R for Stata Users - Online Course
An 8-Hour Livestream Seminar Taught by
Andrew MilesThursday, October 1 —
Friday, October 2, 2026
10:30am-12:30pm (convert to your local time)
1:00pm-3:00pm
R is a free and open-source package for statistical analysis that is widely used in the social, health, physical, and computational sciences. While many excellent analysis packages exist, researchers gravitate to R because it is powerful, flexible, has excellent graphics capabilities, and is supported by a large and rapidly growing community of users.
This course is designed to help Stata users transition to R by learning how to perform familiar data analysis tasks in R. Topics include data management and coding, exploratory data visualizations, and performing basic descriptive, bivariate, and multivariate analyses. Along the way, we will pay special attention to the differences between Stata and R, such as terminology, code syntax, data handling, and default procedures. We’ll also briefly illustrate how prompt-based generative AI models (aka, large language models or LLMs) can be used to support learning R.
Starting October 1, this seminar will be presented as an 8-hour synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 30-minute break. Live attendance is recommended for the best experience. If you can’t join in real time, recordings will be available within 24 hours and accessible for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages, including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
This course is more than just a how-to guide for translating Stata code to R. While these kinds of translations can be helpful, the end goal is to help you become fluent in R. Thus, an important theme will be helping you understand the fundamentals of how R “thinks” so that you can begin to use R independently.
Hands-on practice is a central part of the course. You are encouraged to write code along with the instructor and to participate in the carefully designed exercises that will be interspersed throughout the seminar and assigned as “take-home” practice after the first class session. By the end of the course, you can expect to log more than six hours of guided practice coding in R.
This course is more than just a how-to guide for translating Stata code to R. While these kinds of translations can be helpful, the end goal is to help you become fluent in R. Thus, an important theme will be helping you understand the fundamentals of how R “thinks” so that you can begin to use R independently.
Hands-on practice is a central part of the course. You are encouraged to write code along with the instructor and to participate in the carefully designed exercises that will be interspersed throughout the seminar and assigned as “take-home” practice after the first class session. By the end of the course, you can expect to log more than six hours of guided practice coding in R.
Computing
To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
If you wish to follow along with course sections illustrating the use of generative AI, you will need access to a prompt-based LLM like ChatGPT, Gemini, or Claude. The instructor will be using Gemini.
To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
If you wish to follow along with course sections illustrating the use of generative AI, you will need access to a prompt-based LLM like ChatGPT, Gemini, or Claude. The instructor will be using Gemini.
Who should register?
This course is for Stata users who want to seamlessly transition to R. You should have prior experience with basic data management, bivariate statistics, and linear regression.
If you are looking to transition to R from a different software, check out R for SAS Users or R for SPSS Users with LLM Applications.
This course is for Stata users who want to seamlessly transition to R. You should have prior experience with basic data management, bivariate statistics, and linear regression.
If you are looking to transition to R from a different software, check out R for SAS Users or R for SPSS Users with LLM Applications.
Seminar outline
Introduction
-
- R basics
Data basics
-
- Importing and exporting (Stata) data
- Basic R data structures and Stata equivalents
- How R and Stata manage data sets
- Viewing vs. modifying data in R and Stata
- Missing data
Recoding data
-
- Logical operators in R and Stata
- Common data recoding tasks
Essential R skills
-
- Understanding R’s functions and help files
- Writing understandable R code
Exploring data
-
- Descriptive statistics
- Exploratory data plots
Classic tests
-
- T-test
- Chi-squared test
- Storing and accessing test results in R and Stata
Linear models
-
- Fitting linear models
- Specifying common Stata options in R
- Post-estimation (time permitting)
- Detecting and correcting problems
- Model predictions
Introduction
-
- R basics
Data basics
-
- Importing and exporting (Stata) data
- Basic R data structures and Stata equivalents
- How R and Stata manage data sets
- Viewing vs. modifying data in R and Stata
- Missing data
Recoding data
-
- Logical operators in R and Stata
- Common data recoding tasks
Essential R skills
-
- Understanding R’s functions and help files
- Writing understandable R code
Exploring data
-
- Descriptive statistics
- Exploratory data plots
Classic tests
-
- T-test
- Chi-squared test
- Storing and accessing test results in R and Stata
Linear models
-
- Fitting linear models
- Specifying common Stata options in R
- Post-estimation (time permitting)
- Detecting and correcting problems
- Model predictions
Payment information
The fee of $695 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $695 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.