours_SIR[3]
+  cols <- replace(original_cols, names(dots), dots)
+  # limits = force is needed in ggplot2 3.3.4 and 3.3.5, see here;
+  # https://github.com/tidyverse/ggplot2/issues/4511#issuecomment-866185530
+  ggplot2::scale_discrete_manual(aesthetics = aesthetics, values = cols, limits = force)
+}
+
+#' @rdname plot
+#' @export
+theme_sir <- function() {
+  stop_ifnot_installed("ggplot2")
+  ggplot2::theme_minimal(base_size = 10) +
+    ggplot2::theme(
+      panel.grid.major.x = ggplot2::element_blank(),
+      panel.grid.minor = ggplot2::element_blank(),
+      panel.grid.major.y = ggplot2::element_line(colour = "grey75"),
+      # center title and subtitle
+      plot.title = ggplot2::element_text(hjust = 0.5),
+      plot.subtitle = ggplot2::element_text(hjust = 0.5)
+    )
+}
+
+#' @rdname plot
+#' @export
+labels_sir_count <- function(position = NULL,
+                             x = "antibiotic",
+                             translate_ab = "name",
+                             minimum = 30,
+                             language = get_AMR_locale(),
+                             combine_SI = TRUE,
+                             datalabels.size = 3,
+                             datalabels.colour = "grey15") {
+  stop_ifnot_installed("ggplot2")
+  meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE)
+  meet_criteria(x, allow_class = "character", has_length = 1)
+  meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
+  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
+  language <- validate_language(language)
+  meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
+  meet_criteria(datalabels.size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(datalabels.colour, allow_class = "character", has_length = 1)
+  
+  if (is.null(position)) {
+    position <- "fill"
+  }
+  if (identical(position, "fill")) {
+    position <- ggplot2::position_fill(vjust = 0.5, reverse = TRUE)
+  }
+  x_name <- x
+  ggplot2::geom_text(
+    mapping = ggplot2::aes_string(
+      label = "lbl",
+      x = x,
+      y = "value"
+    ),
+    position = position,
+    inherit.aes = FALSE,
+    size = datalabels.size,
+    colour = datalabels.colour,
+    lineheight = 0.75,
+    data = function(x) {
+      transformed <- sir_df(
+        data = x,
+        translate_ab = translate_ab,
+        combine_SI = combine_SI,
+        minimum = minimum,
+        language = language
+      )
+      transformed$gr <- transformed[, x_name, drop = TRUE]
+      transformed %pm>%
+        pm_group_by(gr) %pm>%
+        pm_mutate(lbl = paste0("n=", isolates)) %pm>%
+        pm_ungroup() %pm>%
+        pm_select(-gr)
+    }
+  )
+}

data-raw/_generate_python_wrapper.sh

@@ -274,7 +274,7 @@ setup(
         'numpy',
         'pandas',
     ],
-    author='Dr. Matthijs Berends',
+    author='Matthijs Berends',
     author_email='m.s.berends@umcg.nl',
     description='A Python wrapper for the AMR R package',
     long_description=open('README.md').read(),

data-raw/gpt_training_text_v2.1.1.9118.txt

@@ -1,5 +1,5 @@
 This files contains all context you must know about the AMR package for R.
-First and foremost, you are trained on version 2.1.1.9117. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9118. Remember this whenever someone asks which AMR package version you’re at.
 --------------------------------
 
 THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'NAMESPACE':
@@ -5365,11 +5365,6 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/ggplot_sir.Rd':
 \name{ggplot_sir}
 \alias{ggplot_sir}
 \alias{geom_sir}
-\alias{facet_sir}
-\alias{scale_y_percent}
-\alias{scale_sir_colours}
-\alias{theme_sir}
-\alias{labels_sir_count}
 \title{AMR Plots with \code{ggplot2}}
 \usage{
 ggplot_sir(
@@ -5408,28 +5403,6 @@ geom_sir(
   combine_SI = TRUE,
   ...
 )
-
-facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
-
-scale_y_percent(
-  breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
-  limits = NULL
-)
-
-scale_sir_colours(..., aesthetics = "fill")
-
-theme_sir()
-
-labels_sir_count(
-  position = NULL,
-  x = "antibiotic",
-  translate_ab = "name",
-  minimum = 30,
-  language = get_AMR_locale(),
-  combine_SI = TRUE,
-  datalabels.size = 3,
-  datalabels.colour = "grey15"
-)
 }
 \arguments{
 \item{data}{a \link{data.frame} with column(s) of class \code{\link{sir}} (see \code{\link[=as.sir]{as.sir()}})}
@@ -5483,23 +5456,20 @@ Use these functions to create bar plots for AMR data analysis. All functions rel
 }
 \details{
 At default, the names of antibiotics will be shown on the plots using \code{\link[=ab_name]{ab_name()}}. This can be set with the \code{translate_ab} argument. See \code{\link[=count_df]{count_df()}}.
-\subsection{The Functions}{
 
 \code{\link[=geom_sir]{geom_sir()}} will take any variable from the data that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) using \code{\link[=sir_df]{sir_df()}} and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
 
