R Data Science Tutorials

君子生非异也,善假于物也。
希望这篇会成为帮助到各位。

这一篇文章总结了数据科学,NLP和机器学习的R教程和包的精选列表。
可以作为几种常见数据分析任务的参考指南,

Learning R

网上课程

网上课程
* tryR on Codeschool
* Introduction to R for Data Science - Microsoft | edX
* Introduction to R on DataCamp
* Data Analysis with R

  • 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

更多相关资源

  • 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

重要的问题

  • 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?

数据框的操作

  • 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 包介绍

  • 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

幻灯片

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

多变量分析

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

时间序列分析

  • 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

贝叶斯

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

机器学习

  • 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

神经网络与机器学习

  • 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

情绪分析

  • 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

数值计算

  • 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}

自然语言处理

  • 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

可视化

  • 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

统计

  • 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

常用的包

  • 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

关联分析

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

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