R for Data Science

This book PDF is perfect for those who love Computers genre, written by Hadley Wickham and published by "O'Reilly Media, Inc." which was released on 12 December 2016 with total hardcover pages 521. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related R for Data Science books below.

R for Data Science
R for Data Science
Author : Hadley Wickham
File Size : 41,8 Mb
Publisher : "O'Reilly Media, Inc."
Language : English
Release Date : 12 December 2016
ISBN : 9781491910368
Pages : 521 pages
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R for Data Science by Hadley Wickham Book PDF Summary

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

R for Data Science

R for Data Science by Hadley Wickham,Garrett Grolemund

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

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Hands On Exploratory Data Analysis with R

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Exploratory Data Analysis Using R

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Exploratory Data Analysis with R

Exploratory Data Analysis with R by Roger Peng

This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed

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Exploratory Multivariate Analysis by Example Using R by Francois Husson,Sebastien Le,Jérôme Pagès

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) a

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Praise for the Second Edition: "The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB." —Adolfo Alvarez Pinto, International Statistical Review "Practitioners of EDA who use MATLAB will want a copy of this book. ...

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Doing Data Science in R

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Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level

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