Eesmärgiks on, et kursusel osaleja oskaks hakata kasutada R keele tööriistu, selleks, et muuta oma toorandmed arusaamiseks ja teadmisteks. Kursusel õpetatakse kuidas andmeid importida, kuidas viia andmed sobiva kujuga tabelisse, tabelis olevaid andmeid summeerida, luua uusi muutujaid ja tulemusi visualiseerida kasutades tidyverse tööriistu.
The aim of the course is to learn how to start using R to turn raw data into insight and knowledge. You will learn how to import raw data to R, how to rearrange your data suitable for analysis, how to summarise and transform, how to visualise data using tidyverse R packages.
This year our examples are mainly centered around hip fractures dataset https://github.com/rstats-tartu/hip-fractures.
If you have specific things you would like to learn to do in R, please let us know. Also, if you have specific problems with your datasets that you would like to solve, maybe we can do this in class as an excersise. If you have anything like that, we would appreciate if you write to us even this week, so that we can prepare this stuff for class.
Töö on kavandatud toimuma Rstudio pilves, kuid siiski on vajalik:
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internetiühendusega sülearvuti olemasolu, kuhu on installeeritud
- R 3.6.1 ("Action of the Toes") https://cran.r-project.org,
- RStudio 1.2.1335 (desktop viimane versioon) https://www.rstudio.com/products/rstudio/download/,
- git https://git-scm.com/book/en/v2/Getting-Started-Installing-Git.
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Github konto https://github.com (jäta salasõna meelde!).
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Rstudio cloud konto https://login.rstudio.cloud/, (logi sisse Githubi kontoga!).
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Work is planned to be carried out in RStudio cloud, but you still need to have following software available in your computer:
- R 3.6.1 ("Action of the Toes") https://cran.r-project.org,
- RStudio 1.2.1335 (desktop last version) https://www.rstudio.com/products/rstudio/download/,
- git https://git-scm.com/book/en/v2/Getting-Started-Installing-Git.
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Github account https://github.com (remember your password!).
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Rstudio cloud account https://login.rstudio.cloud/, (login with your Github account!).
Iga sessioon on jaotatud kaheks: - esimeses pooles vaatame süstemaatiliselt läbi tidy R andmeanalüüsi tööriistad ja - teises pooles tegeleme praktilise andmeanalüüsiga kasutades peamiselt https://github.com/rstats-tartu/hip-fractures andmeid.
Each session is divided into two parts, - in the first half, we go systematically through tidy R data analysis tools and - second half is dedicated to practical data analysis mainly using https://github.com/rstats-tartu/hip-fractures dataset.
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Week 3, 17/18. sept/Sep, 14.15-17
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Sissejuhatus R-i, Rstudio töölaua sisseseadmine/tutvustus.
?helpehk kust otsida abi. -
Sissejuhatus baas R-i.
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Mis on reprodutseeritavus (olen rääkinud sellest tavaliselt lõpus, kuid tegelikult tuleb see kohe üle rääkida) ja andmeanalüüsi töövoog, versioonikontroll (git+github).
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Introduction, setting up RStudio workspace. How to search for R
?help. -
Introduction to base R.
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Introduction to reproducibility, data analysis workflow, version control (git+Github).
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Week 4, 24/25. sept/Sep, 14.15-17
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Andmete visualiseerimine kasutades ggplot2: ggploti komponeerimine, lihtsamad diagrammid, andmete grupeerimine graafikutel kasutades värvi, suurust ja kuju.
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Andmete visualiseerimine kasutades ggplot2: andmete esitamine pisijooniste abil, graafikutüüpide kombineerimine, statistiliste kokkuvõtete plottimine.
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Data visualisation using ggplot2: components/layers of ggplot plot, scatterplots, aesthetics: color, size, shape.
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Data visualisation using ggplot2: facetting, combining different types of geoms, plotting statistical summaries.
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Week 5, 1/2. okt/Oct, 14.15-17
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Andmete transformeerimine kasutades dplyr-i: "5 verbi".
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Andmete transformeerimine kasutades dplyr-i: andmete muteerimine ja selleks kasutatavad funktsioonid.
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%>%(pipe) operaatori kasutamine ja andmete transformeerimise "laused". -
Data transformation using five verbs from dplyr package. filter + logical operators and missing values, arrange rows, select columns, create new variables with mutate.
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Data transformation using dplyr package: data mutating and useful mutating functions.
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How to use
%>%(pipe) operator to compose data transformation sentences.
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Week 6, 8/9. okt/Oct, 14.15-17
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Andmete grupeerimine ja summeerimine, andmete importimine tekstifailidest ja exceli failidest
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Korrastatud andmed, andmetabelite formaatimine laiast pikaks ja vastupidi. Tabelite liitmine üle ridade ja tulpade ning muutuja-põhine liitmine kasutades join-e.
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Data grouping and grouped summaries, data import from text and excel files.
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Tidy data, formatting data frames from wide to long format. Binding data frames by rows and columns and data frame merging by join-s.
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Week 7, 15/16. okt/Oct, 14.15-17
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Itereerimine kasutades map funktsioone, list tulbad, lineaarne mudel ja mudeli fittimine.
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Iterating with map function, list columns in data frame, linear model and fitting linear models. Broom package.
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Week 8, 22/23. okt/Oct, 14.15-17
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Grupitöö: andmeanalüüs kasutades ette antud andmeid.
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Groupwork: data analysis using provided dataset.
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Kursus toimub teisip./kolmap. kell 14.15-17/classes will take place 14.15-17 on Tue in Nooruse 1 room 121 and on Wed in Ostwaldi 1 room B103.
| nädal/Week | teisip./Tue (Nooruse 1-121) | kolmap./Wed (Ostwaldi 1-B103) |
|---|---|---|
| 38 (3) | 17. sept/Sep | 18. sept/Sep |
| 39 (4) | 24. sept/Sep | 25. sept/Sep |
| 40 (5) | 1. okt/Oct | 2. okt/Oct (Ostwaldi 1-A106) |
| 41 (6) | 8. okt/Oct | 9. okt/Oct |
| 42 (7) | 15. okt/Oct | 16. okt/Oct |
| 43 (8) | 22. okt/Oct | 23. okt/Oct |
Teachers:
Ülo Maiväli, PhD, Institute of Technology, University of Tartu, Estonia. Ülo has excellent theoretical background in biological research and biostatistics related issues. User and promotor of Bayesian statistics. Author of the book "Interpreting Biomedical Science: Experiment, Evidence, and Belief". Translator of "Bioloogilise mõtte areng : mitmekesisus, evolutsioon ja pärilikkus" by Ernst Mayr. Laureate of the Estonian National Science Prize in chemistry and molecular biology.
Taavi Päll, PhD, Institute of Biological and Translational Medicine, University of Tartu, Estonia. Taavi is experienced R user and has several years of R stats teaching experience (at University of Tartu and TalTech). His current research project involves quantitative and qualitative analysis of gut virome using NGS data and workflows developed in Python and R.