Linguistic Data: Quantitative Analysis and Visualisation for theoretical linguists

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Course info

Dear students,

Here will be published the materials of the course "Linguistic Data: Quantitative Analysis and Visualisation", taught at the Master programme "Linguistic Theory and Language Description" in 2018-2019 academic year.

  • Instructors: Olga Lyashevskaya, George Moroz, Alla Tambovtseva and Ilya Schurov.
  • Modules: 3-4


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 online version of RStudio.

How to use RStudio?

Read the instruction here.

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


Date Topic of the lecture Seminar Optional
12.01 Something about data: population vs sample
Descriptive statistics

problems1 R-basics
RMarkdown: official page,

19.01 Population and samples. Working with data in R
problems2 R-samples artists.txt
R-vectors R-dataframes orientation.csv

more on basic graphs in R
26.01 Statistical hypotheses testing Binomial-test poetry.csv
02.02 Student's t-test. Central limit theorem
T-test icelandic.csv
asp-paper (Coretta, 2017)
09.02 Confidence Intervals Conf-intervals poetry.csv icelandic.csv
an interactive visualization of CI by K.Magnusson
more on overlapping CI's (by A.Knezevic)

16.02 Data manipulation with tidyverse. Visualisation with ggplot2
class materials

02.03 Chi-squared and Fisher's exact tests
Chi-squared-test elision.csv socling.csv

16.03 Correlation coefficients and simple linear regression
guess correlation game
23.03 Multiple comparisons. ANOVA Anova icelandic.csv
correlograms spurious correlations
06.04 Multiple linear regression
Multiple-regression english.csv
more on visualising coefficients, more tests
13.04 Logistic regression Lab10 [Lab10-solutions]
more on visualising coefficients
27.04 More on model diagnostics. Mixed-effects models Mixed-effects ReductionRussian.txt
LME in R
18.05 Decision trees and random forest. Lab 12. Trees and forests Code
25.05 PCA
class materials

01.06 Clustering swadesh.csv
08.06 NeighborNet. Simulation statistics prefixes.txt R code scores2.csv

R seminars in pdf

12 January: R-basics, 19 January: R-vectors, R-dataframes, R-samples, 26 January: Binomial-test

2 February: T-test, 9 February: Conf-intervals

02 March: Chi-squared-test, 16 March: Corr-regression, 23 March: Anova

6 April: Multiple-regression, 27 April: Mixed-effects

R seminars in .R and .Rmd

12 January: R-basics.R, R-basics.Rmd, 19 January: R-vectors.R, R-vectors.Rmd R-dataframes.R, R-dataframes.Rmd, R-samples.Rmd, 26 January: Binomial-test.Rmd

2 February: T-test.R, T-test.Rmd, 9 February: Conf-intervals.Rmd Conf-intervals.R

2 March: Chi-squared-test.Rmd, Chi-squared-test.R, 16 March: Corr-regression.R, Corr-regression.Rmd, 23 March: Anova.R, Anova.Rmd

6 April: [Multiple-regression.R], [Multiple-regression.Rmd], 27 April: Mixed-models.R, Mixed-models.Rmd


Final project

  • Project topics: link to the table to fill in
  • Projects pre-registration (deadline: 28 April, 23:59): link to submit your file
  • Final versions of projects: link to sumbit your files