Top 100 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. Create Interactive Map
  50. Open and Close URL via R
  51. Wish Christmas with R
  52. Web Scrape Google News
  53. Handle Cookies in Selenium
  54. Translate Webpage while Scraping
  55. R Wrapper for Google Photos API
  56. Integrate R with PHP
  57. Fix R Error : $ operator is invalid for atomic vectors

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. Learn Area under Curve (AUC)
  16. Gini Coefficient, Cumulative Accuracy Profile, AUC
  17. Weighting in Decision Tree and SVM
  18. Decision Tree : Custom CTREE Plot
  19. Train Random Forest with CARET package
  20. Missing Value Imputation with Random Forest
  21. Speeding up Random Forest with R
  22. Variable Selection with Boruta Package
  23. Variable Selection / Reduction with R
  24. Variable Selection - Wald Chi Square
  25. Predicting Transformed Dependent Variable
  26. Ensemble Learning : Stacking (Blending)
  27. Parallelizing Machine Learning Algorithms
  28. Ways to correct Class Imbalances / Rare Events
  29. Missing Imputation with Mice Package
  30. Predict Functions in R
  31. Splitting Data into Training and Validation Datasets
  32. R Function : Gain and Lift Table
  33. Automatically Create Model Formula
  34. Calculating AUC of Training Dataset
  35. R Functions : AUC and KS Statistics
  36. 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

R Programmer / Analyst Jobs - New!

To find jobs in data science using R easier we have collated jobs related to R Programming from various job portals. You will find a variety of job roles based on your years of experience and location. R Jobs

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. Add Loader for Computation Heavy Tasks
  4. Build Login Page in Shiny
  5. Add Dark Mode to Shiny Apps
  6. Value and Info Box in Shiny and RMarkdown
  7. Style HTML Table
  8. Show Game when Shiny Disconnects
  9. Copy to Clipboard in Shiny App
  10. Rich Text Editor in Shiny
  11. Excel like Filter in Shiny
  12. Search Bar with Suggestions 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


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 above and put your question related to the topic in the comment section. You can also sign-up on Stackoverflow and ask your questions

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