Tidy Data Science with the `tidyverse` and `tidymodels`

By W. Jake Thompson in R workshop

June 14, 2021

This workshop provides an introduction to data science using R. Participants learn an entire pipeline of data analysis, from importing data to creating reproducible reports. Along the way we introduce several core tidyverse packages, which work together to make the process of data analysis seamless. These include dplyr for data transformation, tidyr for data tidying, and ggplot2 for creating data visualizations. We then introduce core tidymodels packages for creating modeling pipelines including, parsnip for creating model specifications, recipes for feature engineering, and yardstick for evaluating model performance. Finally, we discuss how to use rmarkdown for creating reproducible reports.

See the workshop websites for more, including links to the GitHub repositories for all workshop materials.

Posted on:
June 14, 2021
1 minute read, 114 words
R workshop
tidyverse tidymodels ggplot2 rmarkdown
See Also:
Tidy Data Science with the tidyverse and tidymodels
Using R for Item Response Theory
Applications of Item Response Theory in R