R

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

• Introduction to R

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