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dr. M.S. (Matthijs) Berends
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R/first_isolate.R
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@ -26,7 +26,6 @@
#' Determine First Isolates
#'
#' 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 *et al.* in 2007 (\doi{10.1086/511864}). To determine patient episodes not necessarily based on microorganisms, use [is_new_episode()] that also supports grouping with the `dplyr` package.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [data.frame] containing isolates. Can be left blank for automatic determination, see *Examples*.
#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column with a date class
#' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
@ -126,7 +125,6 @@
#' - **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#'
#' - Hindler JF and Stelling J (2007). **Analysis and Presentation of Cumulative Antibiograms: A New Consensus Guideline from the Clinical and Laboratory Standards Institute.** Clinical Infectious Diseases, 44(6), 867-873. \doi{10.1086/511864}
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
@ -134,7 +132,7 @@
#' example_isolates[first_isolate(), ]
#' \donttest{
#' # get all first Gram-negatives
#' example_isolates[which(first_isolate() & mo_is_gram_negative()), ]
#' example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ]
#'
#' if (require("dplyr")) {
#' # filter on first isolates using dplyr:
@ -143,12 +141,13 @@
#'
#' # short-hand version:
#' example_isolates %>%
#' filter_first_isolate()
#' filter_first_isolate(info = FALSE)
#'
#' # grouped determination of first isolates (also prints group names):
#' # flag the first isolates per group:
#' example_isolates %>%
#' group_by(hospital_id) %>%
#' mutate(first = first_isolate())
#' mutate(first = first_isolate()) %>%
#' select(hospital_id, date, patient_id, mo, first)
#'
#' # now let's see if first isolates matter:
#' A <- example_isolates %>%
@ -163,6 +162,9 @@
#' 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.