% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/antibiogram.R
\name{antibiogram}
\alias{antibiogram}
\alias{wisca}
\alias{retrieve_wisca_parameters}
\alias{plot.antibiogram}
\alias{autoplot.antibiogram}
\alias{knit_print.antibiogram}
\title{Generate Traditional, Combination, Syndromic, or WISCA Antibiograms}
\source{
\itemize{
\item Bielicki JA \emph{et al.} (2016). \strong{Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data} \emph{Journal of Antimicrobial Chemotherapy} 71(3); \doi{10.1093/jac/dkv397}
\item Bielicki JA \emph{et al.} (2020). \strong{Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries} \emph{JAMA Netw Open.} 3(2):e1921124; \doi{10.1001.jamanetworkopen.2019.21124}
\item Klinker KP \emph{et al.} (2021). \strong{Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms}. \emph{Therapeutic Advances in Infectious Disease}, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
\item Barbieri E \emph{et al.} (2021). \strong{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} \emph{Antimicrobial Resistance & Infection Control} May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
\item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
}
\usage{
antibiogram(x, antimicrobials = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type",
ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(),
minimum = 30, combine_SI = TRUE, sep = " + ", wisca = FALSE,
simulations = 1000, conf_interval = 0.95, interval_side = "two-tailed",
info = interactive(), ...)
wisca(x, antimicrobials = where(is.sir), ab_transform = "name",
syndromic_group = NULL, only_all_tested = FALSE, digits = 1,
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL, language = get_AMR_locale(), combine_SI = TRUE,
sep = " + ", simulations = 1000, conf_interval = 0.95,
interval_side = "two-tailed", info = interactive(), ...)
retrieve_wisca_parameters(wisca_model, ...)
\method{plot}{antibiogram}(x, ...)
\method{autoplot}{antibiogram}(object, ...)
\method{knit_print}{antibiogram}(x, italicise = TRUE,
na = getOption("knitr.kable.NA", default = ""), ...)
}
\arguments{
\item{x}{A \link{data.frame} containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see \code{\link[=as.sir]{as.sir()}}).}
\item{antimicrobials}{A vector specifying the antimicrobials to include in the antibiogram (see \emph{Examples}). Will be evaluated using \code{\link[=guess_ab_col]{guess_ab_col()}}. This can be:
\itemize{
\item Any antimicrobial name or code that matches to a column name in \code{x}
\item A column name in \code{x} that contains SIR values
\item Any \link[=antimicrobial_selectors]{antimicrobial selector}, such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}
\item A combination of the above, using \code{c()}, e.g.:
\itemize{
\item \code{c(aminoglycosides(), "AMP", "AMC")}
\item \code{c(aminoglycosides(), carbapenems())}
}
\item Combination therapy, indicated by using \code{"+"}, with or without \link[=antimicrobial_selectors]{antimicrobial selectors}, e.g.:
\itemize{
\item \code{"cipro + genta"}
\item \code{"TZP+TOB"}
\item \code{c("TZP", "TZP+GEN", "TZP+TOB")}
\item \code{carbapenems() + "GEN"}
\item \code{carbapenems() + c("", "GEN")}
\item \code{carbapenems() + c("", aminoglycosides())}
}
}}
\item{mo_transform}{A character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input or \code{NA} to consider all microorganisms 'unknown'.}
\item{ab_transform}{A character to transform antimicrobial input - must be one of the column names of the \link{antimicrobials} data set (defaults to \code{"name"}): "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be \code{NULL} to not transform the input.}
\item{syndromic_group}{A column name of \code{x}, or values calculated to split rows of \code{x}, e.g. by using \code{\link[=ifelse]{ifelse()}} or \code{\link[dplyr:case_when]{case_when()}}. See \emph{Examples}.}
\item{add_total_n}{\emph{(deprecated in favour of \code{formatting_type})} A \link{logical} to indicate whether \code{n_tested} available numbers per pathogen should be added to the table (default is \code{TRUE}). This will add the lowest and highest number of available isolates per antimicrobial (e.g, if for \emph{E. coli} 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). This option is unavailable when \code{wisca = TRUE}; in that case, use \code{\link[=retrieve_wisca_parameters]{retrieve_wisca_parameters()}} to get the parameters used for WISCA.}
