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dr. M.S. (Matthijs) Berends
2022-08-21 16:37:20 +02:00
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man/get_episode.Rd
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@ -32,28 +32,16 @@ The \code{\link[=first_isolate]{first_isolate()}} function is a wrapper around t
The \code{dplyr} package is not required for these functions to work, but these functions do support \link[dplyr:group_by]{variable grouping} and work conveniently inside \code{dplyr} verbs such as \code{\link[dplyr:filter]{filter()}}, \code{\link[dplyr:mutate]{mutate()}} and \code{\link[dplyr:summarise]{summarise()}}.
}
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:"5"} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}
\section{Read more on Our Website!}{
On our website \url{https://msberends.github.io/AMR/} you can find \href{https://msberends.github.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR data analysis, the \href{https://msberends.github.io/AMR/reference/}{complete documentation of all functions} and \href{https://msberends.github.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
}
\examples{
# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates.
# See ?example_isolates
df <- example_isolates[sample(seq_len(2000), size = 200), ]
get_episode(example_isolates$date, episode_days = 60) # indices
is_new_episode(example_isolates$date, episode_days = 60) # TRUE/FALSE
get_episode(df$date, episode_days = 60) # indices
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
# filter on results from the third 60-day episode only, using base R
example_isolates[which(get_episode(example_isolates$date, 60) == 3), ]
df[which(get_episode(df$date, 60) == 3), ]
# the functions also work for less than a day, e.g. to include one per hour:
get_episode(c(Sys.time(),
@ -64,24 +52,24 @@ get_episode(c(Sys.time(),
if (require("dplyr")) {
# is_new_episode() can also be used in dplyr verbs to determine patient
# episodes based on any (combination of) grouping variables:
example_isolates \%>\%
df \%>\%
mutate(condition = sample(x = c("A", "B", "C"),
size = 2000,
replace = TRUE)) \%>\%
group_by(condition) \%>\%
mutate(new_episode = is_new_episode(date, 365))
mutate(new_episode = is_new_episode(date, 365)) \%>\%
select(patient_id, date, condition, new_episode)
example_isolates \%>\%
df \%>\%
group_by(hospital_id, patient_id) \%>\%
transmute(date,
patient_id,
new_index = get_episode(date, 60),
new_logical = is_new_episode(date, 60))
example_isolates \%>\%
df \%>\%
group_by(hospital_id) \%>\%
summarise(patients = n_distinct(patient_id),
summarise(n_patients = n_distinct(patient_id),
n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),
n_episodes_30 = sum(is_new_episode(date, episode_days = 30)))
@ -89,21 +77,23 @@ if (require("dplyr")) {
# grouping on patients and microorganisms leads to the same
# results as first_isolate() when using 'episode-based':
x <- example_isolates \%>\%
x <- df \%>\%
filter_first_isolate(include_unknown = TRUE,
method = "episode-based")
y <- example_isolates \%>\%
y <- df \%>\%
group_by(patient_id, mo) \%>\%
filter(is_new_episode(date, 365))
filter(is_new_episode(date, 365)) \%>\%
ungroup()
identical(x$patient_id, y$patient_id)
identical(x, y)
# but is_new_episode() has a lot more flexibility than first_isolate(),
# since you can now group on anything that seems relevant:
example_isolates \%>\%
df \%>\%
group_by(patient_id, mo, hospital_id, ward_icu) \%>\%
mutate(flag_episode = is_new_episode(date, 365))
mutate(flag_episode = is_new_episode(date, 365)) \%>\%
select(group_vars(.), flag_episode)
}
}
}