FYI: 100 Free Tutorials for Learning R

Published by 劉正山,


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 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.
  1. Companies using R
  2. Getting Started with R
  3. Data Types (Structures) in R
  4. Create Sample (Dummy) Data in R
  5. Importing Data into R
  6. Exporting Data in R
  7. Copy Data from Excel to R
  8. Loading and Saving Data with R
  9. Data Exploration with R
  10. Data Manipulation with R
  11. Data Manipulation with dplyr package
  12. Data Manipulation with data.table package
  13. If Else and Nested If Else in R
  14. Transpose Data with R
  15. Loops with R
  16. Error Handling in R
  17. Converting a factor to integer
  18. Character Functions
  19. Apply Function on Rows
  20. Keep / Drop Columns with R
  21. Joining and Merging with R
  22. Summarize Data with R
  23. Indexing Operators in List
  24. Split a data frame
  25. Convert data from wide to long format
  26. R Which Function Explained
  27. How to Update R Software
  28. Convert Backslash File Path to Forward Slash
  29. Send Email From R
  30. Run SQL Queries in R
  31. Measuring Execution Time of R Code
  32. Install an archived package
  33. Delete columns where certain % of missing values
  34. Converting multiple numeric variables to factor
  35. Extracting Numeric and Factor Variables
  36. Install R package from GitHub account
  37. Password Generator App with R
  38. Reading large CSV Files
  39. Creating Dummy Columns From Categorical Variables
  40. Convert Categorical Variables to Numeric
  41. CARET Package [Part I]
  42. CARET Package [Part II]
  43. Create Wordcloud with R
  44. Integrate R with PHP

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. Case Studies : Data Science Training
  2. Linear Regression with R
  3. Logistic Regression with R
  4. Cluster Analysis with R
  5. Validate Cluster Analysis
  6. Decision Tree on Credit Data
  7. Random Forest Explained
  8. Gradient Boosting Model (GBM) with R
  9. Support Vector Machine (SVM) in 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 / Reduction with R
  20. Variable Selection - Wald Chi Square
  21. Predicting Transformed Dependent Variable
  22. Ensemble Learning : Stacking (Blending)
  23. Parallelizing Machine Learning Algorithms
  24. Ways to correct Class Imbalances / Rare Events
  25. Missing Imputation with Mice Package
  26. Predict Functions in R
  27. Splitting Data into Training and Validation Datasets
  28. R Function : Gain and Lift Table
  29. Automatically Create Model Formula
  30. Calculating AUC of Training Dataset
  31. R Functions : AUC and KS Statistics
  32. 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.

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