Why R? and other questions

• Transparency
• Reproducibility
• Open science & lowering barriers

• Statistical computing environment
  • Basic statistics (e.g. ANOVA, T-tests)
  • Complex modeling (machine learning, Bayesian stats, simulations…)
• Database manager

• Graphic design
  
• GIS
• Version control (via Github, etc.)
  
• Publisher (basic word processing, web pages, scientific articles, theses, books…)

Download R :http://www.r-cran.org

Download RStudio: http://www.rstudio.com

Other options besides RStudio: Emacs, Jupyter notebooks, Rodeo

• Integrated environment between scripts, output, figures, etc.
• Easy to configure
• Large online user community for help
• Lots of online courses (e.g. Datacamp, Coursera…)

• R for Data Science (by Hadley Wickham) http://r4ds.had.co.nz/

• Coding Club https://ourcodingclub.github.io/tutorials/

• Basics and a lot more http://ohi-science.org/news/Resources-for-R-and-Data-Science

• Guide for reproducible code https://www.britishecologicalsociety.org/wp-content/uploads/2017/12/guide-to-reproducible-code.pdf

• Cookbook for R http://www.cookbook-r.com/
• Stackoverflow http://www.stackoverflow.com
• Quick-R https://www.statmethods.net/
• or do an online search (Google, DuckDuckGo) :
  • ‘R package/function name error message’
  • ‘R how to remove every third line from a data.frame’
    
• Look for local or university user groups (via Slack,Twitter, etc.)

• Open RStudio
  
• File | New Project
• Select either ‘New Directory’ or ‘Existing Directory’
  this will be the working folder for our course (on your computer)
• Locate the project on your computer in a place (e.g. ’~/Courses/Macroecology)
  
• Name your project
  
• Within your project’s folder, create three sub-folders:
  • data
  • code
  • figures

• Open a new file: File | New File | R script

• Type: # Day 1: getting started

• Save file (in ~/code): “0_Code_Day1.R”

• Start coding!