Introduction to R for Data Analysis - Online Course
A 3-Day Livestream Seminar Taught by
Andrew MilesWednesday, February 17 —
Friday, February 19, 2027
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
R is a free and open-source package for statistical analysis that is widely used in the social, health, physical, and computational sciences. Researchers gravitate to R because it is powerful, flexible, and has excellent graphics capabilities. It also has a large and rapidly growing community of users.
This course is designed as an introduction to R for those who are looking to use R for applied statistical tasks. Topics include data coding and management as well as how to perform basic descriptive, bivariate, and multivariate analyses. We will also discuss using plots to explore data and how R can simplify the process of exporting the results from statistical analyses. To be clear, this course does not teach the principles of data management or statistical analysis. Instead, it assumes prior knowledge of these topics and focuses on explaining how they can be implemented in R.
Starting February 17, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour 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
There is no way to cover all the possible uses of R in a single course, so an important theme will be helping you understand the fundamentals of how R “thinks” so that you can begin to use R independently. For this reason, the course focuses on basic R functions and practical issues like interpreting output and getting help. After this course, you will be well-equipped to adapt R to your own research and analytical needs.
This course is thoroughly hands-on. 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” exercises after each class session. By the end of the course, you can expect to log more than a dozen hours of guided practice coding in R.
For those looking to build on these foundational skills, our Essential R Skills for Intermediate Users seminar explores techniques for writing better R code and improving research workflows.
There is no way to cover all the possible uses of R in a single course, so an important theme will be helping you understand the fundamentals of how R “thinks” so that you can begin to use R independently. For this reason, the course focuses on basic R functions and practical issues like interpreting output and getting help. After this course, you will be well-equipped to adapt R to your own research and analytical needs.
This course is thoroughly hands-on. 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” exercises after each class session. By the end of the course, you can expect to log more than a dozen hours of guided practice coding in R.
For those looking to build on these foundational skills, our Essential R Skills for Intermediate Users seminar explores techniques for writing better R code and improving research workflows.
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.
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.
Who should register?
This course is for anyone who wants to learn R. No prior knowledge of R is assumed, though those lacking experience with any type of statistical coding language might find the course more intensive (but doable!). You should also have prior experience with data management and a basic understanding of fundamental bivariate and multivariate statistics including linear regression and the generalized linear model.
This course is for anyone who wants to learn R. No prior knowledge of R is assumed, though those lacking experience with any type of statistical coding language might find the course more intensive (but doable!). You should also have prior experience with data management and a basic understanding of fundamental bivariate and multivariate statistics including linear regression and the generalized linear model.
Seminar outline
Day 1: Working with R, working with data
-
- Introduction: R basics
- Data basics
-
- Importing and exporting data
- Basic data structures in R
- Viewing and modifying objects
- Missing data
- Recoding data
-
- Logical operators
- Functions for recoding data
- Essential R skills
- Understanding R’s functions and help files
-
- Writing understandable R code
Day 2: Exploring and analyzing data in R
-
- Exploring data
-
- Descriptive statistics
- Exploratory data plots
- A few bivariate techniques
-
- Classic statistical tests
- Bivariate plots
- Linear models
-
- Detecting and correcting problems
- Predictions
Day 3: Practical R skills
-
- Generalized linear models
- Visualizing model results
- Getting results out of R
Day 1: Working with R, working with data
-
- Introduction: R basics
- Data basics
-
- Importing and exporting data
- Basic data structures in R
- Viewing and modifying objects
- Missing data
-
- Recoding data
-
- Logical operators
- Functions for recoding data
-
- Essential R skills
- Understanding R’s functions and help files
-
- Writing understandable R code
-
Day 2: Exploring and analyzing data in R
-
- Exploring data
-
- Descriptive statistics
- Exploratory data plots
-
- A few bivariate techniques
-
- Classic statistical tests
- Bivariate plots
-
- Linear models
-
- Detecting and correcting problems
- Predictions
-
- Exploring data
Day 3: Practical R skills
-
- Generalized linear models
- Visualizing model results
- Getting results out of R
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Need funding from your employer or institution? Use our funding request template.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Need funding from your employer or institution? Use our funding request template.
Our Tax ID number is 26-4576270.