Syllabus

  • Course: 070184 UE Methodological course - Digital Humanities Skills: Intro to GIS (2021S), R Edition
  • u:find Link: https://ufind.univie.ac.at/en/course.html?lv=070184&semester=2021S
  • Meeting time: Tu 11:30-13:00
  • Meeting place: due to COVID, all meetings will be held online
  • Instructor: Dr. Maxim Romanov, maxim.romanov@univie.ac.at
  • Language of instruction: English
  • Office hours: Tu 13:30-15:00 (on Zoom; please, contact beforehand!)
  • Office: Department of History, Maria-Theresien-Straße 9, 1090 Wien, Room 1.10

Course Details

Aims, contents and method of the course

GIS, which stands for geographical information system(s), has become an important tool of a historian — to the point that scholars in many humanities disciplines talk about “spatial turns.” Indeed, GIS allows us as historians to pay much closer attention to the spatial dimension in studying the past. To begin with, creating maps became so much easier now. Yet, more importantly, we can now map our data to understand spatial distribution of different phenomena — and how this distribution changed over time; we can map data representing different phenomena and study how they may have affected each other; we can map connections and study how physical space affects the formation of networks; and so much more. This course will focus on the use of the programming language R which shines when it comes to data analysis and creating robust visualizations. The course will consist of three main parts. Part I will introduce you to the basics of R and its philosophy of “tidy data.” Part II will focus on specific methods and techniques of spatial analysis. Part III will require you to focus on your own research projects. The language of the course is English.

Assessment and permitted materials

Assessment will be based on class participation, homework assignments, and a final project (final projects can be prepared either individually or in groups; ideally, your final project should be directly connected to research projects that you carry out for other courses or for your BA, MA, or PhD theses).

Minimum requirements and assessment criteria

No prior programming experience is required, but familiarity with the command line and basic principles of programming will be beneficial.

Course Evaluation

Course evaluation will be a combination of in-class participation (30%), weekly homework assignments (50%), and the final project (20%).

Class Participation

Attendance is required; regular participation is the key to completing the course; all students must come with their laptops; homework assignments must be submitted on time (some can be completed later as a part of the final project, but this must be discussed with the instructor whenever the issue arises); the final project must be submitted on time.

Homework Assignments

  • Homework assignments are due before the beginning of the following class;
  • You will need to complete your worksheet and generate HTML or PDF document with the results, which you then submit to the instructor as attachments via email;
  • In the subject of your email, please, use the following format: CourseID-LessonID-HW-Lastname-matriculationNumber, for example, if I were to submit homework for the first lesson, my subject header would look like: 070184-L01-HW-Romanov-12435687.
  • DH is a collaborative field, so you are most welcome to work on your homework assignments in groups, however: you must still submit it. That is, if a groups of three works on one assignment, there must be three separate submissions emailed from each member’s email.
  • Reading-Reporting Assignments: one of your tasks will be to identify relevant readings (articles, book sections), read them, and provide short summaries (250-500 words) in the shared Zotero library for our course. You need to submit 5 such short summaries for full credit. “Relevant” means that these readings must be relevant to your own research interests. Ideally, you will use these readings as an additional background for your final projects. Some readings will be suggested, but I would encourage you to invest some time into this task and find your own readings (which is now very easy with such available resources as JSTOR).

NB: For most classes you will need to complete and submite worksheets. Some questions in those worksheets will be easy, but some you might find to be difficult and even impossible. We will go over the difficult parts of each worksheet in class, so do not worry. The main goal is to practice, since for most students this course will be very new and very strange. In practical terms this means that you need to to submit your worksheet in time with your best try for each question. The worksheets will be graded by completion, i.e. you will get full credit if you: 1) complete all the easy and moderately difficult questions; 2) give an honest try to the very difficult questions; 3) ask for help in class and/or office hours to make sure that you understand the challenges.

