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| Jan 18 || Introduction. Quantitative linguistic research and data types. R basics || [https://docs.google.com/presentation/d/1VUIUa3Db5n4dsD_HeA3e-mz55zK8uPrko3yu207pKUk/edit?usp=sharing Intro Slides] [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-01-18 Lab 01: intro to R]
 
| Jan 18 || Introduction. Quantitative linguistic research and data types. R basics || [https://docs.google.com/presentation/d/1VUIUa3Db5n4dsD_HeA3e-mz55zK8uPrko3yu207pKUk/edit?usp=sharing Intro Slides] [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-01-18 Lab 01: intro to R]
 
|-
 
|-
| Jan 25 || Hypothesis testing. Binomial test. R: dataframes || [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-01-25 Lab 02]
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| Jan 25 || Hypothesis testing. Binomial test. R: dataframes, tydyverse || [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-01-25 Lab 02] [https://datacamp-community-prod.s3.amazonaws.com/e63a8f6b-2aa3-4006-89e0-badc294b179c tidyverse cheat sheet]
 
|-
 
|-
| Feb 1  
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| Feb 1 || Central limit theorem. Variance. Student's t-test. R: simulating data, boxplots, density plots, binomial test, t-test ||
| Estimating of population mean. Central Limit Theorem
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[https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-02-01 Lab 03: ]
| Simulations. Test of central limit theorem
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[https://raw.githubusercontent.com/LingData2019/LingData2020/master/seminars/2020-02-01/Lab3-ttest-binom-matrices.Rmd Rmd] [https://htmlpreview.github.io/?https://github.com/LingData2019/LingData2020/blob/master/seminars/2020-02-01/Lab3-ttest-binom-matrices.html html] [https://rforpublichealth.blogspot.com/2014/02/ggplot2-cheatsheet-for-visualizing.html Viz. distributions]
 
|-
 
|-
| Feb 8
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| Feb 8 || Two-sample t-test. Paired t-test. Confidence intervals. <!-- TODO: Non-parametric tests --> || [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-02-08 Lab 04: ] [https://raw.githubusercontent.com/LingData2019/LingData2020/master/seminars/2020-02-08/Lab4-confint-pairedttest-anova.Rmd Rmd] [https://github.com/LingData2019/LingData2020/raw/master/seminars/2020-02-08/Lab4-confint-pairedttest-anova.pdf pdf][https://agricolamz.github.io/2018-MAG_R_course/Lec_4_stats.html CI slides] [https://istats.shinyapps.io/ExploreCoverage/ CI demo]
| One sample t-test
 
| Working with dataframes. Selection by condition.
 
 
|-
 
|-
| Two sample t-test
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| Feb 15 || ANOVA. Correlations || [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-02-15 Lab 05:] [https://raw.githubusercontent.com/LingData2019/LingData2020/master/seminars/2020-02-15/Lab5_Corr.Rmd Rmd] [https://github.com/LingData2019/LingData2020/blob/master/seminars/2020-02-15/Lab5_Corr.pdf pdf]
| Usage of <code>t.test</code> to perform two-sample t.test. [https://rpubs.com/ilyaschurov/ttest2-2020 notebook]
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|-
|}
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| Feb 22 || Tests for categorial data. Chi-squared test. Fisher exact test. Effect size || [https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-02-22 Lab 06:] [https://raw.githubusercontent.com/LingData2019/LingData2020/master/seminars/2020-02-22/Lab6-chisq-Fischer-effectsize.Rmd Rmd] [https://github.com/LingData2019/LingData2020/blob/master/seminars/2020-02-22/Lab6-chisq-Fischer-effectsize.pdf pdf] [https://www.datacamp.com/community/tutorials/contingency-tables-r DataCamp: contingency tables]
 
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|-
== Homeworks ==
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| Feb 29 || Linear regression. Multivariate linear regression. Dummy variables ||
{|class='wikitable'
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[https://github.com/LingData2019/LingData2020/tree/master/seminars/2020-02-29 Lab 07:] [https://raw.githubusercontent.com/LingData2019/LingData2020/master/seminars/2020-02-29/Lab7-lm.Rmd Rmd] [https://github.com/LingData2019/LingData2020/blob/master/seminars/2020-02-29/Lab7-lm.pdf pdf]
! id !! links !! due date !! upload link
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[https://htmlpreview.github.io/?https://github.com/LingData2019/LingData2020/blob/master/seminars/2020-02-29/Lab7-lm.html html]  
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[https://lindeloev.github.io/tests-as-linear/linear_tests_cheat_sheet.pdf Common statistical tests & linear models ]
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|-
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|  || Fixed and random effects. Linear mixed-effects models ||
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|-
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|  || Dimensionality reduction. PCA. MDS. t-SNE ||
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|-
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|  || CA, MCA. Clusterization ||
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|-
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|  || Logistic regression. Model selection ||  
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|-
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|  || Bootstrap. Decision trees. Decision forests ||
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|-
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|  || Bayesian statistics ||
 
|-
 
|-
| HW1
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| || Bayesian statistics II ||  
| [https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW1-theo.Rmd Rmd], [https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW1-theo.pdf pdf]
 
| Feb. 9, 23:59:59
 
| [https://www.dropbox.com/request/hblqeftXqVpJLj0miQwd here]
 
 
|-
 
|-
| HW2
 
| [https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW2-theo.Rmd Rmd], [https://github.com/LingData2019/LingData2020/blob/master/hw/hw-pdf/LingData-HW2-theo.pdf pdf]
 
| Feb. 24, 23:59:59
 
| [https://www.dropbox.com/request/TTMCHN0AOZ1MONaPjuav here]
 
 
|}
 
|}
  
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For successful submission of assignments you should be able to create and save R code files (.R) and RMarkdown files (.Rmd).
 
