Research Workshop Series

LATIS offers a series of workshops created by our experts that are free and open to all faculty and graduate students. Join our LATIS Research Workshops Google Group to be the first to learn about workshops. Joining the group is highly recommended as these workshops are popular and often fill to capacity. You can view the slides and materials from past workshops at the LATIS Workshop Materials website.

Please note: Now accepting waitlist registrants for all workshops.

Summer Workshop Series: Fridays with R - Register For The Series Waitlist

R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. Additionally, R is free and designed for reproducible research. This workshop series will teach you how to get started using R to clean, manipulate, summarize,  and visualize data. We will not cover statistical analysis. Rather, this series will focus on all the steps that come *before* you run statistics because getting your data into the right format is often the hardest part of data analysis. We will also cover tools for reproducible research, introducing you to R Markdown, a tool to tie together analysis and text into a report, and github, a tool for version control and sharing.

While this workshop is open to participants from all disciplines, we will focus on issues social scientists often encounter when using data in R.

To be successful, you should have:


Fridays June 22 - July 17 (no workshop July 6)

  • Workshop - 10:00 a.m. - 12:00 p.m.

      • Each week we will cover a new topic in R with a hands-on presentation. See schedule below for the dates

  • Lunch on your own - 12:00 - 1:30 p.m.

  • Bring Your Own Data (BYOD) session - 1:30 - 3:00 p.m. (optional)

      • Try out what you learn on your own data, with R experts available for help. Extra activities will be provided as well. Ask questions, get help with troubleshooting, or get advice on how to approach a task in R. The BYOD sessions will generally be themed to match the workshop topic each week, but questions of any kind are welcome.

Workshops & BYOD sessions are in the same room each week

Introduction to R - June 22 - Bruininks 123 - 10:00 a.m. - Register for Waitlist

This workshop will teach you how to get started using R to explore and clean your data.

You will learn how to:

  • Create an R script (syntax/command file) to capture data cleaning steps in a reproducible way

  • Load a comma-delimited spreadsheet (.csv) into R as a dataset

  • View and examine data in R

  • Check and correct missing values, rename variables, create new variables, and recode values in the data

  • Save cleaned data file in formats for later use in R or other applications

Manipulating Data Using dplyr - June 29 - Bruininks 512A - 10:00 a.m. - Register for Waitlist

This workshop will introduce you to the dplyr package designed for data manipulation in R.

You will learn how to:

  • Subset a dataset to select the column/variables you need

  • Filter rows of the dataset to include only certain cases

  • Sort data by values in a column/variable

  • Chain together multiple R functions in a single command

  • Group and summarize data using descriptive statistics

Visualizing Data with Ggplot2 - July 13 - Bruininks 512A - 10:00 a.m. - Register for Waitlist

Ggplot2 is a popular package that extends R’s capability for data visualization, allowing users to produce attractive and complex graphics in a relatively simple way. This workshop will introduce the logic behind ggplot2 and give users hands-on experience creating data visualizations using this package. 

You will learn how to:

  • Understand the basics of the "grammar of graphics" underlying ggplot2's functionality

  • Create a variety of reproducible data visualizations in R, such as histograms, line charts, scatter plots, heat maps, and density plots

  • Visualize data by groups in multiple ways, including color labeling and faceting

Reshaping and Merging Data - July 20 - Bruininks 512A - 10:00 a.m. - Register for Waitlist

Often data are not in the shape or format you need for analysis. This workshop will teach you how to reshape data using the reshape2 package and how to merge multiple files into a single dataset.

You will learn how to:

  • Transform data from "wide" (single row per individual case; many columns) to "long" (multiple rows per individual case; fewer columns) and vice versa.

  • Check for and remove duplicates in a file before merging.

  • Examine cases that are present in each dataset before merging and determine the overlap

  • Learn about and perform different types of merges depending on what cases you want to keep in your final dataset.

Reproducible Tools: Markdown and Github in R - July 27 - Bruininks 123 - 10:00 a.m. - Register for Waitlist

This workshop explores strategies for creating reproducible code using R and github, as well as how to create reports integrating R code and output using Markdown.

You will learn how to:

  • Use git to version and github to share your code inside of RStudio

  • Create reproducible reports using RMarkdown, including how to format text in markdown, inserting and formatting chunks of R code and output, and other add-ons, such as a table of contents and bibliographies.

Before this workshop, please:

Following the summer session, the next series of workshops will be offered in the fall of 2018. Previous workshop topics have included:

  • Introduction to R for Social Scientists
  • Data Wrangling with R
  • Introduction to NVivo
  • Introduction to Python
  • Introduction to Markdown & LaTeX
  • Introduction to E-Prime
  • Introduction to Researching Computing
  • Introduction to Parallel Computing
  • Introduction to SQL & Research Database
  • Data Wrangling with Python (using Pandas)
  • Workflow in Python
  • Introduction to Atlas
  • Reproducible Experimental Research in Qualtrics
  • Introduction to Linux I & II
  • Qualitative Coding
  • Data Visualization Using Ggplot 2
  • Qualitative Data Collection
  • Gathering Data from the Web
  • Qualitative Research Transparency
  • Using the Open Science Framework for Better Research
  • Data Management in Transition: Strategies for When You Graduate