Final Project

The final project is your own research project that uses collections of texts of your interest and research methods introduced inthe course. You are encouraged to explore methods outside of those covered within the course. Ideally, your research project should be directly related to your main research project that you undertake in your studies. You are welcome to work in groups on your final projects, but make sure to discuss that with the instructor first.

Practice worksheets (R Notebooks)

NB: Worksheets 1-6 have beed developed by Lincoln Mullen. Source: Lincoln A. Mullen, Computational Historical Thinking: With Applications in R (2018–): http://dh-r.lincolnmullen.com.

Study materials

Necessary materials will be provided. The following list includes valuable resources on R:

  • Arnold, Taylor, and Lauren Tilton. Humanities Data in R. New York, NY: Springer Science+Business Media, 2015.
  • Healy, Kieran Data Visualization: A Practical Guide. Princeton University Press, 2018. ISBN: 978-0691181622. http://socviz.co/.
  • Hadley Wickham & Garrett Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly, 2017. ISBN: 978-1491910399. https://r4ds.had.co.nz/.
  • Wickham, Hadley. Advanced R, Second Edition. 2 edition. Boca Raton: Chapman and Hall/CRC, 2019. http://adv-r.had.co.nz/.
  • Also check https://bookdown.org/ for more books on R.

Software, Tools, & Technologies

The main tools for the course will be the programming language R and RStudio, the premier integrated development environment for R.

We will also use a variety of packages:

Schedule

Location: Hörsaal 30 Hauptgebäude, 1.Stock, Stiege 7; due to COVID, all meetings will be held online via video-conferencing

  • Tuesday 02.03. 11:30 - 13:00
  • Tuesday 09.03. 11:30 - 13:00
  • Tuesday 16.03. 11:30 - 13:00
  • Tuesday 23.03. 11:30 - 13:00
  • 2-WEEK BREAK
  • 2-WEEK BREAK
  • Tuesday 13.04. 11:30 - 13:00
  • Tuesday 20.04. 11:30 - 13:00
  • Tuesday 27.04. 11:30 - 13:00
  • Tuesday 04.05. 11:30 - 13:00
  • Tuesday 11.05. 11:30 - 13:00
  • Tuesday 18.05. 11:30 - 13:00
  • Tuesday 01.06. 11:30 - 13:00
  • Tuesday 08.06. 11:30 - 13:00
  • Tuesday 15.06. 11:30 - 13:00
  • Tuesday 22.06. 11:30 - 13:00
  • Tuesday 29.06. 11:30 - 13:00

Lesson Topics

  • [ #01 ] General Introduction: Making Sure Everything Works; Getting to know R
  • [ #02 ] Basics I: Data Structures and Subsetting
  • [ #03 ] Basics II: Data Manipulation & Exploratory Analysis
  • [ #04 ] Basics III: Data Visualization; Functions
  • [ ### ] 2-WEEK BREAK: swirl tutorials @ home
  • [ ### ] 2-WEEK BREAK: swirl tutorials @ home
  • [ #05 ] Data I: Collecting, Organizing, Creating
  • [ #06 ] Data II: Modeling & Manipulating
  • === GIS METHODS ===
  • [ #07 ] GIS Methods I: Georeferencing (with QGIS) and Geocoding (with R)
  • [ #08 ] GIS Methods II: Creating Base maps: Points, Vectors, Polygons
  • [ #09 ] GIS Methods III: Mapping Categorical Data
  • [ #10 ] GIS Methods IV: Mapping Networks
  • [ #11 ] GIS Methods V: GIS without Maps
  • === DEVELOPING FINAL PROJECTS ===
  • [ #12 ] Projects I
  • [ #13 ] Projects II
  • [ #14 ] Projects III
  • [ #15 ] Final Presentations

Note: one of the classes might be canceled; this will be announced separately. Lesson materials will be appearing on the website shortly before each class. Lessons will be accessible via the Lessons link on the left panel.

Additional

Datasets