ggplot2：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》是一本由Hadley Wickham编著的英文原版书籍，专注于介绍在R语言中使用ggplot2包进行高级数据可视化的技术。这本书旨在帮助统计学和大数据领域的专业人士掌握数据可视化艺术，通过实例和语法解析，让读者理解并熟练运用ggplot2构建出美观且具有深度的数据图表。书中的主要内容分为几个部分： 1. **简介**：章节一从欢迎读者入手，简要介绍了ggplot2的价值和与其他R图形系统的对比。它解释了什么是图形语法（Grammar of Graphics），这是一种强大的概念框架，用于组织和构建各种类型的图表。此外，还提到了ggplot2在R语言中的地位以及本书的目的和结构。 2. **入门指南**：第二章“开始使用qplot”引导读者从基本的qplot函数入手，这是一种简化版的ggplot接口。这里涉及数据集的导入、基础用法，以及如何添加颜色、大小、形状等美学属性来增强图表的视觉效果。同时，书中展示了箱线图、直方图、条形图、时间序列图以及分面绘图的使用方法，并比较了与plot函数的区别。 3. **掌握图形语法**：第三章深入探讨图形语法的核心要素，如数据结构、坐标系统、图层、尺度、分面等。以实际案例（如燃油经济性数据）为例，逐步构建复杂的散点图和更深层次的图形结构。这部分内容对于理解和构建自定义图形至关重要。 4. **逐层构建图形**：第四章详细讲解了如何一步步地创建图形，从零开始构建，包括数据处理、图层添加、坐标系统设置和图层组件的管理，让读者对ggplot2的底层工作原理有深入理解。《ggplot2：数据分析的优雅图形》不仅提供了丰富的代码示例，还鼓励读者利用该包的强大功能进行创新和探索。对于学习R语言的数据可视化和从事数据分析工作的专业人士来说，这是一本不可或缺的参考资料，可以帮助他们提升数据可视化能力，从而更好地呈现和理解数据故事。

10 2 Getting 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; and

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 Good 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 Good 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 Getting 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 following geoms enable you

to investigate two-dimensional relationships:

• geom = "point"

draws points to produce 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 explore 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"

draws a histogram,

geom =

"freqpoly"

a frequency polygon, and

geom = "density"

creates a density

plot,

2.5.3. The histogram geom is the default when you only supply an

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 data. In this case you may want to add a smoothed

line to the plot. This is easily done 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()

. The geoms will be overlaid in the

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 impression 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-wise conﬁdence interval shown in grey. If you want to turn the

conﬁdence interval oﬀ, use se = FALSE.

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

method argument:

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