Week 1 Deliverable: Introduction to DH, GitHub & Data Management

Course: Topical Reading: Digital Humanities (BA3 Korean Studies)
Date: October 10
Instructor: Dr. Steven Denney

Objective

There are two main goals for this week:

1. Set up your GitHub repository for the course and reflect on how Digital Humanities (DH) might connect to your thesis research in Korean Studies.
   • Add a data folder, and within it the subfolder nikh, which will contain the corpus we will work with (available in the course repository).
2. Install Orange Data Mining on your computer.

Instructions

1. Create a GitHub account (if you don’t already have one).
2. Create a new repository for this course, following the instructions outlined in the syllabus.
3. Clone the course repository and explore its structure.
   • Via GitHub Desktop (recommended):
     Open the GitHub Desktop app and go to File → Clone Repository. In the URL tab, paste the course repo link:

     https://github.com/scdenney/ba3_text_as_data.git
     

     Choose a folder on your computer to save the repository, then click Clone.

   • Via the GitHub website:
     Go to the course repository, click the green “<> Code” button, and select “Open with GitHub Desktop.” The app will open and prompt you to clone automatically. Click Clone to confirm.
4. After cloning, locate the data/nikh folder in the course repository and copy it into your own repository under data/nikh.
5. Submit the URL of your own repo to the instructor via this Google Sheet.
6. Install Orange Data Mining on your computer.
7. Review the following Orange tutorials from the official YouTube playlist Getting Started with Orange:
   • Welcome to Orange
   • Data Workflows
   • Widgets & Channels

Deliverable

• A README.md file in your repository containing:
  • A short overview of what your repository is for.
  • A brief reflection (≈1 paragraph) on the following questions:
    • What do you expect to learn in this course?
    • How might DH support (or not support) your thesis research?
• In the assignments/week1 folder:
  • Commit a Markdown (.md) file containing your reflection answers.
• In the screenshots/week1 folder:
  • Include a screenshot showing that you have successfully installed Orange Data Mining.
  • Include a screenshot of your local file structure, showing that you have:
    • Successfully set up your own repository locally, and
    • Cloned the course repository (for practice and easy access to shared materials).

Optional Programming Lessons: Swirl R Tutorials

Optional but incentivized; see notes on extra credit and penalties in the Syllabus.

What: Complete Swirl interactive R lessons inside RStudio.
How to start: See the Swirl student guide → https://swirlstats.com/students.html

Required if you opt in:

1. R Programming (Swirl course)
2. Getting and Cleaning Data (Swirl course)
   • Complete at least the first 3–4 lessons (focus on data tidiness and basic transformation).
3. Upload a screenshot of your Swirl progress indicating completion to your screenshots/week1 folder. Note, I require that you do this in R Studio (Swirl recommends it; I require it).

Optional Swirl lessons:

• Data Analysis (selected lessons)
  • Looking at Data
  • Subsetting Vectors

Opt-in & tracking requirements (as noted in the Syllabus):

• If you choose to do the additional programming option, must indicate your participation on the GitHub link Google Sheet:
  https://docs.google.com/spreadsheets/d/1iVdwLTfmVkMn2cQGXPxCC4YIxADawSKAWltZIxD5WMs/edit?gid=0#gid=0

Submission

• Push all files to your GitHub repository.
• Verify that your .md files and screenshots are visible online.