Intro

R has something for everyone. Whether you want to document your work to help remember what you did 2 years ago (or maybe just 1 week ago), automate the analysis of 100’s of monitors, copy a method you saw in Paper X, quickly share your results online, create charts exactly how you want them, or produce the 500 boxplots your boss just demanded you have done by tomorrow… R is here to help.

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R

• R is a free, open-source language and environment for statistical analysis.

• But R is more than a free version of SAS or SPSS.

  R is reproducible research, interactive data visualizations, simulations, GIS, automation, collaborative science, web tools, and a worldwide Community.

• And of course, R is always perfectly happy to do simple stats.

plot(AirQuality, main="Ozone vs. Wind Speed")
abline(h=25)

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Get Started - Tutorials

1. Interactive Pirate Tutorial
   aRRRg! TryR at CodeSchool

2. Switching from From Excel to R

STDEV() in Excel is sd() in R

3. IDEM slide shows by Nathan Byers
   • Load and Plot Data
   • Summary Statistics
   • Clean, Combine, Reshape and Filter data

To grab the first row from a data table, specify the row number inside the brackets

airquality[1, ]

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Take the Next Step

• Helpful Forums and Groups
  • Rseek.org
    
  • StackOverflow
    
  • R-bloggers
    
  • Quick-R

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• Quick Overviews
  • A Very Short Introduction to R.pdf
    
  • R-intro.pdf
    
  • Reference Card of handy commands: R RefCard

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• Special Topics
  • Filter / Subset / Select Data-> Subsetting by Hadley Wickham
    
  • Dplyr is the best thing (easier syntax, built for grouping data)-> Using dplyr
    
  • Time Series, Dates, Seasonal Variation, Calendar Plots, Wind Roses-> Open Air Project
    
  • Spatial Data and Maps-> ggmaps by Wickham

Extra Credit

• Style Guide by Wickham
• Vocab List by Wickham

*Go -here- for an extensive document list

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Videos and Courses

• Two Minute Tutorials: TwoTorials.com

Online Courses (Free)

• Data Scientist’s Toolbox:
  This course gives an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

• Exploratory Data Analysis:
  This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

• Reproducible Research:
  After successfully completing this course you will be able to make visual representations of data using the base, lattice, and ggplot2 plotting systems in R, apply basic principles of data graphics to create rich analytic graphics from different types of datasets, construct exploratory summaries of data in support of a specific question, and create visualizations of multidimensional data using exploratory multivariate statistical techniques.

• R programming:
  The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

• Computing for Data Analysis: This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.

*Go -here- for an extensive video list

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Books

show(books)

Title	Author	Year
The R Book	Michael Crawley	2012
R Graphics Cookbook	Winston Chang	2013
Introductory R: A Beginner’s Guide	Robert Knell	2013
Learning R	Richard Cotton	2013
R Cookbook	Paul Teetor	2011
R in a Nutshell	Joseph Adler	2012
Data Manipulation with R	Phil Spector	2008
Ecological Models and Data in R	Benjamin M. Bolker	2008

*Go -here- for a comprehensive book list

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Credits

• Created using RStudio’s R Markdown

• RStudio can be freely downloaded

• Link to this document: http://rpubs.com/dKvale/LearningR