In this R tutorial, you will learn R programming from basic to advanced, taking you from a beginner to an expert coder.

R is the world's most widely used programming language for statistical analysis and data science. It's popularity is claimed in many recent surveys and studies. It is getting powerful day by day as number of supported packages grows. Some of big tech companies such as Microsoft and IBM also developed R packages and offering enterprise version of R.

R can be run on low-end laptops and desktops having 4GB RAM and i3 Processor. It's a myth that R needs a powerful system. Obviously, if you need to handle large datasets, you will need more system memory with a powerful processor. So while a less expensive laptop is sufficient for practicing R programming and memory-intensive algorithms require a high-performance laptop.

You can download R by visiting official site of R - R Project.
Then click on the link "**CRAN**" located at the left hand side of the page. Choose your country and click on the link available for your location. Download R based on operating system in your laptop.

RStudio can be downloaded from here - RStudio. Once downloaded, installation is straight-forward. Just run and proceed with hitting next button.

R is used for a variety of purposes, from predictive model building to web scrapping. As a first step you should focus on the basics of R. Once done it helps you to jump to more advanced programming concepts. R has several packages for data science that are easy to use but require some background in statistics. If you already have a good knowledge of statistics, these packages are easy. Make your schedule and follow it without cheating. If you are ready for putting efforts in learning how to manipulate data using R and can spend 8 hours a day, you can learn it in a week. When you're done, use various statistical algorithms in R. You can make use of the publicly available datasets for practice

RStudio also use R in processing. RStudio is IDE which gives beautiful interface for writing codes rather than ugly R interface. In nutshell you should focus on learning R. You will get comfortable with RStudio in a matter of minutes, it's a piece of cake.

You can follow the links below and put your question related to the topic in the comment section.

## Best R Tutorials for Beginners and Advanced Users

Here is a list of R Programming tutorials which are designed to guide you through each step, making it easy to master R programming from the beginning. We have covered all the essential R programming concepts, syntax, and best practices in a clear and understandable way. 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
- 10 Ways to Filter Data in R
- If Else and Nested If Else in R
- Transpose Data with R
- Loops with R
- Data Visualization with ggplot2
- 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 Variables in R
- Extracting Character Variables in R
- Extracting Factor Variables in R
- 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
- Install and Load Multiple R Packages
- Create Animation in R
- Take Screenshot of Webpage using R
- Run Python from R
- CARET Package [Part I]
- CARET Package [Part II]
- WebScraping Website with R
- Create Interactive Map
- Open and Close URL via R
- Wish Christmas with R
- Web Scrape Google News
- Handle Cookies in Selenium
- Translate Webpage while Scraping
- R Wrapper for Google Photos API
- Integrate R with PHP
- Integrate ChatGPT into R
- Integrate Google's Gemini AI Model into R
- Fix R Error : $ operator is invalid for atomic vectors
- Fix R Error : Select Unused Arguments

## 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.

- Linear Regression with R
- Logistic Regression with R
- 15 Types of Regresssion
- 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
- K-Nearest Neighbor using R
- Market Basket Analysis
- ARIMA Modeling with R
- Dimensionality Reduction with R
- Correcting Collinearity with R
- Learn Area under Curve (AUC)
- Gini Coefficient, Cumulative Accuracy Profile, AUC
- 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 with Boruta Package
- 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

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.

- Text Mining Basics
- Creating WordCloud with R
- Creating WordCloud by Demographic
- Twitter Analytics with R
- 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.

- Shiny Tutorial with Examples
- Include Javascript and CSS in Shiny
- Add Loader for Computation Heavy Tasks
- Build Login Page in Shiny
- Add Dark Mode to Shiny Apps
- Value and Info Box in Shiny and RMarkdown
- Style HTML Table
- Show Game when Shiny Disconnects
- Copy to Clipboard in Shiny App
- Rich Text Editor in Shiny
- Excel like Filter in Shiny
- Search Bar with Suggestions in Shiny
- How to build ChatGPT clone in Shiny

## R Interview Questions

Following is a list of Top 100 most frequently asked R Interview Questions along with their solutions.

Top 100 R Interview Questions and Answers