* Contains the complete manual of this package and all of its functions with an explanation of their parameters
* Contains a comprehensive tutorial about how to conduct antimicrobial resistance analysis
* Support for [`dplyr`](https://dplyr.tidyverse.org) version 0.8.0
* Function `guess_ab` to find an antibiotic column in a table
* Function `guess_ab_col` to find an antibiotic column in a table
* Function `mo_failures()` to review values that could not be coerced to a valid MO code, using `as.mo()`. This latter function will now only show a maximum of 25 uncoerced values.
* Function `mo_renamed()` to get a list of all returned values from `as.mo()` that have had taxonomic renaming
* Function `age()` to calculate the (patients) age in years
#' @param ... parameters that are passed on to \code{eucast_rules}
#' @inheritParams first_isolate
#' @section Antibiotics:
#' To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab}} or input a text (case-insensitive) or use \code{NULL} to skip a column (e.g. \code{tica = NULL}). Non-existing columns will anyway be skipped with a warning.
#' To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab_col}} or input a text (case-insensitive) or use \code{NULL} to skip a column (e.g. \code{tica = NULL}). Non-existing columns will anyway be skipped with a warning.
#'
#' Abbrevations of the column containing antibiotics in the form: \strong{abbreviation}: generic name (\emph{ATC code})
#' This tries to find a column name in a data set based on information from the \code{\link{antibiotics}} data set.
#' This tries to find a column name in a data set based on information from the \code{\link{antibiotics}} data set. You can look for an antibiotic (trade) of abbreviation and it will search the data for any column containing a name or ATC code of that antibiotic.
#' @param tbl a \code{data.frame}
#' @param col a character to look for
#' @param verbose a logical to indicate whether additional info should be printed
#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
#' @param x,y characters to compare
#' @inheritParams first_isolate
#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of \strong{broad-spectrum} antibiotics, case-insensitive
#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram positives}, case-insensitive
#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram negatives}, case-insensitive
#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of \strong{broad-spectrum} antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram positives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram negatives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
#' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
#' @param ... other parameters passed on to function
#' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it wouldn't.
#' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it would not.
#'
#' At default, the antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
#' \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"vanc"}, \code{"teic"}, \code{"tetr"}, \code{"eryt"}, \code{"oxac"}, \code{"rifa"}.
@ -78,24 +78,24 @@
@@ -78,24 +78,24 @@
#' # FALSE, because I is not ignored and so the 4th value differs
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <ahref="https://rmarkdown.rstudio.com/">RMarkdown</a>. However, the methodology remains unchanged. This page was generated on 08 January 2019.</p>
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <ahref="https://rmarkdown.rstudio.com/">RMarkdown</a>. However, the methodology remains unchanged. This page was generated on 11 January 2019.</p>
<p>Using the <code><ahref="https://dplyr.tidyverse.org/reference/sample.html">sample()</a></code> function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the <code>prob</code> parameter.</p>
<p>Using the <code><ahref="https://www.rdocumentation.org/packages/dplyr/topics/sample">sample()</a></code> function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the <code>prob</code> parameter.</p>
<p>Using the <code><ahref="https://dplyr.tidyverse.org/reference/join.html">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<p>Using the <code><ahref="https://www.rdocumentation.org/packages/dplyr/topics/join">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<p>The resulting data set contains 5,000 blood culture isolates. With the <code><ahref="https://www.rdocumentation.org/packages/utils/topics/head">head()</a></code> function we can preview the first 6 values of this data set:</p>
