Based on the provided solutions, I will suggest an alternative approach that builds upon the second solution. Instead of using nest_join and map, we can use a more straightforward approach with dplyr.

Here’s the modified code:

library(dplyr)

get_medication_name <- function(medication_name_df) {
  medication_name <- medication_name_df %>%
    group_by(id) %>%
    arrange(administered_datetime) %>%
    pull(med_name_one)
}

table_nested <- table_age %>%
  inner_join(table, on = .(id = id))

table_answer <- table_nested %>%
  mutate(
    medication_name = ifelse(is.na(medication_name), NA,
                             get_medication_name(subset(table_nested, administration_datetime == administered_datetime)))
  )

print(table_answer)

This code performs the same operations as the original solution, but with a more concise and readable syntax. The get_medication_name function remains unchanged, while the table_nested and table_answer data frames are created using inner_join and mutate, respectively.

Note that I’ve also added a check for NA values in the medication_name column, to avoid errors when administration_datetime is not equal to any date in table. This ensures that the get_medication_name function is only called for rows where the medication name is not already present.

Last modified on 2024-04-03