R for SAS Users - Online Course
An 8-Hour Livestream Seminar Taught by
Melinda HigginsThursday, May 28 —
Friday, May 29, 2026
10:30am-12:30pm (convert to your local time)
1:00pm-3:00pm
This course is specifically designed for SAS users who want to learn R for statistics and data analysis and visualization. While this course will get you started transitioning from SAS to R, it will go beyond just “translating SAS code to R.” You’ll also learn the power of the R language and the immense open source R programming community.
The course will cover the RStudio computing environment, importing and exporting data, data wrangling, visualization, descriptive statistics, and an introduction to modeling. Each lesson will provide a detailed crosswalk of SAS procedures to R functions with specific attention to the similarities and differences (especially related to default settings).
The course will end with a brief introduction to R Markdown and Quarto for writing automated reproducible analysis reports. You will leave with plenty of analysis examples with R code and workflow pipelines you can apply immediately to your projects.
Starting May 28, 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. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed 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
The first day of the course will begin by immediately getting you comfortable with the computing environment, writing R code, and working with datasets and variables. We will cover data manipulation and wrangling issues, which will introduce you to the tidyverse suite of R packages. Day 1 will also get you started with the ggplot2 package, which applies the “grammar of graphics” principles for data visualization.
The second day of the course will cover additional data merging procedures, including column-wise inner, outer, left and right-joins, and row-wise dataset concatenation. We will also cover statistical hypothesis tests and modeling approaches (ANOVA, linear and logistic regression). Along with these methods, R packages will be covered which are helpful for manipulating model output objects for interpretation and visualization. Time permitting, Day 2 will end with a brief introduction to creating “reproducible reports” using R Markdown and Quarto.
The course will intersperse lecture with slides with hands-on active learning modules. You are encouraged to write code and work through the examples in class with immediate help from the instructor. Take-home exercises will be provided at the end of each day for you to try on your own. We will review the Day 1 take-home exercises at the beginning of Day 2 and the Day 2 take-home “answer key” code will be provided.
The first day of the course will begin by immediately getting you comfortable with the computing environment, writing R code, and working with datasets and variables. We will cover data manipulation and wrangling issues, which will introduce you to the tidyverse suite of R packages. Day 1 will also get you started with the ggplot2 package, which applies the “grammar of graphics” principles for data visualization.
The second day of the course will cover additional data merging procedures, including column-wise inner, outer, left and right-joins, and row-wise dataset concatenation. We will also cover statistical hypothesis tests and modeling approaches (ANOVA, linear and logistic regression). Along with these methods, R packages will be covered which are helpful for manipulating model output objects for interpretation and visualization. Time permitting, Day 2 will end with a brief introduction to creating “reproducible reports” using R Markdown and Quarto.
The course will intersperse lecture with slides with hands-on active learning modules. You are encouraged to write code and work through the examples in class with immediate help from the instructor. Take-home exercises will be provided at the end of each day for you to try on your own. We will review the Day 1 take-home exercises at the beginning of Day 2 and the Day 2 take-home “answer key” code will be provided.
Computing
Who should register?
This course is for SAS users who want to transition to the R programming and computing environment. It is designed for attendees who want to transition to R with a minimal learning curve.
This course is for SAS users who want to transition to the R programming and computing environment. It is designed for attendees who want to transition to R with a minimal learning curve.
Seminar outline
Day 1
-
- Understanding what is similar and different between R and SAS (understanding R packages).
- The RStudio development environment – organizing your analysis project: code, data, output, and other files.
- Importing and exporting data.
- Working with datasets, variables, and other “objects”.
- Getting started with executing code at the command line and writing longer scripts and programs.
- Data wrangling (introduction to the tidyverse and dplyr and tidyr) – part 1 (filtering, selecting, sorting, pivoting, descriptive statistics, and summaries).
- Data visualization (introduction to ggplot2).
Day 2
-
- Data wrangling – part 2 (merging datasets by row or column, performing joins).
- Statistical tests (t-tests, chi-square, and related non-parametric tests) – understanding and manipulating output objects.
- Introduction to linear models (linear and logistic regression and ANOVA).
- Helpful R packages for improving, interpreting, and visualizing model output.
Time permitting
-
- Pulling it all together – brief introduction to R Markdown and Quarto (writing reproducible reports).
Day 1
-
- Understanding what is similar and different between R and SAS (understanding R packages).
- The RStudio development environment – organizing your analysis project: code, data, output, and other files.
- Importing and exporting data.
- Working with datasets, variables, and other “objects”.
- Getting started with executing code at the command line and writing longer scripts and programs.
- Data wrangling (introduction to the tidyverse and dplyr and tidyr) – part 1 (filtering, selecting, sorting, pivoting, descriptive statistics, and summaries).
- Data visualization (introduction to ggplot2).
Day 2
-
- Data wrangling – part 2 (merging datasets by row or column, performing joins).
- Statistical tests (t-tests, chi-square, and related non-parametric tests) – understanding and manipulating output objects.
- Introduction to linear models (linear and logistic regression and ANOVA).
- Helpful R packages for improving, interpreting, and visualizing model output.
Time permitting
-
- Pulling it all together – brief introduction to R Markdown and Quarto (writing reproducible reports).
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.