R functions to divide person-time by year of observation-CodePudding

Been a little stuck on this for a couple days. Let's say I have a cohort of 2 people.

Person 1 was in cohort from 01/01/2000 to 01/03/2001. Person 2 was in cohort from 01/01/1999 to 31/12/2001.

This means person 1 was in the cohort for all of 2000 and 25% of 2001. Person 2 was in the cohort for all of 1999, all of 2000, and all of 2001.

Adding this together means that, in total, the cohort contributed 1 year of person-time in 1999, 2 years of person-time in 2000, and 1.25 years of person-time in 2001.

Does anyone know of any R functions that might help with dividing up/summing time elapsed between dates like this? I could write it all from scratch, but I'd like to use existing functions if they're out there, and Google has got me nowhere.

Thanks!

CodePudding user response：

Using data.table and lubridate:

Data <- Data[, .(Start, Start2 = seq(Start, End, by="year"), End), by=.(Person)]
Data[, End2 := Start2 years(1)-days(1)]
Data[year(Start2) != year(Start), Start := Start2]
Data[year(End2) != year(End), End := End2]
Data[, c("Year", "Contribution") := list(year(Start), (month(End)-month(Start) 1)/12)]
Data <- Data[, .(Contribution = sum(Contribution)), by=.(Year)][order(Year)]

Which gives:

> Data
   Year Contribution
1: 1999         1.00
2: 2000         2.00
3: 2001         1.25

CodePudding user response：

This is a possible generalized tidyverse approach also using lubridate. This creates rows for each year and appropriate time intervals for each person-year. The intersection between the calendar year and person-year interval will be the contribution summed up in the end. Note that Jan 1 to Mar 1 here would be considered 2 months or 1/6 of a year contribution (not 25%).

df <- data.frame(
  person = c("Person 1", "Person 2"),
  start = c("01/01/2000", "01/01/1999"),
  end = c("01/03/2001", "31/12/2001")
)

df$start <- dmy(df$start)
df$end <- dmy(df$end)

library(lubridate)
library(tidyverse)

df %>%
  mutate(date_int = interval(start, end),
         year = map2(year(start), year(end), seq)) %>%
  unnest(year) %>%
  mutate(
    year_int = interval(
      as.Date(paste0(year, '-01-01')), as.Date(paste0(year, '-12-31'))
      ),
    year_sect = intersect(date_int, year_int)
  ) %>%
  group_by(year) %>%
  summarise(contribute = signif(sum(as.numeric(year_sect, "years")), 2))

Output

   year contribute
  <int>      <dbl>
1  1999        1  
2  2000        2  
3  2001        1.2