Coding & Cookies

Single Cookie

Everything is better with cookies. Especially data.

Making your research reproducible is increasingly important as research data become larger and more complex. The Coding & Cookies series provides an introduction to literate programming with R, a popular programming language for statistical computing. Learning to automate data cleaning, analysis, and visualization will make your research more efficient, reliable, and transparent.

Coding & Cookies is offered in collaboration with the Department of Statistics. We will be piloting a flipped classroom approach this fall, with an asynchronous recording that attendees will be expected to watch and follow along with before attending a synchronous session that will review key concepts and work through additional examples. Due to the interactive nature of learning to code, synchronous sessions will be limited to 10 registrants. Workshops will be led by experienced statistics graduate students and facilitated by the Morgan Library Data Management Specialist.

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.

Sessions are free, but space is limited to 10 attendees. 

Got your eID ready?  Register for Coding & Cookies.

Fall 2020 Sessions

R basics

Learning how to code involves an investment of time and effort up front, but will save you time and effort in the long run. In the R basics Coding and Cookies session, the basics of using tabular data in RStudio will be discussed. By the end of this session, you will 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 may not be the class for you.

September 8th, 10:00-11:00am

September 22nd, 10:00-11:00am

Tidy Data in R

The process of generating data can be messy, and what you can do with your data depends strongly on how it is formatted. This month’s coding and cookies will cover the definition of “tidy data”, a standardized way of formatting your data that makes it easier to work with. You will learn how to clean and reformat your data using a collection of R packages called the tidyverse. A basic working knowledge of R and R studio would be helpful for you to get the most out of this session.

October 13th, 10:00-11:00am

Data Visualization using ggplot2

So you’re familiar with R, but want to do more with your plots than the base graphics package.  In this month’s Coding and Cookies, the ggplot2 package in R will be discussed. After this session, you will be able to create a variety of plot types, alter their aesthetics, and create custom themes. A working knowledge of R and R studio and dplyr would be helpful for you to get the most out of this session.

October 27th, 10:00-11:00am

Reproducible Reports using RMarkdown

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. In this month’s Coding and Cookies session, we will cover creating reproducible reports of this type using knitr. After this session, you will be able to create R markdown documents, add formatted text and executable code blocks, and render the R markdown document into a final report.

November 10th, 10:00-11:00am

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