mirror of
https://github.com/msberends/AMR.git
synced 2025-07-08 11:11:54 +02:00
new tibble export
This commit is contained in:
11
man/AMR.Rd
11
man/AMR.Rd
@ -35,19 +35,14 @@ This package can be used for:
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
}
|
||||
|
||||
\section{Read more on Our Website!}{
|
||||
|
||||
On our website \url{https://msberends.github.io/AMR/} you can find \href{https://msberends.github.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR data analysis, the \href{https://msberends.github.io/AMR/reference/}{complete documentation of all functions} and \href{https://msberends.github.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\section{Contact Us}{
|
||||
|
||||
For suggestions, comments or questions, please contact us at:
|
||||
For suggestions, comments or questions, please contact us via:
|
||||
|
||||
Matthijs S. Berends \cr
|
||||
Dr. Matthijs S. Berends \cr
|
||||
m.s.berends [at] umcg [dot] nl \cr
|
||||
University of Groningen
|
||||
Department of Medical Microbiology and Infection Prevention \cr
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{WHONET}
|
||||
\title{Data Set with 500 Isolates - WHONET Example}
|
||||
\format{
|
||||
A \link{data.frame} with 500 observations and 53 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 500 observations and 53 variables:
|
||||
\itemize{
|
||||
\item \verb{Identification number}\cr ID of the sample
|
||||
\item \verb{Specimen number}\cr ID of the specimen
|
||||
@ -41,12 +41,10 @@ WHONET
|
||||
\description{
|
||||
This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antibiotic results are from our \link{example_isolates} data set. All patient names are created using online surname generators and are only in place for practice purposes.
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
\details{
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(WHONET)
|
||||
WHONET
|
||||
}
|
||||
\keyword{datasets}
|
||||
|
||||
@ -104,7 +104,7 @@ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{htt
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
|
||||
@ -59,7 +59,7 @@ age_groups(ages, "children")
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
filter_first_isolate() \%>\%
|
||||
filter(mo == as.mo("E. coli")) \%>\%
|
||||
filter(mo == as.mo("Escherichia coli")) \%>\%
|
||||
group_by(age_group = age_groups(age)) \%>\%
|
||||
select(age_group, CIP) \%>\%
|
||||
ggplot_rsi(x = "age_group",
|
||||
|
||||
@ -166,121 +166,153 @@ The \code{\link[=not_intrinsic_resistant]{not_intrinsic_resistant()}} function c
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
# `example_isolates` is a data set available in the AMR package.
|
||||
# See ?example_isolates.
|
||||
df <- example_isolates[ , c("hospital_id", "mo",
|
||||
"AMP", "AMC", "TZP", "CXM", "CRO", "GEN",
|
||||
"TOB", "COL", "IPM", "MEM", "TEC", "VAN")]
|
||||
example_isolates
|
||||
|
||||
# base R ------------------------------------------------------------------
|
||||
|
||||
# select columns 'IPM' (imipenem) and 'MEM' (meropenem)
|
||||
df[, carbapenems()]
|
||||
example_isolates[, carbapenems()]
|
||||
|
||||
# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
|
||||
df[, c("mo", aminoglycosides())]
|
||||
example_isolates[, c("mo", aminoglycosides())]
|
||||
|
||||
# select only antibiotic columns with DDDs for oral treatment
|
||||
df[, administrable_per_os()]
|
||||
example_isolates[, administrable_per_os()]
|
||||
|
||||
# filter using any() or all()
|
||||
df[any(carbapenems() == "R"), ]
|
||||
subset(df, any(carbapenems() == "R"))
|
||||
example_isolates[any(carbapenems() == "R"), ]
|
||||
subset(example_isolates, any(carbapenems() == "R"))
|
||||
|
||||
# filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
|
||||
df[any(carbapenems()), ]
|
||||
df[all(carbapenems()), ]
|
||||
example_isolates[any(carbapenems()), ]
|
||||
example_isolates[all(carbapenems()), ]
|
||||
|
||||
# filter with multiple antibiotic selectors using c()
|
||||
df[all(c(carbapenems(), aminoglycosides()) == "R"), ]
|
||||
example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
|
||||
|
||||
# filter + select in one go: get penicillins in carbapenems-resistant strains
|
||||
df[any(carbapenems() == "R"), penicillins()]
|
||||
example_isolates[any(carbapenems() == "R"), penicillins()]
|
||||
|
||||
# You can combine selectors with '&' to be more specific. For example,
|
||||
# penicillins() would select benzylpenicillin ('peni G') and
|
||||
# administrable_per_os() would select erythromycin. Yet, when combined these
|
||||
# drugs are both omitted since benzylpenicillin is not administrable per os
|
||||
# and erythromycin is not a penicillin:
|
||||
df[, penicillins() & administrable_per_os()]
|
||||
example_isolates[, penicillins() & administrable_per_os()]
|
||||
|
||||
# ab_selector() applies a filter in the `antibiotics` data set and is thus very
|
||||
# flexible. For instance, to select antibiotic columns with an oral DDD of at
|
||||
# least 1 gram:
|
||||
df[, ab_selector(oral_ddd > 1 & oral_units == "g")]
|
||||
example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
|
||||
|
||||
# dplyr -------------------------------------------------------------------
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
|
||||
# get AMR for all aminoglycosides e.g., per hospital:
|
||||
df \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
