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https://github.com/ujjwalkarn/DataScienceR

a curated list of R tutorials for Data Science, NLP and Machine Learning
https://github.com/ujjwalkarn/DataScienceR

data-science datascience r text-mining

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a curated list of R tutorials for Data Science, NLP and Machine Learning

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README 

        
# R Data Science Tutorials

- 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.



- [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataSciencePython).



- [Comprehensive topic-wise list of Machine Learning and Deep Learning tutorials, codes, articles and other resources](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md).



## Learning R

- Online Courses

    - [tryR on Codeschool](http://tryr.codeschool.com/)

    - [Introduction to R for Data Science - Microsoft | edX](https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x?gclid=CLiyoPb448wCFRJxvAod-RoLsA)

    - [Introduction to R on DataCamp](https://www.datacamp.com/courses/free-introduction-to-r)

    - [Data Analysis with R](https://www.udacity.com/course/data-analysis-with-r--ud651)

- [**Free resources for learning R**](http://stats.stackexchange.com/questions/138/free-resources-for-learning-r)

- [R for Data Science - Hadley Wickham](http://r4ds.had.co.nz/)

- [Advanced R - Hadley Wickham](http://adv-r.had.co.nz/)

- [swirl: Learn R, in R](http://swirlstats.com/)

- [Data Analysis and Visualization Using R](http://varianceexplained.org/RData/)

- [**MANY R PROGRAMMING TUTORIALS**](http://www.listendata.com/p/r-programming-tutorials.html)

- [**A Handbook of Statistical Analyses Using R**](https://cran.r-project.org/web/packages/HSAUR/vignettes/Ch_introduction_to_R.pdf), Find Other Chapters

- [**Cookbook for R**](http://www.cookbook-r.com/)

- [Learning R in 7 simple steps](http://www.datasciencecentral.com/profiles/blogs/learning-r-in-seven-simple-steps)



## More Resources

- [Awesome-R Repository on GitHub](https://github.com/qinwf/awesome-R)

- [R Reference Card: Cheatsheet](https://cran.r-project.org/doc/contrib/Short-refcard.pdf)

- [R bloggers: blog aggregator](http://www.r-bloggers.com/)

- [R Resources on  GitHub](https://github.com/binga/DataScienceArsenal/blob/master/r-resources.md)

- [Awesome R resources](https://github.com/ujjwalkarn/awesome-R)

- [Data Mining with R](https://github.com/ujjwalkarn/Data-Mining-With-R)

- [Rob J Hyndman's R Blog](http://robjhyndman.com/hyndsight/r/)

- [Simple R Tricks and Tools](http://robjhyndman.com/hyndsight/simpler/) [(Video)](https://www.youtube.com/watch?v=Toc__W7L2Qo)

- [RStudio GitHub Repo](https://github.com/rstudio/)

- [Tidying Messy Data in R](http://www.dataschool.io/tidying-messy-data-in-r/) [Video](https://vimeo.com/33727555)

- [Baseball Research with R](http://www.hardballtimes.com/a-short-ish-introduction-to-using-r-for-baseball-research)

- [600 websites about R](http://www.datasciencecentral.com/profiles/blogs/600-websites-about-r)

- [Implementation of 17 classification algorithms in R](http://www.datasciencecentral.com/profiles/blogs/implemetation-of-17-classification-algorithms-in-r)

- [Cohort Analysis and LifeCycle Grids mixed segmentation with R](http://analyzecore.com/2015/04/01/cohort-analysis-and-lifecycle-grids-mixed-segmentation-with-r/)

- [Using R and Tableau](http://www.tableau.com/learn/whitepapers/using-r-and-tableau)

- [COMPREHENSIVE VIEW ON CRAN PACKAGES](http://www.docfoc.com/cran-pdf)

- [Using R for Statistical Tables and Plotting Distributions](http://math.arizona.edu/~jwatkins/R-01.pdf)

- [Extended Model Formulas in R: Multiple Parts and Multiple Responses](https://cran.r-project.org/web/packages/Formula/vignettes/Formula.pdf)

- [R vs Python: head to head data analysis](https://www.dataquest.io/blog/python-vs-r/?utm_content=buffer55639&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)

- [**R for Data Science: Hadley Wickham's Book**](http://r4ds.had.co.nz/)

- [**R Study Group at UPenn**](https://www.ling.upenn.edu/~joseff/rstudy/index.html)

