fix for antibiograms on R < 3.5

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 1.8.2.9142
+Version: 1.8.2.9143
-Date: 2023-02-23
+Date: 2023-02-24
 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 1.8.2.9142
+# AMR 1.8.2.9143
 
 *(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
 

R/aa_helper_pm_functions.R

@@ -988,7 +988,7 @@ pm_summarise.default <- function(.data, ...) {
       if (is.list(x_res)) I(x_res) else x_res
     }
   )
-  res <- as.data.frame(res)
+  res <- as.data.frame(res, stringsAsFactors = FALSE)
   fn_names <- names(fns)
   colnames(res) <- if (is.null(fn_names)) fns else fn_names
   if (pm_groups_exist) res <- cbind(group, res, row.names = NULL)

R/antibiogram.R

@@ -50,6 +50,10 @@
 #'
 #' **Remember that you should filter your data to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use [first_isolate()] to determine them in your data set with one of the four available algorithms.
 #' 
+#' All types of antibiograms as listed below can be plotted (using [ggplot2::autoplot()] or base \R [plot()]/[barplot()]). The `antibiogram` object can also be used directly in R Markdown / Quarto (i.e., `knitr`) for reports. In this case, [knitr::kable()] will be applied automatically and microorganism names will even be printed in italics at default (see argument `italicise`). You can also use functions from specific 'table reporting' packages to transform the output of [antibiogram()] to your needs, e.g. with [`as_flextable()`][flextable::as_flextable()] or [`gt()`][gt::gt()].
+#'
+#' ### Antibiogram Types
+#' 
 #' There are four antibiogram types, as proposed by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()]:
 #'
 #' 1. **Traditional Antibiogram**
@@ -103,8 +107,6 @@
 #'                                           "Study Group", "Control Group"))
 #'    ```
 #'
-#' All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using [ggplot2::autoplot()] or base \R [plot()]/[barplot()]) or directly used into R Markdown / Quarto formats for reports (in the last case, [knitr::kable()] will be applied automatically). Use functions from specific 'table reporting' packages to transform the output of [antibiogram()] to your needs, e.g. `flextable::as_flextable()` or `gt::gt()`.
-#'
 #' Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the `only_all_tested` argument (default is `FALSE`). See this example for two antibiotics, Drug A and Drug B, about how [antibiogram()] works to calculate the %SI:
 #'
 #' ```
@@ -125,6 +127,7 @@
 #'   <NA>      <NA>        -            -            -            -
 #' --------------------------------------------------------------------
 #' ```
+#' 
 #' @source
 #' * Klinker KP *et al.* (2021). **Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms**. *Therapeutic Advances in Infectious Disease*, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
 #' * Barbieri E *et al.* (2021). **Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach** *Antimicrobial Resistance & Infection Control* May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
@@ -209,6 +212,7 @@
 #'             )
 #' )
 #' 
+#'
 #' # Print the output for R Markdown / Quarto -----------------------------
 #' 
 #' ureido <- antibiogram(example_isolates,
@@ -504,6 +508,7 @@ antibiogram <- function(x,
   out <- as_original_data_class(new_df, class(x), extra_class = "antibiogram")
   rownames(out) <- NULL
   structure(out,
+    has_syndromic_group = has_syndromic_group,
     long = long,
     combine_SI = combine_SI
   )
@@ -578,7 +583,8 @@ autoplot.antibiogram <- function(object, ...) {
 }
 
 # will be exported in zzz.R
-#' @param italicise a [logical] to indicate whether the microorganism names in the [knitr][knitr::kable()] table should be made italic, using [italicise_taxonomy()]. This only works when the output format is markdown, such as in HTML output.
+#' @method knit_print antibiogram
+#' @param italicise a [logical] to indicate whether the microorganism names in the [knitr][knitr::kable()] table should be made italic, using [italicise_taxonomy()].
 #' @param na character to use for showing `NA` values
 #' @rdname antibiogram
 knit_print.antibiogram <- function(x, italicise = TRUE, na = getOption("knitr.kable.NA", default = ""), ...) {
@@ -586,31 +592,16 @@ knit_print.antibiogram <- function(x, italicise = TRUE, na = getOption("knitr.ka
   meet_criteria(italicise, allow_class = "logical", has_length = 1)
   meet_criteria(na, allow_class = "character", has_length = 1, allow_NA = TRUE)
 
