Here is topic wise list of R tutorials for Data Science, Time Series Analysis, Natural Language Processing and Machine Learning. This list also serves as a reference guide for several common data analysis tasks. You can also find this list on GitHub where it is updated regularly.

## The R Language

- Awesome-R Repository on GitHub
- R Reference Card: Cheatsheet
- R bloggers: blog aggregator
- R Resources on GitHub
- Awesome R resources
- Data Mining with R
- Rob J Hyndman’s R Blog
- Simple R Tricks and Tools (Video)
- RStudio GitHub Repo
- Tidying Messy Data in R Video
- Baseball Research with R
- 600 websites about R
- Implementation of 17 classification algorithms in R
- Cohort Analysis and LifeCycle Grids mixed segmentation with R
- Using R and Tableau
- COMPREHENSIVE VIEW ON CRAN PACKAGES
- Using R for Statistical Tables and Plotting Distributions
- Extended Model Formulas in R: Multiple Parts and Multiple Responses
- R vs Python: head to head data analysis
- R for Data Science: Hadley Wickham’s Book

## Important Questions

- In R, why is bracket better than
`subset`

? - Subsetting Data in R
- Quickly reading very large tables as dataframes in R
- Using R to show data
- How can I view the source code for a function?
- How to make a great R reproducible example?
- R Grouping functions: sapply vs. lapply vs. apply. vs. tapply vs. by vs. aggregate
- Tricks to manage the available memory in an R session
- Difference between Assignment operators ‘=’ and ‘<-‘ in R
- What is the difference between require() and library()?
- How can I view the source code for a function?
- How can I change fonts for graphs in R?

## Learning R

- Free resources for learning R
- Online Courses
- Introduction to R for Data Science – Microsoft | edX
- Introduction to R on DataCamp
- tryR on Codeschool
- swirl: Learn R, in R
- Data Analysis and Visualization Using R
- MANY R PROGRAMMING TUTORIALS
- A Handbook of Statistical Analyses Using R, Find Other Chapters
- Cookbook for R
- Learning R in 7 simple steps

## Caret Package in R

- Ensembling Models with caret
- Model Training and Tuning
- Caret Model List
- relationship-between-data-splitting-and-traincontrol
- Specify model generation parameters
- Tutorial, Paper
- Ensembling models with R, Ensembling Regression Models in R

## Reference Slides

- Awesome R Reference Card
- Association Rule Mining
- Time Series Analysis
- Data Exploration and Visualisation
- Regression and Classification
- Text Mining on Twitter Data

## Using R for Multivariate Analysis

- Little Book of R for Multivariate Analysis!
- THE FREQPARCOORD PACKAGE FOR MULTIVARIATE VISUALIZATION
- Use of freqparcoord for Regression Diagnostics

## Time Series Analysis

- Time Series Forecasting (Online Book)
- A Little Book of Time Series Analysis in R
- Quick R: Time Series and Forecasting
- Components of Time Series Data
- Unobserved Component Models using R
- The Holt-Winters Forecasting Method
- CRAN Task View: Time Series Analysis

## Bayesian Inference

## Machine Learning using R

- Machine Learning with R
- Using R for Multivariate Analysis (Online Book)
- CRAN Task View: Machine Learning & Statistical Learning
- Machine Learning Using R (Online Book)
- Linear Regression and Regularization Code
- Cheatsheet
- Multinomial and Ordinal Logistic Regression in R

## Neural Networks in R

- Visualizing Neural Nets in R
- nnet package
- Fitting a neural network in R; neuralnet package
- Neural Networks with R – A Simple Example
- NeuralNetTools 1.0.0 now on CRAN
- Introduction to Neural Networks in R
- Step by Step Neural Networks using R
- R for Deep Learning
- Neural Networks using package neuralnet, Paper

## Sentiment Analysis

- Different Approaches
- Sentiment analysis with machine learning in R
- First shot: Sentiment Analysis in R
- qdap package, code
- sentimentr package
- tm.plugin.sentiment package
- Packages other than sentiment
- Sentiment Analysis and Opinion Mining
- tm_term_score
- vaderSentiment Paper, vaderSentiment code

## Imputation in R

- Imputation in R
- Imputation with Random Forests
- How to Identify and Impute Multiple Missing Values using R
- MICE
- error in implementation of random forest in mice r package
- mice.impute.rf {mice}

## NLP and Text Mining in R

- What algorithm I need to find n-grams?
- NLP R Tutorial
- Introduction to the tm Package Text Mining in R
- Adding stopwords in R tm
- Text Mining
- Word Stemming in R
- Classification of Documents using Text Mining Package “tm”
- Text mining tools techniques and applications
- Text Mining: Overview,Applications and Issues
- Text Mining pdf
- Text Mining Another pdf
- Good PPT
- Scraping Twitter and Web Data Using R

## Visualisation in R

- ggplot2 tutorial
- SHINY EXAMPLES
- Comprehensive Guide to Data Visualization in R
- Interactive visualizations with R – a minireview
- Beginner’s guide to R: Painless data visualization
- Data Visualization in R with ggvis
- Multiple Visualization Articles in R

## Statistics with R

- Using R for Biomedical Statistics (Online Book)
- Elementary Statistics with R
- A Hands-on Introduction to Statistics with R
- Quick R: Basic Statistics
- Quick R: Descriptive Statistics
- Explore Statistics with R | edX

## Market Basket Analysis in R

A good exhaustive list about R tutorials for Data Science, this saves a lot of time for users who are specifically looking for tutorials on R.

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thanks for sharing nice information and nice article and very useful information…..

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Data analysis is a thing in which the more you learn the more you learn, the more interesting it becomes. Well, this list of tutorials was worth appreciation. There are a lot of doubts in mind of people regarding data science which will be cleared after reading this post here. Thanks a lot!

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