R is the world's most widely used programming language for statistical analysis and predictive modeling. It's popularity is claimed in many recent surveys and studies. In a study of skills associated with LinkedIn profiles by RJmetrics , "R" was the most associated skill with data scientists in a category of software. In a recent Dice Tech Salary Survey, it was found people who know R programming language fetch more salary than python and SAS programmers.

The R programming tutorials are listed below which are ideal for beginner to advanced users.

The following tutorials are designed for beginners who have no programming background or new to R programming language. These tutorials cover how to get started with R, importing and exporting data, data exploration and manipulation, report generation etc.

These tutorials would help you to understand data mining and statistical techniques with R. It includes tutorials on performing 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 creating word cloud by demographics and sentiment analysis with R,

This tutorial helps you to prepare for interview for R programmers and data scientists roles.
R Interview Questions

R Programming Tutorials |

**R Tutorial**

The following tutorials are designed for beginners who have no programming background or new to R programming language. These tutorials cover how to get started with R, importing and exporting data, data exploration and manipulation, report generation etc.

- Getting Started with R
- Companies using R
- Data Types (Structures) in R
- Create 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
- 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

**Predictive Modeling 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
- 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**