- [Program-Defined Functions in R](http://dni-institute.in/blogs/extracting-data-from-facebook-using-r/)



## Important Questions

- [**In R, why is bracket better than `subset`?**](http://stackoverflow.com/questions/9860090/in-r-why-is-better-than-subset)

- [**Subsetting Data in R**](http://www.statmethods.net/management/subset.html)

- [**Vectorization in R: Why?**](http://www.noamross.net/blog/2014/4/16/vectorization-in-r--why.html)

- [**Quickly reading very large tables as dataframes in R**](http://stackoverflow.com/questions/1727772/quickly-reading-very-large-tables-as-dataframes-in-r)

- [**Using R to show data**](http://www.sr.bham.ac.uk/~ajrs/R/r-show_data.html)

- [How can I view the source code for a function?](http://stackoverflow.com/questions/19226816/how-can-i-view-the-source-code-for-a-function?lq=1)

- [How to make a great R reproducible example?](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example)

- [**R Grouping functions: sapply vs. lapply vs. apply. vs. tapply vs. by vs. aggregate**](https://stackoverflow.com/questions/3505701/r-grouping-functions-sapply-vs-lapply-vs-apply-vs-tapply-vs-by-vs-aggrega)

- [**Tricks to manage the available memory in an R session**](https://stackoverflow.com/questions/1358003/tricks-to-manage-the-available-memory-in-an-r-session)

- [Difference between Assignment operators '=' and '<-' in R](https://stackoverflow.com/questions/1741820/assignment-operators-in-r-and)

- [What is the difference between require() and library()?](https://stackoverflow.com/questions/5595512/what-is-the-difference-between-require-and-library)

- [How can I view the source code for a function?](https://stackoverflow.com/questions/19226816/how-can-i-view-the-source-code-for-a-function)

- [How can I change fonts for graphs in R?](https://stackoverflow.com/questions/27689222/changing-fonts-for-graphs-in-r/)



## Common DataFrame Operations

- [Create an empty data.frame](https://stackoverflow.com/questions/10689055/create-an-empty-data-frame)

- [Sort a dataframe by column(s)](https://stackoverflow.com/questions/1296646/how-to-sort-a-dataframe-by-columns)

- [Merge/Join data frames (inner, outer, left, right)](https://stackoverflow.com/questions/1299871/how-to-join-merge-data-frames-inner-outer-left-right)

- [Drop data frame columns by name](https://stackoverflow.com/questions/4605206/drop-data-frame-columns-by-name)

- [Remove rows with NAs in data.frame](https://stackoverflow.com/questions/4862178/remove-rows-with-nas-in-data-frame)

- [Quickly reading very large tables as dataframes in R](https://stackoverflow.com/questions/1727772/quickly-reading-very-large-tables-as-dataframes-in-r)

- [Drop factor levels in a subsetted data frame](https://stackoverflow.com/questions/1195826/drop-factor-levels-in-a-subsetted-data-frame)

- [Convert R list to data frame](https://stackoverflow.com/questions/4227223/r-list-to-data-frame)

- [Convert data.frame columns from factors to characters](https://stackoverflow.com/questions/2851015/convert-data-frame-columns-from-factors-to-characters)

- [Extracting specific columns from a data frame](https://stackoverflow.com/questions/10085806/extracting-specific-columns-from-a-data-frame)



## Caret Package in R

- [Ensembling Models with caret](http://stats.stackexchange.com/questions/27361/stacking-ensembling-models-with-caret)

- [Model Training and Tuning](http://topepo.github.io/caret/training.html)

- [Caret Model List](http://topepo.github.io/caret/modelList.html)

- [relationship-between-data-splitting-and-traincontrol](http://stackoverflow.com/questions/14968874/caret-relationship-between-data-splitting-and-traincontrol)

- [Specify model generation parameters](http://stackoverflow.com/questions/10498477/carettrain-specify-model-generation-parameters?lq=1)

- [Tutorial](https://www.r-project.org/nosvn/conferences/useR-2013/Tutorials/kuhn/user_caret_2up.pdf), [Paper](www.jstatsoft.org/article/view/v028i05/v28i05.pdf)

- [Ensembling models with R](http://amunategui.github.io/blending-models/), [Ensembling Regression Models in R](http://stats.stackexchange.com/questions/26790/ensembling-regression-models)



## R Cheatsheets

- [R Reference Card](https://cran.r-project.org/doc/contrib/Short-refcard.pdf)