\item{only_all_tested}{(for combination antibiograms): a \link{logical} to indicate that isolates must be tested for all antimicrobials, see \emph{Details}.}
\item{digits}{Number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise.}
\item{formatting_type}{Numeric value (1–22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See \emph{Details} > \emph{Formatting Type} for a list of options.}
\item{col_mo}{Column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
\item{language}{Language to translate text, which defaults to the system language (see \code{\link[=get_AMR_locale]{get_AMR_locale()}}).}
\item{minimum}{The minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see \emph{Source}.}
\item{combine_SI}{A \link{logical} to indicate whether all susceptibility should be determined by results of either S, SDD, or I, instead of only S (default is \code{TRUE}).}
\item{sep}{A separating character for antimicrobial columns in combination antibiograms.}
\item{wisca}{A \link{logical} to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is \code{FALSE}). This will use a Bayesian decision model to estimate regimen coverage probabilities using \href{https://en.wikipedia.org/wiki/Monte_Carlo_method}{Monte Carlo simulations}. Set \code{simulations}, \code{conf_interval}, and \code{interval_side} to adjust.}
\item{simulations}{(for WISCA) a numerical value to set the number of Monte Carlo simulations.}
\item{conf_interval}{A numerical value to set confidence interval (default is \code{0.95}).}
\item{interval_side}{The side of the confidence interval, either \code{"two-tailed"} (default), \code{"left"} or \code{"right"}.}
\item{info}{A \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode.}
\item{...}{When used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use).}
\item{wisca_model}{The outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}.}
\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()}}.}
\item{na}{Character to use for showing \code{NA} values.}
}
\description{
Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
Adhering to previously described approaches (see \emph{Source}) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki \emph{et al.}, these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.
}
\details{
These functions return a table with values between 0 and 100 for \emph{susceptibility}, not resistance.
\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 with one of the four available algorithms: isolate-based, patient-based, episode-based, or phenotype-based.
For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top \emph{n} species encountered in the data. You can filter on this top \emph{n} using \code{\link[=top_n_microorganisms]{top_n_microorganisms()}}. For example, use \code{top_n_microorganisms(your_data, n = 10)} as a pre-processing step to only include the top 10 species in the data.
The numeric values of an antibiogram are stored in a long format as the \link[=attributes]{attribute} \code{long_numeric}. You can retrieve them using \code{attributes(x)$long_numeric}, where \code{x} is the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}. This is ideal for e.g. advanced plotting.
\subsection{Formatting Type}{
The formatting of the 'cells' of the table can be set with the argument \code{formatting_type}. In these examples, \code{5} indicates the antimicrobial coverage (\code{4-6} the confidence level), \code{15} the number of susceptible isolates, and \code{300} the number of tested (i.e., available) isolates:
\enumerate{
\item 5
\item 15
\item 300
\item 15/300
\item 5 (300)
\item 5\% (300)
\item 5 (N=300)
\item 5\% (N=300)
\item 5 (15/300)
\item 5\% (15/300)
\item 5 (N=15/300)
\item 5\% (N=15/300)
\item 5 (4-6)
\item 5\% (4-6\%) - \strong{default for WISCA}
\item 5 (4-6,300)
\item 5\% (4-6\%,300)
\item 5 (4-6,N=300)
\item 5\% (4-6\%,N=300) - \strong{default for non-WISCA}
\item 5 (4-6,15/300)
\item 5\% (4-6\%,15/300)
\item 5 (4-6,N=15/300)
\item 5\% (4-6\%,N=15/300)
}
The default can be set globally with the package option \code{\link[=AMR-options]{AMR_antibiogram_formatting_type}}, e.g. \code{options(AMR_antibiogram_formatting_type = 5)}. Do note that for WISCA, the total numbers of tested and susceptible isolates are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.
Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibility percentages.
