From our last article, you should feel comfortable with the idea of editing and saving data sets in Stata. In this article, we’ll explain how to create new variables in Stata using replace, generate, egen, and clonevar.
recode
Getting Started with Stata Tutorial #10: Four Commands to Create New Variables in Stata
April 29th, 2025 by TAF SupportGetting Started with Stata Tutorial #9: Saving, Reordering, and Dropping Data
March 17th, 2025 by TAF SupportStata makes it a breeze to edit or clean your data. If you’re unfamiliar with using data sets in Stata, check out these blog posts to get a good grasp on importing and browsing data in Stata.
For this tutorial we will be using Stata’s “auto” data set. If you haven’t loaded it in yet, type
Issues in Coding Missing Values
October 11th, 2023 by guest contributerThere’s no mincing words here. Missing values can cause problems for every statistician. That’s true for a lot of reasons, but it can start with simple issues of choices 
made when coding missing values in a data set. Here are a few examples.
Example 1: The Null License Plate
Researcher Joseph Tartaro thought it would be funny to get the following California vanity license plate: (more…)
Recoding a Variable from a Survey Question to Use in a Statistical Model
March 18th, 2019 by Jeff MeyerSurvey questions are often structured without regard for ease of use within a statistical model.
Take for example a survey done by the Centers for Disease Control (CDC) regarding child births in the U.S. One of the variables in the data set is “interval since last pregnancy”. Here is a histogram of the results.
Stata Loops and Macros for Large Data Sets: Quickly Finding Needles in the Hay Stack
August 7th, 2015 by Jeff MeyerI recently opened a very large data set titled “1998 California Work and Health Survey” compiled by the Institute for Health Policy Studies at the University of California, San Francisco. There are 1,771 observations and 345 variables. (more…)
R Is Not So Hard! A Tutorial, Part 18: Re-Coding Values
August 29th, 2014 by David Lillis
One data manipulation task that you need to do in pretty much any data analysis is recode data. It’s almost never the case that the data are set up exactly the way you need them for your analysis.
In R, you can re-code an entire vector or array at once. To illustrate, let’s set up a vector that has missing values.
A <- c(3, 2, NA, 5, 3, 7, NA, NA, 5, 2, 6)
A
[1] 3 2 NA 5 3 7 NA NA 5 2 6
We can re-code all missing values by another number (such as zero) as follows: (more…)