For successful submission of assignments you should be able to create and save R code files (.R) and RMarkdown files (.Rmd).
 +
 +
 +
== Homeworks ==
 +
* Homework 1 (deadline: February 16, 23:59), Chapters 1, 2, 3, and 5 of the [https://www.datacamp.com/courses/free-introduction-to-r DataCamp] course "Introduction to R". Please fill in this [https://docs.google.com/forms/d/e/1FAIpQLSdjgKBM5JSo6D6ajhrWWfFG1ktcKgDfbdK_jQ_ZbW9GwNLzpQ/viewform form]. 
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* Homework 2 (deadline: February 23, 23:59), Chapters 4 and 6 of the [https://www.datacamp.com/courses/free-introduction-to-r DataCamp] course "Introduction to R". 
 +
* Homework 3 (deadline: February 9, 12:00), Hypothesis testing, binomial test, t-test. [https://github.com/LingData2019/LingData2020/blob/master/hw/hw-pdf/LingData-HW3-comp.pdf HW3 pdf] [https://htmlpreview.github.io/?https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW3-comp.html html] [https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW3-comp.Rmd Rmd template]
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* Homework 4 (deadline: February 29, 12:00), T-test and ANOVA, reproducing some results from Leivada & Westergaard 2019 [https://github.com/LingData2019/LingData2020/blob/master/hw/hw-pdf/LingData-HW4-comp.pdf HW4 pdf] [https://htmlpreview.github.io/?https://github.com/LingData2019/LingData2020/blob/master/hw/hw-html/LingData-HW4-comp.html html] [https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW4-comp.Rmd Rmd template] [https://classroom.github.com/a/mvhbEgzD link] to submit your .Rmd file
 +
* Homework 5 (deadline: March 07, 12:00), Contingency tables and tests, linear models  [https://github.com/LingData2019/LingData2020/blob/master/hw/hw-pdf/LingData-HW5-comp.pdf HW4 pdf] [https://htmlpreview.github.io/?https://github.com/LingData2019/LingData2020/blob/master/hw/hw-html/LingData-HW5-comp.html html] [https://github.com/LingData2019/LingData2020/blob/master/hw/LingData-HW5-comp.Rmd Rmd template] [ link] to submit your .Rmd file
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 +
== Final project ==
 +
* Projects description [https://github.com/LingData2019/LingData2020/blob/master/projects.pdf link] 
 +
* Projects pre-registration: link to submit your file TBA 
 +
* Final versions of project papers: link to sumbit your files TBA 
 +
 +
 +
== References ==
 +
* Gries, Stefan (2013). Statistics for Linguistics with R : A Practical Introduction (Vol. 2nd revised edition). Berlin: De Gruyter Mouton. [http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=604318 HSE library link]
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* Levshina, Natalia (2015). How to Do Linguistics with R : Data Exploration and Statistical Analysis. Amsterdam: John Benjamins Publishing Company. [http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1093048 HSE library link]
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* Baayen, Harald (2008). Analyzing Linguistic Data: A practical introduction to statistics. Cambridge UP. [http://www.sfs.uni-tuebingen.de/~hbaayen/publications/baayenCUPstats.pdf pdf]
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 +
* 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 [https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf 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.

Версия 21:40, 1 марта 2020

  • Instructors: Ilya Schurov and Olga Lyashevskaya

Materials

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 Viz. distributions

Feb 8 Two-sample t-test. Paired t-test. Confidence intervals. Lab 04: Rmd pdfCI slides CI demo
Feb 15 ANOVA. Correlations Lab 05: Rmd pdf
Feb 22 Tests for categorial data. Chi-squared test. Fisher exact test. Effect size Lab 06: Rmd pdf DataCamp: contingency tables
Feb 29 Linear regression. Multivariate linear regression. Dummy variables

Lab 07: Rmd pdf html Common statistical tests & linear models

Fixed and random effects. Linear mixed-effects models
Dimensionality reduction. PCA. MDS. t-SNE
CA, MCA. Clusterization
Logistic regression. Model selection
Bootstrap. Decision trees. Decision forests
Bayesian statistics
Bayesian statistics II

Software

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 rstudio.cloud (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).


Homeworks

  • 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".
  • Homework 3 (deadline: February 9, 12:00), Hypothesis testing, binomial test, t-test. HW3 pdf html Rmd template
  • Homework 4 (deadline: February 29, 12:00), T-test and ANOVA, reproducing some results from Leivada & Westergaard 2019 HW4 pdf html Rmd template link to submit your .Rmd file
  • Homework 5 (deadline: March 07, 12:00), Contingency tables and tests, linear models HW4 pdf html Rmd template [ link] to submit your .Rmd file

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


References

  • 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 [https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf 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.