20.1 A Minimal Book Example
This is a sample book written in Markdown.
20.2 A Minimal rTorch Book
by Alfonso R. Reyes
Practically, you can do everything you could with PyTorch within the R ecosystem.
20.3 Apache Arrow R Cookbook
This cookbook aims to provide a number of recipes showing how to perform common tasks using arrow.
20.4 ComplexHeatmap Complete Reference
by Zuguang Gu
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.
20.5 Create, Publish, and Analyze Personal Websites Using R and RStudio
by Danny Morris
A free, digital handbook with step-by-step instructions for launching your own personal website using R, RStudio, and other freely available technologies including GitHub, Hugo, Netlify, and Google Analytics.
20.6 data.table in R The Complete Beginners Guide
by Selva Prabhakaran
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.
20.7 ggplot2 Elegant Graphics for Data Analysis
by Hadley Wickham
ggplot2 is an R package for producing statistical, or data, graphics. Unlike most other graphics packages, ggplot2 has an underlying grammar, based on the Grammar of Graphics (Wilkinson 2005), that allows you to compose graphs by combining independent components. This makes ggplot2 powerful. Rather than being limited to sets of pre-defined graphics, you can create novel graphics that are tailored to your specific problem.
20.8 GT Cookbook
by Thomas Mock
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
20.9 Highcharter Cookbook
by Tom Bishop
by Yihui Xie
Dynamic documents with R and knitr!
The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package.
20.11 mlr3 book
by Michel Lang
The mlr3 package and ecosystem provide a generic, object-oriented, and extensible framework for classification, regression, survival analysis, and other machine learning tasks for the R language. They do not implement any learners, but provide a unified interface to many existing learners in R.
20.12 The caret Package
by Max Kuhn
The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.
20.13 The Data Validation Cookbook
The purposes of this book include demonstrating the main tools and workflows of the validate package, giving examples of common data validation tasks, and showing how to analyze data validation results.
20.14 The lidR package
by Jean-Romain Roussel, Tristan R.H. Goodbody, Piotr Tompalski
lidR is an R package for manipulating and visualizating airborne laser scanning (ALS) data with an emphasis on forestry applications. The package is entirely open source and is integrated within the geospatial R ecosytem (i.e. raster, sp, sf, rgdal etc.). This guide has been written to help both the ALS novice, as well as seasoned point cloud processing veterans.
20.15 The targets R Package User Manual
by Will Landau
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.
20.16 The Tidyverse Cookbook
by 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.
Created and maintained by Oscar Baruffa
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