# get AMR for all aminoglycosides e.g., per ward:
|
||||
example_isolates \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(across(aminoglycosides(), resistance))
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# You can combine selectors with '&' to be more specific:
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
select(penicillins() & administrable_per_os())
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# get AMR for only drugs that matter - no intrinsic resistance:
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
filter(mo_genus() \%in\% c("Escherichia", "Klebsiella")) \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(across(not_intrinsic_resistant(), resistance))
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# get susceptibility for antibiotics whose name contains "trim":
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
filter(first_isolate()) \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(across(ab_selector(name \%like\% "trim"), susceptibility))
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
select(carbapenems())
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
select(mo, aminoglycosides())
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# any() and all() work in dplyr's filter() too:
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
filter(any(aminoglycosides() == "R"),
|
||||
all(cephalosporins_2nd() == "R"))
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# also works with c():
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
filter(any(c(carbapenems(), aminoglycosides()) == "R"))
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# not setting any/all will automatically apply all():
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
filter(aminoglycosides() == "R")
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
select(mo, ab_class("mycobact"))
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# get bug/drug combinations for only glycopeptides in Gram-positives:
|
||||
df \%>\%
|
||||
example_isolates \%>\%
|
||||
filter(mo_is_gram_positive()) \%>\%
|
||||
select(mo, glycopeptides()) \%>\%
|
||||
bug_drug_combinations() \%>\%
|
||||
format()
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
data.frame(some_column = "some_value",
|
||||
J01CA01 = "S") \%>\% # ATC code of ampicillin
|
||||
select(penicillins()) # only the 'J01CA01' column will be selected
|
||||
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# with recent versions of dplyr this is all equal:
|
||||
x <- df[carbapenems() == "R", ]
|
||||
y <- df \%>\% filter(carbapenems() == "R")
|
||||
z <- df \%>\% filter(if_all(carbapenems(), ~.x == "R"))
|
||||
identical(x, y)
|
||||
identical(y, z)
|
||||
x <- example_isolates[carbapenems() == "R", ]
|
||||
y <- example_isolates \%>\% filter(carbapenems() == "R")
|
||||
z <- example_isolates \%>\% filter(if_all(carbapenems(), ~.x == "R"))
|
||||
identical(x, y) && identical(y, z)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
\alias{antivirals}
|
||||
\title{Data Sets with 566 Antimicrobial Drugs}
|
||||
\format{
|
||||
\subsection{For the \link{antibiotics} data set: a \link{data.frame} with 464 observations and 14 variables:}{
|
||||
\subsection{For the \link{antibiotics} data set: a [tibble\link[tibble:tibble]{tibble::tibble} with 464 observations and 14 variables:}{
|
||||
\itemize{
|
||||
\item \code{ab}\cr Antibiotic ID as used in this package (such as \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
|
||||
\item \code{cid}\cr Compound ID as found in PubChem
|
||||
@ -25,7 +25,7 @@
|
||||
}
|
||||
}
|
||||
|
||||
\subsection{For the \link{antivirals} data set: a \link{data.frame} with 102 observations and 9 variables:}{
|
||||
\subsection{For the \link{antivirals} data set: a [tibble\link[tibble:tibble]{tibble::tibble} with 102 observations and 9 variables:}{
|
||||
\itemize{
|
||||
\item \code{atc}\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC
|
||||
\item \code{cid}\cr Compound ID as found in PubChem
|
||||
@ -39,7 +39,7 @@
|
||||
}
|
||||
}
|
||||
|
||||
An object of class \code{data.frame} with 102 rows and 9 columns.
|
||||
An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 102 rows and 9 columns.
|
||||
}
|
||||
\source{
|
||||
World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): \url{https://www.whocc.no/atc_ddd_index/}
|
||||
@ -60,24 +60,9 @@ Properties that are based on an ATC code are only available when an ATC is avail
|
||||
Synonyms (i.e. trade names) were derived from the Compound ID (\code{cid}) and consequently only available where a CID is available.
|
||||
\subsection{Direct download}{
|
||||
|
||||
These data sets are available as 'flat files' for use even without \R - you can find the files here:
|
||||
\itemize{
|
||||
\item \url{https://github.com/msberends/AMR/raw/main/data-raw/antibiotics.txt}
|
||||
\item \url{https://github.com/msberends/AMR/raw/main/data-raw/antivirals.txt}
|
||||
}
|
||||
|
||||
Files in \R format (with preserved data structure) can be found here:
|
||||
\itemize{
|
||||
\item \url{https://github.com/msberends/AMR/raw/main/data/antibiotics.rda}
|
||||
\item \url{https://github.com/msberends/AMR/raw/main/data/antivirals.rda}
|
||||
Like all data sets in this package, these data sets are publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
}
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
}
|
||||
|
||||
\section{WHOCC}{
|
||||
|
||||
