I am working with a long-format longitudinal dataset where each person has 1, 2 or 3 time points. In order to perform certain analyses I need to make sure that each person has the same number of rows even if it consists of NAs because they did not complete the certain time point.
Here is a sample of the data before adding the rows:
structure(list(Values = c(23, 24, 45, 12, 34, 23), P_ID = c(1,
1, 2, 2, 2, 3), Event_code = c(1, 2, 1, 2, 3, 1), Site_code = c(1,
1, 3, 3, 3, 1)), class = "data.frame", row.names = c(NA, -6L))
This is the data I aim to get after adding the relevant rows:
structure(list(Values = c(23, 24, NA, 45, 12, 34, 23, NA, NA),
P_ID = c(1, 1, 1, 2, 2, 2, 3, 3, 3), Event_code = c(1, 2,
3, 1, 2, 3, 1, 2, 3), Site_code = c(1, 1, 1, 3, 3, 3, 1,
1, 1)), class = "data.frame", row.names = c(NA, -9L))
I want to come up with code that would automatically add rows to the dataset conditionally on whether the participant has had 1, 2 or 3 visits. Ideally it would make rest of data all NAs while copying Participant_ID and site_code but if not possible I would be satisfied just with creating the right number of rows.
We could use fill after doing a complete
library(dplyr)
library(tidyr)
ExpandedDataset %>%
complete(P_ID, Event_code) %>%
fill(Site_code)