}
\subsection{Antibiogram Types}{
There are various antibiogram types, as summarised by Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}), and they are all supported by \code{\link[=antibiogram]{antibiogram()}}.
For clinical coverage estimations, \strong{use WISCA whenever possible}, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki \emph{et al.} (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section \emph{Explaining WISCA} on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
\enumerate{
\item \strong{Traditional Antibiogram}
Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to piperacillin/tazobactam (TZP)
Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antimicrobials = "TZP")
}\if{html}{\out{
}}
\item \strong{Combination Antibiogram}
Case example: Additional susceptibility of \emph{Pseudomonas aeruginosa} to TZP + tobramycin versus TZP alone
Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{
}}
\item \strong{Syndromic Antibiogram}
Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to TZP among respiratory specimens (obtained among ICU patients only)
Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antimicrobials = penicillins(),
syndromic_group = "ward")
}\if{html}{\out{
}}
\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
WISCA can be applied to any antibiogram, see the section \emph{Explaining WISCA} on this page for more information.
Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
wisca = TRUE)
# this is equal to:
wisca(your_data,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{
}}
WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
}
}
\subsection{Grouped tibbles}{
For any type of antibiogram, grouped \link[tibble:tibble]{tibbles} can also be used to calculate susceptibilities over various groups.
Code example:
\if{html}{\out{}}\preformatted{library(dplyr)
your_data \%>\%
group_by(has_sepsis, is_neonate, sex) \%>\%
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{
}}
}
\subsection{Stepped Approach for Clinical Insight}{
In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:
\enumerate{
\item \strong{Initial Empirical Therapy (Admission / Pre-Culture Data)}
At admission, no pathogen information is available.
\itemize{
\item Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms. Using the pathogen-agnostic yet incidence-weighted WISCA is preferred.
\item Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antimicrobials = selected_regimens,
mo_transform = NA) # all pathogens set to `NA`
# preferred: use WISCA
wisca(your_data,
antimicrobials = selected_regimens)
}\if{html}{\out{
}}
}
\item \strong{Refinement with Gram Stain Results}
When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).
\itemize{
\item Action: narrow coverage based on Gram stain-specific resistance patterns.
\item Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antimicrobials = selected_regimens,
mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
}\if{html}{\out{
}}
}
\item \strong{Definitive Therapy Based on Species Identification}
After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.
\itemize{
\item Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.
\item Code example:
\if{html}{\out{}}\preformatted{antibiogram(your_data,
antimicrobials = selected_regimens,
mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
}\if{html}{\out{
}}
}
}
By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.
}
\subsection{Inclusion in Combination Antibiograms}{
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 antimicrobials, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
\if{html}{\out{}}\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B considered considered considered considered
susceptible tested susceptible tested
-------- -------- ----------- ---------- ----------- ----------
S or I S or I X X X X
R S or I X X X X
S or I X X - -
S or I R X X X X
R R - X - X
R - - - -
S or I X X - -
R - - - -
- - - -
--------------------------------------------------------------------
}\if{html}{\out{
}}
}
\subsection{Plotting}{
All types of antibiograms as listed above can be plotted (using \code{\link[ggplot2:autoplot]{ggplot2::autoplot()}} or base \R's \code{\link[=plot]{plot()}} and \code{\link[=barplot]{barplot()}}). As mentioned above, the numeric values of an antibiogram are stored in a long format as the \link[=attributes]{attribute} \code{long_numeric}. You can retrieve them using \code{attributes(x)$long_numeric}, where \code{x} is the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}.
The outcome of \code{\link[=antibiogram]{antibiogram()}} 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{flextable::as_flextable()} or \code{gt::gt()}.
}
}
\section{Explaining WISCA}{
WISCA, as outlined by Bielicki \emph{et al.} (\doi{10.1093/jac/dkv397}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian decision model with random effects for pathogen incidence and susceptibility, enabling robust estimates in the presence of sparse data.
