Coding & Cookies
Everything is better with cookies. Especially data.
All sessions are held 10:00 a.m. – 11:30 a.m. at Morgan Library, Computer Classroom 175
Good data management practices are becoming increasingly important in the digital age. Because we now have the technology to freely share research data, and also because funding agencies want to do more with decreasing research funds, many funding agencies and journals require authors and grantees to share their research data. Coding & Cookies sessions are focused on coding in R, a programming language for statistical computing and graphics.
New to R or RStudio? We encourage you to attend the first session, R Basics. A basic working knowledge of R and RStudio is helpful to get the most out of the rest of the sessions.
Got your eID ready? Register for Coding & Cookies.
Fall 2017 Sessions
Thu., Sept. 28 or Thu., Oct. 26 – R Basics
Learning how to code can be intimidating, but will save you time and effort in the long run. The first session will cover the basics of using of tabular data in RStudio. By the end of this session, you’ll be able to load data into R, calculate summary statistics, and create exploratory graphs using R’s basic graphics package. This session is geared toward beginners, so if you have experience using R, this isn’t the class for you.
Thu., Nov. 2 – Data Cleaning Using R
The process of generating data can be messy, especially when data are hand collected by multiple people. We’ll discuss how to wrangle messy tabular data using R. After this session, you’ll be able to import tabular data into R, convert data to an appropriate type, re-code categorical variables, convert dates, manipulate strings, and detect and fix errors in your data.
Thu., Nov. 30 – Data Wrangling Using R
What you can do with your data depends strongly on how its formatted. We’ll cover how to manipulate datasets using an R package called dplyr. After this session, you will be able to subset, reformat and summarize your data.
Thu., Feb. 15 – Data Visualization Using R
So you’re familiar with R, but want to do more with your plots than the base graphics package. We’ll show you how to use the ggplot2 package in R. After this session, you will be able to create a variety of plot types, alter their aesthetics, and create custom themes.
Thu., Mar. 15 – Version Control Using Git
We’ve all intuitively used some type of version control in our work such as saving multiple versions of a document. While easy, it can cause file bloat and ultimately become more complicated. Luckily, formal version control systems have been developed to streamline this process. This session, we’ll be covering version control using git. After this session, you’ll be able to create a git repository, make and add changes to the repository, and use GitHub to remotely store your repository.
Thu., Apr. 19 – Reproducible Reports with R Markdown
Documenting your analysis in a way that is understandable to a colleague (or yourself 3 months later) can be challenging. One way to make reports more readable, even by people who don’t code, is to alternate human readable text with machine readable code. We’ll learn about creating reproducible reports of this type using knitr. After this session, you’ll be able to create R markdown documents, add formatted text and executable code blocks, and render the R markdown document into a final report.