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. You’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 you’ve learned along the way.
You’ll learn how to:
Wrangle—transform your datasets into a form convenient for analysis
Program—learn powerful R tools for solving data problems with greater clarity and ease
Explore—examine your data, generate hypotheses, and quickly test them
Model—provide a low-dimensional summary that captures true "signals" in your dataset
Communicate—learn R Markdown for integrating prose, code, and results
Hadley Wickham is an Assistant Professor and the Dobelman FamilyJunior Chair in Statistics at Rice University. He is an active memberof the R community, has written and contributed to over 30 R packages, and won the John Chambers Award for Statistical Computing for his work developing tools for data reshaping and visualization. His research focuses on how to make data analysis better, faster and easier, with a particular emphasis on the use of visualization to better understand data and models.
Garrett Grolemund is a statistician, teacher and R developer who currently works for RStudio. He sees data analysis as a largely untapped fountain of value for both industry and science. Garrett received his Ph.D at Rice University in Hadley Wickham's lab, where his research traced the origins of data analysis as a cognitive process and identified how attentional and epistemological concerns guide every data analysis.
Garrett is passionate about helping people avoid the frustration and unnecessary learning he went through while mastering data analysis. Even before he finished his dissertation, he started teaching corporate training in R and data analysis for Revolutions Analytics. He's taught at Google, eBay, Axciom and many other companies, and is currently developing a training curriculum for RStudio that will make useful know-how even more accessible.
Outside of teaching, Garrett spends time doing clinical trials research, legal research, and financial analysis. He also develops R software, he's co-authored the lubridate R package--which provides methods to parse, manipulate, and do arithmetic with date-times--and wrote the ggsubplot package, which extends the ggplot2 package.
The animal on the cover of R for Data Science is the kakapo (Strigops habroptilus). Also known as the owl parrot, the kakapo is a large flightless bird native to New Zealand. Adult kakapos can grow up to 64 centimeters in height and 4 kilograms in weight. Their feathers are generally yellow and green, although there is significant variation between individuals. Kakapos are nocturnal and use their robust sense of smell to navigate at night. Although they cannot fly, kakapos have strong legs that enable them to run and climb much better than most birds.
The name kakapo comes from the language of the native Maori people of New Zealand. Kakapos were an important part of Maori culture, both as a food source and as a part of Maori mythology. Kakapo skin and feathers were also used to make cloaks and capes.
Due to the introduction of predators to New Zealand during European colonization, kakapos are now critically endangered, with less than 200 individuals currently living. The government of New Zealand has been actively attempting to revive the kakapo population by providing special conservation zones on three predator-free islands.
I'm frustrated with errors in the book. Can't get some of the code to run, and the site supposed to be up for errataa, comments, etc. (http://bit.ly/r-for-data-science) doesn't seem to exist---i just get funneled into O'Reilly's "BUY, BUY, BUY..." page.
Bottom Line No, I would not recommend this to a friend
The suite of functions in the tidy data universe hasn't before been presented so comprehensively. I am redoing all my code. I am thinking of new approaches. I am saving time writing new code. I have time to pursue new fun projects in addition to the required ones.
Bottom Line Yes, I would recommend this to a friend