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(v1.3.0.9035) mdro() for EUCAST 3.2, examples cleanup
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@ -46,18 +46,19 @@
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#'
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#' All isolates with a microbial ID of `NA` will be excluded as first isolate.
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#'
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#' 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:
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#' 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:
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#' ```
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#' x %>% filter(first_isolate(., ...))
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#' x[first_isolate(x, ...), ]
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#' x %>% filter(first_isolate(x, ...))
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#' ```
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#' The function [filter_first_weighted_isolate()] is essentially equal to:
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#' ```
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#' x %>%
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#' mutate(keyab = key_antibiotics(.)) %>%
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#' mutate(only_weighted_firsts = first_isolate(x,
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#' col_keyantibiotics = "keyab", ...)) %>%
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#' filter(only_weighted_firsts == TRUE) %>%
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#' select(-only_weighted_firsts, -keyab)
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#' x %>%
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#' mutate(keyab = key_antibiotics(.)) %>%
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#' mutate(only_weighted_firsts = first_isolate(x,
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#' col_keyantibiotics = "keyab", ...)) %>%
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#' filter(only_weighted_firsts == TRUE) %>%
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#' select(-only_weighted_firsts, -keyab)
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#' ```
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#' @section Key antibiotics:
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#' There are two ways to determine whether isolates can be included as first *weighted* isolates which will give generally the same results:
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@ -80,50 +81,41 @@
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#' @examples
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#' # `example_isolates` is a dataset available in the AMR package.
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#' # See ?example_isolates.
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#'
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#' \dontrun{
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#' library(dplyr)
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#' # Filter on first isolates:
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#' example_isolates %>%
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#' mutate(first_isolate = first_isolate(.)) %>%
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#' filter(first_isolate == TRUE)
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#'
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#' # Now let's see if first isolates matter:
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#' A <- example_isolates %>%
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#' group_by(hospital_id) %>%
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#' summarise(count = n_rsi(GEN), # gentamicin availability
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#' resistance = resistance(GEN)) # gentamicin resistance
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#'
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#' B <- example_isolates %>%
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#' filter_first_weighted_isolate() %>% # the 1st isolate filter
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#' group_by(hospital_id) %>%
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#' summarise(count = n_rsi(GEN), # gentamicin availability
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#' resistance = resistance(GEN)) # gentamicin resistance
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#'
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#' # Have a look at A and B.
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#' # B is more reliable because every isolate is counted only once.
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#' # Gentamicin resistance in hospital D appears to be 3.7% higher than
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#' # when you (erroneously) would have used all isolates for analysis.
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#'
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#'
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#' ## OTHER EXAMPLES:
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#' # basic filtering on first isolates
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#' example_isolates[first_isolate(example_isolates), ]
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#'
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#' # Short-hand versions:
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#' example_isolates %>%
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#' filter_first_isolate()
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#' \donttest{
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#' if (require("dplyr")) {
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#' # Filter on first isolates:
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#' example_isolates %>%
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#' mutate(first_isolate = first_isolate(.)) %>%
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#' filter(first_isolate == TRUE)
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#'
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#' # Short-hand versions:
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#' example_isolates %>%
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#' filter_first_isolate()
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#'
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#' example_isolates %>%
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#' filter_first_weighted_isolate()
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#'
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#' example_isolates %>%
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#' filter_first_weighted_isolate()
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#'
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#'
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#' # set key antibiotics to a new variable
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#' x$keyab <- key_antibiotics(x)
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#'
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#' x$first_isolate <- first_isolate(x)
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#'
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#' x$first_isolate_weighed <- first_isolate(x, col_keyantibiotics = 'keyab')
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#'
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#' x$first_blood_isolate <- first_isolate(x, specimen_group = "Blood")
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#' # Now let's see if first isolates matter:
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#' A <- example_isolates %>%
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#' group_by(hospital_id) %>%
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#' summarise(count = n_rsi(GEN), # gentamicin availability
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#' resistance = resistance(GEN)) # gentamicin resistance
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#'
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#' B <- example_isolates %>%
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#' filter_first_weighted_isolate() %>% # the 1st isolate filter
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#' group_by(hospital_id) %>%
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#' summarise(count = n_rsi(GEN), # gentamicin availability
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#' resistance = resistance(GEN)) # gentamicin resistance
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#'
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#' # Have a look at A and B.
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#' # B is more reliable because every isolate is counted only once.
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#' # Gentamicin resistance in hospital D appears to be 3.7% higher than
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#' # when you (erroneously) would have used all isolates for analysis.
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#' }
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#' }
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first_isolate <- function(x,
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col_date = NULL,
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