Split date range into several chunks ending by YYYY-12-31

Question

df <- data.frame(group = c("a", "a", "b", "b"),
                 start = c("2017-05-01", "2019-04-03", "2011-03-03", "2014-05-07"),
                 end = c("2018-09-01", "2020-04-03", "2012-05-03", "2016-04-02"))  

Let's say I have the following df:

  group      start        end
1     a 2017-05-01 2018-09-01
2     a 2019-04-03 2020-04-03
3     b 2011-03-03 2012-05-03
4     b 2014-05-07 2016-04-02

I want to get it into this format, with each record split into starting date and 31/12 of that and subsequent years:

  group      start        end
1     a 2017-05-01 2017-12-31
2     a 2018-01-01 2018-09-01
3     a 2019-04-03 2019-12-31
4     a 2020-01-01 2020-04-03
5     b 2011-03-03 2011-12-31
6     b 2012-01-01 2012-05-03
7     b 2014-05-07 2014-12-31
8     b 2015-01-01 2015-12-31
9     b 2016-01-01 2016-04-02

Any ideas on how to tackle this?

Edit:

My primary concerns are not the date ranges that are within the same year. However, as chinsoon12 pointed out, it would indeed be helpful if the approach could handle them as well, as for instance in this dataset:

df <- data.frame(group = c("a", "a", "b", "b", "c"),
                 start = c("2017-05-01", "2019-04-03", "2011-03-03", "2014-05-07", "2017-02-01"),
                 end = c("2018-09-01", "2020-04-03", "2012-05-03", "2016-04-02", "2017-04-05")) 

The end result would leave the last line as it was:

   group      start        end
1      a 2017-05-01 2017-12-31
2      a 2018-01-01 2018-09-01
3      a 2019-04-03 2019-12-31
4      a 2020-01-01 2020-04-03
5      b 2011-03-03 2011-12-31
6      b 2012-01-01 2012-05-03
7      b 2014-05-07 2014-12-31
8      b 2015-01-01 2015-12-31
9      b 2016-01-01 2016-04-02
10     c 2017-02-01 2017-04-05  

Answer 1

A possible solution with data.table:

library(data.table)
setDT(df)

df[df[, rep(.I, 1 + year(end) - year(start))]
   ][, `:=` (start = pmax(start[1], as.Date(paste0(year(start[1]) + 0:(.N-1), '-01-01'))),
             end = pmin(end[.N], as.Date(paste0(year(end[.N]) - (.N-1):0, '-12-31'))))
     , by = .(group, rleid(start))][]

which gives:

    group      start        end
 1:     a 2017-05-01 2017-12-31
 2:     a 2018-01-01 2018-09-01
 3:     a 2019-04-03 2019-12-31
 4:     a 2020-01-01 2020-04-03
 5:     b 2011-03-03 2011-12-31
 6:     b 2012-01-01 2012-05-03
 7:     b 2014-05-07 2014-12-31
 8:     b 2015-01-01 2015-12-31
 9:     b 2016-01-01 2016-04-02
10:     c 2017-02-01 2017-04-05

Two alternative solutions with data.table:

# alternative 1:
df[, ri := rowid(group)
   ][df[, rep(.I, 1 + year(end) - year(start))]
     ][, `:=` (start = if (.N == 1) start else c(start[1], as.Date(paste0(year(start[1]) + 1:(.N-1), '-01-01') )),
               end = if (.N == 1) end else c(as.Date(paste0(year(end[.N]) - (.N-1):1, '-12-31') ), end[.N]))
       , by = .(group, ri)][, ri := NULL][]

# alternative 2:
df[, ri := rowid(group)
   ][df[, rep(.I, 1 + year(end) - year(start))]
     ][, `:=` (start = pmax(start[1], as.Date(paste0(year(start[1]) + 0:(.N-1), '-01-01'))),
               end = pmin(end[.N], as.Date(paste0(year(end[.N]) - (.N-1):0, '-12-31'))))
       , by = .(group, ri)][, ri := NULL][]