-\code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
-
-\code{\link[=scale_y_percent]{scale_y_percent()}} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2:scale_continuous]{ggplot2::scale_y_continuous()}}.
-
-\code{\link[=scale_sir_colours]{scale_sir_colours()}} sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
-
-\code{\link[=theme_sir]{theme_sir()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
-
-\code{\link[=labels_sir_count]{labels_sir_count()}} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2:geom_text]{ggplot2::geom_text()}}.
+Additional functions include:
+\itemize{
+\item \code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
+\item \code{\link[=scale_y_percent]{scale_y_percent()}} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2:scale_continuous]{ggplot2::scale_y_continuous()}}.
+\item \code{\link[=scale_sir_colours]{scale_sir_colours()}} sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
+\item \code{\link[=theme_sir]{theme_sir()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
+\item \code{\link[=labels_sir_count]{labels_sir_count()}} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2:geom_text]{ggplot2::geom_text()}}.
+}
 
 \code{\link[=ggplot_sir]{ggplot_sir()}} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\verb{\%>\%}). See \emph{Examples}.
 }
-}
 \examples{
 \donttest{
 if (require("ggplot2") && require("dplyr")) {
@@ -7437,7 +7407,12 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/plot.Rd':
 \alias{plot.sir}
 \alias{autoplot.sir}
 \alias{fortify.sir}
-\title{Plotting for Classes \code{sir}, \code{mic} and \code{disk}}
+\alias{facet_sir}
+\alias{scale_y_percent}
+\alias{scale_sir_colours}
+\alias{theme_sir}
+\alias{labels_sir_count}
+\title{Plotting Helpers for AMR Data Analysis}
 \usage{
 scale_x_mic(keep_operators = "edges", mic_range = NULL, drop = FALSE, ...)
 
@@ -7535,6 +7510,32 @@ scale_fill_mic(keep_operators = "edges", mic_range = NULL, drop = FALSE, ...)
 )
 
 \method{fortify}{sir}(object, ...)
+
+facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
+
+scale_y_percent(
+  breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
+  limits = NULL
+)
+
+scale_sir_colours(
+  ...,
+  aesthetics = "fill",
+  colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B")
+)
+
+theme_sir()
+
+labels_sir_count(
+  position = NULL,
+  x = "antibiotic",
+  translate_ab = "name",
+  minimum = 30,
+  language = get_AMR_locale(),
+  combine_SI = TRUE,
+  datalabels.size = 3,
+  datalabels.colour = "grey15"
+)
 }
 \arguments{
 \item{keep_operators}{a \link{character} specifying how to handle operators (such as \code{>} and \code{<=}) in the input. Accepts one of three values: \code{"all"} (or \code{TRUE}) to keep all operators, \code{"none"} (or \code{FALSE}) to remove all operators, or \code{"edges"} to keep operators only at both ends of the range.}
@@ -7583,6 +7584,17 @@ The interpretation of "I" will be named "Increased exposure" for all EUCAST guid
 For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2024", "EUCAST 2023", "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2024", "CLSI 2023", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", and "CLSI 2011".
 
 Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline.
+\subsection{Additional \code{ggplot2} Functions}{
+
+This package contains several functions that extend the \code{ggplot2} package, to help in visualising AMR data results. All these functions are internally used by \code{\link[=ggplot_sir]{ggplot_sir()}} too.
+\itemize{
+\item \code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
+\item \code{\link[=scale_y_percent]{scale_y_percent()}} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2:scale_continuous]{ggplot2::scale_y_continuous()}}.
+\item \code{\link[=scale_sir_colours]{scale_sir_colours()}} sets colours to the bars (green for S, yellow for I, and red for R). Has multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
+\item \code{\link[=theme_sir]{theme_sir()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
+\item \code{\link[=labels_sir_count]{labels_sir_count()}} print datalabels on the bars with percentage and number of isolates, using \code{\link[ggplot2:geom_text]{ggplot2::geom_text()}}.
+}
+}
 }
 \examples{
 some_mic_values <- random_mic(size = 100)

man/ggplot_sir.Rd

@@ -3,11 +3,6 @@
 \name{ggplot_sir}
 \alias{ggplot_sir}
 \alias{geom_sir}
-\alias{facet_sir}
-\alias{scale_y_percent}
-\alias{scale_sir_colours}
-\alias{theme_sir}
-\alias{labels_sir_count}
 \title{AMR Plots with \code{ggplot2}}
 \usage{
 ggplot_sir(
@@ -46,28 +41,6 @@ geom_sir(
   combine_SI = TRUE,
   ...
 )
-
-facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
-
-scale_y_percent(
-  breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
-  limits = NULL
-)
-
-scale_sir_colours(..., aesthetics = "fill")
-
-theme_sir()
-
-labels_sir_count(
-  position = NULL,
-  x = "antibiotic",
-  translate_ab = "name",
-  minimum = 30,
-  language = get_AMR_locale(),
-  combine_SI = TRUE,
-  datalabels.size = 3,
-  datalabels.colour = "grey15"
-)
 }
 \arguments{
 \item{data}{a \link{data.frame} with column(s) of class \code{\link{sir}} (see \code{\link[=as.sir]{as.sir()}})}
@@ -121,23 +94,20 @@ Use these functions to create bar plots for AMR data analysis. All functions rel
 }
 \details{
 At default, the names of antibiotics will be shown on the plots using \code{\link[=ab_name]{ab_name()}}. This can be set with the \code{translate_ab} argument. See \code{\link[=count_df]{count_df()}}.
-\subsection{The Functions}{
 
