匹配 data.table r 中开始和结束日期相同或接近的行

Question

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 .

Answer 1

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"))