+  if (isTRUE(italicise)) {
+    # make all microorganism names italic, according to nomenclature
+    names_col <- ifelse(isTRUE(attributes(x)$has_syndromic_group), 2, 1)
+    x[[names_col]] <- italicise_taxonomy(x[[names_col]], type = "markdown")
+  }
+
   old_option <- getOption("knitr.kable.NA")
   options(knitr.kable.NA = na)
   on.exit(options(knitr.kable.NA = old_option))
-  out <- knitr::kable(x, ..., output = FALSE)
 
-  format <- attributes(out)$format
+  out <- paste(c("", "", knitr::kable(x, ..., output = FALSE)), collapse = "\n")
-  if (isTRUE(italicise) &&
+  knitr::asis_output(out)
-      !is.null(format) &&
-      format %in% c("markdown", "pipe")) {
-    # try to italicise the output
-    rows_with_txt <- which(out %like% "[a-z]")
-    rows_without_txt <- setdiff(seq_len(length(out)), rows_with_txt)
-    out[rows_with_txt] <- gsub("^[|]", "| ", out[rows_with_txt])
-    # put hyphen directly after second character
-    out[rows_without_txt] <- gsub("^[|](.)", "|\\1-", out[rows_without_txt])
-    out_ita <- italicise_taxonomy(as.character(out), type = "markdown")
-    if (length(unique(nchar(out_ita))) != 1) {
-      # so there has been alterations done by italicise_taxonomy()
-      to_fill <- which(nchar(out_ita) < max(nchar(out_ita)))
-      out_ita[intersect(to_fill, rows_with_txt)] <- gsub("(^[|].*?)([|])(.*)", "\\1  \\2\\3", out_ita[intersect(to_fill, rows_with_txt)], perl = TRUE)
-      out_ita[intersect(to_fill, rows_without_txt)] <- gsub("(^[|].*?)([|])(.*)", "\\1--\\2\\3", out_ita[intersect(to_fill, rows_without_txt)], perl = TRUE)
-    }
-    attributes(out_ita) <- attributes(out)
-    out <- out_ita
-  }
-  res <- paste(c("", "", out), collapse = "\n")
-  knitr::asis_output(res)
 }

R/bug_drug_combinations.R

@@ -39,7 +39,7 @@
 #' @param ... arguments passed on to `FUN`
 #' @inheritParams sir_df
 #' @inheritParams base::formatC
-#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_SI = TRUE` (default) to test R vs. S+I and `combine_SI = FALSE` to test R+I vs. S.
+#' @details The function [format()] calculates the resistance per bug-drug combination and returns a table ready for reporting/publishing. Use `combine_SI = TRUE` (default) to test R vs. S+I and `combine_SI = FALSE` to test R+I vs. S. This table can also directly be used in R Markdown / Quarto without the need for e.g. [knitr::kable()].
 #' @export
 #' @rdname bug_drug_combinations
 #' @return The function [bug_drug_combinations()] returns a [data.frame] with columns "mo", "ab", "S", "I", "R" and "total".
@@ -327,7 +327,15 @@ format.bug_drug_combinations <- function(x,
   }
 
   rownames(y) <- NULL
-  as_original_data_class(y, class(x.bak)) # will remove tibble groups
+  as_original_data_class(y, class(x.bak), extra_class = "formatted_bug_drug_combinations") # will remove tibble groups
+}
+
+# will be exported in zzz.R
+knit_print.formatted_bug_drug_combinations <- function(x, ...) {
+  stop_ifnot_installed("knitr")
+  # make columns with MO names italic according to nomenclature
+  colnames(x)[3:NCOL(x)] <- italicise_taxonomy(colnames(x)[3:NCOL(x)], type = "markdown")
+  knitr::asis_output(paste("", "", knitr::kable(x, ...), collapse = "\n"))
 }
 
