This function joins 2 tibbles and generates unique columns to record non-NA values when there are 2 identical column names.
Arguments
- tibble_names
A vector of tibbles' names in your R environment.
- join_type
It should be one of 'full_join', 'left_join', 'inner_join'.
- by_cols
It should include the columns in a format that `
join_by` can interpret.
Details
The motivation to create this function is that when Bolin extracts SNAC-N variables, he finds that for wave 3, there is overlapping information from different sources.
For example, for physician variables, there are 3 files:
All wave 3 participants.
Only cohort 1's follow-up at wave 3.
Only cohort 2's baseline.
There are overlapping of both participants and variables.
In addition, for the same participant and same variable, some are NA in one file whereas not NA in the other file.
If only using a join function, the common columns will be separated to '.x' and '.y'. And we have to use coalesce to pick one without NA. And then we delete the '.x' and '.y' columns.
To avoid repetitive work, Bolin wrote this function to integrate information from different data files.
Examples
if (FALSE) { # \dontrun{
## fake_snacn_ph_wave3 and fake_snacn_ph_fu contains same variable 'ph121'.
## but for some obs, in one file in NA, in the other file is not NA. E.g. Lopnr = 10
left_join(fake_snacn_ph_wave3, fake_snacn_ph_fu, by = join_by(Lopnr == N1lopnr, age))
## let's just combine the info and only keep the one without NA.
get_unique_join(tibble_names = c("fake_snacn_ph_wave3", "fake_snacn_ph_fu"),
join_type = "full_join",
by_cols = "Lopnr == N1lopnr, age")
} # }