Linguistic Data: Quantitative Analysis and Visualisation: computational linguistics

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  • Instructors: Ilya Schurov and Olga Lyashevskaya


Data Topics Links
Jan 18 Introduction. Quantitative linguistic research and data types. R basics Intro Slides Lab 01: intro to R
Jan 25 Hypothesis testing. Binomial test. R: dataframes, tydyverse Lab 02 tidyverse cheat sheet
Feb 1 Central limit theorem. Variance. Student's t-test. R: simulating data, boxplots, density plots, binomial test, t-test

Lab 03: Rmd html

Feb 8 Two-sample t-test. Paired t-test. ANOVA. Confidence intervals. Non-parametric tests Lab 04: Rmd pdfCI slides CI demo
Feb 15 Tests for categorial data. Chi-squared test. Fisher exact test. Effect size.
Feb 22 Correlations. Linear regression Common statistical tests & linear models
Feb 29 Multivariate linear regression. Dummy variables
Dimensionality reduction. PCA. MDS. t-SNE
CA, MCA. Clusterization
Logistic regression. Model selection
Fixed and random effects. Linear mixed-effects models
Bootstrap. Decision trees. Decision forests
Bayesian statistics
Bayesian statistics II


During this course we will use R as a programming language and RStudio as a GUI.

How to install R and RStudio?

1. Download R (you can choose another mirror here if you wish) and install it on your computer. Make sure you did it before installing RStudio.

2. Download RStudio (you need RStudio Desktop Open Source License) and install it on your computer. It is recommended to create a shortcut for RStudio during installation.

It is possible avoid installing anything on your PC, using (an online version of RStudio).

For successful submission of assignments you should be able to create and save R code files (.R) and RMarkdown files (.Rmd).


  • Homework 1 (deadline: February 16, 23:59), Chapters 1, 2, 3, and 5 of the DataCamp course "Introduction to R". Please fill in this form.
  • Homework 2 (deadline: February 23, 23:59), Chapters 4 and 6 of the DataCamp course "Introduction to R".

After completing the course please provide either the Statement of Accomplishment or a screenshot of your learning progress via [link TBA]. Deadlines for Homework 1 and 2 are cancelled due to unavailability of the free version of the datacamp online course. Stay tuned!

Final project

  • Projects description link
  • Projects pre-registration: link to submit your file TBA
  • Final versions of project papers: link to sumbit your files TBA


  • Gries, Stefan (2013). Statistics for Linguistics with R : A Practical Introduction (Vol. 2nd revised edition). Berlin: De Gruyter Mouton. HSE library link
  • Levshina, Natalia (2015). How to Do Linguistics with R : Data Exploration and Statistical Analysis. Amsterdam: John Benjamins Publishing Company. HSE library link
  • Baayen, Harald (2008). Analyzing Linguistic Data: A practical introduction to statistics. Cambridge UP. pdf
  • Gries, Stefan (2017). Quantitative Corpus Linguistics with R : A Practical Introduction (Vol. Second edition). Milton Park, Abingdon, Oxon: Routledge. eBook
  • Empirical Bayes
  • Harney, H. L. (2016). Bayesian Inference : Data Evaluation and Decisions (Vol. 2nd ed). Springer. eBook
  • McElreath, R. (2016). Statistical Rethinking : A Bayesian Course with Examples in R and Stan. eBook
  • ggplot2
  • Hadley, W. (2016). Ggplot2 : Elegant Graphics for Data Analysis. Springer. eBook
  • R markdown [ Rmd Cheat Sheet

Course Info

This page contains the materials of the course "Linguistic Data: Quantitative Analysis and Visualisation", taught at the HSE Master's program "Computational Linguistics" in 2019-2020 academic year. Modules: 3-4.