17.1 The caret Package
The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.
This book is free online.
17.2 ComplexHeatmap Complete Reference
The ComplexHeatmap package is used to generate heatmap visualizations. It is a highly flexible tool to arrange multiple heatmaps and supports various annotation graphics for high-dimensional data. These visualizations are efficient to visualize visualizations between different sources of data sets and reveal potential patterns.
This book here contains the full documentation to using the ComplexHeatmap package effectively with plenty of small and complex examples to help you create your own complex heatmap data vizualization.
17.3 data.table in R – The Complete Beginners Guide
data.table is a package is used for working with tabular data in R. It provides the efficient data.table object which is a much improved version of the default data.frame. It is super fast and has intuitive and terse syntax. If you know R language and haven’t picked up the data.table package yet, then this tutorial guide is a great place to start.
17.4 GT Cookbook
This cookbook attempts to walk through many of the example usecases for gt, and provide useful commentary around the use of the various gt functions. The full gt documentation has other more succinct examples and full function arguments.
For advanced use cases, make sure to check out the Advanced Cookbook
17.5 Highcharter Cookbook
17.6 A Minimal rTorch Book
Alfonso R. Reyes
Practically, you can do everything you could with PyTorch within the R ecosystem.
17.7 The Tidyverse Cookbook
Edited by Garrett Grolemund
This book collects code recipes for doing data science with R’s tidyverse. Each recipe solves a single common task, with a minimum of discussion.
17.8 The targets R Package User Manual
The targets package is a Make-like pipeline toolkit for Statistics and data science in R. With targets, you can maintain a reproducible workflow without repeating yourself. targets learns how your pipeline fits together, skips costly runtime for tasks that are already up to date, runs only the necessary computation, supports implicit parallel computing, abstracts files as R objects, and shows tangible evidence that the results match the underlying code and data.