ggplot2：R中数据分析的优雅图形指南

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《ggplot2：数据分析的优雅图形》是Hadley Wickham撰写的一本专著，作为Springer系列“使用R”（UseR!）的一部分，它旨在帮助读者掌握R语言中的高级绘图技术。本书适合那些熟悉统计学和编程基础，特别是对R语言有兴趣深入探索数据分析可视化的人群。ggplot2是R语言中一个强大的数据可视化包，它强调“分层”（layered）的方式来构建图形，使得复杂的数据可视化变得直观且易于理解。作者Hadley Wickham，作为一名知名的R语言专家，他在书中分享了他的设计理念，即通过将数据视为图形的构建块来创建清晰、一致和美观的图表。这本书不仅仅是一份教程，更是一个实践指南，涵盖了ggplot2的核心概念，如数据框映射、几何对象、统计变换、坐标系统、颜色编码、标签和图例等。书中包含了大量的示例和实际应用案例，让读者能够在实际操作中学习如何使用ggplot2进行各类统计图形的绘制，包括散点图、线图、柱状图、直方图、箱线图、小提琴图等，并能够处理缺失值、异常值和多变量数据的可视化。此外，作者还讨论了如何利用ggplot2进行地图制作、时间序列分析和网络图的创建，以及如何与其他R包（如dplyr和tidyr）集成，进一步提升数据处理和图形设计的效率。《ggplot2：数据分析的优雅图形》不仅适合数据分析师和研究人员，也适合教学工作者，因为它提供了丰富的教学材料和扩展资源，可以帮助读者深化对R语言的理解，提高数据可视化技能。无论你是R语言初学者还是高级用户，这本书都是提升数据可视化能力的宝贵参考资料。通过阅读这本书，读者不仅能学习到ggplot2的基本用法，还能了解到如何利用这个工具进行高效、美观的数据表达，从而在数据驱动决策和沟通中发挥重要作用。同时，对于R语言开发者来说，这本书也是理解R生态系统中图形库设计原则和最佳实践的重要参考书。

10 2 G etting started with qplot

2.2 Datasets

In this chapter we’ll just use one data source, so you can get familiar with

the plotting details rather than having to familiarise yourself with diﬀerent

datasets. The diamonds dataset consists of prices and quality information

about 54,000 diamonds, and is included in the ggplot2 package. The data

contains the four C ’s of diamond quality, carat, cut, colour and clarity; an d

ﬁve physical measurements, depth, table, x, y and z, as described in Figure 2.1.

The ﬁrst few rows of the data are shown in Table 2.1.

carat cut color clarity depth table price x y z

0.2 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43

0.2 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31

0.2 Go od E VS1 56.9 65.0 327 4.05 4.07 2.31

0.3 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63

0.3 Go od J SI2 63.3 58.0 335 4.34 4.35 2.75

0.2 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48

Table 2.1: diamonds dataset. The variables depth, table, x, y and z refer to the

dimensions of the diamond as shown in Figure 2.1

table width

depth

depth = z depth / z * 100

table = table width / x * 100

Fig. 2.1: How the variables x, y, z, table and depth are measured.

The dataset has not been well cleaned, so as well as demonstrating inter -

esting relationships about diamonds, it also demonstrates some data quality

problems. We’ll also use another dataset, dsmall, which is a random sample

of 100 diamonds. We’ll use this data for plots that are more appropriate for

smaller datasets.

> set.seed(1410) # Make the sample reproducible

14 2 G etting started with qplot

of object that is used to display the data. Some geoms have an associated

statistical transformation, for example, a histogram is a binning statistic plus

a bar geom. These diﬀerent components are described in the next chapter.

Here we’ll introduce the most common and useful geoms, organised by the

dimensionality of data that they work with. The foll owing geoms enable you

to investigate two-dimensional relationships:

• geom = "point" draws points to pr oduce a scatterplot. This is the default

when you supply both x and y arguments to qplot().

• geom = "smooth" ﬁts a smoother to the data and displays the smooth and

its standard error, §2.5.1.

• geom = "boxplot" produces a box-and-whisker plot to summarise the

distribution of a set of points, §2.5.2.

• geom = "path" and geom = "line" draw lines between the data points.

Traditionally these are used to explor e relationships between time and

another variable, but lines may be used to join observations connected in

some other way. A line plot is constrained to produce lines that travel from

left to right, while paths can go in any direction, §2.5.5.

For 1d distributions, your choice of geoms is guided by the variable type:

• For continuous variables , geom = "histogram" dr aws a histogram, geom =

"freqpoly" a frequency polygon, and geom = "density" creates a de ns ity

plot, §2.5.3. The histogram geom is the default when you only supply an x

value to qplot().

• For discrete variables, geom = "bar" makes a bar chart, §2.5.4.

2.5.1 Adding a smoother to a plot

If you have a scatterplot with many data points, it can be hard to see exactly

what trend is shown by the d ata. In this case you may want to add a smoothed

line to the plot. This is easily d on e using the smooth geom as shown in

Figure 2.4. Notice that we have combined multiple geoms by supplying a

vector of geom names created with c().Thegeomswillbeoverlaidinthe

order in which they appear.

qplot(carat, price, data = dsmall, geom = c("point", "smooth"))

qplot(carat, price, data = diamonds, geom = c("point", "smooth"))

Despite overplotting, our impre s s ion of an exponential relationship between

price and carat was correct. There are few diamonds bigger than three carats,

and our uncertainty in the form of the relationship increases as illustrated

by the point-wis e conﬁdence interval shown in grey. If you want to turn the

conﬁdence interval oﬀ,usese = FALSE.

There are many diﬀerent smoothers you can choose between by using the

method argument:

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