IMMERSE Pre-Training Materials

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The Institute of Mixture Modeling for Equity-Oriented Researchers, Scholars, and Educators (IMMERSE) is an IES funded training grant (R305B220021) to support education scholars in integrating mixture modeling into their research.

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Day 1 (May 19, 2023): Introductions, training goals, introduction to data science

Learning Outcomes:

1. Fellows will be able to create a first draft of their equity-focused research goals that can be addressed with mixture modeling in the upcoming year.

2. Fellows will be able to identify how the IMMERSE training goals and opportunities can help them to apply mixture modeling training their research goals

Synchronous Activity:

• Slide deck here
• Workspace

Asynchronous Activity:

• Activity 1: Thoughts on articles from expert consultants (starting on slide 17 of the Workspace slides)

• Activity 2: Create a draft of your research goals (starting on slide 20 of the Workspace slides]

Pre-Training Day 1 Video

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Day 2 (May 24, 2023): Introduction to Mplus and MplusAutomation in R

Prepartion:

• Make sure you have Mplus, R, and RStudio downloaded and ready to go!

Learning Outcomes:

1. Using Mplus software, fellows will learn the basic skeleton of an Mplus input (.inp) syntax, run basic descriptive statistics, and evaluate output (.out)

2. Using the MplusAutomation package, fellows will will be able to run descriptive statistics in R.

3. This workshop will cover the creation of R-projects and R markdowns and discuss the benefits of organization of the R workflow.

4. Fellows will have the opportunity to learn how to enhance and customize their R markdowns by applying themes to their documents

Synchronous Activity:

• Introduction to MplusAutomation
• Slide deck here

Asynchronous Activity:

• Complete Part 3 of “Introduction to MplusAutomation”: Descriptive Statistics
• How to make your R markdown pretty!

Anonymous Feedback Survey

Pre-Training Day 2 Video

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Day 3 (May 26, 2023): Data wrangling, exploration and visualization

Learning Outcomes:

1. Fellows will be able to describe principles and challenges of reproducible data science.
2. Fellows will be able to explain tidy data principles and recognize when those principles might not apply, depending on the needs of an analysis method.
3. Using R in RStudio, Fellows will be able to wrangle data with categorical variables, encode continuous variables to categorical, and transform data frames from wide to long format and back.

Preparation:

• Work through the Intro to R and R Studio tutorial if not already done, including installing R and R Studio.
• Read “Why don’t we share data and code? Perceived barriers and benefits to public archiving practices” (Gomes et al. 2022) and reflect on your coding and sharing practices and challenges, in preparation for a brief discussion in the synchronous session.

Synchronous Activity (GitHub repo here):

• Discussion of benefits and challenges of coding, reproducible research, and collaboration (Google slides)
• Live coding: data wrangling with categorical data (.Rmd | .pdf)
  • Set up RStudio workspace in global options

Asynchronous Activity (GitHub repo here):

• Homework, day 3
  • Additional data wrangling: self-guided tour: Download the zip, follow directions in part three of the homework document.

Pre-Training Day 3 Video

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Day 4 (May 31, 2023): Collaborative, reproducible workflows with R, RStudio, git and GitHub

Learning Outcomes:

1. Using Git and GitHub, Fellows will be able to fork an existing repository, and clone it to an R project using RStudio.
2. Using R in RStudio, Fellows will be able to use simple loops and functions to automate and iterate coding tasks.
3. Using R in RStudio, Fellows will be able to use the stringr package and simple regular expressions to wrangle text data.

Preparation:

• Sign up for a GitHub account, and set up Git on your computer
• Link RStudio and Github, and set up a personal access token PAT
• Read “Better Science in Less Time” (Lowndes et al. 2017). Table 1 provides a good overview of tools available to improve open data science.
• Read “Good enough practices in scientific computing” (Wilson et al. 2017)

Synchronous Activity (Github repo here):

• Live coding: loops and functions; text wrangling. We will also practice with GitHub!
• Continue discussion on benefits and challenges of reproducible, collaborative, open data science.

Asynchronous Activity:

• Additional text wrangling: self-guided tour: Fork then clone this repository to your computer. Code along with the .HTML files in numeric order, or run line by line through the .Rmd scripts, as you prefer. Note, some of 1_stringr_tutorial will be duplicated in the Day 4 live coding session, but this script contains additional details and more complex wrangling cases.

Pre-Training Day 4 Video

Anonymous Feedback Survey

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Day 5 (June 2, 2023): Data science principles: data organization, storage, sharing, and code

Learning Outcomes:

1. Fellows will be able to create GitHub repositories on GitHub and in R/RStudio.
2. Fellows will be able to work with others to collaborate on a coding script; they will learn strategies to safely collaborate (to avoid merge conflicts), and will learn how to resolve a merge conflict if (when!) one does occur.
3. Fellows will explore additional collaboration and communication tools using R/RStudio and Git/Github.

Preparation:

• Read “Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution” (Braga et al 2023). Before reading, look at Fig. 2 - it nicely organizes many of the ways GitHub can facilitate collaboration, ordered by technical difficulty. As you read, choose three or four of those skills/activities that interest you most, and prepare to discuss those during the synchronous session.

Synchronous Activity:

• Recaps of ideas from pre-training days 1-4: Slide deck here
• Reflections and discussions on challenges of coding, reproducibility, and how to effectively implement data science principles

Asynchronous Activity:

• Watch (and follow along with) video walkthrough of Git/GitHub workflow using RStudio functionality
• Watch, and follow along with a collaboration partner, video of safely collaborating via GitHub, and also creating then resolving a merge conflict
  • Follow along with the instructions here
  • As part of the instructions, you’ll need to download this file and put it in a newly created R project
• GitHub Pages - build a website using R/RStudio and GitHub - try one or both as suits your needs and preferences!
  • Personal site using distill and postcards:
    • follow the instructions in this blog post from Alison Hill
    • follow carefully, and every time you modify anything in a .yml file or YAML header, be VERY CAREFUL about the spacing and indents!
  • Course site based on Allison Horst’s template for the UCSB MEDS program:
    • Step-by-step instructions
    • GitHub Repository of template (Here’s the README)

Pre-Training Day 5 Video

Anonymous Feedback Survey

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Helpful Links:

• Please visit our website to learn more.

• Visit our GitHub account to access all the IMMERSE materials.

• Follow us on Twitter!

How to reference this workshop: Institute of Mixture Modeling for Equity-Oriented Researchers, Scholars, and Educators (2023). IMMERSE Pre-Training Workshop (IES No. 305B220021). Institute of Education Sciences. https://immerse-ucsb.github.io/cohort-one/pre-training

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