\if{html}{\figure{logo_who.png}{options: height="60" style=margin-bottom:"5"} \cr}
|
||||
@ -91,8 +76,8 @@ The WHOCC is located in Oslo at the Norwegian Institute of Public Health and fun
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(antibiotics)
|
||||
head(antivirals)
|
||||
antibiotics
|
||||
antivirals
|
||||
}
|
||||
\seealso{
|
||||
\link{microorganisms}, \link{intrinsic_resistant}
|
||||
|
||||
@ -61,7 +61,7 @@ The WHOCC is located in Oslo at the Norwegian Institute of Public Health and fun
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
|
||||
@ -103,9 +103,16 @@ as.rsi(x = as.mic(c(0.01, 2, 4, 8)),
|
||||
# plot MIC values, see ?plot
|
||||
plot(mic_data)
|
||||
plot(mic_data, mo = "E. coli", ab = "cipro")
|
||||
autoplot(mic_data, mo = "E. coli", ab = "cipro")
|
||||
autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "nl") # Dutch
|
||||
autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "uk") # Ukrainian
|
||||
|
||||
if (require("ggplot2")) {
|
||||
autoplot(mic_data, mo = "E. coli", ab = "cipro")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "nl") # Dutch
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "uk") # Ukrainian
|
||||
}
|
||||
}
|
||||
\seealso{
|
||||
\code{\link[=as.rsi]{as.rsi()}}
|
||||
|
||||
@ -173,7 +173,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
|
||||
@ -156,7 +156,7 @@ This AMR package honours this (new) insight. Use \code{\link[=susceptibility]{su
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
|
||||
@ -25,7 +25,7 @@ availability(example_isolates)
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
filter(mo == as.mo("E. coli")) \%>\%
|
||||
filter(mo == as.mo("Escherichia coli")) \%>\%
|
||||
select_if(is.rsi) \%>\%
|
||||
availability()
|
||||
}
|
||||
|
||||
@ -25,7 +25,7 @@ bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)
|
||||
)
|
||||
}
|
||||
\arguments{
|
||||
\item{x}{data with antibiotic columns, such as \code{amox}, \code{AMX} and \code{AMC}}
|
||||
\item{x}{a data set with antibiotic columns, such as \code{amox}, \code{AMX} and \code{AMC}}
|
||||
|
||||
\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
|
||||
|
||||
@ -74,7 +74,7 @@ bug_drug_combinations(example_isolates,
|
||||
FUN = mo_gramstain)
|
||||
|
||||
bug_drug_combinations(example_isolates,
|
||||
FUN = function(x) ifelse(x == as.mo("E. coli"),
|
||||
FUN = function(x) ifelse(x == as.mo("Escherichia coli"),
|
||||
"E. coli",
|
||||
"Others"))
|
||||
}
|
||||
|
||||
@ -40,13 +40,13 @@ catalogue_of_life_version()
|
||||
mo_shortname("Chlamydophila psittaci")
|
||||
|
||||
# Get any property from the entire taxonomic tree for all included species
|
||||
mo_class("E. coli")
|
||||
mo_class("Escherichia coli")
|
||||
|
||||
mo_family("E. coli")
|
||||
mo_family("Escherichia coli")
|
||||
|
||||
mo_gramstain("E. coli") # based on kingdom and phylum, see ?mo_gramstain
|
||||
mo_gramstain("Escherichia coli") # based on kingdom and phylum, see ?mo_gramstain
|
||||
|
||||
mo_ref("E. coli")
|
||||
mo_ref("Escherichia coli")
|
||||
|
||||
# Do not get mistaken - this package is about microorganisms
|
||||
mo_kingdom("C. elegans")
|
||||
|
||||
@ -155,7 +155,7 @@ susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX)
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(R = count_R(CIP),
|
||||
I = count_I(CIP),
|
||||
S = count_S(CIP),
|
||||
@ -166,7 +166,7 @@ if (require("dplyr")) {
|
||||
# Number of available isolates for a whole antibiotic class
|
||||
# (i.e., in this data set columns GEN, TOB, AMK, KAN)
|
||||
example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(across(aminoglycosides(), n_rsi))
|
||||
|
||||
# Count co-resistance between amoxicillin/clav acid and gentamicin,
|
||||
@ -188,8 +188,8 @@ if (require("dplyr")) {
|
||||
|
||||
# It also supports grouping variables
|
||||
example_isolates \%>\%
|
||||
select(hospital_id, AMX, CIP) \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
select(ward, AMX, CIP) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
count_df(translate = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
custom_eucast_rules(...)
|
||||
}
|
||||
\arguments{
|
||||
\item{...}{rules in \link[=`~`]{formula} notation, see \emph{Examples}}
|
||||
\item{...}{rules in \link[base:tilde]{formula} notation, see \emph{Examples}}
|
||||
}
|
||||
\value{
|
||||
A \link{list} containing the custom rules
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{dosage}
|
||||
\title{Data Set with Treatment Dosages as Defined by EUCAST}
|
||||
\format{
|
||||
A \link{data.frame} with 169 observations and 9 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 169 observations and 9 variables:
|
||||
\itemize{
|
||||
\item \code{ab}\cr Antibiotic ID as used in this package (such as \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
|
||||
\item \code{name}\cr Official name of the antimicrobial agent as used by WHONET/EARS-Net or the WHO
|
||||
@ -25,14 +25,13 @@ dosage
|
||||
EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved with \code{\link[=eucast_dosage]{eucast_dosage()}}.
|
||||
}
|
||||
\details{
|
||||
\href{https://www.eucast.org/clinical_breakpoints/}{'EUCAST Clinical Breakpoint Tables' v11.0} (2021) are based on the dosages in this data set.
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
This data set is based on \href{https://www.eucast.org/clinical_breakpoints/}{'EUCAST Clinical Breakpoint Tables' v11.0} (2021).
