Replace missing data by using another data table for multiple columns

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)

BENY · Accepted Answer · 2018-07-27 21:39:07Z

2

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

edited Jul 27, 2018 at 21:39

answered Jul 27, 2018 at 21:29

BENY

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1 Comment

C.Lee Over a year ago

This works but I need a function that matches on ID and only pulls in records from Old_Data table that has matches to the New_Data based on ID, I do not want any records from Old_Data that does not exist in New_Data table.

MKR · Accepted Answer · 2018-07-27 23:18:21Z

1

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

answered Jul 27, 2018 at 23:18

MKR

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Comments

jayke · Accepted Answer · 2018-07-27 21:44:04Z

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")

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