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
About the Author
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 (the tidyverse: ggplot2, dplyr, tidyr, purrr, readr, …), and principled software development (roxygen2, test that, devtools). He is also a writer, educator, and frequent speaker promoting the use of R for data science.
Garrett maintains shiny.rstudio.com, the development center for the Shiny R package, and is the author of Hands-On Programming with R as well as R for Data Science, a forthcoming book by O’Reilly Media (watch him write it at http://r4ds.had.co.nz/). Garrett is a Data Scientist and Professional Educator at RStudio, Inc. In his own words: I specialize in teaching people how to use R – and especially Hadley Wickham’s R packages – to do insightful, reliable data science. Hadley was my dissertation advisor at Rice University, where I gained a first-hand understanding of his R libraries. While at Rice, I taught (and helped developed) the courses “Statistics 405: Introduction to Data Analysis,” and “Visualization in R with ggplot2”. Before that, I taught introductory statistics as a Teaching Fellow at Harvard University. I’m very passionate about helping people analyze data better. I have travelled as far as New Zealand, where R was born, to learn new ways to teach data science. I worked alongside some of the original developers of R to hone my programming skills, and I collaborated with the New Zealand government in a nationwide project to improve how New Zealand teaches data analysis to new statisticians. Back in the states, I focused my doctoral research on developing pragmatic principles that guide data science. These principles create a foundation for learning R, which is a bit of a layer cake. R is a set of tools for implementing statistical methods, and statistical methods are themselves a set of tools for learning from data. Like all toolkits, R gives its best results to those who use it wisely. Outside of teaching, I have spent time doing clinical trials research, legal research, and financial analysis. I also develop R software. I co-authored the `lubridate` R package, which provides methods to parse, manipulate, and do arithmetic with date-times, and I wrote the `ggsubplot` package, which extends `ggplot2`. I’m also the Editor-in-chief of RStudio’s Shiny Development Center (shiny.rstudio.com), the official resource for learning to use the shiny package to make interactive web apps with R.
Author: Hadley Wickham
Condition Type: New
Country Origin: India
Gift Wrap: No
Publication Date: 2017