The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
\deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
\deqn{y \sim \text{Binomial}(n, \theta)}
Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
\deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
Pathogen incidence, representing the proportion of infections caused by different pathogens, is modelled using a Dirichlet distribution, which is the natural conjugate prior for multinomial outcomes. The Dirichlet distribution is parameterised by a vector of concentration parameters \eqn{\alpha}, where each \eqn{\alpha_i} corresponds to a specific pathogen. The prior is typically chosen to be uniform (\eqn{\alpha_i = 1}), reflecting an assumption of equal prior probability across pathogens.
The posterior distribution of pathogen incidence is then given by:
\deqn{\text{Dirichlet}(\alpha_1 + n_1, \alpha_2 + n_2, \dots, \alpha_K + n_K)}
where \eqn{n_i} is the number of infections caused by pathogen \eqn{i} observed in the data. For practical implementation, pathogen incidences are sampled from their posterior using normalised Gamma-distributed random variables:
\deqn{x_i \sim \text{Gamma}(\alpha_i + n_i, 1)}
\deqn{p_i = \frac{x_i}{\sum_{j=1}^K x_j}}
where \eqn{x_i} represents unnormalised pathogen counts, and \eqn{p_i} is the normalised proportion for pathogen \eqn{i}.
For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using \code{dplyr}'s \code{\link[dplyr:group_by]{group_by()}} as a pre-processing step before running \code{\link[=wisca]{wisca()}}. Posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
\deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
By combining empirical data with prior knowledge, WISCA overcomes the limitations of traditional combination antibiograms, offering disease-specific, patient-stratified estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial stewardship programs and empirical treatment guideline refinement.
\strong{Note:} WISCA never gives an output on the pathogen/species level, as all incidences and susceptibilities are already weighted for all species.
}
\examples{
# example_isolates is a data set available in the AMR package.
# run ?example_isolates for more info.
example_isolates
\donttest{
# Traditional antibiogram ----------------------------------------------
antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems())
)
antibiogram(example_isolates,
antimicrobials = aminoglycosides(),
ab_transform = "atc",
mo_transform = "gramstain"
)
antibiogram(example_isolates,
antimicrobials = carbapenems(),
ab_transform = "name",
mo_transform = "name"
)
# Combined antibiogram -------------------------------------------------
# combined antimicrobials yield higher empiric coverage
antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain"
)
# you can use any antimicrobial selector with `+` too:
antibiogram(example_isolates,
antimicrobials = ureidopenicillins() + c("", "GEN", "tobra"),
mo_transform = "gramstain"
)
# names of antimicrobials do not need to resemble columns exactly:
antibiogram(example_isolates,
antimicrobials = c("Cipro", "cipro + genta"),
mo_transform = "gramstain",
ab_transform = "name",
sep = " & "
)
# Syndromic antibiogram ------------------------------------------------
# the data set could contain a filter for e.g. respiratory specimens
antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward"
)
# now define a data set with only E. coli
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
# with a custom language, though this will be determined automatically
# (i.e., this table will be in Spanish on Spanish systems)
antibiogram(ex1,
antimicrobials = aminoglycosides(),
ab_transform = "name",
syndromic_group = ifelse(ex1$ward == "ICU",
"UCI", "No UCI"
),
language = "es"
)
# WISCA antibiogram ----------------------------------------------------
# WISCA are not stratified by species, but rather on syndromes
antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
syndromic_group = "ward",
wisca = TRUE
)
# Print the output for R Markdown / Quarto -----------------------------
ureido <- antibiogram(example_isolates,
antimicrobials = ureidopenicillins(),
syndromic_group = "ward",
wisca = TRUE
)
# in an Rmd file, you would just need to return `ureido` in a chunk,
# but to be explicit here:
if (requireNamespace("knitr")) {
cat(knitr::knit_print(ureido))
}
# Generate plots with ggplot2 or base R --------------------------------
ab1 <- antibiogram(example_isolates,
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain"
)
ab2 <- antibiogram(example_isolates,
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
syndromic_group = "ward"
)
if (requireNamespace("ggplot2")) {
ggplot2::autoplot(ab1)
}
if (requireNamespace("ggplot2")) {
ggplot2::autoplot(ab2)
}
plot(ab1)
plot(ab2)
}
}
\author{
Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
}