R Tutorials : Step by Step Guide

In this R tutorial, you will learn R programming from basic to advance. This tutorial is ideal for both beginners and advanced programmers. R 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

Complete 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. Data Visualization with ggplot2
  17. Error Handling in R
  18. Converting a factor to integer
  19. Character Functions
  20. Apply Function on Rows
  21. Keep / Drop Columns with R
  22. Joining and Merging with R
  23. Summarize Data with R
  24. Indexing Operators in List
  25. Split a data frame
  26. Convert data from wide to long format
  27. R Which Function Explained
  28. How to Update R Software
  29. Convert Backslash File Path to Forward Slash
  30. Send Email From R
  31. Run SQL Queries in R
  32. Measuring Execution Time of R Code
  33. Install an archived package
  34. Delete columns where certain % of missing values
  35. Converting multiple numeric variables to factor
  36. Extracting Numeric and Factor Variables
  37. Install R package from GitHub account
  38. Password Generator App with R
  39. Reading large CSV Files
  40. Creating Dummy Columns From Categorical Variables
  41. Convert Categorical Variables to Numeric
  42. Install and Load Multiple R Packages
  43. Create Animation in R
  44. Take Screenshot of Webpage using R
  45. Run Python from R
  46. CARET Package [Part I]
  47. CARET Package [Part II]
  48. WebScraping Website with R
  49. 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. Linear Regression with R
  2. Logistic Regression with R
  3. 15 Types of Regresssion
  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. K-Nearest Neighbor using R
  11. Market Basket Analysis
  12. ARIMA Modeling with R
  13. Dimensionality Reduction with R
  14. Correcting Collinearity with R
  15. Weighting in Decision Tree and SVM
  16. Decision Tree : Custom CTREE Plot
  17. Train Random Forest with CARET package
  18. Missing Value Imputation with Random Forest
  19. Speeding up Random Forest with R
  20. Variable Selection with Boruta Package
  21. Variable Selection / Reduction with R
  22. Variable Selection - Wald Chi Square
  23. Predicting Transformed Dependent Variable
  24. Ensemble Learning : Stacking (Blending)
  25. Parallelizing Machine Learning Algorithms
  26. Ways to correct Class Imbalances / Rare Events
  27. Missing Imputation with Mice Package
  28. Predict Functions in R
  29. Splitting Data into Training and Validation Datasets
  30. R Function : Gain and Lift Table
  31. Automatically Create Model Formula
  32. Calculating AUC of Training Dataset
  33. R Functions : AUC and KS Statistics
  34. 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

Shiny Tutorials

In this section we covered resources related to shiny package. It's a very powerful package for building web app in R. Unlike other licensed data visualization tools like Tableau, Qlikview and PowerBI, it is available for free. It is very flexible in terms of customization. You can customize it as much as you want
  1. Shiny Tutorial with Examples
  2. Include Javascript and CSS in Shiny
  3. Build Login Page in Shiny

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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