- [R Reference Card 2.0](https://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf)

- [Data Wrangling in R](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)

- [ggplot2 Cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/08/ggplot2-cheatsheet.pdf)

- [Shiny Cheatsheet](http://shiny.rstudio.com/images/shiny-cheatsheet.pdf)

- [devtools Cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/06/devtools-cheatsheet.pdf)

- [markdown Cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf), [reference](https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf)

- [Data Exploration Cheatsheet](http://www.analyticsvidhya.com/blog/2015/10/cheatsheet-11-steps-data-exploration-with-codes/)



## Reference Slides

- [R Reference Card](https://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf)

- [Association Rule Mining](https://78462f86-a-e2d7344e-s-sites.googlegroups.com/a/rdatamining.com/www/docs/RDataMining-slides-association-rules.pdf?attachauth=ANoY7crD9hRI7333KWhK0TVPsS1VfgWoW4BuIsmL8B0NANfntEOq6QbcwJk-aCRUy2N6CmUeJsyrlOOd5bo1CqRUYXkEbSl1JbTniVbb-GSR3cyTt9Qq6xB3ZasMEdACaS9j1fZDiLVn_zLFbrF--aJM7gAu54JwRBhvKuQPOPyeMTosWcTmmrJdRNWH4ZqD5kYEJlmHDcXB8Bp-DWbUxZG2T8sAGbcHGUqkPTTJ_u03wvKyw5MGMrGU7q4xIyyUmBas_PqEDi6q&attredirects=0)

- [Time Series Analysis](https://78462f86-a-e2d7344e-s-sites.googlegroups.com/a/rdatamining.com/www/docs/RDataMining-slides-time-series-analysis.pdf?attachauth=ANoY7cphtFEj6IMGuupE5ygQn5flMH5-QPE4yNgJ9fYv3WqfY0qU8LWGgiECZKs6P63Rhx5Nml8lQXQnX7QH7OZm1hoi_Kl0m9sLOAC0tc4sQipWC8DprQVoYSDyw0EdeJfZWAQor0AyjMWeFHPY6nqxIGAaj4arrwZcnR1dYC7nQK4dTVQM80ARrN5Yzq9rNbGic30X-xKwNQxOXL4fO54ThpzmNB4wLKv5geo_hDqPkwtKBmNR7u_kGPOymJHGvxP3nr02aJsB&attredirects=0)

- [Data Exploration and Visualisation](https://78462f86-a-e2d7344e-s-sites.googlegroups.com/a/rdatamining.com/www/docs/RDataMining-slides-data-exploration-visualization.pdf?attachauth=ANoY7cpqnCTmCv1omsIoKmefAn8q6M_j4Hizv_1enJlu3nRPIxIhzjBlf-9B_sIxMxpUx-XN5cAw74GUr18Dn0EcaiIm9MVeCtqT-2dcPNo0dfhRJvnb5J8EHKBX_w7Y6mYgb7UAoIUbjdmVGR9VCIfJf6PGQqAlupywcb1yGbT4pv61bQzOzrU4-eICfgHmORdi8YgBqscyT2ThaKHPSeGXD0dd3g08pGN3bY70MKM02ZaqarewbII91KTNH1-zmELEcvatl_sMxmGgNnIDm6MaxEWQ1pIrTQ%3D%3D&attredirects=0)

- [Regression and Classification](https://78462f86-a-e2d7344e-s-sites.googlegroups.com/a/rdatamining.com/www/docs/RDataMining-slides-regression-classification.pdf?attachauth=ANoY7cq0yqcj_65pafTfUqHazTYvp4E4r-5OB1kLv3swVKJhVydaJ0YU5yEPiOciQC0k_P1QzO6z1vD0r9E05KU8y7Mn6NTesQOOq_mmwlMqAe7D2mnqkHZBqFT6tk2hJ3g3fK40mvfyU5ggoGMxMYn9nVhihKwcIYJy9A8zlbFo4r9a35kpTDr6jJjAw5eQwSEMe-bvT5iyZuyMS7QS-tvlgHjJ40ZGhPro7GcWXfb7qqaPeTe9NyeU7MxAy2Z_lAzxn0vSnqe6&attredirects=0)

