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(v1.3.0.9035) mdro() for EUCAST 3.2, examples cleanup

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dr. M.S. (Matthijs) Berends
2020-09-29 23:35:46 +02:00
parent 68e6e1e329
commit 4e0374af29
94 changed files with 1143 additions and 1165 deletions
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90
R/first_isolate.R
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@ -46,18 +46,19 @@
#'
#' All isolates with a microbial ID of `NA` will be excluded as first isolate.
#'
#' The functions [filter_first_isolate()] and [filter_first_weighted_isolate()] are helper functions to quickly filter on first isolates. The function [filter_first_isolate()] is essentially equal to one of:
#' The functions [filter_first_isolate()] and [filter_first_weighted_isolate()] are helper functions to quickly filter on first isolates. The function [filter_first_isolate()] is essentially equal to either:
#' ```
#' x %>% filter(first_isolate(., ...))
#' x[first_isolate(x, ...), ]
#' x %>% filter(first_isolate(x, ...))
#' ```
#' The function [filter_first_weighted_isolate()] is essentially equal to:
#' ```
#' x %>%
#' mutate(keyab = key_antibiotics(.)) %>%
#' mutate(only_weighted_firsts = first_isolate(x,
#' col_keyantibiotics = "keyab", ...)) %>%
#' filter(only_weighted_firsts == TRUE) %>%
#' select(-only_weighted_firsts, -keyab)
#' x %>%
#' mutate(keyab = key_antibiotics(.)) %>%
#' mutate(only_weighted_firsts = first_isolate(x,
#' col_keyantibiotics = "keyab", ...)) %>%
#' filter(only_weighted_firsts == TRUE) %>%
#' select(-only_weighted_firsts, -keyab)
#' ```
#' @section Key antibiotics:
#' There are two ways to determine whether isolates can be included as first *weighted* isolates which will give generally the same results:
@ -80,50 +81,41 @@
#' @examples
#' # `example_isolates` is a dataset available in the AMR package.
#' # See ?example_isolates.
#'
#' \dontrun{
#' library(dplyr)
#' # Filter on first isolates:
#' example_isolates %>%
#' mutate(first_isolate = first_isolate(.)) %>%
#' filter(first_isolate == TRUE)
#'
#' # 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_weighted_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.
#' # B is more reliable because every isolate is counted only once.
#' # Gentamicin resistance in hospital D appears to be 3.7% higher than
#' # when you (erroneously) would have used all isolates for analysis.
#'
#'
#' ## OTHER EXAMPLES:
#' # basic filtering on first isolates
#' example_isolates[first_isolate(example_isolates), ]
#'
#' # Short-hand versions:
#' example_isolates %>%
#' filter_first_isolate()
#' \donttest{
#' if (require("dplyr")) {
#' # Filter on first isolates:
#' example_isolates %>%
#' mutate(first_isolate = first_isolate(.)) %>%
#' filter(first_isolate == TRUE)
#'
#' # Short-hand versions:
#' example_isolates %>%
#' filter_first_isolate()
#'
#' example_isolates %>%
#' filter_first_weighted_isolate()
#'
#' example_isolates %>%
#' filter_first_weighted_isolate()
#'
#'
#' # set key antibiotics to a new variable
#' x$keyab <- key_antibiotics(x)
#'
#' x$first_isolate <- first_isolate(x)
#'
#' x$first_isolate_weighed <- first_isolate(x, col_keyantibiotics = 'keyab')
#'
#' x$first_blood_isolate <- first_isolate(x, specimen_group = "Blood")
#' # 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_weighted_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.
#' # B is more reliable because every isolate is counted only once.
#' # Gentamicin resistance in hospital D appears to be 3.7% higher than
#' # when you (erroneously) would have used all isolates for analysis.
#' }
#' }
first_isolate <- function(x,
col_date = NULL,