 #' @method print bug_drug_combinations

R/zzz.R

@@ -128,8 +128,9 @@ if (utf8_supported && !is_latex) {
   s3_register("ggplot2::fortify", "sir")
   s3_register("ggplot2::fortify", "mic")
   s3_register("ggplot2::fortify", "disk")
-  # Support for knitr / R Markdown
+  # Support for knitr (R Markdown/Quarto)
   s3_register("knitr::knit_print", "antibiogram")
+  s3_register("knitr::knit_print", "formatted_bug_drug_combinations")
   # Support vctrs package for use in e.g. dplyr verbs
   # S3: ab_selector
   s3_register("vctrs::vec_ptype2", "character.ab_selector")

data-raw/antibiograms.Rmd

@@ -2,7 +2,7 @@
 title: "Generating antibiograms with the AMR package"
 author: "AMR package developers"
 date: "`r Sys.Date()`"
-output: html_document
+output: pdf_document
 ---
 
 ```{r setup, include=FALSE}

data-raw/antibiograms.html

@@ -11,7 +11,7 @@
 
 <meta name="author" content="AMR package developers" />
 
-<meta name="date" content="2023-02-23" />
+<meta name="date" content="2023-02-24" />
 
 <title>Generating antibiograms with the AMR package</title>
 
@@ -299,23 +299,17 @@ overflow-y: auto;
 border: 1px solid #ddd;
 border-radius: 4px;
 }
-.tabset-dropdown > .nav-tabs > li.active:before {
+.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
-content: "";
+content: "\e259";
 font-family: 'Glyphicons Halflings';
 display: inline-block;
 padding: 10px;
 border-right: 1px solid #ddd;
 }
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
-content: "";
+content: "\e258";
-border: none;
-}
-.tabset-dropdown > .nav-tabs.nav-tabs-open:before {
-content: "";
 font-family: 'Glyphicons Halflings';
-display: inline-block;
+border: none;
-padding: 10px;
-border-right: 1px solid #ddd;
 }
 .tabset-dropdown > .nav-tabs > li.active {
 display: block;
@@ -359,7 +353,7 @@ display: none;
 <h1 class="title toc-ignore">Generating antibiograms with the AMR
 package</h1>
 <h4 class="author">AMR package developers</h4>
-<h4 class="date">2023-02-23</h4>
+<h4 class="date">2023-02-24</h4>
 
 </div>
 
@@ -370,26 +364,25 @@ package</h1>
 looks like:</p>
 <pre class="r"><code>example_isolates</code></pre>
 <pre><code>## # A tibble: 2,000 × 46
-##    date       patient   age gender ward   mo           PEN  
+##    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
-##    &lt;date&gt;     &lt;chr&gt;   &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt;  &lt;mo&gt;         &lt;sir&gt;
+##    &lt;date&gt;     &lt;chr&gt;   &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt;    &lt;mo&gt;         &lt;sir&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt;
-##  1 2002-01-02 A77334     65 F      Clini… B_ESCHR_COLI R    
+##  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA   
-##  2 2002-01-03 A77334     65 F      Clini… B_ESCHR_COLI R    
+##  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA   
-##  3 2002-01-07 067927     45 F      ICU    B_STPHY_EPDR R    
+##  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
-##  4 2002-01-07 067927     45 F      ICU    B_STPHY_EPDR R    
+##  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
-##  5 2002-01-13 067927     45 F      ICU    B_STPHY_EPDR R    
+##  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
-##  6 2002-01-13 067927     45 F      ICU    B_STPHY_EPDR R    
+##  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
-##  7 2002-01-14 462729     78 M      Clini… B_STPHY_AURS R    
+##  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R    
-##  8 2002-01-14 462729     78 M      Clini… B_STPHY_AURS R    
+##  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R    
-##  9 2002-01-16 067927     45 F      ICU    B_STPHY_EPDR R    
+##  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
-## 10 2002-01-17 858515     79 F      ICU    B_STPHY_EPDR R    
+## 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA   
-## # … with 1,990 more rows, and 39 more variables: OXA &lt;sir&gt;,
+## # … with 1,990 more rows, and 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;,
-## #   FLC &lt;sir&gt;, AMX &lt;sir&gt;, AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;,
+## #   TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;, CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;,
-## #   CZO &lt;sir&gt;, FEP &lt;sir&gt;, CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;,