- [Text Mining on Twitter Data](https://78462f86-a-e2d7344e-s-sites.googlegroups.com/a/rdatamining.com/www/docs/RDataMining-slides-text-mining.pdf?attachauth=ANoY7cquEwmhHFNHxiKNhv6C2wquNdaib8A_BeTRFaGFXZ2deivENdTK-GS7mSZjermC7b_-L6KtCWhfF1ZOzOF9XaLkIaw6InCEnjdO1fWUhJFujaGwwbcbExJKEVuMmwlBX_SDUFZYgjuTbIb2llgKRMQc3Dd241HNZHTvGVuPG26vHKN_jU_WoEj7uIilRJWFTDvNrZWGWrvImWr0aCNou56qAB-zmBG_cvRS4QOQroiEetLpR7k%3D&attredirects=0)



## Using R for Multivariate Analysis

- [Little Book of R for Multivariate Analysis!](http://little-book-of-r-for-multivariate-analysis.readthedocs.io/en/latest/)

- [THE FREQPARCOORD PACKAGE FOR MULTIVARIATE VISUALIZATION](https://matloff.wordpress.com/2014/03/30/the-freqparcoord-package-for-multivariate-visualization/)

- [Use of freqparcoord for Regression Diagnostics](http://www.r-bloggers.com/use-of-freqparcoord-for-regression-diagnostics/)



## Time Series Analysis

- [**Time Series Forecasting (Online Book)**](https://www.otexts.org/fpp)

- [**A Little Book of Time Series Analysis in R**](http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/src/timeseries.html)

- [Quick R: Time Series and Forecasting](http://www.statmethods.net/advstats/timeseries.html)

- [Components of Time Series Data](https://www.linkedin.com/pulse/component-time-series-data-jeffrey-strickland-ph-d-cmsp)

- [Unobserved Component Models using R](https://www.linkedin.com/pulse/unobserved-component-models-r-jeffrey-strickland-ph-d-cmsp)

- [The Holt-Winters Forecasting Method](http://webarchive.nationalarchives.gov.uk/20080726235635/http://statistics.gov.uk/iosmethodology/downloads/Annex_B_The_Holt-Winters_forecasting_method.pdf)

- [**CRAN Task View: Time Series Analysis**](https://cran.r-project.org/web/views/TimeSeries.html)



## Bayesian Inference

- [Packages for Bayesian Inference](https://github.com/ujjwalkarn/awesome-R#bayesian)

- [Bayesian Inference in R: Video](https://www.youtube.com/watch?v=fiWIK7ONX3U)

- [R and Bayesian Statistics](http://www.r-bloggers.com/r-and-bayesian-statistics/)



## Machine Learning using R

- [Machine Learning with R](https://github.com/jhashanti/Machine-Learning-with-R)

- [Using R for Multivariate Analysis (Online Book)](http://little-book-of-r-for-multivariate-analysis.readthedocs.org/en/latest/src/multivariateanalysis.html)

- [CRAN Task View: Machine Learning & Statistical Learning](https://cran.r-project.org/web/views/MachineLearning.html)

- [Machine Learning Using R (Online Book)](https://www.otexts.org/sfml)

- [Linear Regression and Regularization Code](http://rpubs.com/justmarkham/linear-regression-salary)

- [Cheatsheet](http://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/)

- [**Multinomial and Ordinal Logistic Regression in R**](http://www.analyticsvidhya.com/blog/2016/02/multinomial-ordinal-logistic-regression/)

- [**Evaluating Logistic Regression Models in R**](https://www.r-bloggers.com/evaluating-logistic-regression-models/)

 

## Neural Networks in R

- [Visualizing Neural Nets in R](https://beckmw.wordpress.com/2013/11/14/visualizing-neural-networks-in-r-update/)

- [nnet package](http://stackoverflow.com/questions/21788817/r-nnet-with-a-simple-example-of-2-classes-with-2-variables)

- [Fitting a neural network in R; neuralnet package](http://www.r-bloggers.com/fitting-a-neural-network-in-r-neuralnet-package/)

- [Neural Networks with R – A Simple Example](http://gekkoquant.com/2012/05/26/neural-networks-with-r-simple-example/)

- [NeuralNetTools 1.0.0 now on CRAN](https://beckmw.wordpress.com/tag/neural-network/)

- [Introduction to Neural Networks in R](http://www.louisaslett.com/Courses/Data_Mining/ST4003-Lab5-Introduction_to_Neural_Networks.pdf)

- [Step by Step Neural Networks using R](https://bicorner.com/2015/05/13/neural-networks-using-r/)

