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Group across multiple observations of overlapping time intervals, with defined start and end dates, or events within a static/fixed or rolling window of time. These are commonly used with inpatient HES/SUS data to group spells with defined start and end dates, or to group positive specimen tests, based on specimen dates together into infection episodes.
Usage
group_time(
x,
date_start,
date_end,
window,
window_type = c("rolling", "static"),
group_vars,
indx_varname = "indx",
min_varname = "date_min",
max_varname = "date_max",
.forceCopy = FALSE
)Arguments
- x
data frame, this will be converted to a data.table
- date_start
column containing the start dates for the grouping, provided quoted
- date_end
column containing the end dates for the interval, quoted
- window
an integer representing a time window in days which will be applied to the start date for grouping events
- window_type
character, to determine if a 'rolling' or 'static' grouping method should be used when grouping events
- group_vars
in a vector, the all columns used to group records, quoted
- indx_varname
a character string to set variable name for the index column which provides a grouping key; default is indx
- min_varname
a character string to set variable name for the time period minimum
- max_varname
a character string set variable name for the time period maximum
- .forceCopy
default FALSE; TRUE will force data.table to take a copy instead of editing the data without reference
Value
the original data.frame as a data.table with the following new fields:
indx; renamed usingindx_varnamean id field for the new aggregated events/intervals; note that where the
date_startis NA, anindxvalue will also be NAmin_date; renamed usingmin_varnamethe start date for the aggregated events/intervals
max_date; renamed usingmax_varnamethe end date for the aggregated events/intervals
Examples
episode_test <- structure(
list(
pat_id = c(1L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L),
species = c(rep("E. coli",7),rep("K. pneumonia",7)),
spec_type = c(rep("Blood",7),rep("Blood",4),rep("Sputum",3)),
sp_date = structure(c(18262, 18263, 18281, 18282, 18262, 18263, 18281,
18265, 18270, 18281, 18283, 18259, 18260, 18281),
class = "Date")
),
row.names = c(NA, -14L), class = "data.frame")
group_time(x=episode_test,
date_start='sp_date',
window=14,
window_type = 'static',
indx_varname = 'static_indx',
group_vars=c('pat_id','species','spec_type'))[]
#> pat_id species spec_type sp_date static_indx
#> <int> <char> <char> <Date> <char>
#> 1: 1 E. coli Blood 2020-01-01 1.4.1
#> 2: 1 E. coli Blood 2020-01-02 1.4.1
#> 3: 1 E. coli Blood 2020-01-20 1.4.2
#> 4: 1 E. coli Blood 2020-01-21 1.4.2
#> 5: 1 K. pneumonia Blood 2020-01-04 2.4.1
#> 6: 1 K. pneumonia Blood 2020-01-09 2.4.1
#> 7: 1 K. pneumonia Blood 2020-01-20 2.4.2
#> 8: 1 K. pneumonia Blood 2020-01-22 2.4.2
#> 9: 2 E. coli Blood 2020-01-01 3.3.1
#> 10: 2 E. coli Blood 2020-01-02 3.3.1
#> 11: 2 E. coli Blood 2020-01-20 3.3.2
#> 12: 2 K. pneumonia Sputum 2019-12-29 4.3.1
#> 13: 2 K. pneumonia Sputum 2019-12-30 4.3.1
#> 14: 2 K. pneumonia Sputum 2020-01-20 4.3.2
spell_test <- data.frame(
id = c(rep(99,6),rep(88,4),rep(3,3)),
provider = c("YXZ",rep("ZXY",5),rep("XYZ",4),rep("YZX",3)),
spell_start = as.Date(
c(
"2020-03-01",
"2020-07-07",
"2020-02-08",
"2020-04-28",
"2020-03-15",
"2020-07-01",
"2020-01-01",
"2020-01-12",
"2019-12-25",
"2020-03-28",
"2020-01-01",
rep(NA,2)
)
),
spell_end = as.Date(
c(
"2020-03-10",
"2020-07-26",
"2020-05-22",
"2020-04-30",
"2020-05-20",
"2020-07-08",
"2020-01-23",
"2020-03-30",
"2020-01-02",
"2020-04-20",
"2020-01-01",
rep(NA,2)
)
)
)
group_time(x = spell_test,
date_start = 'spell_start',
date_end = 'spell_end',
group_vars = c('id','provider'),
indx_varname = 'spell_id',
min_varname = 'spell_min_date',
max_varname = 'spell_max_date')[]
#> id provider spell_start spell_end spell_id spell_min_date
#> <num> <char> <Date> <Date> <char> <Date>
#> 1: 88 XYZ 2019-12-25 2020-01-02 1.4.0 2019-12-25
#> 2: 88 XYZ 2020-01-01 2020-01-23 1.4.0 2019-12-25
#> 3: 3 YZX 2020-01-01 2020-01-01 2.1.0 2020-01-01
#> 4: 88 XYZ 2020-01-12 2020-03-30 1.4.0 2019-12-25
#> 5: 99 ZXY 2020-02-08 2020-05-22 3.5.0 2020-02-08
#> 6: 99 YXZ 2020-03-01 2020-03-10 4.1.0 2020-03-01
#> 7: 99 ZXY 2020-03-15 2020-05-20 3.5.0 2020-02-08
#> 8: 88 XYZ 2020-03-28 2020-04-20 1.4.0 2019-12-25
#> 9: 99 ZXY 2020-04-28 2020-04-30 3.5.0 2020-02-08
#> 10: 99 ZXY 2020-07-01 2020-07-08 3.5.1 2020-07-01
#> 11: 99 ZXY 2020-07-07 2020-07-26 3.5.1 2020-07-01
#> 12: 3 YZX <NA> <NA> <NA> <NA>
#> 13: 3 YZX <NA> <NA> <NA> <NA>
#> spell_max_date
#> <Date>
#> 1: 2020-04-20
#> 2: 2020-04-20
#> 3: 2020-01-01
#> 4: 2020-04-20
#> 5: 2020-05-22
#> 6: 2020-03-10
#> 7: 2020-05-22
#> 8: 2020-04-20
#> 9: 2020-05-22
#> 10: 2020-07-26
#> 11: 2020-07-26
#> 12: <NA>
#> 13: <NA>