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. 

In Fall 2020, these workshops have been adapted to support online learning. The new format for the workshops will be a flipped classroom approach. Learners will watch a video prior to a session and an online, live workshop will be used to review key concepts and work through additional problems. 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.  

Fall Workshops and Schedule  

Registration is required, and capped at 10 attendees to ensure learners get the attention they need in an online learning environment. If researchers are new to the R programming language or RStudio, they are encouraged to sign up for the first session, R Basics.  

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