- [**R for Deep Learning**](http://www.parallelr.com/r-deep-neural-network-from-scratch/)

- [Neural Networks using package neuralnet](http://www.di.fc.ul.pt/~jpn/r/neuralnets/neuralnets.html), [Paper](https://journal.r-project.org/archive/2010-1/RJournal_2010-1_Guenther+Fritsch.pdf)



## Sentiment Analysis

- [Different Approaches](https://drive.google.com/open?id=0By_wg-rXnp_6U1JLNVA3cnAxZ3M)

- [**Sentiment analysis with machine learning in R**](http://datascienceplus.com/sentiment-analysis-with-machine-learning-in-r/)

- [**First shot: Sentiment Analysis in R**](http://andybromberg.com/sentiment-analysis/)

- [qdap package](https://github.com/trinker/qdap), [code](http://stackoverflow.com/questions/22774913/estimating-document-polarity-using-rs-qdap-package-without-sentsplit)

- [sentimentr package](https://github.com/trinker/sentimentr)

- [tm.plugin.sentiment package](https://github.com/mannau/tm.plugin.sentiment)

- [Packages other than sentiment](http://stackoverflow.com/questions/15194436/is-there-any-other-package-other-than-sentiment-to-do-sentiment-analysis-in-r)

- [Sentiment Analysis and Opinion Mining](https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html)

- [tm_term_score](http://www.inside-r.org/packages/cran/tm/docs/tm_term_score)

- [**vaderSentiment Paper**](http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf), [**vaderSentiment code**](https://github.com/cjhutto/vaderSentiment)



## Imputation in R

- [**Imputation in R**](http://stackoverflow.com/questions/13114812/imputation-in-r)

- [Imputation with Random Forests](http://stats.stackexchange.com/questions/49270/imputation-with-random-forests)

- [How to Identify and Impute Multiple Missing Values using R](http://www.unt.edu/rss/class/Jon/Benchmarks/MissingValueImputation_JDS_Nov2010.pdf)

- MICE

    - [error in implementation of random forest in mice r package](http://stackoverflow.com/questions/23974026/error-in-implementation-of-random-forest-in-mice-r-package)

    - [mice.impute.rf {mice}](http://www.inside-r.org/packages/cran/mice/docs/mice.impute.rf)



## NLP and Text Mining in R

- [**What algorithm I need to find n-grams?**](http://stackoverflow.com/questions/8161167/what-algorithm-i-need-to-find-n-grams)

- [NLP R Tutorial](http://www.r-bloggers.com/natural-language-processing-tutorial/)

- [Introduction to the tm Package Text Mining in R](https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf)

- [Adding stopwords in R tm](http://stackoverflow.com/questions/18446408/adding-stopwords-in-r-tm)

- [Text Mining](http://www.r-bloggers.com/text-mining/)

- [Word Stemming in R](http://www.omegahat.net/Rstem/stemming.pdf)

- [**Classification of Documents using Text Mining Package “tm”**](http://web.letras.up.pt/bhsmaia/EDV/apresentacoes/Bradzil_Classif_withTM.pdf)

- [Text mining tools techniques and applications](http://slidegur.com/doc/1830649/text-mining)

- [Text Mining: Overview,Applications and Issues ](http://www3.cs.stonybrook.edu/~cse634/G8present.pdf)

- [**Text Mining pdf**](http://www3.cs.stonybrook.edu/~cse634/presentations/TextMining.pdf)

- [Text Mining Another pdf](http://www.stat.columbia.edu/~madigan/W2025/notes/IntroTextMining.pdf)

- [Good PPT](http://studylib.net/doc/5800473/topic7-textmining)

- [**Scraping Twitter and Web Data Using R**](http://www.nyu.edu/projects/politicsdatalab/localdata/workshops/twitter.pdf)



## Visualisation in R

- [ggplot2 tutorial](http://www.ling.upenn.edu/~joseff/avml2012/)

- [SHINY EXAMPLES](https://github.com/rstudio/shiny-examples)

- [**Top 50 ggplot2 Visualizations**](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html)

- [Comprehensive Guide to Data Visualization in  R](http://www.analyticsvidhya.com/blog/2015/07/guide-data-visualization-r/)

- [Interactive visualizations with R – a minireview](http://www.r-bloggers.com/interactive-visualizations-with-r-a-minireview/)