+## #   CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;, TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;,
-## #   CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;, TOB &lt;sir&gt;, AMK &lt;sir&gt;,
+## #   TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;, FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;,
-## #   KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;, FOS &lt;sir&gt;,
+## #   MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;, TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;,
-## #   LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,
+## #   ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;, IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;,
-## #   TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, …
+## #   CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, RIF &lt;sir&gt;</code></pre>
-## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names</code></pre>
 <div id="traditional-antibiogram" class="section level3">
 <h3>Traditional Antibiogram</h3>
 <pre class="r"><code>antibiogram(example_isolates,
@@ -397,7 +390,7 @@ looks like:</p>
 <table>
 <thead>
 <tr class="header">
-<th align="left">Pathogeen (N min-max)</th>
+<th align="left">Pathogen (N min-max)</th>
 <th align="right">AMK</th>
 <th align="right">GEN</th>
 <th align="right">IPM</th>
@@ -408,7 +401,7 @@ looks like:</p>
 </thead>
 <tbody>
 <tr class="odd">
-<td align="left">CNS (43-309)</td>
+<td align="left">CoNS (43-309)</td>
 <td align="right">0</td>
 <td align="right">86</td>
 <td align="right">52</td>
@@ -507,7 +500,7 @@ looks like:</p>
 <table>
 <thead>
 <tr class="header">
-<th align="left">Pathogeen (N min-max)</th>
+<th align="left">Pathogen (N min-max)</th>
 <th align="right">TZP</th>
 <th align="right">TZP + GEN</th>
 <th align="right">TZP + TOB</th>
@@ -515,7 +508,7 @@ looks like:</p>
 </thead>
 <tbody>
 <tr class="odd">
-<td align="left">CNS (29-274)</td>
+<td align="left">CoNS (29-274)</td>
 <td align="right">30</td>
 <td align="right">97</td>
 <td align="right"></td>
@@ -577,10 +570,20 @@ looks like:</p>
              antibiotics = c(aminoglycosides(), carbapenems()),
              syndromic_group = &quot;ward&quot;)</code></pre>
 <table>
+<colgroup>
+<col width="25%" />
+<col width="37%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+</colgroup>
 <thead>
 <tr class="header">
-<th align="left">Syndroomgroep</th>
+<th align="left">Syndromic Group</th>
-<th align="left">Pathogeen (N min-max)</th>
+<th align="left">Pathogen (N min-max)</th>
 <th align="right">AMK</th>
 <th align="right">GEN</th>
 <th align="right">IPM</th>
@@ -592,7 +595,7 @@ looks like:</p>
 <tbody>
 <tr class="odd">
 <td align="left">Clinical</td>
-<td align="left">CNS (23-205)</td>
+<td align="left">CoNS (23-205)</td>
 <td align="right"></td>
 <td align="right">89</td>
 <td align="right">57</td>
@@ -602,7 +605,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">ICU</td>
-<td align="left">CNS (10-73)</td>
+<td align="left">CoNS (10-73)</td>
 <td align="right"></td>
 <td align="right">79</td>
 <td align="right"></td>
@@ -612,7 +615,7 @@ looks like:</p>
 </tr>
 <tr class="odd">
 <td align="left">Outpatient</td>
-<td align="left">CNS (3-31)</td>
+<td align="left">CoNS (3-31)</td>
 <td align="right"></td>
 <td align="right">84</td>
 <td align="right"></td>
@@ -622,7 +625,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">Clinical</td>
-<td align="left">E. <em>coli</em> (0-299)</td>
+<td align="left"><em>E. coli</em> (0-299)</td>
 <td align="right">100</td>
 <td align="right">98</td>
 <td align="right">100</td>
@@ -632,7 +635,7 @@ looks like:</p>
 </tr>
 <tr class="odd">
 <td align="left">ICU</td>
-<td align="left">E. <em>coli</em> (0-137)</td>
+<td align="left"><em>E. coli</em> (0-137)</td>
 <td align="right">100</td>
 <td align="right">99</td>
 <td align="right">100</td>
@@ -642,7 +645,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">Clinical</td>
-<td align="left">K. <em>pneumoniae</em> (0-51)</td>
+<td align="left"><em>K. pneumoniae</em> (0-51)</td>
 <td align="right"></td>
 <td align="right">92</td>
 <td align="right">100</td>
@@ -652,7 +655,7 @@ looks like:</p>
 </tr>
 <tr class="odd">
 <td align="left">Clinical</td>
-<td align="left">P. <em>mirabilis</em> (0-30)</td>