|
||||
\subsection{Direct download}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(dosage)
|
||||
dosage
|
||||
}
|
||||
\keyword{datasets}
|
||||
|
||||
@ -36,7 +36,7 @@ eucast_rules(
|
||||
eucast_dosage(ab, administration = "iv", version_breakpoints = 11)
|
||||
}
|
||||
\arguments{
|
||||
\item{x}{data with antibiotic columns, such as \code{amox}, \code{AMX} and \code{AMC}}
|
||||
\item{x}{a data set with antibiotic columns, such as \code{amox}, \code{AMX} and \code{AMC}}
|
||||
|
||||
\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
|
||||
|
||||
@ -83,26 +83,26 @@ Custom rules can be created using \code{\link[=custom_eucast_rules]{custom_eucas
|
||||
AMC == "I" & genus == "Klebsiella" ~ aminopenicillins == "I")
|
||||
|
||||
eucast_rules(example_isolates, rules = "custom", custom_rules = x, info = FALSE)
|
||||
#> # A tibble: 2,000 x 49
|
||||
#> date hospit~1 ward_~2 ward_~3 ward_~4 age gender patie~5 mo
|
||||
#> * <date> <fct> <lgl> <lgl> <lgl> <dbl> <chr> <chr> <mo>
|
||||
#> 1 2002-01-02 D FALSE TRUE FALSE 65 F A77334 B_ESCHR_COLI
|
||||
#> 2 2002-01-03 D FALSE TRUE FALSE 65 F A77334 B_ESCHR_COLI
|
||||
#> 3 2002-01-07 B TRUE FALSE FALSE 45 F 067927 B_STPHY_EPDR
|
||||
#> 4 2002-01-07 B TRUE FALSE FALSE 45 F 067927 B_STPHY_EPDR
|
||||
#> 5 2002-01-13 B TRUE FALSE FALSE 45 F 067927 B_STPHY_EPDR
|
||||
#> 6 2002-01-13 B TRUE FALSE FALSE 45 F 067927 B_STPHY_EPDR
|
||||
#> 7 2002-01-14 D FALSE TRUE FALSE 78 M 462729 B_STPHY_AURS
|
||||
#> 8 2002-01-14 D FALSE TRUE FALSE 78 M 462729 B_STPHY_AURS
|
||||
#> 9 2002-01-16 B TRUE FALSE FALSE 45 F 067927 B_STPHY_EPDR
|
||||
#> 10 2002-01-17 B TRUE FALSE FALSE 79 F 858515 B_STPHY_EPDR
|
||||
#> # ... with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>,
|
||||
#> # AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>,
|
||||
#> # CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>,
|
||||
#> # TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>,
|
||||
#> # FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>,
|
||||
#> # TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>,
|
||||
#> # IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, ...
|
||||
#> # A tibble: 2,000 x 46
|
||||
#> date patient age gender ward mo PEN OXA FLC AMX
|
||||
#> * <date> <chr> <dbl> <chr> <chr> <mo> <rsi> <rsi> <rsi> <rsi>
|
||||
#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
|
||||
#> 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 NA R NA
|
||||
#> 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 NA R NA
|
||||
#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S 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 NA R NA
|
||||
#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA
|
||||
#> # ... with 1,990 more rows, and 36 more variables: AMC <rsi>, AMP <rsi>,
|
||||
#> # TZP <rsi>, CZO <rsi>, FEP <rsi>, CXM <rsi>, FOX <rsi>, CTX <rsi>,
|
||||
#> # CAZ <rsi>, CRO <rsi>, GEN <rsi>, TOB <rsi>, AMK <rsi>, KAN <rsi>,
|
||||
#> # TMP <rsi>, SXT <rsi>, NIT <rsi>, FOS <rsi>, LNZ <rsi>, CIP <rsi>,
|
||||
#> # MFX <rsi>, VAN <rsi>, TEC <rsi>, TCY <rsi>, TGC <rsi>, DOX <rsi>,
|
||||
#> # ERY <rsi>, CLI <rsi>, AZM <rsi>, IPM <rsi>, MEM <rsi>, MTR <rsi>,
|
||||
#> # CHL <rsi>, COL <rsi>, MUP <rsi>, RIF <rsi>
|
||||
#> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
@ -131,7 +131,7 @@ Amikacin (\code{AMK}, \href{https://www.whocc.no/atc_ddd_index/?code=J01GB06&sho
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
|
||||
@ -5,32 +5,27 @@
|
||||
\alias{example_isolates}
|
||||
\title{Data Set with 2,000 Example Isolates}
|
||||
\format{
|
||||
A \link{data.frame} with 2,000 observations and 49 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 2,000 observations and 46 variables:
|
||||
\itemize{
|
||||
\item \code{date}\cr date of receipt at the laboratory
|
||||
\item \code{hospital_id}\cr ID of the hospital, from A to D
|
||||
\item \code{ward_icu}\cr \link{logical} to determine if ward is an intensive care unit
|
||||
\item \code{ward_clinical}\cr \link{logical} to determine if ward is a regular clinical ward
|
||||
\item \code{ward_outpatient}\cr \link{logical} to determine if ward is an outpatient clinic
|
||||
\item \code{age}\cr age of the patient
|
||||
\item \code{gender}\cr gender of the patient
|
||||
\item \code{patient_id}\cr ID of the patient
|
||||
\item \code{mo}\cr ID of microorganism created with \code{\link[=as.mo]{as.mo()}}, see also \link{microorganisms}
|
||||
\item \code{PEN:RIF}\cr 40 different antibiotics with class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}}); these column names occur in the \link{antibiotics} data set and can be translated with \code{\link[=ab_name]{ab_name()}}
|
||||
\item \code{date}\cr Date of receipt at the laboratory
|
||||
\item \code{patient}\cr ID of the patient
|
||||
\item \code{age}\cr Age of the patient
|
||||
\item \code{gender}\cr Gender of the patient, either "F" or "M"
|
||||
\item \code{ward}\cr Ward type where the patient was admitted, either "Clinical", "ICU" or "Outpatient"
|
||||
\item \code{mo}\cr ID of microorganism created with \code{\link[=as.mo]{as.mo()}}, see also the \link{microorganisms} data set
|
||||
\item \code{PEN:RIF}\cr 40 different antibiotics with class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}}); these column names occur in the \link{antibiotics} data set and can be translated with \code{\link[=set_ab_names]{set_ab_names()}} or \code{\link[=ab_name]{ab_name()}}
|
||||
}
|
||||
}
|
||||
\usage{
|
||||
example_isolates
|
||||
}
|
||||
\description{
|
||||
A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR data analysis. For examples, please read \href{https://msberends.github.io/AMR/articles/AMR.html}{the tutorial on our website}.
|
||||
A data set containing 2,000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read \href{https://msberends.github.io/AMR/articles/AMR.html}{the tutorial on our website}.
