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.

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.

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.

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.

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

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.

- Companies using R
- Getting Started with R
- Data Types (Structures) in R
- Create Sample (Dummy) Data in R
- Importing Data into R
- Exporting Data in R
- Copy Data from Excel to R
- Loading and Saving Data with R
- Data Exploration with R
- Data Manipulation with R
- Data Manipulation with dplyr package
- Data Manipulation with data.table package
- If Else and Nested If Else in R
- Transpose Data with R
- Loops with R
- Error Handling in R
- Converting a factor to integer
- Character Functions
- Apply Function on Rows
- Keep / Drop Columns with R
- Joining and Merging with R
- Summarize Data with R
- Indexing Operators in List
- Split a data frame
- Convert data from wide to long format
- R Which Function Explained
- How to Update R Software
- Convert Backslash File Path to Forward Slash
- Send Email From R
- Run SQL Queries in R
- Measuring Execution Time of R Code
- Install an archived package
- Delete columns where certain % of missing values
- Converting multiple numeric variables to factor
- Extracting Numeric and Factor Variables
- Install R package from GitHub account
- Password Generator App with R
- Reading large CSV Files
- Creating Dummy Columns From Categorical Variables
- Convert Categorical Variables to Numeric
- CARET Package [Part I]
- CARET Package [Part II]
- Create Wordcloud with R
- Integrate R with PHP

**Data Science with R Tutorials**

- Linear Regression with R
- Logistic Regression with R
- Cluster Analysis with R
- Validate Cluster Analysis
- Decision Tree on Credit Data
- Random Forest Explained
- Gradient Boosting Model (GBM) with R
- Support Vector Machine (SVM) in R
- Market Basket Analysis
- ARIMA Modeling with R
- Dimensionality Reduction with R
- Correcting Collinearity with R
- Weighting in Decision Tree and SVM
- Decision Tree : Custom CTREE Plot
- Train Random Forest with CARET package
- Missing Value Imputation with Random Forest
- Speeding up Random Forest with R
- Variable Selection / Reduction with R
- Variable Selection - Wald Chi Square
- Predicting Transformed Dependent Variable
- Ensemble Learning : Stacking (Blending)
- Parallelizing Machine Learning Algorithms
- Ways to correct Class Imbalances / Rare Events
- Missing Imputation with Mice Package
- Predict Functions in R
- Splitting Data into Training and Validation Datasets
- R Function : Gain and Lift Table
- Automatically Create Model Formula
- Calculating AUC of Training Dataset
- R Functions : AUC and KS Statistics
- Two ways to train a model with R

**Text Mining with R**

**R Interview Questions and Answers**

R Interview Questions