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

Defining Tidy Data

John Mount shares thoughts about the concept of tidy data: A question is: is such a data set “tidy”? The paper itself claims the above definitions are “Codd’s 3rd normal form.” So, no the above table is not “tidy” under that paper’s definition. The the winner’s date of birth is a fact about the winner […]

Read More

Visualizing Earthquake Data

Giorgio Garziano continues a series on analyzing earthquake data: This is the third part of our post series about the exploratory analysis of a publicly available dataset reporting earthquakes and similar events within a specific 30 days time span. In this post, we are going to show static, interactive and animated earthquakes maps of different flavors by […]

Read More


December 2018
« Nov Jan »