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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

Reader Interactions

Comments

  1. Muhammad zaman says

    June 23, 2018 at 11:49 pm

    very good and excellent for new leaner

    Reply

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