Data science with r

• resources
• data-science
• R-language

posted on 18 May 2020 under category note

R Data Science Tutorials

• This 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.

Learning R

• Online Courses
  • tryR on Codeschool
• 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

• Packages for Bayesian Inference
• Bayesian Inference in R: Video
• R and Bayesian Statistics

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
  • 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
• 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](https://www.edx.org/course/explore-statistics-r-kix-kiexplorx-0)

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
  • Code Files in this Repo
  • dplyr tutorial 1, dplyr tutorial 2
  • Do your “data janitor work” like a boss with dplyr
• ggplot2
  • ggplot2 tutorial
  • Good Tutorial!
  • Introduction to ggplot2, GitHub
  • A quick introduction to ggplot()
  • R Graphics cookbook
  • ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R”
• A speed test comparison of plyr, data.table, and dplyr
• data.table
  • Introduction to the data.table package in R
  • Fast summary statistics in R with 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

Market Basket Analysis in R

• Market Basket Analysis with R
• Step by Step explanation of Market Basket

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