Skip to main content

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

Update (10 March 2013): FillIn is now part of the budding DataCombine package.


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 <- FillIn(D1 = naDF, D2 = fillDF, 
                       Var1 = "fNA", Var2 = "fFull", KeyVar = c("a", "b"))

## [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

Comments

Popular posts from this blog

Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines'). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values.So, I’ve been putting together the simPH R p…

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame.I've found the various R methods for doing this hard to remember and usually need to look at old blogposts. Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command–slide–to the DataCombine R package (v0.1.5).Building on the shift function TszKin Julian posted on his blog, slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data.Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996, 1997. ExamplesNot…

Do Political Scientists Care About Effect Sizes: Replication and Type M Errors

Reproducibility has come a long way in political science. Many major journals now require replication materials be made available either on their websites or some service such as the Dataverse Network. Most of the top journals in political science have formally committed to reproducible research best practices by signing up to the The (DA-RT) Data Access and Research Transparency Joint Statement.This is certainly progress. But what are political scientists actually supposed to do with this new information? Data and code availability does help avoid effort duplication--researchers don't need to gather data or program statistical procedures that have already been gathered or programmed. It promotes better research habits. It definitely provides ''procedural oversight''. We would be highly suspect of results from authors that were unable or unwilling to produce their code/data.However, there are lots of problems that data/code availability requirements do not address.…