LATIS offers a series of workshops 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. You can view the videos, slides, and materials from past workshops at the LATIS Workshop Materials website.

Summer 2021 Workshops

R Summer Workshop Series 

Register Here

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. 

While these workshops are open to participants from all disciplines, we will focus on issues social and behavioral scientists often encounter when using data in R. You can sign up for all five workshops in the series, or attend just one. 

Live workshop & help sessions will be held via Zoom on Wednesdays from 10am-noon

Each workshop will have the following format:

  • Asynchronous materials to review before the workshop
  • 10:00am - 10:15am: Open help session/time to review materials
  • 10:15am - 11:30am: Live Demonstration
  • 11:30am - Noon: Open help session/time to work on activities


June 30 | 10am-noon | Introduction to R

July 7 | 10am-noon | Manipulating data using dplyr

July 14 | 10am-noon | Visualizing data with Ggplot2

July 21 | 10am-noon | Reshaping and merging data

July 28 | 10am-noon | Reproducible research in R

June 30: Introduction to R

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


July 7: Manipulating data using dplyr

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


July 14: Visualizing data with Ggplot2

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 demonstrate how to create 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, heatmaps, and density plots
  • Visualize data by groups in multiple ways, including color labeling and faceting 


July 21: Reshaping and merging data

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

Your 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. 


July 28: Reproducible research in R

R is a powerful tool for reproducible research because it allows you to chain together many steps in your workflow within a single tool, from data cleaning to analysis to reporting. In this workshop, we will discuss packages, coding strategies, and tips to make your work in R more reproducible, allowing you and others to re-run your code future and ensure you get the same results. 

You will learn how to: 

  • Write an R script that incorporates best practices for reproducible research
  • Review and evaluate your code for understandability and use by others (or future you)
  • Record and manage package and software versions
  • Extend your R practice by incorporating other tools for reproducibility, such as GitHub and R Markdown