**R Tutorial for Beginners and Advanced Users**

R language 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.

R Tutorials for Beginners to Advanced Users |

The R language tutorials are listed below which are ideal for beginners to advanced users.

**R Tutorial**

The following R language tutorial 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.

- 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

**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**