Replace missing data by using another data table for multiple columns - r

Question

I have many columns in a table where there is missing data. I want to be able to pull in the information from another table if the data is missing for a particular record based on ID. I thought about possibly joining the two tables and writing a for loop where if column X is NA then pull in information from column Y, however, I have many columns and would require writing many of these conditions.
I want to create a function or a loop where I can pass in the data column names with the missing data and be able to pass in the column name from another table to get the information from.
Reproducible Example:
ID <- c(1,2,3,4,5,6)
Year <- c(1990,1987,NA,NA,1968,1992)
Month <- c(1,NA,8,12,NA,5)
Day <- c(3,NA,NA,NA,NA,30)
New_Data = data.frame(ID=ID,Year=Year,Month=Month,Day=Day)
ID <- c(2,3,4,5)
Year <- c(NA,1994,1967,NA)
Month <- c(4,NA,NA,10)
Day <- c(23,12,16,9)
Old_Data = data.frame(ID=ID,Year=Year,Month=Month,Day=Day)
Expected Output:
ID <- c(1,2,3,4,5,6)
Year <- c(1990,1987,1994,1967,1968,1992)
Month <- c(1,4,8,12,10,5)
Day <- c(3,23,12,16,9,30)
New_Data = data.frame(ID=ID,Year=Year,Month=Month,Day=Day)

Answer 1

Using rbind combine two dataframe , then we using group_by with summarise_all
library(dplyr)
rbind(New_Data,Old_Data)%>%group_by(ID)%>%dplyr::summarise_all(function(x) x[!is.na(x)][1])
# A tibble: 6 x 4
ID Year Month Day
<dbl> <dbl> <dbl> <dbl>
1 1 1990 1 3
2 2 1987 4 23
3 3 1994 8 12
4 4 1967 12 16
5 5 1968 10 9
6 6 1992 5 30

Answer 2

An option using dplyr::left_join and dplyr::coalesce can be as:
library(dplyr)
New_Data %>% left_join(Old_Data, by="ID") %>%
mutate(Year = coalesce(Year.x, Year.y),
Month = coalesce(Month.x, Month.y),
Day = coalesce(Day.x, Day.y)) %>%
select(ID, Year, Month, Day)
# ID Year Month Day
# 1 1 1990 1 3
# 2 2 1987 4 23
# 3 3 1994 8 12
# 4 4 1967 12 16
# 5 5 1968 10 9
# 6 6 1992 5 30

Answer 3

Here's a solution using only base functions from another SO question
I modified it to your needs (created a function, and made an argument for the key column name):
fill_missing_data = function(df1, df2, keyColumn) {
commonNames <- names(df1)[which(colnames(df1) %in% colnames(df2))]
commonNames <- commonNames[commonNames != keyColumn]
dfmerge<- merge(df1,df2,by="ID",all=T)
for(i in commonNames){
left <- paste(i, ".x", sep="")
right <- paste(i, ".y", sep="")
dfmerge[is.na(dfmerge[left]),left] <- dfmerge[is.na(dfmerge[left]),right]
dfmerge[right]<- NULL
colnames(dfmerge)[colnames(dfmerge) == left] <- i
}
return(dfmerge)
}
result = fill_missing_data(New_Data, Old_Data, "ID")