+<td align="left"><em>P. mirabilis</em> (0-30)</td>
 <td align="right"></td>
 <td align="right">100</td>
 <td align="right"></td>
@@ -662,7 +665,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">Clinical</td>
-<td align="left">S. <em>aureus</em> (2-150)</td>
+<td align="left"><em>S. aureus</em> (2-150)</td>
 <td align="right"></td>
 <td align="right">99</td>
 <td align="right"></td>
@@ -672,7 +675,7 @@ looks like:</p>
 </tr>
 <tr class="odd">
 <td align="left">ICU</td>
-<td align="left">S. <em>aureus</em> (0-66)</td>
+<td align="left"><em>S. aureus</em> (0-66)</td>
 <td align="right"></td>
 <td align="right">100</td>
 <td align="right"></td>
@@ -682,7 +685,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">Clinical</td>
-<td align="left">S. <em>epidermidis</em> (4-79)</td>
+<td align="left"><em>S. epidermidis</em> (4-79)</td>
 <td align="right"></td>
 <td align="right">82</td>
 <td align="right"></td>
@@ -692,7 +695,7 @@ looks like:</p>
 </tr>
 <tr class="odd">
 <td align="left">ICU</td>
-<td align="left">S. <em>epidermidis</em> (4-75)</td>
+<td align="left"><em>S. epidermidis</em> (4-75)</td>
 <td align="right"></td>
 <td align="right">72</td>
 <td align="right"></td>
@@ -702,7 +705,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">Clinical</td>
-<td align="left">S. <em>hominis</em> (1-45)</td>
+<td align="left"><em>S. hominis</em> (1-45)</td>
 <td align="right"></td>
 <td align="right">96</td>
 <td align="right"></td>
@@ -712,7 +715,7 @@ looks like:</p>
 </tr>
 <tr class="odd">
 <td align="left">Clinical</td>
-<td align="left">S. <em>pneumoniae</em> (5-78)</td>
+<td align="left"><em>S. pneumoniae</em> (5-78)</td>
 <td align="right">0</td>
 <td align="right">0</td>
 <td align="right"></td>
@@ -722,7 +725,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">ICU</td>
-<td align="left">S. <em>pneumoniae</em> (5-30)</td>
+<td align="left"><em>S. pneumoniae</em> (5-30)</td>
 <td align="right">0</td>
 <td align="right">0</td>
 <td align="right"></td>
@@ -742,9 +745,9 @@ looks like:</p>
             syndromic_group = ifelse(example_isolates$age &gt;= 65 &amp;
                                        example_isolates$gender == &quot;M&quot;,
                                      &quot;WISCA Group 1&quot;, &quot;WISCA Group 2&quot;))</code></pre>
-<table style="width:100%;">
+<table>
 <colgroup>
-<col width="22%" />
+<col width="23%" />
 <col width="35%" />
 <col width="5%" />
 <col width="14%" />
@@ -753,8 +756,8 @@ looks like:</p>
 </colgroup>
 <thead>
 <tr class="header">
-<th align="left">Syndroomgroep</th>
+<th align="left">Syndromic Group</th>
-<th align="left">Pathogeen (N min-max)</th>
+<th align="left">Pathogen (N min-max)</th>
 <th align="right">AMC</th>
 <th align="right">AMC + CIP</th>
 <th align="right">TZP</th>
@@ -764,7 +767,7 @@ looks like:</p>
 <tbody>
 <tr class="odd">
 <td align="left">WISCA Group 1</td>
-<td align="left">Gram-negatief (261-285)</td>
+<td align="left">Gram-negative (261-285)</td>
 <td align="right">76</td>
 <td align="right">95</td>
 <td align="right">89</td>
@@ -772,7 +775,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">WISCA Group 2</td>
-<td align="left">Gram-negatief (380-442)</td>
+<td align="left">Gram-negative (380-442)</td>
 <td align="right">76</td>
 <td align="right">98</td>
 <td align="right">88</td>
@@ -780,7 +783,7 @@ looks like:</p>
 </tr>
 <tr class="odd">
 <td align="left">WISCA Group 1</td>
-<td align="left">Gram-positief (123-406)</td>
+<td align="left">Gram-positive (123-406)</td>
 <td align="right">76</td>
 <td align="right">89</td>
 <td align="right">81</td>
@@ -788,7 +791,7 @@ looks like:</p>
 </tr>
 <tr class="even">
 <td align="left">WISCA Group 2</td>
-<td align="left">Gram-positief (222-732)</td>
+<td align="left">Gram-positive (222-732)</td>
 <td align="right">76</td>
 <td align="right">89</td>
 <td align="right">88</td>

data-raw/reproduction_of_poorman.R

@@ -107,3 +107,4 @@ contents <- c(
 writeLines(contents, "R/aa_helper_pm_functions.R")
 
 # note: pm_left_join() will be overwritten by aaa_helper_functions.R, which contains a faster implementation
+# replace `res <- as.data.frame(res)` with  `res <- as.data.frame(res, stringsAsFactors = FALSE)`

man/antibiogram.Rd

@@ -35,7 +35,7 @@ antibiogram(
 
 \method{autoplot}{antibiogram}(object, ...)
 
