GitHub
A GitHub account and GitHub Desktop. We will create and organize the private course repository during Session 1.
Before Sep. 18
Start here
Complete the setup once, test it, and arrive ready to work. No previous programming experience is required.
Course time and location
Regular classes meet Fridays from 09:15 to 11:00 in the Digital Humanities Lab, Johan Huizinga room 0.09, Doelensteeg 16, 2311 VL Leiden. The final hackathon meets Nov. 13 from 09:15 to 13:15 in the same room.
Regular classes meet Fridays from 09:15 to 11:00 in the Digital Humanities Lab, Johan Huizinga room 0.09, Doelensteeg 16, 2311 VL Leiden. The final hackathon meets Nov. 13 from 09:15 to 13:15 in the same room.
Before the first class on Sep. 18
Complete the installation, account setup, sign-in checks, and software tests on this page before you arrive. We will review the setup briefly in Session 1. Do not wait until class to begin. The course repository is created in Session 1, and the first Swirl, DataCamp, and demo assignments are due in Week 2.
Complete the installation, account setup, sign-in checks, and software tests on this page before you arrive. We will review the setup briefly in Session 1. Do not wait until class to begin. The course repository is created in Session 1, and the first Swirl, DataCamp, and demo assignments are due in Week 2.
This preparation is also your first exercise in independent troubleshooting. Follow the linked guides and read any error message carefully. Record the step and the exact message if a problem remains after one sensible attempt to fix it. Bring that information to the DH Lab so that we can help efficiently.
What must be ready
R + RStudio
R is the programming language. RStudio is the interface. Install both and confirm that the Console runs a simple command.
Orange Data Mining
Install Orange and its Text Mining add-on. Open the program once and confirm that the text-analysis widgets appear.
Your setup checklist
Complete these steps before the first session.
- Follow Orange’s official installation and first-steps guide, then add Text Mining through Options → Add-ons.
- Install R first, then install RStudio Desktop.
- Create or sign into your DataCamp account. Everyone will be added to the online course classroom by the start of the course, and some invitations may arrive earlier. DataCamp will send you an email notification when you are added. You do not need to upload DataCamp evidence.
- Create or confirm your GitHub account and install GitHub Desktop.
- Confirm that GitHub Desktop is signed into the same account.
- Choose a sensible local folder where you can find your course files.
- Bring the account details or recovery access needed to sign in during Session 1.
If Orange runs slowly on your computer
Text mining, embeddings, and topic models can use substantial memory. Digital Humanities Lab computers will be available if you need one. Complete the GitHub and R/RStudio setup. Tell the instructor before Session 1 if possible so that a lab workstation can be prepared.
Text mining, embeddings, and topic models can use substantial memory. Digital Humanities Lab computers will be available if you need one. Complete the GitHub and R/RStudio setup. Tell the instructor before Session 1 if possible so that a lab workstation can be prepared.
Bring your laptop. Devices are used for the opening survey and the demonstration. During the lecture block, laptops are closed and phones are put away unless you have an approved accommodation.
Recommended repository structure
You will create this folder structure during the Session 1 demonstration.
ba3tad-26-surname/
├── README.md
├── .gitignore
├── data/
│ └── README.md
├── demos/
│ ├── 01-setup/
│ ├── 02-preprocessing/
│ ├── 03-descriptive-patterns/
│ ├── 04-clustering/
│ ├── 05-sentiment/
│ └── 06-topic-modeling/
└── r-learning/
Each demo folder should contain a short README.md, a workflow/ folder, and an output/ folder. Download the student repository template before Session 1 so that it is available during the repository demonstration.
FAIR in practical terms
FAIR does not require you to upload everything. For this course, it guides how you organize and document the work.
- Use clear filenames, a useful README, and a predictable folder structure so that files are easy to find.
- Record permissions and link to data sources. Keep restricted data restricted.
- Prefer open formats such as
.md,.csv,.png,.svg,.R, and Orange.owsworkflows. - Record the data source, changes, software, and known limitations so that another person can reuse the work.
Never commit passwords, API keys, personal data, attendance information, or restricted course materials.
R foundations
Everyone completes a short set of R exercises across the course.
- Swirl · R Programming includes six selected lessons on basic commands, files, vectors, subsetting, data frames, and inspecting data.
- DataCamp · Introduction to R includes all six chapters. They are divided across Sessions 2–4 in the course classroom.
- DataCamp · Intermediate R includes all five chapters. They are divided across Sessions 5–6 in the course classroom.
The first R deadline is Sep. 25 at 09:15, when Session 2 begins. Complete the assigned Swirl lessons and upload one screenshot showing completion as r-learning/swirl-r-programming.png in your private course repository. DataCamp work is checked through the course dashboard and requires no upload. The R foundations assignment gives the exact chapters, dates, and marks. If DataCamp access is delayed, use the posted local replacement. BA2 review students may repeat the foundations or request approved alternatives.
Test before class
- Orange opens and the Text Mining widgets are visible.
- RStudio opens and accepts
1 + 1in the Console. - You can sign in to the DataCamp account that will be added to the course classroom.
- GitHub Desktop is signed into your GitHub account.
- You know where your files are stored locally.
If one of these checks fails, keep the notes you made while troubleshooting and bring them to the DH Lab. Avoid repeated installation attempts until you know what failed.