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 severalpart 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: Subsetting

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: ReCoding 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 NonLinear 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
{ 1 comment… read it below or add one }
very good and excellent for new leaner