-knit_print.antibiogram(
+\method{knit_print}{antibiogram}(
   x,
   italicise = TRUE,
   na = getOption("knitr.kable.NA", default = ""),
@@ -75,7 +75,7 @@ knit_print.antibiogram(
 
 \item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
 
-\item{italicise}{a \link{logical} to indicate whether the microorganism names in the \link[knitr:kable]{knitr} table should be made italic, using \code{\link[=italicise_taxonomy]{italicise_taxonomy()}}. This only works when the output format is markdown, such as in HTML output.}
+\item{italicise}{a \link{logical} to indicate whether the microorganism names in the \link[knitr:kable]{knitr} table should be made italic, using \code{\link[=italicise_taxonomy]{italicise_taxonomy()}}.}
 
 \item{na}{character to use for showing \code{NA} values}
 }
@@ -87,6 +87,9 @@ This function returns a table with values between 0 and 100 for \emph{susceptibi
 
 \strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set with one of the four available algorithms.
 
+All types of antibiograms as listed below can be plotted (using \code{\link[ggplot2:autoplot]{ggplot2::autoplot()}} or base \R \code{\link[=plot]{plot()}}/\code{\link[=barplot]{barplot()}}). The \code{antibiogram} object can also be used directly in R Markdown / Quarto (i.e., \code{knitr}) for reports. In this case, \code{\link[knitr:kable]{knitr::kable()}} will be applied automatically and microorganism names will even be printed in italics at default (see argument \code{italicise}). You can also use functions from specific 'table reporting' packages to transform the output of \code{\link[=antibiogram]{antibiogram()}} to your needs, e.g. with \code{\link[flextable:as_flextable]{as_flextable()}} or \code{\link[gt:gt]{gt()}}.
+\subsection{Antibiogram Types}{
+
 There are four antibiogram types, as proposed by Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}), and they are all supported by \code{\link[=antibiogram]{antibiogram()}}:
 \enumerate{
 \item \strong{Traditional Antibiogram}
@@ -134,8 +137,6 @@ your_data \%>\%
 }\if{html}{\out{</div>}}
 }
 
-All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using \code{\link[ggplot2:autoplot]{ggplot2::autoplot()}} or base \R \code{\link[=plot]{plot()}}/\code{\link[=barplot]{barplot()}}) or directly used into R Markdown / Quarto formats for reports (in the last case, \code{\link[knitr:kable]{knitr::kable()}} will be applied automatically). Use functions from specific 'table reporting' packages to transform the output of \code{\link[=antibiogram]{antibiogram()}} to your needs, e.g. \code{flextable::as_flextable()} or \code{gt::gt()}.
-
 Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the \code{only_all_tested} argument (default is \code{FALSE}). See this example for two antibiotics, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
@@ -156,6 +157,7 @@ Note that for combination antibiograms, it is important to realise that suscepti
 --------------------------------------------------------------------
 }\if{html}{\out{</div>}}
 }
+}
 \examples{
 # example_isolates is a data set available in the AMR package.
 # run ?example_isolates for more info.
@@ -233,6 +235,7 @@ antibiogram(example_isolates,
             )
 )
 
+
 # Print the output for R Markdown / Quarto -----------------------------
 
 ureido <- antibiogram(example_isolates,

man/bug_drug_combinations.Rd

@@ -55,7 +55,7 @@ The function \code{\link[=bug_drug_combinations]{bug_drug_combinations()}} retur
 Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use \code{\link[=format]{format()}} on the result to prettify it to a publishable/printable format, see \emph{Examples}.
 }
 \details{
-The function \code{\link[=format]{format()}} calculates the resistance per bug-drug combination. Use \code{combine_SI = TRUE} (default) to test R vs. S+I and \code{combine_SI = FALSE} to test R+I vs. S.
+The function \code{\link[=format]{format()}} calculates the resistance per bug-drug combination and returns a table ready for reporting/publishing. Use \code{combine_SI = TRUE} (default) to test R vs. S+I and \code{combine_SI = FALSE} to test R+I vs. S. This table can also directly be used in R Markdown / Quarto without the need for e.g. \code{\link[knitr:kable]{knitr::kable()}}.
 }
 \examples{
 # example_isolates is a data set available in the AMR package.