- [Beginner's guide to R: Painless data visualization](http://www.computerworld.com/article/2497304/business-intelligence-beginner-s-guide-to-r-painless-data-visualization.html)

- [Data Visualization in R with ggvis](https://www.datacamp.com/courses/ggvis-data-visualization-r-tutorial)

- [Multiple Visualization Articles in R](http://www.r-statistics.com/tag/visualization/)



## Statistics with R

- [Using R for Biomedical Statistics (Online Book)](http://a-little-book-of-r-for-biomedical-statistics.readthedocs.org/en/latest/src/biomedicalstats.html)

- [Elementary Statistics with R](http://www.r-tutor.com/elementary-statistics)

- [A Hands-on Introduction to Statistics with R](https://www.datacamp.com/introduction-to-statistics)

- [Quick R: Basic Statistics](http://www.statmethods.net/stATS/index.html)

- [Quick R: Descriptive Statistics](http://www.statmethods.net/stats/descriptives.html)

- [Explore Statistics with R | edX](https://www.edx.org/course/explore-statistics-r-kix-kiexplorx-0)



## Useful R Packages

- [**TIDY DATA HADLEY PAPER**](https://www.jstatsoft.org/article/view/v059i10)

    - 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](https://github.com/dgrtwo/broom)

- [**plyr, stringr, reshape2 tutorial**](http://www.dataschool.io/tidying-messy-data-in-r/) [Video](https://vimeo.com/33727555), [CODE](https://github.com/justmarkham/tidy-data)

- dplyr

    - [Code Files in this Repo](https://github.com/ujjwalkarn/DataScienceR/tree/master/Intro%20to%20dplyr)

    - [dplyr tutorial 1](http://www.dataschool.io/dplyr-tutorial-for-faster-data-manipulation-in-r/), [dplyr tutorial 2](http://www.dataschool.io/dplyr-tutorial-part-2/)

    - [Do your "data janitor work" like a boss with dplyr](http://www.gettinggeneticsdone.com/2014/08/do-your-data-janitor-work-like-boss.html)

- ggplot2

    - [ggplot2 tutorial](http://www.ling.upenn.edu/~joseff/avml2012/)

    - [Good Tutorial!](https://github.com/jennybc/ggplot2-tutorial)

    - [Introduction to ggplot2](https://speakerdeck.com/karthik/introduction-to-ggplot2), [GitHub](https://github.com/karthik/ggplot-lecture)

    - [A quick introduction to ggplot()](http://www.noamross.net/blog/2012/10/5/ggplot-introduction.html)

    - [R Graphics cookbook](http://www.cookbook-r.com/Graphs/index.html)

    - [ggplot2 Version of Figures in “Lattice: Multivariate Data Visualization with R” ](https://learnr.wordpress.com/2009/06/28/ggplot2-version-of-figures-in-lattice-multivariate-data-visualization-with-r-part-1/)

- [A speed test comparison of plyr, data.table, and dplyr](http://www.r-statistics.com/2013/09/a-speed-test-comparison-of-plyr-data-table-and-dplyr/)

- data.table

    - [Introduction to the data.table package in R](https://cran.r-project.org/web/packages/data.table/vignettes/datatable-intro.pdf)   

    - [Fast summary statistics in R with data.table](http://blog.yhat.com/posts/fast-summary-statistics-with-data-dot-table.html)

- 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](https://cran.r-project.org/web/packages/Ryacas/vignettes/Ryacas.pdf)

    - [Scatterplot3d – an R package for Visualizing Multivariate Data](https://cran.r-project.org/web/packages/scatterplot3d/vignettes/s3d.pdf)

    - [tm.plugin.webmining intro](https://cran.r-project.org/web/packages/tm.plugin.webmining/vignettes/ShortIntro.pdf)

    - [Solving Differential Equations in R - ODE examples](https://cran.r-project.org/web/packages/diffEq/vignettes/ODEinR.pdf)

    - [Structural Equation Modeling With the sem Package in R](http://socserv.socsci.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf)

    - [prettyScree - prettyGraphs](http://www.inside-r.org/packages/cran/prettyGraphs/docs/prettyScree)



## Market Basket Analysis in R

- [Market Basket Analysis with R](http://www.salemmarafi.com/code/market-basket-analysis-with-r/)

- [Step by Step explanation of Market Basket](http://dni-institute.in/blogs/market-basket-analysis-step-by-step-approach-using-r/)