mirror of https://github.com/msberends/AMR.git
269 lines
12 KiB
R
Executable File
269 lines
12 KiB
R
Executable File
# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' Determine Clinical or Epidemic Episodes
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#'
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#' These functions determine which items in a vector can be considered (the start of) a new episode. This can be used to determine clinical episodes for any epidemiological analysis. The [get_episode()] function returns the index number of the episode per group, while the [is_new_episode()] function returns `TRUE` for every new [get_episode()] index. Both absolute and relative episode determination are supported.
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#' @param x vector of dates (class `Date` or `POSIXt`), will be sorted internally to determine episodes
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#' @param episode_days episode length in days to specify the time period after which a new episode begins, can also be less than a day or `Inf`, see *Details*
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#' @param case_free_days (inter-epidemic) interval length in days after which a new episode will start, can also be less than a day or `Inf`, see *Details*
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#' @param ... ignored, only in place to allow future extensions
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#' @details Episodes can be determined in two ways: absolute and relative.
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#'
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#' 1. Absolute
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#'
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#' This method uses `episode_days` to define an episode length in days, after which a new episode will start. A common use case in AMR data analysis is microbial epidemiology: episodes of *S. aureus* bacteraemia in ICU patients for example. The episode length could then be 30 days, so that new *S. aureus* isolates after an ICU episode of 30 days will be considered a different (or new) episode.
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#'
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#' Thus, this method counts **since the start of the previous episode**.
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#'
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#' 2. Relative
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#'
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#' This method uses `case_free_days` to quantify the duration of case-free days (the inter-epidemic interval), after which a new episode will start. A common use case is infectious disease epidemiology: episodes of norovirus outbreaks in a hospital for example. The case-free period could then be 14 days, so that new norovirus cases after that time will be considered a different (or new) episode.
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#'
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#' Thus, this methods counts **since the last case in the previous episode**.
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#'
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#' In a table:
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#'
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#' | Date | Using `episode_days = 7` | Using `case_free_days = 7` |
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#' |:----------:|:------------------------:|:--------------------------:|
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#' | 2023-01-01 | 1 | 1 |
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#' | 2023-01-02 | 1 | 1 |
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#' | 2023-01-05 | 1 | 1 |
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#' | 2023-01-08 | 2** | 1 |
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#' | 2023-02-21 | 3 | 2*** |
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#' | 2023-02-22 | 3 | 2 |
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#' | 2023-02-23 | 3 | 2 |
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#' | 2023-02-24 | 3 | 2 |
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#' | 2023-03-01 | 4 | 2 |
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#'
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#' ** This marks the start of a new episode, because 8 January 2023 is more than 7 days since the start of the previous episode (1 January 2023). \cr
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#' *** This marks the start of a new episode, because 21 January 2023 is more than 7 days since the last case in the previous episode (8 January 2023).
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#'
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#' Either `episode_days` or `case_free_days` must be provided in the function.
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#'
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#' ### Difference between `get_episode()` and `is_new_episode()`
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#'
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#' The [get_episode()] function returns the index number of the episode, so all cases/patients/isolates in the first episode will have the number 1, all cases/patients/isolates in the second episode will have the number 2, etc.
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#'
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#' The [is_new_episode()] function on the other hand, returns `TRUE` for every new [get_episode()] index.
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#'
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#' To specify, when setting `episode_days = 365` (using method 1 as explained above), this is how the two functions differ:
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#'
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#' | patient | date | `get_episode()` | `is_new_episode()` |
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#' |:---------:|:----------:|:---------------:|:------------------:|
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#' | A | 2019-01-01 | 1 | TRUE |
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#' | A | 2019-03-01 | 1 | FALSE |
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#' | A | 2021-01-01 | 2 | TRUE |
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#' | B | 2008-01-01 | 1 | TRUE |
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#' | B | 2008-01-01 | 1 | FALSE |
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#' | C | 2020-01-01 | 1 | TRUE |
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#'
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#' ### Other
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#'
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#' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods.
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#'
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#' The `dplyr` package is not required for these functions to work, but these episode functions do support [variable grouping][dplyr::group_by()] and work conveniently inside `dplyr` verbs such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
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#' @return
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#' * [get_episode()]: an [integer] vector
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#' * [is_new_episode()]: a [logical] vector
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#' @seealso [first_isolate()]
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#' @rdname get_episode
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#' @export
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#' @examples
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#' # difference between absolute and relative determination of episodes:
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#' x <- data.frame(dates = as.Date(c(
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#' "2021-01-01",
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#' "2021-01-02",
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#' "2021-01-05",
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#' "2021-01-08",
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#' "2021-02-21",
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#' "2021-02-22",
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#' "2021-02-23",
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#' "2021-02-24",
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#' "2021-03-01",
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#' "2021-03-01"
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#' )))
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#' x$absolute <- get_episode(x$dates, episode_days = 7)
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#' x$relative <- get_episode(x$dates, case_free_days = 7)
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#' x
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#'
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#'
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#' # `example_isolates` is a data set available in the AMR package.
