The statistical programming language R is becoming a popular means for analyzing data. But it’s not always easy to use. We have a number of resources about learning and using R, including a several-part tutorial blog series.

## Live Online R Workshops

Past Workshops

## R Tutorial Series

- R is Not So Hard! A Tutorial, Part 1: Syntax
- R is Not So Hard! A Tutorial, Part 2: Variable Creation
- R Is Not So Hard! A Tutorial, Part 3: Regressions and Plots
- R Is Not So Hard! A Tutorial, Part 4: Advanced Regression
- R Is Not So Hard! A Tutorial, Part 5: Fitting an Exponential Model
- R Is Not So Hard! A Tutorial, Part 6: Basic Plotting in R
- R Is Not So Hard! A Tutorial, Part 7: More Plotting in R
- R Is Not So Hard! A Tutorial, Part 8: Basic Commands
- R Is Not So Hard! A Tutorial, Part 9: Sub-setting
- R Is Not So Hard! A Tutorial, Part 10: Creating Summary tables with aggregate()
- R Is Not So Hard! A Tutorial, Part 11: Creating Bar Charts
- R is Not So Hard! A Tutorial, Part 12: Creating Histograms & Setting Bin Widths
- R Is Not So Hard! A Tutorial, Part 13: Box Plots
- R Is Not So Hard! A Tutorial, Part 14: Pie Charts
- R Is Not So Hard! A Tutorial, Part 15: Counting Elements in a Data Set
- R Is Not So Hard! A Tutorial, Part 16: Counting Values within Cases
- R Is Not So Hard! A Tutorial, Part 17: Testing for Existence of Particular Values
- R Is Not So Hard! A Tutorial, Part 18: Re-Coding Values
- R Is Not So Hard! A Tutorial, Part 19: Multiple Graphs and par(mfrow=(A,B))
- R is Not So Hard! A Tutorial, Part 20: Useful Commands for Exploring Data
- R is Not So Hard! A Tutorial, Part 21: Pearson and Spearman Correlation
- R is Not So Hard! A Tutorial, Part 22: Creating and Customizing Scatter Plots
- Graphing Non-Linear Mathematical Expressions in R
- Doing Scatterplots in R
- (R Programming) Plotting with Color: qplot
- (R Programming) Plotting with Color Part 2: qplot
- Linear Models in R: Plotting Regression Lines
- Linear Models in R: Diagnosing Our Regression Model
- Linear Models in R: Improving Our Regression Model
- Generalized Linear Models in R, Part 1: Calculating Predicted Probability in Binary Logistic Regression
- Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output
- Generalized Linear Models in R, Part 3: Plotting Predicted Probabilities
- Generalized Linear Models in R, Part 4: Options, Link Functions, and Interpretation

## Other Blog posts about R and Stat Software

- Ways to Customize a Scatter Plot in R Commander
- What R Commander Can do in R Without Coding–More Than You Would Think
- Random Sample from a Uniform Distribution in R Commander
- Ten Ways Learning a Statistical Software Package is Like Learning a New Language
- The 3 Stages of Mastering Statistical Analysis
- SPSS, SAS, R, Stata, JMP? Choosing a Statistical Software Package or Two.
- Do I Really Need to Learn R?
- R Programming Video: 15 Tips for The Beginner
- Repeated Measures Workshop

Muhammad zaman says

very good and excellent for new leaner