The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R’s quirks and shows how some...
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This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding experience. This is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists,...
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This book introduces the blogdown R package to create websites using R Markdown and Hugo. There are two major highlights of blogdown:
1) It produces a static website, meaning the website only consists of static files such as HTML, CSS,...
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This book introduces an R package, bookdown, to change your workflow of writing books.
With bookdown,
- It should be technically easy to write a book,
- visually pleasant to view the book,
- fun to...
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This website explains and partially documents the R package plotly, a high-level interface to the open source JavaScript graphing library plotly.js (which powers plot.ly).
The R package already has numerous examples and documentation on...
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This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data...
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This book will teach you how to create an R package.
Contents:
1. Getting started
- Introduction
- Package structure
2. Package components
- Code (R/)
- Package metadata...
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With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr.
