100 Free Tutorials for Learning R

R programming language tutorials are listed below which are ideal for beginners to advanced users. R language is the world's most widely used programming language for statistical analysis, predictive modeling and data science. It's popularity is claimed in many recent surveys and studies. R programming language is getting powerful day by day as number of supported packages grows. Some of big IT companies such as Microsoft and IBM have also started developing packages on R and offering enterprise version of R.
R Tutorials for Beginners to Advanced Users

R Tutorial

The following R language tutorial are designed for novice users who have no programming background or new to R programming language. These tutorials help them to get started with R. Once you understand basics and fundamentals of R such as importing and exporting data, data exploration and manipulation, you can switch to advanced R tutorials such as how to apply loop and creating functions in R.

Data Science with R Tutorials

These tutorials aimed at people who want to build a career in predictive modeling and data science. It covers various data mining, machine learning and statistical techniques with R. It explains how to perform descriptive and inferential statistics, linear and logistic regression, time series, variable selection and dimensionality reduction, classification, market basket analysis, random forest, ensemble technique, clustering and more.
  1. Linear Regression with R
  2. Logistic Regression with R
  3. Cluster Analysis with R
  4. Validate Cluster Analysis
  5. Decision Tree on Credit Data
  6. Random Forest Explained
  7. Gradient Boosting Model (GBM) with R
  8. Support Vector Machine (SVM) in R
  9. K-Nearest Neighbor using R
  10. Market Basket Analysis
  11. ARIMA Modeling with R
  12. Dimensionality Reduction with R
  13. Correcting Collinearity with R
  14. Weighting in Decision Tree and SVM
  15. Decision Tree : Custom CTREE Plot
  16. Train Random Forest with CARET package
  17. Missing Value Imputation with Random Forest
  18. Speeding up Random Forest with R
  19. Variable Selection with Boruta Package
  20. Variable Selection / Reduction with R
  21. Variable Selection - Wald Chi Square
  22. Predicting Transformed Dependent Variable
  23. Ensemble Learning : Stacking (Blending)
  24. Parallelizing Machine Learning Algorithms
  25. Ways to correct Class Imbalances / Rare Events
  26. Missing Imputation with Mice Package
  27. Predict Functions in R
  28. Splitting Data into Training and Validation Datasets
  29. R Function : Gain and Lift Table
  30. Automatically Create Model Formula
  31. Calculating AUC of Training Dataset 
  32. R Functions : AUC and KS Statistics
  33. Two ways to train a model with R

Text Mining with R

These tutorials would help you to understand the basics of text mining with R. It includes tutorials on extracting and analysing data from Facebook and Twitter. It also explains how to create word cloud by demographics and perform sentiment analysis with R.
  1. Text Mining Basics
  2. Creating WordCloud with R
  3. Creating WordCloud by Demographic
  4. Twitter Analytics with R
  5. Facebook Data Mining with R

R Interview Questions and Answers

This tutorial helps you to prepare for interview for R programmers and data scientists roles. It includes more than 75 interview questions with detailed answers. After completing this tutorial, you would have fair chance to crack technical R interview.
 R Interview Questions
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