FillIn: a function for filling in missing data in one data frame with info from another

[This article was first published on Christopher Gandrud (간드루드 크리스토파), and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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Sometimes I want to use R to fill in values that are missing in one data frame with values from another. For example, I have data from the World Bank on government deficits. However, there are some country-years with missing data. I gathered data from Eurostat on deficits and want to use this data to fill in some of the values that are missing from my World Bank data.

Doing this is kind of a pain so I created a function that would do it for me. It’s called FillIn.

An Example

Here is an example using some fake data. (This example and part of the function was inspired by a Stack Exchange conversation between JD Long and Josh O’Brien.)

First let’s make two data frames: one with missing values in a variable called fNA. And a data frame with a more complete variable called fFull.

# Create data set with missing values
naDF <- data.frame(a = sample(c(1,2), 100, rep=TRUE), 
                   b = sample(c(3,4), 100, rep=TRUE), 
                   fNA = sample(c(100, 200, 300, 400, NA), 100, rep=TRUE))
                   
# Created full data set
fillDF <- data.frame(a = c(1,2,1,2), 
                     b = c(3,3,4,4),
                     fFull = c(100, 200, 300, 400))

Now we just enter some information into FillIn about what the data set names are, what variables we want to fill in, and what variables to join the data sets on.

# Fill in missing f's from naDF with values from fillDF
FilledInData 
## [1] "16 NAs were replaced."
## [1] "The correlation between fNA and fFull is 0.313"

D1 and Var1 are for the data frame and variables you want to fill in. D2 and Var2 are what you want to use to fill them in with. KeyVar specifies what variables you want to use to joint the two data frames.

FillIn lets you know how many missing values it is filling in and what the correlation coefficient is between the two variables you are using. Depending on your missing data issues, this could be an indicator of whether or not Var2 is an appropriate substitute for Var1.

Installation

FillIn is currently available as a GitHub Gist and can be installed with this code:

devtools::source_gist("4959237")

You will need the devtools package to install it. For it to work properly you will also need the data.table package.

The Full Code

To leave a comment for the author, please follow the link and comment on their blog: Christopher Gandrud (간드루드 크리스토파).

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