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
\details{
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(example_isolates)
|
||||
example_isolates
|
||||
}
|
||||
\keyword{datasets}
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{example_isolates_unclean}
|
||||
\title{Data Set with Unclean Data}
|
||||
\format{
|
||||
A \link{data.frame} with 3,000 observations and 8 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 3,000 observations and 8 variables:
|
||||
\itemize{
|
||||
\item \code{patient_id}\cr ID of the patient
|
||||
\item \code{date}\cr date of receipt at the laboratory
|
||||
@ -20,12 +20,10 @@ example_isolates_unclean
|
||||
\description{
|
||||
A data set containing 3,000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
\details{
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(example_isolates_unclean)
|
||||
example_isolates_unclean
|
||||
}
|
||||
\keyword{datasets}
|
||||
|
||||
@ -58,7 +58,7 @@ filter_first_isolate(
|
||||
|
||||
\item{col_specimen}{column name of the specimen type or group}
|
||||
|
||||
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
|
||||
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU). This can also be a \link{logical} vector with the same length as rows in \code{x}.}
|
||||
|
||||
\item{col_keyantimicrobials}{(only useful when \code{method = "phenotype-based"}) column name of the key antimicrobials to determine first isolates, see \code{\link[=key_antimicrobials]{key_antimicrobials()}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' or 'antimicrobials' (case insensitive). Use \code{col_keyantimicrobials = FALSE} to prevent this. Can also be the output of \code{\link[=key_antimicrobials]{key_antimicrobials()}}.}
|
||||
|
||||
@ -87,7 +87,7 @@ filter_first_isolate(
|
||||
\item{...}{arguments passed on to \code{\link[=first_isolate]{first_isolate()}} when using \code{\link[=filter_first_isolate]{filter_first_isolate()}}, otherwise arguments passed on to \code{\link[=key_antimicrobials]{key_antimicrobials()}} (such as \code{universal}, \code{gram_negative}, \code{gram_positive})}
|
||||
}
|
||||
\value{
|
||||
A \code{\link{logical}} vector
|
||||
A \link{logical} vector
|
||||
}
|
||||
\description{
|
||||
Determine first isolates of all microorganisms of every patient per episode and (if needed) per specimen type. These functions support all four methods as summarised by Hindler \emph{et al.} in 2007 (\doi{10.1086/511864}). To determine patient episodes not necessarily based on microorganisms, use \code{\link[=is_new_episode]{is_new_episode()}} that also supports grouping with the \code{dplyr} package.
|
||||
@ -177,36 +177,22 @@ if (require("dplyr")) {
|
||||
# filter on first isolates using dplyr:
|
||||
example_isolates \%>\%
|
||||
filter(first_isolate())
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# short-hand version:
|
||||
example_isolates \%>\%
|
||||
filter_first_isolate(info = FALSE)
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# flag the first isolates per group:
|
||||
example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
mutate(first = first_isolate()) \%>\%
|
||||
select(hospital_id, date, patient_id, mo, first)
|
||||
|
||||
# now let's see if first isolates matter:
|
||||
A <- example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(count = n_rsi(GEN), # gentamicin availability
|
||||
resistance = resistance(GEN)) # gentamicin resistance
|
||||
|
||||
B <- example_isolates \%>\%
|
||||
filter_first_isolate() \%>\% # the 1st isolate filter
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(count = n_rsi(GEN), # gentamicin availability
|
||||
resistance = resistance(GEN)) # gentamicin resistance
|
||||
|
||||
# Have a look at A and B.
|
||||
A
|
||||
B
|
||||
|
||||
# B is more reliable because every isolate is counted only once.
|
||||
# Gentamicin resistance in hospital D appears to be 4.2\% higher than
|
||||
# when you (erroneously) would have used all isolates for analysis.
|
||||
select(ward, date, patient, mo, first)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -54,26 +54,34 @@ if (require("dplyr")) {
|
||||
# episodes based on any (combination of) grouping variables:
|
||||
df \%>\%
|
||||
mutate(condition = sample(x = c("A", "B", "C"),
|
||||
size = 2000,
|
||||
size = 200,
|
||||
replace = TRUE)) \%>\%
|
||||
group_by(condition) \%>\%
|
||||
mutate(new_episode = is_new_episode(date, 365)) \%>\%
|
||||
select(patient_id, date, condition, new_episode)
|
||||
select(patient, date, condition, new_episode)
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
df \%>\%
|
||||
group_by(hospital_id, patient_id) \%>\%
|
||||
group_by(ward, patient) \%>\%
|
||||
transmute(date,
|
||||
patient_id,
|
||||
patient,
|
||||
new_index = get_episode(date, 60),
|
||||
new_logical = is_new_episode(date, 60))
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
df \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(n_patients = n_distinct(patient_id),
|
||||
group_by(ward) \%>\%
|
||||
summarise(n_patients = n_distinct(patient),
|
||||
n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
|
||||
n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),
|
||||
n_episodes_30 = sum(is_new_episode(date, episode_days = 30)))
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# grouping on patients and microorganisms leads to the same
|
||||
# results as first_isolate() when using 'episode-based':
|
