Use new part headings

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hadley 2016-04-21 08:01:34 -05:00
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# Communicate your work
# (PART) Communicate your work {-}
# Introduction
Reproducible, literate code is the data science equivalent of the Scientific Report (i.e, Intro, Methods and materials, Results, Discussion).

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# Welcome
# Welcome {-}
This is the book site for __"R for data science"__. This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data. (__R for Data Science__ was formerly called __Data Science with R__ in __Hands-On Programming with R__)

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# Programming
# (PART) Programming {-}
# Introduction
Code is a tool of communication, not just to the computer, but to other people. This is important because every project you undertake is fundamentally collaborative. Even if you're not working with other people, you'll definitely be working with future-you. You want to write clear code so that future-you doesn't curse present-you when you look at a project again after several months have passed.

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# Do science with data
# (PART) Do science with data {-}
# Introduction
The scientific method guides data science. Data science solves known problems with the scientific method.

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# Understand your data
# (PART) Understand your data {-}
# Introduction
Data poses a cognitive problem; Data comprehension is a skill.

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# Work with your data
# (PART) Work with your data {-}
# Introduction
With data, the relationships between values matter as much as the values themselves. Tidy data encodes those relationships.