Welcome to SISMID Workshop: Introduction to R

Amy Winter
Zane Billings

Welcome to SISMID Workshop: Introduction to R!

Amy Winter (she/her)

Assistant Professor, Department of Epidemiology and Biostatistics

Email: awinter@uga.edu


Zane Billings (he/him)

PhD Candidate, Department of Epidemiology and Biostatistics

Email: Wesley.Billings@uga.edu

Introductions

  • Name?
  • Current position / institution?
  • Past experience with other statistical programs, including R?
  • Why do you want to learn R?
  • Favorite useful app
  • Favorite guilty pleasure app

Course website

  • All of the materials for this course can be found online here: here.
  • This contains the schedule, course resources, and online versions of all of our slide decks.
  • The Course Resources page contains download links for all of the data, exercises, and slides for this class.
  • Please feel free to download these resources and share them – all of the course content is under the Creative Commons BY-NC 4.0 license.

What is R?

  • R is a language and environment for statistical computing and graphics developed in 1991

  • R is the open source implementation of the S language, which was developed by Bell laboratories in the 70s.

  • The aim of the S language, as expressed by John Chambers, is “to turn ideas into software, quickly and faithfully”

What is R?

R logo

What is R?

  • R possesses an extensive catalog of statistical and graphical methods
    • includes machine learning algorithm, linear regression, time series, statistical inference to name a few.
  • Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicate the results

What is R?

  • Program: R is a clear and accessible programming tool
  • Transform: R is made up of a collection of packages/libraries designed specifically for statistical computing
  • Discover: Investigate the data, refine your hypothesis and analyze them
  • Model: R provides a wide array of tools to capture the right model for your data
  • Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world

Why R?

  • Free (open source)

  • High level language designed for statistical computing

  • Powerful and flexible - especially for data wrangling and visualization

  • Extensive add-on software (packages)

  • Strong community

Why not R?

  • Little centralized support, relies on online community and package developers

  • Annoying to update

  • Slower, and more memory intensive, than the more traditional programming languages (C, Perl, Python)

Is R Difficult?

  • Short answer – It has a steep learning curve, like all programming languages
  • Years ago, R was a difficult language to master.
  • Hadley Wickham developed a collection of packages called tidyverse. Data manipulation became trivial and intuitive. Creating a graph was not so difficult anymore.

Overall Workshop Objectives

By the end of this workshop, you should be able to

  1. start a new project, read in data, and conduct basic data manipulation, analysis, and visualization
  2. know how to use and find packages/functions that we did not specifically learn in class
  3. troubleshoot errors

This workshop differs from “Introduction to Tidyverse”

We will focus this class on using Base R functions and packages, i.e., pre-installed into R and the basis for most other functions and packages! If you know Base R then are will be more equipped to use all the other useful/pretty packages that exit.

The Tidyverse is one set of useful/pretty sets of packages, designed to can make your code more intuitive as compared to the original older Base R. Tidyverse advantages:

  • consistent structure - making it easier to learn how to use different packages
  • particularly good for wrangling (manipulating, cleaning, joining) data
  • more flexible for visualizing data
Tidyverse hex sticker

Workshop Overview

14 lecture blocks that will each:

  • Start with learning objectives
  • End with summary slides
  • Include mini-exercise(s) or a full exercise

Themes that will show up throughout the workshop:

Reproducibility

  • Reproducible research: the idea that other people should be able to verify the claims you make – usually by being able to see your data and run your code.
  • 2023 was the US government’s year of open science – specific aspects of reproducibility will be mandated for federally funded research!
  • Sharing and documenting your code is a massive step towards making your work reproducible, and the R ecosystem can play a big role in that!

Useful (+ Free) Resources

Want more?

  • R for Data Science: http://r4ds.had.co.nz/
    (great general information)

  • Fundamentals of Data Visualization: https://clauswilke.com/dataviz/

  • R for Epidemiology: https://www.r4epi.com/

  • The Epidemiologist R Handbook: https://epirhandbook.com/en/

  • R basics by Rafael A. Irizarry: https://rafalab.github.io/dsbook/r-basics.html (great general information)

  • Open Case Studies: https://www.opencasestudies.org/
    (resource for specific public health cases with statistical implementation and interpretation)

Useful (+Free) Resources

Need help?

  • Various “Cheat Sheets”: https://github.com/rstudio/cheatsheets/

  • R reference card: http://cran.r-project.org/doc/contrib/Short-refcard.pdf

  • R jargon: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf

  • R vs Stata: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf

  • R terminology: https://cran.r-project.org/doc/manuals/r-release/R-lang.pdf

Installing R

Hopefully everyone has pre-installed R and RStudio. We will take a moment to go around and make sure everyone is ready to go. Please open up your RStudio and leave it open as we check everyone’s laptops.