R for Reproducible Scientific Analysis

By Adam H. Sparks, Ahsan Ali Khoja, Amy Lee, Ana Costa Conrado, Andrew Boughton, Andrew Lonsdale, Andrew MacDonald, Andris Jankevics, Andy Teucher, Antonio Berlanga-Taylor, Ashwin Srinath, Ben Bolker, Bill Mills, bippuspm, Bret Beheim, butterflyskip, Clare Sloggett, Daniel, Dave Bridges, David J. Harris, David Mawdsley, Dean Attali, Diego Rabatone Oliveira, Drew Tyre, Elise Morrison, Erin Alison Becker, Fernando Mayer, François Michonneau, Giulio Valentino Dalla Riva, Gordon McDonald, Greg Wilson, Harriet Dashnow, Ido Bar, Jaime Ashander, James Balamuta, James Mickley, Jamie McDevitt-Irwin, Jeffrey Arnold, Jeffrey Oliver, John Blischak, Jonah Duckles, Josh Quan, Julia Piaskowski, Kara Woo, Kate Hertweck, Katherine Koziar, Katrin Leinweber, Kellie Ottoboni, Kevin Weitemier, Kiana Ashley West, Kieran Samuk, Kunal Marwaha, Kyriakos Chatzidimitriou, Lachlan Deer, Lex Nederbragt, Liz Ing-Simmons, Lucy Chang, Luke W Johnston, Luke Zappia, Marc Sze, Marie-Helene Burle, Marieke Frassl, Mark Dunning, Martin John Hadley, Mary Donovan, Matt Clark, Melissa Kardish, Mike Jackson, Murray Cadzow, Narayanan Raghupathy, Naupaka Zimmerman, Nelly Sélem, Nicholas Lesniak, Nicholas Potter, Nima Hejazi, Nora Mitchell, Olivia Rata Burge, Paula Andrea Martinez, Pete Bachant, Phil Bouchet, Philipp Boersch-Supan, Piotr Banaszkiewicz, Raniere Silva, Rayna Michelle Harris, Remi Daigle, Rémi Emonet, Research Bazaar, Richard Barnes, Robert Bagchi, Sam Penrose, Sandra Brosda, Sarah Munro, Sasha Lavrentovich, Scott Allen Funkhouser, Scott Ritchie, Sebastien Renaut, Thea Van Rossum, Timothy Eoin Moore, Timothy Rice, Tobin Magle, Trevor Bekolay, Tyler Crawford Kelly, Vicken Hillis, waiteb5, Yuka Takemon.
Analysis Data Education Open Scholarship Tools and Technologies R Reproducibility Research Data Management Tools Researchers

Abstract

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. The lesson contains more material than can be taught in a day. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop. A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

Link to resource: https://swcarpentry.github.io/r-novice-gapminder/

Type of resources: Module

Education level(s): Graduate / Professional

Primary user(s): student, teacher

Subject area(s): Computer Science, Information Science, Measurement and Data

Language(s): English