ICS02: 8. Introduction to R

Sunoikisis Digital Classics, Spring 2019

Session 8. Introduction to programming through R

Thursday Feb 28, 16:00 UK = 18:00 EET

Convenors: Christopher Ohge & Gabriel Bodard (University of London)

YouTube link: https://youtu.be/X8iCDZVgWSA

Download R Notebook

Access the HTML version of the notebook, with the visualisations

Session outline

This session will introduce will introduce basic programming concepts with the R language. After an introductory lesson on regular expressions, R syntax, and basic R functions, we will use the tidy text library package to perform text analysis tasks.

Plan

1. Christopher [CO] reads outline

2. Preliminary remarks (5 min), Gabby [GB] and CO:

• Most common programming languages in DH

• Why R & why is it important?

3. Regular expressions (15 min), GB:

• Exercises on gutenberg texts

4. Intro to R and tidytext (40 min), CO

Installing R and RStudio

Before the session, make sure to download the R software package from http://www.r-project.org/.

• Click on "download R."

• Choose the appropriate CRAN mirror in your area for downloading (for me it's the UK > Imperial College London link).

• Download and install the appropriate R 3.5.2 binary for your operating system.

Then download the latest version of RStudio at https://www.rstudio.com.

• Click on "Download RStudio."

• Download the RStudio Desktop (free) version.

• Chose the appropriate installer: Most of you will use either RStudio 1.1.463 - Windows Vista/7/8/10 or Mac OS X 10.6+.

Seminar readings

• Hawkins, Laura F. 'Computational Models for Analyzing Data Collected from Reconstructed Cuneiform Syllabaries.' Digital Humanities Quarterly 12.1 (2018). Available: http://digitalhumanities.org:8081/dhq/vol/12/1/000368/000368.html (Wayback Machine version)
• Rockwell, G. 'What is Text Analysis, Really?' Literary and Linguistic Computing 18.2 (2003): 209-219. Available: http://www.geoffreyrockwell.com/publications/WhatIsTAnalysis.pdf

Further reading

• Rydberg-Cox, Jeff. Statistical Methods for Studying Literature in R. Available: https://daedalus.umkc.edu/StatisticalMethods/index.html
• Silge, Julia, and David Robinson. Text Mining with R: A Tidy Approach. Available: https://www.tidytextmining.com/. Especially chapters 1–3.
• Jockers, Matthew. Text Analysis with R for Students of Literature (Springer, 2014). Especially Chapters 1, 2, 6, 7, and 11

Other resources

• Regex Tester: https://www.regextester.com/
• Regex Quickstart: https://www.rexegg.com/regex-quickstart.html

Essay title

• tba

Exercise

1. Download a txt file from Project Gutenberg or the Perseus Project. Load the text file into RStudio and use a regular expression(s) to limit your results.

2. Using the gutenbergr package, load some new text files (more than one, please) that interest you. Create a tidy tibble of the textual data and chose a visualisation method for displaying your results.

3. Based on your results, posit a new question--or questions--about what you would like to investigate further. Modify a code block(s) from Part I of the R Notebook to answer your question.