||||
@ -82,16 +90,19 @@ if (require("dplyr")) {
|
||||
method = "episode-based")
|
||||
|
||||
y <- df \%>\%
|
||||
group_by(patient_id, mo) \%>\%
|
||||
group_by(patient, mo) \%>\%
|
||||
filter(is_new_episode(date, 365)) \%>\%
|
||||
ungroup()
|
||||
|
||||
identical(x, y)
|
||||
|
||||
}
|
||||
if (require("dplyr")) {
|
||||
|
||||
# but is_new_episode() has a lot more flexibility than first_isolate(),
|
||||
# since you can now group on anything that seems relevant:
|
||||
df \%>\%
|
||||
group_by(patient_id, mo, hospital_id, ward_icu) \%>\%
|
||||
group_by(patient, mo, ward) \%>\%
|
||||
mutate(flag_episode = is_new_episode(date, 365)) \%>\%
|
||||
select(group_vars(.), flag_episode)
|
||||
}
|
||||
|
||||
@ -142,11 +142,14 @@ At default, the names of antibiotics will be shown on the plots using \code{\lin
|
||||
}
|
||||
\examples{
|
||||
\donttest{
|
||||
if (require("ggplot2") & require("dplyr")) {
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# get antimicrobial results for drugs against a UTI:
|
||||
ggplot(example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
|
||||
geom_rsi()
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# prettify the plot using some additional functions:
|
||||
df <- example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)
|
||||
@ -156,16 +159,25 @@ if (require("ggplot2") & require("dplyr")) {
|
||||
scale_rsi_colours() +
|
||||
labels_rsi_count() +
|
||||
theme_rsi()
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# or better yet, simplify this using the wrapper function - a single command:
|
||||
example_isolates \%>\%
|
||||
select(AMX, NIT, FOS, TMP, CIP) \%>\%
|
||||
ggplot_rsi()
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# get only proportions and no counts:
|
||||
example_isolates \%>\%
|
||||
select(AMX, NIT, FOS, TMP, CIP) \%>\%
|
||||
ggplot_rsi(datalabels = FALSE)
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# add other ggplot2 arguments as you like:
|
||||
example_isolates \%>\%
|
||||
@ -175,11 +187,17 @@ if (require("ggplot2") & require("dplyr")) {
|
||||
size = 1,
|
||||
linetype = 2,
|
||||
alpha = 0.25)
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# you can alter the colours with colour names:
|
||||
example_isolates \%>\%
|
||||
select(AMX) \%>\%
|
||||
ggplot_rsi(colours = c(SI = "yellow"))
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# but you can also use the built-in colour-blind friendly colours for
|
||||
# your plots, where "S" is green, "I" is yellow and "R" is red:
|
||||
@ -189,34 +207,42 @@ if (require("ggplot2") & require("dplyr")) {
|
||||
ggplot() +
|
||||
geom_col(aes(x = x, y = y, fill = z)) +
|
||||
scale_rsi_colours(Value4 = "S", Value5 = "I", Value6 = "R")
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# resistance of ciprofloxacine per age group
|
||||
example_isolates \%>\%
|
||||
mutate(first_isolate = first_isolate()) \%>\%
|
||||
filter(first_isolate == TRUE,
|
||||
mo == as.mo("E. coli")) \%>\%
|
||||
mo == as.mo("Escherichia coli")) \%>\%
|
||||
# age_groups() is also a function in this AMR package:
|
||||
group_by(age_group = age_groups(age)) \%>\%
|
||||
select(age_group, CIP) \%>\%
|
||||
ggplot_rsi(x = "age_group")
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# a shorter version which also adjusts data label colours:
|
||||
example_isolates \%>\%
|
||||
select(AMX, NIT, FOS, TMP, CIP) \%>\%
|
||||
ggplot_rsi(colours = FALSE)
|
||||
|
||||
|
||||
}
|
||||
if (require("ggplot2") && require("dplyr")) {
|
||||
|
||||
# it also supports groups (don't forget to use the group var on `x` or `facet`):
|
||||
example_isolates \%>\%
|
||||
filter(mo_is_gram_negative()) \%>\%
|
||||
filter(mo_is_gram_negative(), ward != "Outpatient") \%>\%
|
||||
# select only UTI-specific drugs
|
||||
select(hospital_id, AMX, NIT, FOS, TMP, CIP) \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
ggplot_rsi(x = "hospital_id",
|
||||
select(ward, AMX, NIT, FOS, TMP, CIP) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
ggplot_rsi(x = "ward",
|
||||
facet = "antibiotic",
|
||||
nrow = 1,
|
||||
title = "AMR of Anti-UTI Drugs Per Hospital",
|
||||
x.title = "Hospital",
|
||||
title = "AMR of Anti-UTI Drugs Per Ward",
|
||||
x.title = "Ward",
|
||||
datalabels = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{intrinsic_resistant}
|
||||
\title{Data Set with Bacterial Intrinsic Resistance}
|
||||
\format{
|
||||
A \link{data.frame} with 134,956 observations and 2 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 134,956 observations and 2 variables:
|
||||
\itemize{
|
||||
\item \code{mo}\cr Microorganism ID
|
||||
\item \code{ab}\cr Antibiotic ID
|
||||
@ -18,16 +18,15 @@ intrinsic_resistant
|
||||
Data set containing defined intrinsic resistance by EUCAST of all bug-drug combinations.
|
||||
}
|
||||
\details{
|
||||
The repository of this \code{AMR} package contains a file comprising this data set with full taxonomic and antibiotic names: \url{https://github.com/msberends/AMR/blob/main/data-raw/intrinsic_resistant.txt}. This file \strong{allows for machine reading EUCAST guidelines about intrinsic resistance}, which is almost impossible with the Excel and PDF files distributed by EUCAST. The file is updated automatically.
|
||||
|
||||
This data set is based on \href{https://www.eucast.org/expert_rules_and_expected_phenotypes/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021).
