Working With Missing Values In R

Kevin Feasel



Anisa Dhana has a few examples of ways we can work with data containing missing values in R:

Imputation is a complex process that requires a good knowledge of your data. For example, it is crucial to know whether the missing is at random or not before you impute the data. I have read a nice tutorial which visualize the missing data and help to understand the type of missing, and another post showing how to impute the data with MICE package.

In this short post, I will focus on management of the missing data using the tidyverse package. Specifically, I will show how to manage missings in the long data format (i.e., more than one observation for id).

Anisa shows a few different techniques, depending upon what you need to do with the data.  I’d caution about using mean in the second example and instead typically prefer median, as replacing missing values with the median won’t alter the distribution in the way that it can with mean.

Related Posts

Economic Articles With Data Included

Sebastian Kranz has a Shiny app to help you find economic papers with included data: One gets some information about the size of the data files and the used code files. I also tried to find and extract a README file from each supplement. Most README files explain whether all results can be replicated with […]

Read More

Giving A Name To The R Pipe

John Mount noodles an idea from Hadley Wickham: I’d say this fails on at least two counts, the first “%then%” doesn’t seem grammatical (as d is a noun), and magrittr pipes can’t be associated with a new name (as they are implemented by looking for theirselves by name in captured unevaluated code). However, the wrapr dot arrow pipe can take on new names. […]

Read More


December 2018
« Nov Jan »