**Tutorial Slides & Resources:****Intro to R Tutorial**files (7 April 2016, UNR):**Slides**here**tutorial-intro-040716.pdf**;**R cripts**etc. here tutorial-files-040716.zip.

This tutorial was for a mathematician-heavy audience, and introduces a range of basic examples (see slides, zip file for details).**R Bootcamp**: A statistics-focused self tutorial maintained by Kevin Shoemaker, used for our annual introductory graduate student workshop. http://naes.unr.edu/shoemaker/teaching/R-Bootcamp/**R Markdown Intro:**RmdIntro.Rmd and RmdIntro.html

**LaTeX users!**To configure the LaTeX editor**TeXstudio**(www.texstudio.org) to compile documents with embedded R code (this is how I made the beamer slides above) using the`knitr`package, see**www.pauljhurtado.com/latex/texstudio.html**.

**RStudio**also compiles`knitr`documents (see yihui.name/knitr/demo/rstudio/, and**R Markdown**(see rmarkdown.rstudio.com).**Keyboard shortcuts**(for Windows and OS X): To help you type/code more efficiently!**Non-english Speakers:**R documentation in Chinese, German and Russian can be found at www.r-project.org/other-docs.html, but**additional documentation**for other languages is at cran.r-project.org/other-docs.html#nenglish

- First, download and install
**R**from www.r-project.org. - Second, install
**R-Studio**from www.rstudio.com -- you'll learn R more quickly if you use R-Studio as your primary way of using R! - Short, interactive lessons online via the Try R website at tryr.codeschool.com
- Interactive lessons in R via the
`swirl`package: www.swirlstats.com - Getting Started with R from the folks at R-Studio.
**Write R code well**by periodically comparing your R scripts to the Google's Style Guide for R- My Introduction to R (PDF), and a few similar resources:
- Work through Appendix A "A sample session" at the end of "
**An Introduction to R**" (the first of the R "Manuals" on the R website). - Steve Ellner & John Guckenheimer have this Introduction to R (PDF); the lab manual for their book Dynamic Models in Biology.
- Ben Bolker has a related introductory document, lab 1 (PDF); a supplement to his book Ecological Models and Data in R.
- Yet another R Introduction (YaRI), by Andreas Handel, is based in part on the above works.

- Work through Appendix A "A sample session" at the end of "

**Quick-R**is an excellent resource! See www.statmethods.net/- Among other resources on the
**RStudio**website are the**Cheat Sheets**at www.rstudio.com/resources/cheatsheets/. - Manuals on the R website are free, and include
**An Introduction to R**,**R Data Import/Export**, and**The R Reference Index**. - CRAN Task Views: Take a moment and skim these
**comprehensive overviews of R packages for various tasks**(e.g. Baysian Inference, Differential Equations, Probability Distributions, Experimental Design, Multivariate Statistics, Numerical Mathematics, Optimization, Robust Statistics, Spatial Data Analysis, Time Series Analysis, etc) - Introduction to R is a six part online course through DataCamp: www.datacamp.com/courses/introduction-to-r

- The
**Quick-R**website sections Graphics and Advanced Graphics are a good place to start. - research.stowers-institute.org/efg/R/Graphics/Basics/mar-oma/index.htm -- R Graphics Basics: Plot area, mar (margins), oma (outer margin area), mfrow, mfcol (multiple figures)
- flowingdata.com/category/tutorials/ -- Tutorials from FlowingData.com, a data visualization website.
- ggplot2.org/ -- ggplot2 graphics package website
- 10 tips for making your R graphics look their best.

- The
**Quick-R**website sections on data Input (into R) and Management. - See also the RStudio cheat sheet on Data Wrangling, and the
`readxl`package for importing data from MS Excel spreadsheets. - The R website manual on R Data Import / Export.
- Coding systems for categorical variables: http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm

**Rpplane!**: 2D phase plane analysis. A (very!) beta version of pplane for R (Rpplane) based on code from Daniel Kaplan (Mcalester College) that had been modified by Stephen P. Ellner (Cornell University) for use with his 2006 book co-authored with J. Guckenheimer. Ultimately this will wind up on github and then become available as an R package, with documentation for both the GUI/shiny version and functions for command-line functions. Please let me know if you find bugs or have suggestions for cleaning up the code, or if you have any feature requests!**R's assignment operators explained: ‘=’ vs. ‘<-’ vs '<<-'**: csgillespie.wordpress.com/2010/11/16/assignment-operators-in-r-vs/- Revolutions -- The staff blog from Revolution Analytics.
- Microsoft
~~Revolution Analytics~~offers an enhanced version of R called Microsoft R Open (MRO). - Infographic:
**R vs Python**(www.python.org/): www.datacamp.com/community/tutorials/r-or-python-for-data-analysis - SciViews.org/ -- Open source software to supplement R.
- Presentation:
**Intro to R: data manipulation, visualization, communities, networks**(presentation by Scott Chamberlain): schamberlain.github.io/posterstalks/sfu/workshop2/ - Presentation:
**R Resources**(presentation by Scott Chamberlain): rpubs.com/recology_/rresources - Others GUIs/IDEs for R:
- Windows users: a good (free) R code editor is Notepad++ with NppToR
- Mac users: while R's native editor is decent, another good (free) R code editor is TextWrangler.
- ...or for $70, the Sublime Text editor (v2). See this link for using it with R.
- www.rcommander.com/ -- R Commander, a super basic GUI for simple stats in R.
- Additional (older) R GUIs can be found at www.sciviews.org/_rgui/ -- see the sidebar for details.
- Looking for a menu-driven alternative? Check out the R-based software iNZight! Developed for New Zealand high school students to explore data and statistical concepts in a programming-free environment. Read how it compares to Minitab, SPSS, Excel in this blog post.