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2016 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

ggplot2

Elegant Graphics for Data Analysis

verfasst von: Hadley Wickham

Verlag: Springer International Publishing

Buchreihe : Use R!

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to:

produce handsome, publication-quality plots with automatic legends created from the plot specificationsuperimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scalesadd customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regressionsave any ggplot2 plot (or part thereof) for later modification or reusecreate custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plotsapproach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot

This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.

Inhaltsverzeichnis

Frontmatter

Getting Started

Frontmatter
Chapter 1. Introduction
Abstract
ggplot2 is an R package for producing statistical, or data, graphics, but it is unlike most other graphics packages because it has a deep underlying grammar. This grammar, based on the Grammar of Graphics (Wilkinson, 2005), is made up of a set of independent components that can be composed in many different ways. This makes ggplot2 very powerful because you are not limited to a set of pre-specified graphics, but you can create new graphics that are precisely tailored for your problem. This may sound overwhelming, but because there is a simple set of core principles and very few special cases, ggplot2 is also easy to learn (although it may take a little time to forget your preconceptions from other graphics tools).
Hadley Wickham
Chapter 2. Getting Started with ggplot2
Abstract
The goal of this chapter is to teach you how to produce useful graphics with ggplot2 as quickly as possible. You’ll learn the basics of ggplot() along with some useful “recipes” to make the most important plots. ggplot() allows you to make complex plots with just a few lines of code because it’s based on a rich underlying theory, the grammar of graphics. Here we’ll skip the theory and focus on the practice, and in later chapters you’ll learn how to use the full expressive power of the grammar.
Hadley Wickham
Chapter 3. Toolbox
Abstract
The layered structure of ggplot2 encourages you to design and construct graphics in a structured manner. You’ve learned the basics in the previous chapter, and in this chapter you’ll get a more comprehensive task-based introduction. The goal here is not to exhaustively explore every option of every geom, but instead to show the most important tools for a given task. For more information about individual geoms, along with many more examples illustrating their use, see the documentation.
Hadley Wickham

The Grammar

Frontmatter
Chapter 4. Mastering the Grammar
Abstract
In order to unlock the full power of ggplot2, you’ll need to master the underlying grammar. By understanding the grammar, and how its components fit together, you can create a wider range of visualizations, combine multiple sources of data, and customise to your heart’s content.
Hadley Wickham
Chapter 5. Build a Plot Layer by Layer
Abstract
One of the key ideas behind ggplot2 is that it allows you to easily iterate, building up a complex plot a layer at a time. Each layer can come from a different dataset and have a different aesthetic mapping, making it possible to create sophisticated plots that display data from multiple sources.
Hadley Wickham
Chapter 6. Scales, Axes and Legends
Abstract
Scales control the mapping from data to aesthetics. They take your data and turn it into something that you can see, like size, colour, position or shape. Scales also provide the tools that let you read the plot: the axes and legends. Formally, each scale is a function from a region in data space (the domain of the scale) to a region in aesthetic space (the range of the scale). The axis or legend is the inverse function: it allows you to convert visual properties back to data.
Hadley Wickham
Chapter 7. Positioning
Abstract
This chapter discusses position, particularly how facets are laid out on a page, and how coordinate systems within a panel work. There are four components that control position. You have already learned about two of them that work within a facet:
Hadley Wickham
Chapter 8. Themes
Abstract
In this chapter you will learn how to use the ggplot2 theme system, which allows you to exercise fine control over the non-data elements of your plot. The theme system does not affect how the data is rendered by geoms, or how it is transformed by scales. Themes don’t change the perceptual properties of the plot, but they do help you make the plot aesthetically pleasing or match an existing style guide. Themes give you control over things like fonts, ticks, panel strips, and backgrounds.
Hadley Wickham

Data Analysis

Frontmatter
Chapter 9. Data Analysis
Abstract
So far, every example in this book has started with a nice dataset that’s easy to plot. That’s great for learning (because you don’t want to struggle with data handling while you’re learning visualisation), but in real life, datasets hardly ever come in exactly the right structure. To use ggplot2 in practice, you’ll need to learn some data wrangling skills. Indeed, in my experience, visualisation is often the easiest part of the data analysis process: once you have the right data, in the right format, aggregated in the right way, the right visualisation is often obvious.
Hadley Wickham
Chapter 10. Data Transformation
Abstract
Tidy data is important, but it’s not the end of the road. Often you won’t have quite the right variables, or your data might need a little aggregation before you visualise it. This chapter will show you how to solve these problems (and more!) with the dplyr package.
Hadley Wickham
Chapter 11. Modelling for Visualisation
Abstract
Modelling is an essential tool for visualisation. There are two particularly strong connections between modelling and visualisation that I want to explore in this chapter:
  • Using models as a tool to remove obvious patterns in your plots. This is useful because strong patterns mask subtler effects. Often the strongest effects are already known and expected, and removing them allows you to see surprises more easily.
  • Other times you have a lot of data, too much to show on a handful of plots. Models can be a powerful tool for summarising data so that you get a higher level view.
Hadley Wickham
Chapter 12. Programming with ggplot2
Abstract
A major requirement of a good data analysis is flexibility. If your data changes, or you discover something that makes you rethink your basic assumptions, you need to be able to easily change many plots at once. The main inhibitor of flexibility is code duplication. If you have the same plotting statement repeated over and over again, you’ll have to make the same change in many different places. Often just the thought of making all those changes is exhausting! This chapter will help you overcome that problem by showing you how to program with ggplot2.
Hadley Wickham
Backmatter
Metadaten
Titel
ggplot2
verfasst von
Hadley Wickham
Copyright-Jahr
2016
Electronic ISBN
978-3-319-24277-4
Print ISBN
978-3-319-24275-0
DOI
https://doi.org/10.1007/978-3-319-24277-4

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