CODEWITHSUNDEEP
rtime-seriespanel-data
RuthQuinn
RuthQuinn

Reputation: 33

Creating start date based on end date of prior observation for multiple observations by group

I have a dataset of episodes by ID, where for each person I have a start date and a length of the episode (df.have).

I'd like to create start and end dates for each episode where the start date for one episode is one day after the end date for the previous episode (df.want)

I know I need to lag the prior start date but I don't know how to do that repeatedly (i.e., I can do it for the second episode, but not the third).

df.have  <- data.frame(id=c(1,1,1,2,2,2,3,3,3),
               episode_num=c(1,2,3,1,2,3,1,2,3),
               start_date=as.Date(c("1/1/2001", NA, NA, "5/1/2001", NA, NA, "10/1/1001", NA, NA), "%m/%d/%y"),
               episode_length=c(10,4,5,20,3,2,1,9,8))

df.want <- df <- data.frame(id=c(1,1,1,2,2,2,3,3,3),
               episode_num=c(1,2,3,1,2,3,1,2,3),
               start_date=as.Date(c("1/1/01", "1/12/01","1/17/01","5/1/01","5/22/01","5/26/01","10/1/01","10/3/01","10/13/01"),"%m/%d/%y"),
               end_date= as.Date(c("1/11/01","1/16/01","1/22/01","5/21/01","5/25/01","5/28/01","10/2/01","10/12/01","10/21/01"), "%m/%d/%y"),
               episode_length=c(10,4,5,20,3,2,1,9,8))

Upvotes: 1

Views: 104

Answers (1)

fdetsch
fdetsch

Reputation: 5308

Unless you want to split() your input data set by ID and loop over the resulting list, there will ultimately be some kind of grouping involved. An easy solution to solve this would be to switch to data.table and calculate the remaining start dates per group by adding the cumsum() of the episode lengths to the initial start date per ID. The end dates can then be calculated by adding the episode lengths to the resulting start dates.

Note that this approach assumes that episode numbers per ID are in order.

library(data.table)

dt.have = data.table(
  df.have
)

## calculate remaining start dates per id
dt.have[
  , start_date := min(
    start_date
    , na.rm = TRUE
  ) + c(
    0
    , cumsum(
      episode_length[-.N] + 1 # exclude last episode_length per group from `cumsum()`
    )
  )
  , by = id
]

## append end dates
dt.have[
  , end_date := start_date + episode_length
]

If you don't feel comfortable working with data.table, you can simply convert the output object back to data.frame using

data.frame(
  dt.have
)

# id episode_num start_date episode_length   end_date
#  1           1 2020-01-01             10 2020-01-11
#  1           2 2020-01-12              4 2020-01-16
#  1           3 2020-01-17              5 2020-01-22
#  2           1 2020-05-01             20 2020-05-21
#  2           2 2020-05-22              3 2020-05-25
#  2           3 2020-05-26              2 2020-05-28
#  3           1 2010-10-01              1 2010-10-02
#  3           2 2010-10-03              9 2010-10-12
#  3           3 2010-10-13              8 2010-10-21

Since this was explicitly asked in the comments @TomHoel, here's the dplyr analog of the above code (just replace the native |> with %>% for R versions lower than 4.1.0):

library(dplyr)

df.have |> 
  
  # group by id
  group_by(id) |> 
  
  mutate(
    # calculate remaining start dates per id
    start_date = min(
      start_date
      , na.rm = TRUE
    ) + c(
      0
      , cumsum(
        episode_length[-n()] + 1 # exclude last episode_length per group from `cumsum()`
      )
    )
    # append end date
    , end_date = start_date + episode_length
  )

Upvotes: 2

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