Graphics Reference
In-Depth Information
h
<-
ggplot(faithful, aes(x
=
waiting))
# Save the base object for reuse
h
+
geom_histogram(binwidth
=
8
, fill
=
"white"
, colour
=
"black"
, origin
=
31
)
h
+
geom_histogram(binwidth
=
8
, fill
=
"white"
, colour
=
"black"
, origin
=
35
)
Figure 6-3. Different appearance of histograms with the origin at 31 and 35
The results look quite different, even though they have the same bin size. The
faithful
data
set is not particularly small, with 272 observations; with smaller data sets, this is even more of
an issue. When visualizing your data, it's a good idea to experiment with different bin sizes and
boundary points.
Also, if your data has discrete values, it may matter that the histogram bins are asymmetrical.
They are closedon the lower bound and openon the upper bound. If you have bin boundaries at
1, 2, 3, etc., then the bins will be [1, 2), [2, 3), and so on. In other words, the first bin contains 1
but not 2, and the second bin contains 2 but not 3.
It is also possible to use
geom_bar(stat="bin")
for the same effect, although I find it easier to
interpret the code if it uses
geom_histogram()
.
See Also
Frequency polygons provide a better way of visualizing multiple distributions without the bars
interfering with each other. See
Making a Frequency Polygon
.
