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(v1.5.0.9006) major documentation update

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dr. M.S. (Matthijs) Berends
2021-01-18 16:57:56 +01:00
parent e95218c0d1
commit 4eab095306
174 changed files with 1488 additions and 1071 deletions
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12
man/count.Rd
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@@ -12,7 +12,7 @@
\alias{count_all}
\alias{n_rsi}
\alias{count_df}
\title{Count available isolates}
\title{Count Available Isolates}
\usage{
count_resistant(..., only_all_tested = FALSE)
@@ -43,7 +43,7 @@ count_df(
\arguments{
\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link[=as.rsi]{as.rsi()}} if needed.}
\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}): a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below}
\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}): a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination Therapy} below}
\item{data}{a \link{data.frame} containing columns with class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}})}
@@ -59,7 +59,7 @@ count_df(
An \link{integer}
}
\description{
These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{summarise()} from the \code{dplyr} package and also support grouped variables, please see \emph{Examples}.
These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{summarise()} from the \code{dplyr} package and also support grouped variables, see \emph{Examples}.
\code{\link[=count_resistant]{count_resistant()}} should be used to count resistant isolates, \code{\link[=count_susceptible]{count_susceptible()}} should be used to count susceptible isolates.
}
@@ -72,7 +72,7 @@ The function \code{\link[=n_rsi]{n_rsi()}} is an alias of \code{\link[=count_all
The function \code{\link[=count_df]{count_df()}} takes any variable from \code{data} that has an \code{\link{rsi}} class (created with \code{\link[=as.rsi]{as.rsi()}}) and counts the number of S's, I's and R's. It also supports grouped variables. The function \code{\link[=rsi_df]{rsi_df()}} works exactly like \code{\link[=count_df]{count_df()}}, but adds the percentage of S, I and R.
}
\section{Stable lifecycle}{
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
@@ -95,7 +95,7 @@ A microorganism is categorised as \emph{Susceptible, Increased exposure} when th
This AMR package honours this new insight. Use \code{\link[=susceptibility]{susceptibility()}} (equal to \code{\link[=proportion_SI]{proportion_SI()}}) to determine antimicrobial susceptibility and \code{\link[=count_susceptible]{count_susceptible()}} (equal to \code{\link[=count_SI]{count_SI()}}) to count susceptible isolates.
}
\section{Combination therapy}{
\section{Combination Therapy}{
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
@@ -126,7 +126,7 @@ and that, in combination therapies, for \code{only_all_tested = FALSE} applies t
Using \code{only_all_tested} has no impact when only using one antibiotic as input.
}
\section{Read more on our website!}{
\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 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}. As we would like to better understand the backgrounds and needs of our users, please \href{https://msberends.github.io/AMR/survey.html}{participate in our survey}!
}