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
\subsection{Direct download}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
}
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
|
||||
They \strong{allow for machine reading EUCAST and CLSI guidelines}, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI.
|
||||
}
|
||||
}
|
||||
\examples{
|
||||
head(intrinsic_resistant)
|
||||
intrinsic_resistant
|
||||
}
|
||||
\keyword{datasets}
|
||||
|
||||
21
man/join.Rd
21
man/join.Rd
@ -47,21 +47,22 @@ If the \code{dplyr} package is installed, their join functions will be used. Oth
|
||||
left_join_microorganisms(as.mo("K. pneumoniae"))
|
||||
left_join_microorganisms("B_KLBSL_PNMN")
|
||||
|
||||
df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
to = as.Date("2018-01-07"),
|
||||
by = 1),
|
||||
bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
|
||||
"E. coli", "E. coli", "E. coli")),
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(df)
|
||||
|
||||
df_joined <- left_join_microorganisms(df, "bacteria")
|
||||
colnames(df_joined)
|
||||
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
left_join_microorganisms() \%>\%
|
||||
colnames()
|
||||
|
||||
df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
to = as.Date("2018-01-07"),
|
||||
by = 1),
|
||||
bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
|
||||
"E. coli", "E. coli", "E. coli")),
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(df)
|
||||
df_joined <- left_join_microorganisms(df, "bacteria")
|
||||
colnames(df_joined)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -60,11 +60,9 @@ a \%unlike\% b
|
||||
a[1] \%like\% b
|
||||
a \%like\% b[1]
|
||||
|
||||
# get isolates whose name start with 'Ent' or 'ent'
|
||||
example_isolates[which(mo_name(example_isolates$mo) \%like\% "^ent"), ]
|
||||
\donttest{
|
||||
# faster way, since mo_name() is context-aware:
|
||||
example_isolates[which(mo_name() \%like\% "^ent"), ]
|
||||
# get isolates whose name start with 'Entero' (case-insensitive)
|
||||
example_isolates[which(mo_name() \%like\% "^entero"), ]
|
||||
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{microorganisms}
|
||||
\title{Data Set with 70,764 Microorganisms}
|
||||
\format{
|
||||
A \link{data.frame} with 70,764 observations and 16 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 70,764 observations and 16 variables:
|
||||
\itemize{
|
||||
\item \code{mo}\cr ID of microorganism as used by this package
|
||||
\item \code{fullname}\cr Full name, like \code{"Escherichia coli"}
|
||||
@ -65,9 +65,7 @@ For convenience, some entries were added manually:
|
||||
|
||||
\subsection{Direct download}{
|
||||
|
||||
This data set is available as 'flat file' for use even without \R - you can find the file here: \url{https://github.com/msberends/AMR/raw/main/data-raw/microorganisms.txt}.
|
||||
|
||||
The file in \R format (with preserved data structure) can be found here: \url{https://github.com/msberends/AMR/raw/main/data/microorganisms.rda}.
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
}
|
||||
\section{About the Records from LPSN (see \emph{Source})}{
|
||||
@ -85,13 +83,8 @@ This package contains the complete taxonomic tree of almost all microorganisms (
|
||||
\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which versions of the CoL and LPSN were included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
|
||||
}
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(microorganisms)
|
||||
microorganisms
|
||||
}
|
||||
\seealso{
|
||||
\code{\link[=as.mo]{as.mo()}}, \code{\link[=mo_property]{mo_property()}}, \link{microorganisms.codes}, \link{intrinsic_resistant}
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{microorganisms.codes}
|
||||
\title{Data Set with 5,604 Common Microorganism Codes}
|
||||
\format{
|
||||
A \link{data.frame} with 5,604 observations and 2 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 5,604 observations and 2 variables:
|
||||
\itemize{
|
||||
\item \code{code}\cr Commonly used code of a microorganism
|
||||
\item \code{mo}\cr ID of the microorganism in the \link{microorganisms} data set
|
||||
@ -17,11 +17,9 @@ microorganisms.codes
|
||||
\description{
|
||||
A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with \code{\link[=set_mo_source]{set_mo_source()}}. They will all be searched when using \code{\link[=as.mo]{as.mo()}} and consequently all the \code{\link[=mo_property]{mo_*}} functions.
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
\details{
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\section{Catalogue of Life}{
|
||||
|
||||
\if{html}{\figure{logo_col.png}{options: height="40" style=margin-bottom:"5"} \cr}
|
||||
@ -31,7 +29,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(microorganisms.codes)
|
||||
microorganisms.codes
|
||||
}
|
||||
\seealso{
|
||||
\code{\link[=as.mo]{as.mo()}} \link{microorganisms}
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{microorganisms.old}
|
||||
\title{Data Set with Previously Accepted Taxonomic Names}
|
||||
\format{
|
||||
A \link{data.frame} with 14,338 observations and 4 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 14,338 observations and 4 variables:
|
||||
\itemize{
|
||||
\item \code{fullname}\cr Old full taxonomic name of the microorganism
|
||||
\item \code{fullname_new}\cr New full taxonomic name of the microorganism
|
||||
@ -24,6 +24,9 @@ microorganisms.old
|
||||
\description{
|
||||
A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by \code{\link[=as.mo]{as.mo()}}.
