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
z
table width
x
x
y
z
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
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