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About Hadley Wickham
Hadley is Chief Scientist at RStudio and a member of the R Foundation. He builds tools (both computational and cognitive) that make data science easier, faster, and more fun. His work includes packages for data science (ggplot2, dplyr, tidyr), data ingest (readr, readxl, haven), and principled software development (roxygen2, testthat, devtools). He is also a writer, educator, and frequent speaker promoting the use of R for data science. Learn more on his homepage, http://hadley.nz.
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Books By Hadley Wickham
Learn how to use R to turn raw data into insight, knowledge, and understanding. 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.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youâ??ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youâ??ve learned along the way.
Youâ??ll learn how to:
- Wrangleâ??transform your datasets into a form convenient for analysis
- Programâ??learn powerful R tools for solving data problems with greater clarity and ease
- Exploreâ??examine your data, generate hypotheses, and quickly test them
- Modelâ??provide a low-dimensional summary that captures true "signals" in your dataset
- Communicateâ??learn R Markdown for integrating prose, code, and results
This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to:
- produce handsome, publication-quality plots with automatic legends created from the plot specification
- superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales
- add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression
- save any ggplot2 plot (or part thereof) for later modification or reuse
- create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots
- approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot
This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.
Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special.
This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising
By reading this book, you will learn:
- The difference between an object and its name, and why the distinction is important
- The important vector data structures, how they fit together, and how you can pull them apart using subsetting
- The fine details of functions and environments
- The condition system, which powers messages, warnings, and errors
- The powerful functional programming paradigm, which can replace many for loops
- The three most important OO systems: S3, S4, and R6
- The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation
- Effective debugging techniques that you can deploy, regardless of how your code is run
- How to find and remove performance bottlenecks
The second edition is a comprehensive update:
- New foundational chapters: "Names and values," "Control flow," and "Conditions"
- comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them
- Much deeper coverage of metaprogramming, including the new tidy evaluation framework
- use of new package like rlang (http://rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming
- Use of color in code chunks and figures
Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.
Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.
Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You’ll learn to focus on what you want your package to do, rather than think about package structure.
- Learn about the most useful components of an R package, including vignettes and unit tests
- Automate anything you can, taking advantage of the years of development experience embodied in devtools
- Get tips on good style, such as organizing functions into files
- Streamline your development process with devtools
- Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN)
- Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr
The packaging industry is under pressure from regulators, customers and other stakeholders to improve packaging’s sustainability by reducing its environmental and societal impacts. This is a considerable challenge because of the complex interactions between products and their packaging, and the many roles that packaging plays in the supply chain.
Packaging for Sustainability is a concise and readable handbook for practitioners who are trying to implement sustainability strategies for packaging. Industry case studies are used throughout the book to illustrate possible applications and scenarios. Packaging for Sustainability draws on the expertise of researchers and industry practitioners to provide information on business benefits, environmental issues and priorities, environmental evaluation tools, design for environment, marketing strategies, and challenges for the future.
This book offers solutions to all 284 exercises in Advanced R, Second Edition. All the solutions have been carefully documented and made to be as clear and accessible as possible. Working through the exercises and their solutions will give you a deeper understanding of a variety of programming challenges, many of which are relevant to everyday work. This will expand your set of tools on a technical and conceptual level. You will be able to transfer many of the specific programming schemes directly and will discover far more elegant solutions to everyday problems.
- When R creates copies, and how it affects memory usage and code performance
- Everything you could ever want to know about functions
- The differences between calling and exiting handlers
- How to employ functional programming to solve modular tasks
- The motivation, mechanics, usage, and limitations of R's highly pragmatic S3 OO system
- The R6 OO system, which is more like OO programming in other languages
- The rules that R uses to parse and evaluate expressions
- How to use metaprogramming to generate HTML or LaTeX with elegant R code
- How to identify and resolve performance bottlenecks
Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more.
- Get started: Discover how the major pieces of a Shiny app fit together
- Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques
- Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components
- Apply best practices: Examine useful techniques for making your Shiny apps work well in production
Os autores Hadley Wickham e Garret Grolemund te guiam através dos passos de importar, fazer data wrangle, explorar e modelar seus dados e comunicar os resultados. Você obterá uma compreensão completa do quadro geral do ciclo de ciência de dados, junto das ferramentas básicas que você precisa para administrar os detalhes.
Você aprenderá a:
Data Wrangle — transforme seus conjuntos de dados em uma forma conveniente para análise.
Programar — aprenda ferramentas poderosas de R para resolver problemas de dados com mais
clareza e facilidade.
Explorar — examine seus dados, gere hipóteses e teste-as rapidamente.
Modelar — forneça um resumo de baixa dimensão que capture "sinais" verdadeiros em seu conjunto de dados.
Comunicar — aprenda R Markdown para integrar prosa, código e resultados.
An Essential Reference for Intermediate and Advanced R Programmers
Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.
The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn:
- The fundamentals of R, including standard data types and functions
- Functional programming as a useful framework for solving wide classes of problems
- The positives and negatives of metaprogramming
- How to write fast, memory-efficient code
This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.
Provides both rich theory and powerful applications
Figures are accompanied by code required to produce them
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