**How Should I Learn R?**

There are lots of good reasons to learn R, even if it won’t be your main statistical package. One very good reason is most of the seminars that we offer now use R as a primary tool.

The best path forward depends on what you already know. Check out the recommendations below for total beginners or for those who are looking to improve on some existing R proficiency.

**I AM A BEGINNER**

If you are new to R, the best way to learn it is by taking our course, Introduction to R for Data Analysis. Or if you’re coming from an SPSS background, try our new 8-hour course, R for SPSS Users.

What if you want to learn just enough R to feel comfortable in one of our seminars that use R? In that case, our first recommendation is to watch a one-hour video that was prepared especially for participants in our seminars.

**I ALREADY KNOW SOME R**

If you have already started your R journey, you might still benefit from our Introduction to R for Data Analysis course. But if you’re ready to move beyond the basics, there are a few directions you can take to increase your R coding skills:

- Data Wrangling with R will help you learn data management skills using the tidyverse.
- Data Visualization with R will teach you to use ggplot2 to make stunning graphs.
- Reproducible Reports with Quarto and R Markdown will help you generate reports and presentations directly in RStudio.
- Workflow of Data Analysis in R will show you how to integrate R into an organized project workflow that will make you more productive.
- Beyond these coding-forward classes, we also have many other courses focused on learning new methods and approaches using the R language.

**R REFERENCES**

In addition to these options, there are many references that you can use to refresh or expand your knowledge. We recommend the following as particularly useful:

**R FOR DATA SCIENCE**

This is the gold standard for developing R programming, data management, and visualization skills. This book has many short chapters. Even just going through chapters 2-6 would give you a basic familiarity with R.

Click here to read *R for Data Science*.

**MODERN DIVE**

This online book provides a balanced introduction to R with a strong emphasis on data wrangling and visualization. After going through the first two parts, you would be ready for any of our R courses.

Click here to read *Modern Dive*.

### OUR **SEMINARS THAT USE R INCLUDE:**

__Advanced Machine Learning with R__- Analysis of Biological Aging
__Analysis of Complex Survey Data____Analyzing Text Data Using Sentiment Analysis____Applied Bayesian Data Analysis____Applied Bayesian Data Analysis: A Second Course____Categorical Data Analysis____Causal Inference in Econometrics____Causal Mediation Analysis____Data Visualization Using R____Data Wrangling with R____Design and Analysis of Simulation Studies____Difference in Differences____Experimental Methods____Exploratory Factor Analysis____Exploratory Graph Analysis with R____Extracting and Analyzing Web and Social Media Data__- How to Choose a Model for Longitudinal Data
- Interactive Visual Dashboards Using R Shiny
__Interpreting and Communicating Statistical Results with R____Introduction to R for Data Analysis____Introduction to Social Network Analysis____Introduction to Statistical Genetics____Introduction to Structural Equation Modeling____Introduction to Text as Data__- Introduction to the Analysis of Electronic Health Records
__Item Response Theory__- Latent Growth Curve Modeling
__Longitudinal Data Analysis Using R__- Machine Learning
__Machine Learning for Estimating Causal Effects____Matching and Weighting for Causal Inference with R__- Mediation, Moderation, and Conditional Process Analysis
__Missing Data Using R____Missing Data Using R (for students)____Multilevel and Mixed Models Using R____Nonparametric and Semiparametric Statistics____Power Analysis and Sample Size Planning____Propensity Score Analysis: Advanced____Propensity Score Analysis: Basics____Psychometrics__- R for SPSS Users
- Regression Discontinuity Designs
__Reproducible Reports with Quarto and R Markdown____Sample Size Justification____Sensitivity Analysis for Causal Inference____Social Networks: Statistical Approaches____Structural Equation Modeling Done Right____Structural Equation Modeling with Categorical Data____Survival Analysis Using R____Time Series Analysis__- Two Key Techniques for Quantifying the Robustness of Causal Inferences
__Using Large Language Transformer Models for Research in R____Workflow of Data Analysis Using R__