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R to wrangle, analyze, and visualize data faster and better
- 1. 1 Up your data game: How to use R to wrangle, analyze, and visualize data faster and better MERL Tech DC 2018 Charles Guedenet, MEL Technical Advisor, IREX Jonathan Seiden, Learning Research Specialist, Save the Children Please: Plug in your laptop and check its wifi download your dataset and r files here: http://bit.ly/merl-r
- 2. 2 Objectives By the end of this session, you should: 1. Have R and Rstudio setup on your computer 2. Have a better understanding of what R programming is and what it can do for you 3. Learn about useful R tools (functions and packages) that you can use in your own work 4. Feel intrigued (and excited?) enough about R to pursue further learning
- 3. 3 What is R? A programming language for data manipulation Command-line driven vs. Not point-and-click Who uses it? • Academics, journalists, statisticians, open data enthusiasts
- 5. 5 Pro #1: a reproducible workflow!
- 6. 6 Pro #2: top notch data visualization! https://www.r-graph-gallery.com/
- 7. 7 Pro #3: Flexible & Comprehensive Work with data across the data life cycle Get data Cleaning Analysis Data viz Reporting
- 8. 8 Pro #4: Large & active community Tutorials, blogs, websites R-bloggers.com – news and tutorials by 750+ bloggers Stats.stackexchange.com So much free code! Copy + paste Kaggle.com Github.com A package for everything - +13k packages www.r-pkg.org
- 9. 9 Pro #5: It’s free! Compare with: ANNUAL Cost SPSS $1,200 (statistics only) + $$ for addons SAS $8,700 first year (basic Analytics Pro) STATA $595 - $1,500
- 10. 10 Con #1: Steep learning curve
- 11. 11 Pros and Cons Programming language = reproducible work & huge efficiency gains Top notch data visualization capabilities Flexible & comprehensive Active R community Free and open source A steep learning curve for programming newbies Colleagues/friends may still prefer STATA SPSS users Pros Cons
- 12. 12 Getting setup 1. Install R on your computer http://lib.stat.cmu.edu/R/CRAN/ Windows or MacOS - choose one of the precompiled binary distributions (i.e., ready-to-run applications) linked at the top of the R Project’s webpage. 2. Install RStudio https://www.rstudio.com/products/rstudio/download/
- 13. 13 Introduction to Rstudio Go to your Start menu and in programs start Rstudio by clicking on the Rstudio icon
- 14. 14 Rstudio basics This is what the Rstudio interface looks like
- 15. 15 Rstudio basics: the console
- 16. 16 Introduction to Rstudio: Source tabs This is a built-in text editor Open an empty script File New File R Script You can write a script and then execute it in the console using Ctrl Shift Enter
- 17. 17 Global Environment & History Environment tab - where you can see the values and functions that you’ve created or imported History tab –where you can see a list of key strokes you’ve entered in console
- 18. 18 Files, plots, packages, help Files – navigate your computer’s files Packages – find and install packages Help – search and find help with functions and packages
- 19. 19 Files, plots, packages, help A function is a set of statements (or instructions) organized to perform a specific task. e.g. sqrt(), sd(), mean() Packages are collections of R functions, data, and compiled code in a well-defined format. The library is where packages are storied on your computer.
- 20. 20 Basic tips 1. To run a command/function, click the “Run” button or press Ctrl + Enter 2. R is case sensitive. Make sure your spelling and capitalization are correct. 3. The $ symbol is used to select a particular column within a table (e.g., table$column). 4. The # symbol: Any text that you do not want R to act on (such as comments, notes, or instructions) needs to be preceded by the # symbol (a.k.a. hash-tag, comment, pound, or number symbol). R ignores the remainder of the script line following #. From <http://ncss-tech.github.io/stats_for_soil_survey/chapters/1_introduction/1_introduction.html>
Editor's Notes
- Intro: R programming language is one of the most popular languages to do data science, used by tons of companies and universities around the globe in all sorts of fields. *The name “R” comes from the initials of the two men who first developed the language at the University of Auckland, Robert Gentleman and Ross Ihaka
- Image from https://www.quora.com/Who-even-uses-R-language . (James McInnes) Intro: R programming language is one of the most popular languages to do data science, used by tons of companies and universities around the globe in all sorts of fields. Who uses it and to do what? Why learn R and how does it compare to other programs like Excel, STATA, SPSS, and others? Ford uses R to improve the design of its vehicles. Basically, Twitter uses R to monitor user experience. The US National Weather Service uses R to predict severe flooding. The Human Rights Data Analysis Group uses R to quantify the impact of war. R is being used by The New York Times to create infographics. Google uses R to calculate the ROI of advertising campaigns
- 1. R is an actual programming language, with a command-line interface for executing code versus point-and-click Efficiency gains – Useful if you need to reformat every graph or chart at once OR If your data changes after already making your calculations Reproducible - Just like creating a recipe for a meal, a recipe with code makes it possible for anyone to reproduce your work (including your future self). Every step of your analysis is recorded. (It's also useful for validating with other researchers).
- https://www.r-graph-gallery.com/
- Obtain and reads virtually any type of data – Excel, web scraping, databases (MySQL), foreign (SPSS, SAS) Cleaning and wrangling Analysis – Quant, Qualitative, network analysis, Machine learning, Text mining Data visualization Reporting – publish static results in Word or pdf or make it interactive with rmarkdown and Shiny packages
- https://www.r-bloggers.com/ https://cran.r-project.org/web/packages/available_packages_by_name.html 3. Active community It's easy to create so-called packages, which are extensions to R. R's very active community has created thousands of these packages for many different fields. Github.com and stats.stackexchange.com are full of freely available programming code
- PROS 1. R is an actual programming language, with a command-line interface for executing code versus point-and-click Efficiency gains – Useful if you need to reformat every graph or chart at once OR If your data changes after already making your calculations 2. Data visualization 3. Flexible & comprehensive - CONS Steep learning curve Resistance from colleagues/friends
- Setup If you are running Windows or MacOS, you should choose one of the precompiled binary distributions (i.e., ready-to-run applications) linked at the top of the R Project’s webpage. Rstudio R Studio is an “Integrated Development Environment”, or IDE. This means it is a front-end for R that makes it much easier to work with. R Studio is also free, and available for Windows, Mac, and Linux platforms.
- Rstudio is a particularly user-friendly interface for working with R. It has drop-down menus and many customization options. **The first time you open Rstuio, you’ll see three windows. By default, a fourth window is hidden. Open it by clicking on the File drop-down menu, then New file, and R Script.
- The Console is where you can type code and execute. It’s also where you warnings and error messages appear to help you with debugging your code
- The benefits are writing code in a script is that your work is saved and can be changed later if, for example, your data changes, you want to make edits to your charts, or you want to share your work with others.
- Almost everything in R is done through functions. A function is a set of statements organized together to perform a specific task. R has a large number of in-built functions and the user can create their own functions. Functions may include arguments to help the function accomplish a task. (for example, do not include NA values) Numeric functions Sqrt() Character functions e.g. strsplit(data, split)
- From <http://ncss-tech.github.io/stats_for_soil_survey/chapters/1_introduction/1_introduction.html>