Data Science with R

Disclaimer

This repository is build on the work of Garrett Grolemund from posit. In particular, it reuses an important part of the material he developed for tidyverse-related workshops, which is available at https://github.com/rstudio-education/remaster-the-tidyverse under the Creative Commons BY-SA 4.0 copyright.

Material

The main webpage is at https://astamm.github.io/data-science-with-r/.

Outline

Data wrangling with R

The class is organised in 9 parts each of which has its own set of slides and exercises. The slides are available in the above Data Wranging - Slides tab and the exercises in the above Data Wranging - Labs tab. The slides are written partly with Keynote (exported as PDFs) and partly in Quarto reveajs slides. The exercises are written in Quarto.

Part	Title	Slides	Exercises	Suppl. Material
1	Introduction	PDF	Quarto
2	Visualize Data	PDF	Quarto
3	Transform Data	PDF	Quarto	CSV
4	Model Data	PDF	Quarto	ZIP
5	Communicate Data	PDF	Quarto	Quarto
6	Tidy Data	PDF	Quarto
7	Join Data	PDF	Quarto
8	Manipulate Data Types	PDF	Quarto
9	Manipulate Lists	PDF	Quarto

Exploratory Data Analysis with R

The class is organised in 4 parts each of which has its own set of slides and exercises. The slides are available in the above Exploratory Data Analysis - Slides tab and the exercises in the the above Exploratory Data Analysis - Labs tab. The slides are written in Quarto revealjs slides. The exercises are written in Quarto.

Part	Title	Slides	Exercises	Suppl. Material
1	Hypothesis Testing	Quarto	Quarto
2	Linear Regression	Quarto	Quarto	ZIP
3	Principal Component Analysis	Quarto	Quarto
4	Clustering	Quarto	Quarto

Requirements

• R: https://www.r-project.org

• RStudio: https://posit.co/download/rstudio-desktop/

• Quarto: https://quarto.org/docs/get-started/

• Quarto Drop extension: https://github.com/r-wasm/quarto-drop

• R packages:

  • {babynames}
  • {jtools}
  • {plotly}
  • {tidyverse}