These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. 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 values TRUE/FALSE for where get_episode() returns 1, and is thus equal to get_episode(...) == 1.
Arguments
- x
vector of dates (class
DateorPOSIXt), will be sorted internally to determine episodes- episode_days
required episode length in days, can also be less than a day or
Inf, see Details- ...
ignored, only in place to allow future extensions
Details
Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least episode_days days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least episode_days days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored.
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.
The dplyr package is not required for these functions to work, but these episode functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise().
Examples
# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates
df <- example_isolates[sample(seq_len(2000), size = 100), ]
get_episode(df$date, episode_days = 60) # indices
#> [1] 24 25 6 9 9 40 28 6 15 42 34 3 1 21 39 3 5 22 30 13 14 31 12 41 36
#> [26] 34 38 46 18 25 45 43 2 5 17 44 23 43 8 37 17 28 31 43 26 47 43 6 1 28
#> [51] 18 12 30 22 20 29 31 34 18 13 48 12 31 15 4 3 16 2 7 35 6 19 29 11 24
#> [76] 16 12 28 14 32 1 23 13 4 6 9 46 38 37 10 13 17 12 40 33 30 7 27 48 38
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE
# filter on results from the third 60-day episode only, using base R
df[which(get_episode(df$date, 60) == 3), ]
#> # A tibble: 3 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2003-02-26 869648 64 M Outpatie… B_STPHY_AURS R NA R R
#> 2 2003-03-22 E44854 60 F ICU B_STRPT_PNMN S NA NA S
#> 3 2003-01-25 088256 73 F ICU B_STPHY_HMNS R NA R R
#> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>,
#> # FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>,
#> # GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>,
#> # NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>,
#> # TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>,
#> # AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>,
#> # MUP <sir>, RIF <sir>
# the functions also work for less than a day, e.g. to include one per hour:
get_episode(c(Sys.time(),
Sys.time() + 60 * 60),
episode_days = 1 / 24)
#> [1] 1 2
# \donttest{
if (require("dplyr")) {
# is_new_episode() can also be used in dplyr verbs to determine patient
# episodes based on any (combination of) grouping variables:
df %>%
mutate(condition = sample(
x = c("A", "B", "C"),
size = 200,
replace = TRUE
)) %>%
group_by(condition) %>%
mutate(new_episode = is_new_episode(date, 365)) %>%
select(patient, date, condition, new_episode)
}
#> Error in mutate(., condition = sample(x = c("A", "B", "C"), size = 200, replace = TRUE)): ℹ In argument: `condition = sample(x = c("A", "B", "C"), size = 200,
#> replace = TRUE)`.
#> Caused by error:
#> ! `condition` must be size 100 or 1, not 200.
if (require("dplyr")) {
df %>%
group_by(ward, patient) %>%
transmute(date,
patient,
new_index = get_episode(date, 60),
new_logical = is_new_episode(date, 60)
)
}
#> # A tibble: 100 × 5
#> # Groups: ward, patient [96]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <dbl> <lgl>
#> 1 Clinical 2009-12-30 6D2377 1 TRUE
#> 2 Clinical 2010-04-05 192353 1 TRUE
#> 3 Clinical 2004-02-02 136315 1 TRUE
#> 4 ICU 2004-09-22 F35553 1 TRUE
#> 5 ICU 2004-11-03 D65308 1 TRUE
#> 6 Clinical 2015-08-14 A84726 1 TRUE
#> 7 Clinical 2011-04-25 023456 1 TRUE
#> 8 Clinical 2004-03-03 1435C8 1 TRUE
#> 9 Clinical 2006-05-26 54890C 1 TRUE
#> 10 ICU 2016-01-28 845227 1 TRUE
#> # … with 90 more rows
if (require("dplyr")) {
df %>%
group_by(ward) %>%
summarise(
n_patients = n_distinct(patient),
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))
)
}
#> # A tibble: 3 × 5
#> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30
#> <chr> <int> <int> <int> <int>
#> 1 Clinical 61 2 1 1
#> 2 ICU 31 6 2 1
#> 3 Outpatient 4 2 1 1
if (require("dplyr")) {
# is_new_episode() has a lot more flexibility than first_isolate(),
# since you can group on anything that seems relevant:
df %>%
group_by(patient, mo, ward) %>%
mutate(flag_episode = is_new_episode(date, 365)) %>%
select(group_vars(.), flag_episode)
}
#> # A tibble: 100 × 4
#> # Groups: patient, mo, ward [98]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 6D2377 B_FSBCT Clinical TRUE
#> 2 192353 B_STRPT_PNMN Clinical TRUE
#> 3 136315 B_STRPT Clinical TRUE
#> 4 F35553 B_STRPT_ORLS ICU TRUE
#> 5 D65308 B_STPHY_EPDR ICU TRUE
#> 6 A84726 B_STPHY_HMNS Clinical TRUE
#> 7 023456 B_PROTS_MRBL Clinical TRUE
#> 8 1435C8 B_ESCHR_COLI Clinical TRUE
#> 9 54890C B_ESCHR_COLI Clinical TRUE
#> 10 845227 B_STRPT_PNMN ICU TRUE
#> # … with 90 more rows
# }