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When & Where
Thu, February 27, 2020 - 1:00 PM to 4:00 PM
D-Lab Convening Room

LaTeX is a widely used document creation software which can help you improve the presentation of homework, papers, academic articles and even presentations. It produces perfectly formatted equations, allows for the easy integration of multiple files, and can make creating bibliographies easy. In this workshop, we'll teach you how to make the most out of LaTeX, and provide you with templates and code that you can use going forward. Specifically, this workshop will cover:

  • Creating tables and figures
  • Footnotes and references/bibliographies
  • Exporting well-formatted tables from Stata/R to LaTeX

Prior knowledge: This workshop assumes a basic, working knowledge of LaTeX (i.e. how to compile a document and other such tasks as are taught in the first workshop). The workshop will discuss using Stata and R, but you're welcome to attend even if you've never used either and without having either installed on your computer.

Technology requirement: Please bring a laptop with LaTeX installed! 

To install LaTeX, you'll need to install two separate softwares - 1) a distributor and 2) a writing environment. For the distributor, your options are:

The files are quite large for most of these; I recommend tackling the installation somewhere with a strong wifi connection. All three are completely free. For the writing environment, please install:

There are versions available for all major operating systems. You are welcome to come early to the workshop if you need any help with installation.

Primary Tool: 
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
Hands-on, interactive
Participant Technology Requirement: 

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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