R: Pivoting multi-year spells into multiple year rows - r

Question

I have a dataset where each row represents a continuous spell with start and end months and years. For spells which are over more than one year, I want to pivot them so that there is one row per year.
Input:
library(data.table)
dat <- data.table(id = c(1,1,2), b_sp_y = c(2008, 2009, 2011), b_sp_m = c(3, 8, 6),
e_sp_y = c(2008, 2010, 2013), e_sp_m = c(5, 1, 9))
id b_sp_y b_sp_m e_sp_y e_sp_m
1: 1 2008 3 2008 5
2: 1 2009 8 2010 1
3: 2 2011 6 2013 9
Here is my truly horrifyingly ugly code:
dat[, y_dif := e_sp_y - b_sp_y]
res <- dat[y_dif == 0][, c("e_sp_y", "y_dif") := NULL]
setnames(res, "b_sp_y", "year")
tmp <- dat[y_dif > 0]
for(i in 1:nrow(tmp)){
foo <- tmp[i, ]
foo2 <- data.table(year = foo$b_sp_y:(foo$b_sp_y + foo$y_dif))[,id := foo$id]
foo2[, b_sp_m := c(foo$b_sp_m, rep(1, foo$y_dif))]
foo2[, e_sp_m := c(rep(12, foo$y_dif), foo$e_sp_m)]
res <- rbind(res, foo2)
}
Output:
id year b_sp_m e_sp_m
1: 1 2008 3 5
2: 1 2009 8 12
3: 1 2010 1 1
4: 2 2011 6 12
5: 2 2012 1 12
6: 2 2013 1 9
This is ugly and slow to a crawl, but I couldn't really come up with anything better.
Thanks for your help!

Answer 1

Proceeding by row fill in the three columns using summarize as shown.
library(data.table)
library(dplyr)
dat %>%
rowwise() %>%
summarize(id = id,
year = b_sp_y:e_sp_y,
b_sp_m = replace(1 + 0 * year, 1, b_sp_m),
e_sp_m = replace(12 + 0 * year, length(year), e_sp_m))
giving:
# A tibble: 6 × 4
id year b_sp_m e_sp_m
<dbl> <int> <dbl> <dbl>
1 1 2008 3 5
2 1 2009 8 12
3 1 2010 1 1
4 2 2011 6 12
5 2 2012 1 12
6 2 2013 1 9
or using only data.table:
library(data.table)
dat[, .(id = id,
year = b_sp_y:e_sp_y,
b_sp_m = replace(1 + 0 * b_sp_y:e_sp_y, 1, b_sp_m),
e_sp_m = replace(12 + 0 * b_sp_y:e_sp_y, e_sp_y - b_sp_y + 1, e_sp_m)),
by = 1:nrow(dat)][, -1]
Added
Here are some slightly more compact variations of the above:
library(data.table)
library(dplyr)
dat %>%
rowwise() %>%
summarize(id = id,
year = b_sp_y:e_sp_y,
b_sp_m = c(b_sp_m, year[-1]^0),
e_sp_m = c(12 * year[-1]^0, e_sp_m))
library(data.table)
dat[, {
year <- b_sp_y:e_sp_y
.(id = id,
year = year,
b_sp_m = c(b_sp_m, year[-1]^0),
e_sp_m = c(12 * year[-1]^0, e_sp_m))
},
by = 1:nrow(dat)][, -1]

Answer 2

I'd suggest: make a date sequence for each id/row, group by id and year, summarize first and last month.
library(dplyr); library(lubridate)
dat %>%
mutate(start = ymd(paste(b_sp_y, b_sp_m, "01", sep = "-")),
end = ymd(paste(e_sp_y, e_sp_m, "01", sep = "-"))) %>%
group_by(id, row = row_number()) %>%
summarize(months = seq.Date(start, end, by = "month")) %>%
group_by(id, year = year(months)) %>%
summarize(from = month(min(months)),
to = month(max(months)), .groups = "drop")
Result:
# A tibble: 6 × 4
id year from to
<dbl> <dbl> <dbl> <dbl>
1 1 2008 3 5
2 1 2009 8 12
3 1 2010 1 1
4 2 2011 6 12
5 2 2012 1 12
6 2 2013 1 9

Answer 3

We create a sequence column 'rn', loop over the year columns, get the sequence in a list, unnest the column, and do a group by the 'rn' and replace the 'b', 'e' columns where there are duplicates to 1 and 12 respectively
library(dplyr)
library(purrr)
library(tidyr)
dat %>%
mutate(rn=row_number(),
year = map2(b_sp_y, e_sp_y, `:`),
b_sp_y= NULL,
e_sp_y = NULL) %>%
unnest(year) %>%
group_by(rn) %>%
mutate(b_sp_m = replace(b_sp_m, duplicated(b_sp_m), 1),
e_sp_m = replace(e_sp_m, duplicated(e_sp_m, fromLast = TRUE) &
n() > 1, 12)) %>%
ungroup %>%
select(-rn) %>%
relocate(year, .after = 1)
-output
# A tibble: 6 × 4
id year b_sp_m e_sp_m
<dbl> <int> <dbl> <dbl>
1 1 2008 3 5
2 1 2009 8 12
3 1 2010 1 1
4 2 2011 6 12
5 2 2012 1 12
6 2 2013 1 9
OP's output of 'res'
> res
id year b_sp_m e_sp_m
<num> <num> <num> <num>
1: 1 2008 3 5
2: 1 2009 8 12
3: 1 2010 1 1
4: 2 2011 6 12
5: 2 2012 1 12
6: 2 2013 1 9