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Fix for antibiogram() in R <=3.4

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dr. M.S. (Matthijs) Berends 2023-02-13 10:21:43 +01:00
parent 45a9697c84
commit b6d2b1398d
5 changed files with 40 additions and 37 deletions
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 1.8.2.9121
Date: 2023-02-12
Version: 1.8.2.9122
Date: 2023-02-13
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

2
NEWS.md
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@ -1,4 +1,4 @@
# AMR 1.8.2.9121
# AMR 1.8.2.9122
*(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*

44
R/antibiogram.R
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@ -29,7 +29,7 @@
#' Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA)
#'
#' Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker *et al.* (2021, \doi{10.1177/20499361211011373}) and Barbieri *et al.* (2021, \doi{10.1186/s13756-021-00939-2}), and allow reporting in e.g. R Markdown and Quarto as well.
#' Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker *et al.* and Barbieri *et al.* (see *Source*), and allow reporting in e.g. R Markdown and Quarto as well.
#' @param x a [data.frame] containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see [as.sir()])
#' @param antibiotics vector of column names, or (any combinations of) [antibiotic selectors][antibiotic_class_selectors] such as [aminoglycosides()] or [carbapenems()]. For combination antibiograms, this can also be column names separated with `"+"`, such as "TZP+TOB" given that the data set contains columns "TZP" and "TOB". See *Examples*.
#' @param mo_transform a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, quotes = TRUE)`. Can also be `NULL` to not transform the input.
@ -92,10 +92,14 @@
#' Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' syndromic_group = ifelse(your_data$age >= 65 & your_data$gender == "Male",
#' "Group 1", "Group 2"))
#' library(dplyr)
#' your_data %>%
#' filter(ward == "ICU" & specimen_type == "Respiratory") %>%
#' antibiogram(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' syndromic_group = ifelse(.$age >= 65 &
#' .$gender == "Male" &
#' .$condition == "Heart Disease",
#' "Study Group", "Control Group"))
#' ```
#'
#' All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using [ggplot2::autoplot()] or base \R [plot()]/[barplot()]) or printed into R Markdown / Quarto formats for reports. Use functions from specific 'table reporting' packages to transform the output of [antibiogram()] to your needs, e.g. `flextable::as_flextable()` or `gt::gt()`.
@ -193,11 +197,11 @@
#'
#' # Weighted-incidence syndromic combination antibiogram (WISCA) ---------
#'
#' # the data set could contain a filter for e.g. respiratory specimens
#' # the data set could contain a filter for e.g. respiratory specimens/ICU
#' antibiogram(example_isolates,
#' antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain",
#' minimum = 10, # this should be >= 30, but now just as example
#' minimum = 10, # this should be >=30, but now just as example
#' syndromic_group = ifelse(example_isolates$age >= 65 &
#' example_isolates$gender == "M",
#' "WISCA Group 1", "WISCA Group 2"
@ -348,7 +352,18 @@ antibiogram <- function(x,
FUN = function(x) x
)
counts <- out
if (isTRUE(combine_SI)) {
out$numerator <- out$S + out$I
} else {
out$numerator <- out$S
}
if (any(out$total < minimum, na.rm = TRUE)) {
message_("NOTE: ", sum(out$total < minimum, na.rm = TRUE), " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
out <- out %pm>%
subset(total >= minimum)
}
# regroup for summarising
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
@ -358,19 +373,6 @@ antibiogram <- function(x,
out <- out %pm>%
pm_group_by(mo, ab)
}
if (isTRUE(combine_SI)) {
out$numerator <- out$S + out$I
} else {
out$numerator <- out$S
}
out$minimum <- minimum
if (any(out$total < out$minimum, na.rm = TRUE)) {
message_("NOTE: ", sum(out$total < out$minimum, na.rm = TRUE), " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
out <- out %pm>%
subset(total >= minimum)
}
out <- out %pm>%
pm_summarise(SI = numerator / total)

9
inst/tinytest/test-mo.R
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@ -124,8 +124,8 @@ expect_identical(as.character(as.mo(" ")), NA_character_)
expect_warning(as.mo("ab"))
expect_identical(
suppressWarnings(as.character(as.mo(c("Qq species", "", "MRSA", "K. pneu rhino", "esco")))),
c("UNKNOWN", NA_character_, "B_STPHY_AURS", "B_KLBSL_PNMN_RHNS", "B_ESCHR_COLI")
suppressWarnings(as.character(as.mo(c("Qq species", "MRSA", "K. pneu rhino", "esco")))),
c("UNKNOWN", "B_STPHY_AURS", "B_KLBSL_PNMN_RHNS", "B_ESCHR_COLI")
)
# check for Becker classification
@ -317,10 +317,7 @@ expect_warning(x[[1]] <- "invalid code")
expect_warning(c(x[1], "test"))
# ignoring patterns
expect_identical(
as.character(as.mo(c("E. coli", "E. coli ignorethis"), ignore_pattern = "this")),
c("B_ESCHR_COLI", NA)
)
expect_true(is.na(as.mo("E. coli ignorethis", ignore_pattern = "this")))
# frequency tables
if (AMR:::pkg_is_available("cleaner")) {

18
man/antibiogram.Rd
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@ -70,7 +70,7 @@ antibiogram(
\item{as_kable}{a \link{logical} to indicate whether the printing should be done using \code{\link[knitr:kable]{knitr::kable()}} (which is the default in non-interactive sessions)}
}
\description{
Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}) and Barbieri \emph{et al.} (2021, \doi{10.1186/s13756-021-00939-2}), and allow reporting in e.g. R Markdown and Quarto as well.
Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker \emph{et al.} and Barbieri \emph{et al.} (see \emph{Source}), and allow reporting in e.g. R Markdown and Quarto as well.
}
\details{
This function returns a table with values between 0 and 100 for \emph{susceptibility}, not resistance.
@ -113,10 +113,14 @@ Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to TZP among respi
Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
syndromic_group = ifelse(your_data$age >= 65 & your_data$gender == "Male",
"Group 1", "Group 2"))
\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(dplyr)
your_data \%>\%
filter(ward == "ICU" & specimen_type == "Respiratory") \%>\%
antibiogram(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
syndromic_group = ifelse(.$age >= 65 &
.$gender == "Male" &
.$condition == "Heart Disease",
"Study Group", "Control Group"))
}\if{html}{\out{</div>}}
}
@ -210,11 +214,11 @@ antibiogram(ex1,
# Weighted-incidence syndromic combination antibiogram (WISCA) ---------
# the data set could contain a filter for e.g. respiratory specimens
# the data set could contain a filter for e.g. respiratory specimens/ICU
antibiogram(example_isolates,
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
minimum = 10, # this should be >= 30, but now just as example
minimum = 10, # this should be >=30, but now just as example
syndromic_group = ifelse(example_isolates$age >= 65 &
example_isolates$gender == "M",
"WISCA Group 1", "WISCA Group 2"