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#' # See ?example_isolates
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#' df <- example_isolates[sample(seq_len(2000), size = 100), ]
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#'
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#' get_episode(df$date, episode_days = 60) # indices
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#' is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
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#'
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#' # filter on results from the third 60-day episode only, using base R
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#' df[which(get_episode(df$date, 60) == 3), ]
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#'
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#' # the functions also work for less than a day, e.g. to include one per hour:
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#' get_episode(
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#' c(
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#' Sys.time(),
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#' Sys.time() + 60 * 60
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#' ),
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#' episode_days = 1 / 24
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#' )
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#'
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#' \donttest{
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#' if (require("dplyr")) {
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#' # is_new_episode() can also be used in dplyr verbs to determine patient
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#' # episodes based on any (combination of) grouping variables:
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#' df %>%
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#' mutate(condition = sample(
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#' x = c("A", "B", "C"),
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#' size = 100,
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#' replace = TRUE
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#' )) %>%
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#' group_by(patient, condition) %>%
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#' mutate(new_episode = is_new_episode(date, 365)) %>%
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#' select(patient, date, condition, new_episode) %>%
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#' arrange(patient, condition, date)
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#' }
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#'
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#' if (require("dplyr")) {
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#' df %>%
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#' group_by(ward, patient) %>%
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#' transmute(date,
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#' patient,
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#' new_index = get_episode(date, 60),
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#' new_logical = is_new_episode(date, 60)
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#' ) %>%
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#' arrange(patient, ward, date)
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#' }
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#'
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#' if (require("dplyr")) {
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#' df %>%
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#' group_by(ward) %>%
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#' summarise(
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#' n_patients = n_distinct(patient),
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#' n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
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#' n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),
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#' n_episodes_30 = sum(is_new_episode(date, episode_days = 30))
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#' )
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#' }
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#'
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#' # grouping on patients and microorganisms leads to the same
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#' # results as first_isolate() when using 'episode-based':
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#' if (require("dplyr")) {
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#' x <- df %>%
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#' filter_first_isolate(
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#' include_unknown = TRUE,
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#' method = "episode-based"
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#' )
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#'
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#' y <- df %>%
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#' group_by(patient, mo) %>%
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#' filter(is_new_episode(date, 365)) %>%
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#' ungroup()
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#'
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#' identical(x, y)
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#' }
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#'
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#' # but is_new_episode() has a lot more flexibility than first_isolate(),
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#' # since you can now group on anything that seems relevant:
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#' if (require("dplyr")) {
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#' df %>%
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#' group_by(patient, mo, ward) %>%
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#' mutate(flag_episode = is_new_episode(date, 365)) %>%
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#' select(group_vars(.), flag_episode)
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#' }
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#' }
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get_episode <- function(x, episode_days = NULL, case_free_days = NULL, ...) {
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meet_criteria(x, allow_class = c("Date", "POSIXt"), allow_NA = TRUE)
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meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE, allow_NULL = TRUE)
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meet_criteria(case_free_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE, allow_NULL = TRUE)
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as.integer(exec_episode(x, episode_days, case_free_days, ...))
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}
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#' @rdname get_episode
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#' @export
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is_new_episode <- function(x, episode_days = NULL, case_free_days = NULL, ...) {
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meet_criteria(x, allow_class = c("Date", "POSIXt"), allow_NA = TRUE)
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meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE, allow_NULL = TRUE)
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meet_criteria(case_free_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE, allow_NULL = TRUE)
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!duplicated(exec_episode(x, episode_days, case_free_days, ...))
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}
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exec_episode <- function(x, episode_days, case_free_days, ...) {
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stop_ifnot(is.null(episode_days) || is.null(case_free_days),
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"either argument `episode_days` or argument `case_free_days` must be set.",
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call = -2
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)
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# running as.double() on a POSIXct object will return its number of seconds since 1970-01-01
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x <- as.double(as.POSIXct(x)) # as.POSIXct() required for Date classes
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# since x is now in seconds, get seconds from episode_days as well
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episode_seconds <- episode_days * 60 * 60 * 24
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case_free_seconds <- case_free_days * 60 * 60 * 24
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if (length(x) == 1) { # this will also match 1 NA, which is fine
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return(1)
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} else if (length(x) == 2 && all(!is.na(x))) {
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if ((length(episode_seconds) > 0 && (max(x) - min(x)) >= episode_seconds) ||
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(length(case_free_seconds) > 0 && (max(x) - min(x)) >= case_free_seconds)) {
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if (x[1] <= x[2]) {
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return(c(1, 2))
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} else {
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return(c(2, 1))
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}
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} else {
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return(c(1, 1))
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}
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}
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run_episodes <- function(x, episode_seconds, case_free) {
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NAs <- which(is.na(x))
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x[NAs] <- 0
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indices <- integer(length = length(x))
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start <- x[1]
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ind <- 1
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indices[ind] <- 1
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for (i in 2:length(x)) {
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if ((length(episode_seconds) > 0 && (x[i] - start) >= episode_seconds) ||
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(length(case_free_seconds) > 0 && (x[i] - x[i - 1]) >= case_free_seconds)) {
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ind <- ind + 1
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start <- x[i]
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}
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indices[i] <- ind
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}
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indices[NAs] <- NA
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indices
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}
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ord <- order(x)
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out <- run_episodes(x[ord], episode_seconds, case_free_seconds)[order(ord)]
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out[is.na(x) & ord != 1] <- NA # every NA expect for the first must remain NA
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out
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}
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