 \code{\link[=geom_sir]{geom_sir()}} will take any variable from the data that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) using \code{\link[=sir_df]{sir_df()}} and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
 
-\code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
-
-\code{\link[=scale_y_percent]{scale_y_percent()}} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2:scale_continuous]{ggplot2::scale_y_continuous()}}.
-
-\code{\link[=scale_sir_colours]{scale_sir_colours()}} sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
-
-\code{\link[=theme_sir]{theme_sir()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
-
-\code{\link[=labels_sir_count]{labels_sir_count()}} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2:geom_text]{ggplot2::geom_text()}}.
+Additional functions include:
+\itemize{
+\item \code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
+\item \code{\link[=scale_y_percent]{scale_y_percent()}} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2:scale_continuous]{ggplot2::scale_y_continuous()}}.
+\item \code{\link[=scale_sir_colours]{scale_sir_colours()}} sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
+\item \code{\link[=theme_sir]{theme_sir()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
+\item \code{\link[=labels_sir_count]{labels_sir_count()}} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2:geom_text]{ggplot2::geom_text()}}.
+}
 
 \code{\link[=ggplot_sir]{ggplot_sir()}} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\verb{\%>\%}). See \emph{Examples}.
 }
-}
 \examples{
 \donttest{
 if (require("ggplot2") && require("dplyr")) {

man/plot.Rd

@@ -15,7 +15,12 @@
 \alias{plot.sir}
 \alias{autoplot.sir}
 \alias{fortify.sir}
-\title{Plotting for Classes \code{sir}, \code{mic} and \code{disk}}
+\alias{facet_sir}
+\alias{scale_y_percent}
+\alias{scale_sir_colours}
+\alias{theme_sir}
+\alias{labels_sir_count}
+\title{Plotting Helpers for AMR Data Analysis}
 \usage{
 scale_x_mic(keep_operators = "edges", mic_range = NULL, drop = FALSE, ...)
 
@@ -113,6 +118,32 @@ scale_fill_mic(keep_operators = "edges", mic_range = NULL, drop = FALSE, ...)
 )
 
 \method{fortify}{sir}(object, ...)
+
+facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
+
+scale_y_percent(
+  breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
+  limits = NULL
+)
+
+scale_sir_colours(
+  ...,
+  aesthetics = "fill",
+  colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B")
+)
+
+theme_sir()
+
+labels_sir_count(
+  position = NULL,
+  x = "antibiotic",
+  translate_ab = "name",
+  minimum = 30,
+  language = get_AMR_locale(),
+  combine_SI = TRUE,
+  datalabels.size = 3,
+  datalabels.colour = "grey15"
+)
 }
 \arguments{
 \item{keep_operators}{a \link{character} specifying how to handle operators (such as \code{>} and \code{<=}) in the input. Accepts one of three values: \code{"all"} (or \code{TRUE}) to keep all operators, \code{"none"} (or \code{FALSE}) to remove all operators, or \code{"edges"} to keep operators only at both ends of the range.}
@@ -161,6 +192,17 @@ The interpretation of "I" will be named "Increased exposure" for all EUCAST guid
 For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2024", "EUCAST 2023", "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2024", "CLSI 2023", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", and "CLSI 2011".
 
 Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline.
+\subsection{Additional \code{ggplot2} Functions}{
+
+This package contains several functions that extend the \code{ggplot2} package, to help in visualising AMR data results. All these functions are internally used by \code{\link[=ggplot_sir]{ggplot_sir()}} too.
+\itemize{
+\item \code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
+\item \code{\link[=scale_y_percent]{scale_y_percent()}} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2:scale_continuous]{ggplot2::scale_y_continuous()}}.
+\item \code{\link[=scale_sir_colours]{scale_sir_colours()}} sets colours to the bars (green for S, yellow for I, and red for R). Has multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
+\item \code{\link[=theme_sir]{theme_sir()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
+\item \code{\link[=labels_sir_count]{labels_sir_count()}} print datalabels on the bars with percentage and number of isolates, using \code{\link[ggplot2:geom_text]{ggplot2::geom_text()}}.
+}
+}
 }
 \examples{
 some_mic_values <- random_mic(size = 100)

pkgdown/extra.css

@@ -213,6 +213,11 @@ pre .co, .co {
   color: var(--amr-green-dark) !important;
   font-style: italic !important;
 }
+div.sourceCode  pre .co,
+div.sourceCode .co {
+  /* comments in example sections, since functions are already green too */
+  color: var(--bs-gray-600) !important;
+}
 pre code .r-out,
 pre code .r-msg {
   /* output of functions */