Following data.table
df <- data.table(id=c(1,2,2,2,3,3,4,4,4),
start_date=c("2019-05-08","2019-08-01","2019-07-12","2017-05-24","2016-05-08","2017-08-01","2019-06-12","2017-02-24","2017-08-24"),
end_date=c("2019-09-08","2019-12-01","2019-07-30","2017-11-24","2017-07-25","2018-08-01","2019-12-12","2017-08-24","2018-08-24"),
variable1=c("a","c","c","d","a",NA,"a","a","b"))
df
id start_date end_date variable1
1: 1 2019-05-08 2019-09-08 a
2: 2 2019-08-01 2019-12-01 c
3: 2 2019-07-12 2019-07-30 c
4: 2 2017-05-24 2017-11-24 d
5: 3 2016-05-08 2017-07-25 a
6: 3 2017-08-01 2018-08-01 <NA>
7: 4 2019-06-12 2019-12-12 a
8: 4 2017-02-24 2017-08-24 a
9: 4 2017-08-24 2018-08-24 b
Within the same ID, I want to compare the start_date
and end_date
. If the end_date
of one row is within 30 days of the start_date
of another row, I want to combine the rows. So that it looks like this:
id start_date end_date variable1
1: 1 2019-05-08 2019-09-08 a
2: 2 2019-07-12 2019-12-01 c
3: 2 2017-05-24 2017-11-24 d
4: 3 2016-05-08 2018-08-01 a
5: 4 2019-06-12 2019-12-12 a
6: 4 2017-02-24 2017-08-24 a
7: 4 2017-08-24 2018-08-24 b
If the other variables of the rows are the same, rows should be combined with the earliest start_date
and latest end_date
as id
number 2. If the variable1
is NA
it should be replaced with values from the matching row as id
number 3. If the variable1
has different values, rows should remain separate as id
number 4. The data.table
contains more variables and objects than displayed here. Preferable a function in data.table
.
Not clear what happens if an id has 3 overlapping rows with variable1 = c('a', NA, 'b')
, what should the variable1
be for the NA for this case? a
or b
?
If we just choose the first variable1
when there are multiple matches, here is an option to first fill the NA and then borrow the idea from David Aurenburg's solution here
setorder(df, id, start_date, end_date)
df[, end_d := end_date + 30L]
df[is.na(variable1), variable1 :=
df[!is.na(variable1)][.SD, on=.(id, start_date<=start_date, end_d>=start_date), mult="first", x.variable1]]
df[, g:= c(0L, cumsum(shift(start_date, -1L) > cummax(as.integer(end_d)))[-.N]), id][,
.(start_date=min(start_date), end_date=max(end_date)), .(id, variable1, g)]
output:
id variable1 g start_date end_date
1: 1 a 0 2019-05-08 2019-09-08
2: 2 d 0 2017-05-24 2017-11-24
3: 2 c 1 2019-07-12 2019-12-01
4: 3 a 0 2016-05-08 2018-08-01
5: 4 a 0 2017-02-24 2017-08-24
6: 4 b 0 2017-08-24 2018-08-24
7: 4 a 1 2019-06-12 2019-12-12
data:
library(data.table)
df <- data.table(id=c(1,2,2,2,3,3,4,4,4),
start_date=as.IDate(c("2019-05-08","2019-08-01","2019-07-12","2017-05-24","2016-05-08","2017-08-01","2019-06-12","2017-02-24","2017-08-24")),
end_date=as.IDate(c("2019-09-08","2019-12-01","2019-07-30","2017-11-24","2017-07-25","2018-08-01","2019-12-12","2017-08-24","2018-08-24")),
variable1=c("a","c","c","d","a",NA,"a","a","b"))
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