AJL14157 R for Students of Literature

Faculty of Arts
Spring 2021
Extent and Intensity
0/2/0. 6 credit(s). Type of Completion: zk (examination).
Taught online.
Teacher(s)
Mgr. Stefan Veleski, B.A., Ph.D. (lecturer)
Guaranteed by
Stephen Paul Hardy, Ph.D.
Department of English and American Studies – Faculty of Arts
Contact Person: Tomáš Hanzálek
Supplier department: Department of English and American Studies – Faculty of Arts
Timetable
Thu 18:00–19:40 D41
Prerequisites (in Czech)
AJL01002 Practical English II || AJ01002 Practical English II
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20, only registered with preference (fields directly associated with the programme): 0/20
fields of study / plans the course is directly associated with
there are 11 fields of study the course is directly associated with, display
Course objectives
This course aims to introduce students to the R programming language. The course is intended to give students a gentle push towards the path of programming, which may either take them towards a more data driven approach in their MA studies, or towards careers that mix insight from the humanities and programming and/or data analysis, such as technical writing. By the end of the course, the students will be acquainted with the fundamentals of R, version control with Github, the basics of computational analysis of text, R Markdown as a markup language, and how to create a website in R Studio with the "blogdown" package. Moreover, the students will begin to think about the application of quantitative approaches to the study of literature and will acquire the necessary knowledge to continue independent work and further develop their programming skills according to their academic or professional needs.
Learning outcomes
By the end of the course, the student will be acquainted with:
- The basics of R, objects, data types;
- The most common functions, subsetting;
- Loops, for loops, apply family of loop functions;
- Using R in for analyzing tabular data — "tidyverse" collection of packages;
- Word frequency and token distribution analysis;
- Sentiment analysis ("syuzhet" and "sentimentr" packages);
- Topic modeling;
- Version control with Github;
- R Markdown (markup and documentation);
- "Blogdown" package (making a website in R Studio).
Literature
    required literature
  • SILGE, Julia and David ROBINSON. Text mining with R : a tidy approach. First edition. Boston: O'Reilly. xii, 178. ISBN 9781491981658. 2017. info
  • WICKHAM, Hadley and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O'Reilly. xxv, 492. ISBN 9781491910399. 2016. info
  • PENG, Roger D. R Programming for Data Science. Leanpub. 182 pp. 2015. info
  • JOCKERS, Matthew Lee. Text analysis with R for students of literature. Cham: Springer. xvi, 194. ISBN 9783319349190. 2014. info
Assessment methods
Grades will be based on the following percentages:
Class Participation 10%
Quizzes (2) 10%
Assignments (3) 30%
Final Project 50%

The quizzes are aimed at testing the practical knowledge of R and tidyverse functionality and will be concentrated in the early stages of the course. The assignments will consist of certain problems that will require critical thinking and applying the skills acquired in the first five weeks of the course. The final project will be a small research project (5-6 pages including the sources and visualizations) that should be sent to me as an R Markdown PDF file at the end of the semester. Students can choose any of the techniques covered in the course, and apply them on a topic of their choosing. This should be discussed with me in advance, in order to ensure feasibility of the study and availability of the data.

Letter grade distribution: ≤59 F, 60-69 E, 70-75 D, 76-79 C, 80-85 B, >= 86 A
Language of instruction
English
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