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