Session 1 · Introduction to Text as Data

Objective

By the end of class, you can distinguish a broad topic from an answerable text-as-data question. You can also describe what belongs in a corpus and organize the first version of your course repository.

Before class

• Complete the Start here setup checklist.
• Bring a laptop with Orange, RStudio, and GitHub Desktop installed.
• Confirm that you can sign in to GitHub, and create a DataCamp account if you do not have one. You are added to the course classroom after this class.
• You do not need to choose a hackathon topic yet.

No assessed assignment is due before this class. Demo 1 and the Swirl lessons are due Sep. 25 at 09:15, when Session 2 begins. DataCamp starts after Session 2. See the Week 2 assignments →

Today’s route

Time	Activity
09:15–09:25	Attendance and starter questions
09:25–09:50	Primary sources, course overview, and tools
09:50–10:15	Setup clinic
10:15–10:45	Repository workshop
10:45–11:00	Submission checklist and next-class preparation

Attendance and starter questions

The first survey records attendance and asks a few baseline questions about research questions, corpora, and prior experience. There are no right answers and no preparation is expected.

Primary sources, the course, and your toolkit

With screens away, we start with word clouds from Korean presidential speeches: the corpus overall, then two groups of presidents. What becomes prominent? What questions might we ask? Word frequencies alone cannot establish meaning or explain why a pattern appears. We need to return to the speeches and their context.

A corpus is a selected collection of documents assembled for analysis. We use these speeches to consider what belongs in a corpus and how Korean-language primary sources can help answer a research question. Your knowledge of Korean matters when interpreting the results.

We then review the course schedule, assessments, and two tracks before introducing the tools:

• Orange Data Mining connects tasks into a visual analysis workflow. Today we see the interface; we build a text workflow in Session 2.
• R and RStudio provide a programming route. Everyone completes the R foundations assignments; BA2 review students also use R for assessed demos and analysis.
• GitHub and GitHub Desktop keep a record of your work and make submissions available to the instructor.

The Course guide explains the tracks, assessment, and course policies.

Setup clinic

Now open your laptop. We have 25 minutes to check the tools and troubleshoot remaining problems. Confirm that Orange opens with its Text Mining widgets, RStudio answers 1 + 1, GitHub Desktop is signed in, GitHub opens in your browser, and your local course folder and downloaded template are ready. See Start here for installation instructions.

If something fails, show the error. We will check the Text Mining add-on, use browser sign-in where needed, and help you continue with repository setup. Ask about a Digital Humanities Lab workstation if your laptop cannot run Orange.

DataCamp classroom invitations follow after class; enrollment is not a setup gate today. BA2 review students should see the instructor after class.

Repository workshop

Create a private repository on github.com, invite scdenney as a collaborator, and clone it with GitHub Desktop. Move the template contents into the repository, update the root README, then commit, push, and verify the files on github.com.

Next, start data/README.md: where do the texts come from, what do they cover, and what may you share? Use not yet chosen where needed. This introduces FAIR: Findable, Accessible, Interoperable, Reusable. Make a second meaningful commit and push it.

BA2 review track: also create an RStudio Project in the cloned repository. Add session-01.R in demos/01-setup/workflow/; it should create and print a three-row data frame and end with sessionInfo(). Commit and push the project file and script. Orange is not permitted for assessed work on this track.

Before you leave

Review the Demo 1 checklist below and the Session 2 preparation. You can refine README wording after class, but the required files and at least two meaningful commits must be pushed by the deadline. The slides include an unnumbered glossary appendix for reviewing today’s terms.

Demo 1 · Repository foundations

Due Sep. 25 at 09:15

Upload the following items.

• A root README.md naming the course, owner, purpose, and folder structure.
• data/README.md with source, access, license, and “do not upload” placeholders.
• The six demos/ folders and r-learning/ folder.
• A .gitignore that excludes .DS_Store, .Rhistory, .Rproj.user/, credentials, and local data where appropriate.
• At least two meaningful commits visible on GitHub.
• BA2 review students also upload the RStudio Project and session-01.R.

Before you submit

• The repository is private and scdenney is a collaborator. You must do this so I can track your progress/grade you.
• Every required folder exists and opens on github.com.
• The README tells a new reader what the repository contains.
• No passwords, personal data, restricted data, or large corpora are committed.
• The final commit is pushed and visible on github.com.

This demo is graded for checklist completion. Prose quality is not assessed.