|
||||
}
|
||||
\details{
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
\section{Catalogue of Life}{
|
||||
|
||||
\if{html}{\figure{logo_col.png}{options: height="40" style=margin-bottom:"5"} \cr}
|
||||
@ -32,13 +35,8 @@ This package contains the complete taxonomic tree of almost all microorganisms (
|
||||
\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which versions of the CoL and LPSN were included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
|
||||
}
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(microorganisms.old)
|
||||
microorganisms.old
|
||||
}
|
||||
\seealso{
|
||||
\code{\link[=as.mo]{as.mo()}} \code{\link[=mo_property]{mo_property()}} \link{microorganisms}
|
||||
|
||||
@ -41,7 +41,7 @@ Since \code{AMR} version 1.8.1, common microorganism abbreviations are ignored i
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
|
||||
@ -187,7 +187,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
|
||||
|
||||
\section{Reference Data Publicly Available}{
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
}
|
||||
|
||||
\examples{
|
||||
|
||||
@ -158,12 +158,12 @@ proportion_R(example_isolates$AMX)
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(r = resistance(CIP),
|
||||
n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi
|
||||
|
||||
example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(R = resistance(CIP, as_percent = TRUE),
|
||||
SI = susceptibility(CIP, as_percent = TRUE),
|
||||
n1 = count_all(CIP), # the actual total; sum of all three
|
||||
@ -195,7 +195,7 @@ if (require("dplyr")) {
|
||||
|
||||
|
||||
example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),
|
||||
cipro_n = count_all(CIP),
|
||||
genta_p = susceptibility(GEN, as_percent = TRUE),
|
||||
@ -211,8 +211,8 @@ if (require("dplyr")) {
|
||||
# It also supports grouping variables
|
||||
# (use rsi_df to also include the count)
|
||||
example_isolates \%>\%
|
||||
select(hospital_id, AMX, CIP) \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
select(ward, AMX, CIP) \%>\%
|
||||
group_by(ward) \%>\%
|
||||
rsi_df(translate = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
@ -142,7 +142,7 @@ if (require("dplyr")) {
|
||||
filter_first_isolate() \%>\%
|
||||
filter(mo_genus(mo) == "Staphylococcus") \%>\%
|
||||
resistance_predict("PEN", model = "binomial")
|
||||
plot(x)
|
||||
print(plot(x))
|
||||
|
||||
# get the model from the object
|
||||
mymodel <- attributes(x)$model
|
||||
@ -150,7 +150,7 @@ if (require("dplyr")) {
|
||||
}
|
||||
|
||||
# create nice plots with ggplot2 yourself
|
||||
if (require("dplyr") & require("ggplot2")) {
|
||||
if (require("dplyr") && require("ggplot2")) {
|
||||
|
||||
data <- example_isolates \%>\%
|
||||
filter(mo == as.mo("E. coli")) \%>\%
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{rsi_translation}
|
||||
\title{Data Set for R/SI Interpretation}
|
||||
\format{
|
||||
A \link{data.frame} with 20,369 observations and 11 variables:
|
||||
A [tibble\link[tibble:tibble]{tibble::tibble} with 20,369 observations and 11 variables:
|
||||
\itemize{
|
||||
\item \code{guideline}\cr Name of the guideline
|
||||
\item \code{method}\cr Either "DISK" or "MIC"
|
||||
@ -27,15 +27,12 @@ rsi_translation
|
||||
Data set containing reference data to interpret MIC and disk diffusion to R/SI values, according to international guidelines. Currently implemented guidelines are EUCAST (2011-2022) and CLSI (2011-2022). Use \code{\link[=as.rsi]{as.rsi()}} to transform MICs or disks measurements to R/SI values.
|
||||
}
|
||||
\details{
|
||||
The repository of this \code{AMR} package contains a file comprising this exact data set: \url{https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt}. This file \strong{allows for machine reading EUCAST and CLSI guidelines}, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. The file is updated automatically and the \code{mo} and \code{ab} columns have been transformed to contain the full official names instead of codes.
|
||||
}
|
||||
\section{Reference Data Publicly Available}{
|
||||
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
|
||||
|
||||
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
|
||||
They \strong{allow for machine reading EUCAST and CLSI guidelines}, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI.
|
||||
}
|
||||
|
||||
\examples{
|
||||
head(rsi_translation)
|
||||
rsi_translation
|
||||
}
|
||||
\seealso{
|
||||
\link{intrinsic_resistant}
|
||||
|
||||
@ -10,14 +10,14 @@
|
||||
\usage{
|
||||
get_AMR_locale()
|
||||
|
||||
set_AMR_locale(lang)
|
||||
set_AMR_locale(language)
|
||||
|
||||
reset_AMR_locale()
|
||||
|
||||
translate_AMR(x, language = get_AMR_locale())
|
||||
}
|
||||
\arguments{
|
||||
\item{lang}{language to choose. Use one of these supported language names or ISO-639-1 codes: "English" ("en"), "Chinese" ("zh"), "Danish" ("da"), "Dutch" ("nl"), "French" ("fr"), "German" ("de"), "Greek" ("el"), "Italian" ("it"), "Japanese" ("ja"), "Polish" ("pl"), "Portuguese" ("pt"), "Russian" ("ru"), "Spanish" ("es"), "Swedish" ("sv"), "Turkish" ("tr"), "Ukrainian" ("uk").}
|
||||
\item{language}{language to choose. Use one of these supported language names or ISO-639-1 codes: "English" ("en"), "Chinese" ("zh"), "Danish" ("da"), "Dutch" ("nl"), "French" ("fr"), "German" ("de"), "Greek" ("el"), "Italian" ("it"), "Japanese" ("ja"), "Polish" ("pl"), "Portuguese" ("pt"), "Russian" ("ru"), "Spanish" ("es"), "Swedish" ("sv"), "Turkish" ("tr"), "Ukrainian" ("uk").}
|
||||
|
||||
\item{x}{text to translate}
|
||||
}
|
||||
@ -30,7 +30,7 @@ The currently 16 supported languages are English, Chinese, Danish, Dutch, French
|
||||
Please read about adding or updating a language in \href{https://github.com/msberends/AMR/wiki/}{our Wiki}.
|
||||
\subsection{Changing the Default Language}{
|
||||
|
||||
The system language will be used at default (as returned by \code{Sys.getenv("LANG")} or, if \code{LANG} is not set, \link{Sys.getlocale("LC_COLLATE")}), if that language is supported. But the language to be used can be overwritten in two ways and will be checked in this order:
|
||||
The system language will be used at default (as returned by \code{Sys.getenv("LANG")} or, if \code{LANG} is not set, \code{\link[=Sys.getlocale]{Sys.getlocale("LC_COLLATE")}}), if that language is supported. But the language to be used can be overwritten in two ways and will be checked in this order:
|
||||
\enumerate{
|
||||
\item Setting the R option \code{AMR_locale}, either by using \code{set_AMR_locale()} or by running e.g. \code{options(AMR_locale = "de")}.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user