Data Analysis using R
What is R?
R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years.
Style Guide for R Programming
- Install R - https://cran.r-project.org/ - This R core software.
- Install RStudio Desktop - https://rstudio.staging.wpengine.com/products/RStudio/ - User friendly IDE for R
Both are open source software.
Basic of Statistics
It is thriving and rich source for R, machine learning related topics.
Common R packages
Data Science Resources:
- UCLA’s IDRE collection of examples using R, SAS, SPSS, and Stata
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani’s Introduction to Statistical Learning, with Applications in R.
- Jeff Leek’s The Elements of Data Analytic Style.
- Andrew Gelman’s Statistical Modeling, Causal Inference, and Social Science
- Rafa Irizarry, Roger Peng, and Jeff Leek’s Simply Statistics
- Nathan Yau’s Flowing Data
- Randall Munroe’s xkcd
- Dolph Schluter’s R tips pages at the University of BC
- Rob Kabacoff’s Quick-R site
- R-Bloggers, a digest of R news and tutorials contributed by R users
- Mad (Data) Scientist, Norman Matloff’s personal blog on R and data science
- RStudio’s library of cheatsheets
- RevolutionR blog