---

Used data:

df <- data.frame(group = c("a", "a", "b", "b", "c"),
                 start = c("2017-05-01", "2019-04-03", "2011-03-03", "2014-05-07", "2017-02-01"),
                 end = c("2018-09-01", "2020-04-03", "2012-05-03", "2016-04-02", "2017-04-05")) 
df[2:3] <- lapply(df[2:3], as.Date)

Answer 2

library(tidyverse)
library(lubridate)
df%>%
  mutate(end=as.Date(end),
     start=as.Date(start),
     diff=Map(":",0,1+year(end)-year(start)-1))%>%
  unnest()%>%
  mutate(end=pmin(end,as.Date(paste0(year(start)+diff,"-12-31"))),
         start=pmax(start,as.Date(paste0(year(start)+diff,"-1-1"))),
         diff=NULL)

A tibble: 9 x 3
  group start      end       
  <fct> <date>     <date>    
1 a     2017-05-02 2017-12-31
2 a     2018-01-01 2018-09-01
3 a     2019-04-03 2019-12-31
4 a     2020-01-01 2020-04-03
5 b     2020-01-01 2011-12-31
6 b     2012-01-01 2012-05-03
7 b     2014-05-07 2014-12-31
8 b     2015-01-01 2015-12-31
9 b     2015-01-01 2016-04-02

with the updated data just ran this exact function you will get:

 group      start        end
1      a 2017-05-01 2017-12-31
2      a 2018-01-01 2018-09-01
3      a 2019-04-03 2019-12-31
4      a 2020-01-01 2020-04-03
5      b 2011-03-03 2011-12-31
6      b 2012-01-01 2012-05-03
7      b 2014-05-07 2014-12-31
8      b 2015-01-01 2015-12-31
9      b 2016-01-01 2016-04-02
10     c 2017-02-01 2017-04-05

Answer 3

Here's a no-tidyverse/no-data.table version:

df <- data.frame(group = c("a", "a", "b", "b"),
                 start = c("2017-05-01", "2019-04-03", "2011-03-03", "2014-05-07"),
                 end = c("2018-09-01", "2020-04-03", "2012-05-03", "2016-04-02"), stringsAsFactors=FALSE)
# added stringsAsFactors =FALSE to your df for sanity
# reformatting start and end as Date  
df$start <- as.Date(df$start)
df$end <- as.Date(df$end)

dfs <- split(df, rownames(df))
# split the data frame by rows

res <- do.call(rbind, lapply(dfs, function(.){
  s <- seq(from=.$start, to=.$end, by="day")
  # sequence form df$start to df$end, by days
  y <- format(s, "%Y")
  # years of that sequence
  s2 <- as.character(s)
  # formatting s as character -- otherwise sapply will get rid of the 
  # Date class and the result will look as numeric
  ys <- split(s2,y)
  # split the sequence by years
  data.frame(group=.$group, start=sapply(ys, head,1), end = sapply(ys, tail, 1), stringsAsFactors=FALSE)
  # take the first and last element from each "sub-vector" of the split sequence
}))
rownames(res) <- NULL # kill the nasty rownames
res

  group      start        end
1     a 2017-05-01 2017-12-31
2     a 2018-01-01 2018-09-01
3     a 2019-04-03 2019-12-31
4     a 2020-01-01 2020-04-03
5     b 2011-03-03 2011-12-31
6     b 2012-01-01 2012-05-03
7     b 2014-05-07 2014-12-31
8     b 2015-01-01 2015-12-31
9     b 2016-01-01 2016-04-02

Notice that the result has start and end columns as character as it was in your original data frame.

I'm sorry for the way base R treats Date (and POSIXct) objects - you never know when they may lose their class and become simple numbers. Here I avoided this "feature" by treating dates as character, except when date operations were needed e.g in creating the sequence of days.