R for data science : import, tidy, transform, visualize, and model data / Hadley Wickham, Garrett Grolemund.

Includes bibliographical references and index.

Part I. Explore. Data visualization with ggplot2 -- Workflow: basics -- Data transformation with dplyr -- Workflow: scripts -- Exploratory data analysis -- Workflow: projects -- Part II. Wrangle. Tibbles with tibble -- Data import with readr --Tidy data with tidyr -- Relational data with dplyr -- Strings with stringr -- Factors with forcats -- Dates and times with lubridate -- Part III. Program. Pipes with magrittr -- Functions -- Vectors -- Iteration with purrr -- Part IV. Model. Model basics with modelr -- Model building -- Many models with purrr and broom -- Part V. Communicate. R Markdown -- Graphics for communication with ggplot2 -- R Markdown formats -- R Markdown workflow.

This work 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, it is designed to get you doing data science as quickly as possible. $b 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.