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first isolate missing dates fix

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dr. M.S. (Matthijs) Berends
2019-05-13 14:56:23 +02:00
parent c4aa92b4a7
commit cc403169c6
15 changed files with 200 additions and 146 deletions
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50
man/first_isolate.Rd
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@ -9,22 +9,22 @@
Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
\usage{
first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
first_isolate(x, col_date = NULL, col_patient_id = NULL,
col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365,
testcodes_exclude = NULL, icu_exclude = FALSE,
specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE,
points_threshold = 2, info = TRUE, ...)
filter_first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
filter_first_isolate(x, col_date = NULL, col_patient_id = NULL,
col_mo = NULL, ...)
filter_first_weighted_isolate(tbl, col_date = NULL,
filter_first_weighted_isolate(x, col_date = NULL,
col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL,
...)
}
\arguments{
\item{tbl}{a \code{data.frame} containing isolates.}
\item{x}{a \code{data.frame} containing isolates.}
\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class}
@ -70,16 +70,16 @@ To conduct an analysis of antimicrobial resistance, you should only include the
The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to:
\preformatted{
tbl \%>\%
mutate(only_firsts = first_isolate(tbl, ...)) \%>\%
x \%>\%
mutate(only_firsts = first_isolate(x, ...)) \%>\%
filter(only_firsts == TRUE) \%>\%
select(-only_firsts)
}
The function \code{filter_first_weighted_isolate} is essentially equal to:
\preformatted{
tbl \%>\%
x \%>\%
mutate(keyab = key_antibiotics(.)) \%>\%
mutate(only_weighted_firsts = first_isolate(tbl,
mutate(only_weighted_firsts = first_isolate(x,
col_keyantibiotics = "keyab", ...)) \%>\%
filter(only_weighted_firsts == TRUE) \%>\%
select(-only_weighted_firsts)
@ -144,39 +144,39 @@ B <- septic_patients \%>\%
\dontrun{
# set key antibiotics to a new variable
tbl$keyab <- key_antibiotics(tbl)
x$keyab <- key_antibiotics(x)
tbl$first_isolate <-
first_isolate(tbl)
x$first_isolate <-
first_isolate(x)
tbl$first_isolate_weighed <-
first_isolate(tbl,
x$first_isolate_weighed <-
first_isolate(x,
col_keyantibiotics = 'keyab')
tbl$first_blood_isolate <-
first_isolate(tbl,
x$first_blood_isolate <-
first_isolate(x,
specimen_group = 'Blood')
tbl$first_blood_isolate_weighed <-
first_isolate(tbl,
x$first_blood_isolate_weighed <-
first_isolate(x,
specimen_group = 'Blood',
col_keyantibiotics = 'keyab')
tbl$first_urine_isolate <-
first_isolate(tbl,
x$first_urine_isolate <-
first_isolate(x,
specimen_group = 'Urine')
tbl$first_urine_isolate_weighed <-
first_isolate(tbl,
x$first_urine_isolate_weighed <-
first_isolate(x,
specimen_group = 'Urine',
col_keyantibiotics = 'keyab')
tbl$first_resp_isolate <-
first_isolate(tbl,
x$first_resp_isolate <-
first_isolate(x,
specimen_group = 'Respiratory')
tbl$first_resp_isolate_weighed <-
first_isolate(tbl,
x$first_resp_isolate_weighed <-
first_isolate(x,
specimen_group = 'Respiratory',
col_keyantibiotics = 'keyab')
}