Coding and Cookies

Automating data cleaning and analysis using R.

Welcome to Coding and Cookies


  1. Researchers are producing more data than ever before.
  2. It's literally impossible to analyze all of these data by hand.
  3. Research is really repetitive
  4. Automating data processing and analysis will streamline your research

The Coding and Cookies series will teach you the basics of how to use R programming and version control using git to make your research more efficient and reproducible. 

Coding & Cookies is offered in collaboration with the Department of Statistics. After adapting our approach to support online learning in 2020, we will continue to use a flipped classroom format in Fall 2021. Attendees will be expected to watch a recorded video and follow along with the exercises presented before attending the live workshops, which will be held in Morgan Library*. At the in person sessions, we will review key concepts and work through additional examples and questions, with individual help available. Learning materials will continue to be made publicly available on this guide (see links to the left). If sessions are full, interested students are encouraged to watch the videos and get in touch with the instructors for follow-up questions. 

Sessions will be led by experienced statistics graduate students and facilitated by Mara Sedlins, PhD, Data Management Specialist at the CSU Libraries, and Julia Sharp, Associate Professor of Statistics and Director of the Graybill Statistics and Data Science Laboratory.  

*We will be piloting one hybrid workshop on September 21st; participants will choose between an in-person session and an online (Zoom) session. The workshop will be live broadcast to the Zoom session, with a facilitator to answer questions.


Fall 2021 Workshops and Schedule

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. 

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 7th, 10:00-11:30am 
Registration link:  

September 21st, 10:00-11:30am (hybrid session) 
Registration link (in person) 
Registration link (online)  

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 12th, 10:00-11:30am 
Registration link:  

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 26th, 10:00-11:30am 
Registration link:  

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 9th, 10:00-11:30am 
Registration link:  


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