R Data Science Tutorials
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This repo contains a curated list of R tutorials and packages for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks.
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Curated list of Python tutorials for Data Science, NLP and Machine Learning.
Learning R
- Online Courses
- Free resources for learning R
- R for Data Science - Hadley Wickham
- Advanced R - Hadley Wickham
- 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
More Resources
- 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
- R Study Group at UPenn
- Program-Defined Functions in R
Important Questions
- In R, why is bracket better than
subset? - Subsetting Data in R
- Vectorization in R: Why?
- 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?
Common DataFrame Operations
- Create an empty data.frame
- Sort a dataframe by column(s)
- Merge/Join data frames (inner, outer, left, right)
- Drop data frame columns by name
- Remove rows with NAs in data.frame
- Quickly reading very large tables as dataframes in R
- Drop factor levels in a subsetted data frame
- Convert R list to data frame
- Convert data.frame columns from factors to characters
- Extracting specific columns from a data frame
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
R Cheatsheets
- R Reference Card
- R Reference Card 2.0
- Data Wrangling in R
- ggplot2 Cheatsheet
- Shiny Cheatsheet
- devtools Cheatsheet
- markdown Cheatsheet, reference
- Data Exploration Cheatsheet
Reference Slides
- 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
- Evaluating Logistic Regression Models 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
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
- Top 50 ggplot2 Visualizations
- 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
Useful R Packages
- TIDY DATA HADLEY PAPER
- Package ‘tidyr’: tidyr is an evolution of reshape2. It's design specifically for data tidying (not general reshaping or aggregating) and works well with dplyr data pipelines.
- BROOM
- plyr, stringr, reshape2 tutorial Video, CODE
- dplyr
- ggplot2
- A speed test comparison of plyr, data.table, and dplyr
- data.table
- Other Packages
- Package 'e1071'
- Package ‘AppliedPredictiveModeling’
- Package ‘stringr’: stringr is a set of simple wrappers that make R's string functions more consistent, simpler and easier to use.
- Package ‘stringdist’: Implements an approximate string matching version of R's native 'match' function. Can calculate various string distances based on edits (damerau-levenshtein, hamming, levenshtein, optimal sting alignment), qgrams or heuristic metrics
- Package ‘FSelector’: This package provides functions for selecting attributes from a given dataset
- Ryacas – an R interface to the yacas computer algebra system
- Scatterplot3d – an R package for Visualizing Multivariate Data
- tm.plugin.webmining intro
- Solving Differential Equations in R - ODE examples
- Structural Equation Modeling With the sem Package in R
- prettyScree - prettyGraphs