There are a lot of visualizations in the world
and we don't have time for them all.
So let's focus on one particularly abused plot
type, the pie chart.
We have a specimen right here.
This is a pie chart of phone application crashes,
showing what percentage of all crashes
took place in each mobile operating system.
This data set contains information
for all versions of Apple's iOS, which is used in the iPhone,
as well as the various versions of Google's Android.
There are many things wrong with this plot,
but let's break down exactly what.
Putting aside, for a moment, that there are far too
many labels, check out the ordering of the labels
corresponding to iOS.
Two sensible ways of ordering iOS data
might be by decreasing percentage
or by version number.
Instead, we start at the top with iOS 3.13, with 0%,
and then jump to iOS 4.2.10, with 12.64%,
before going back down to iOS 3.2, with 0.00% again.
Which brings us to the number of labels.
Many of the segments are so narrow that they
can't be seen, although technically, all data is
retained, because every segment is labeled.
If we look at iOS, we see that there
are only three major versions, 3, 4, and 5, suggesting
we can compress down the iOS segments to just three
segments, while retaining most of the information.
At the least, the versions that differ in the third number
should be combined, and all data points of 0% should be removed.
The more fundamental concern of this visualization
is that it might really be showing
the percentage of the phone market
using each operating system, and says nothing about whether one
operating system crashes more than the other, which
is the focus of this visualization.
Our next pie chart has its own share of problems.
This is a plot of how many shark attacks have
been attributed to each type of shark.
Firstly, the pie chart is, for some reason,
plotted on a hemisphere, a graphical effect that
adds nothing, but has the effect of vertically compressing
the pie chart.
Next, there is the issue of label orientation.
While the caption, "Shark species (total/deaths)",
and the label, "White shark", are horizontal,
the rest are vertical and hard to read.
They are in order, however, which does help.
Although the "Others" segment is unfortunately large,
which is unclear if it is due to there being a lot of attacks
by many species, or if the species is not
known for many attacks.
Finally, at a glance, it is hard to distinguish
the magnitude of differences between the orange, green,
blue, and brown segments in the top part of the pie chart,
and we must resort to the labels to distinguish between them.
There is no meaning in the colors, they are arbitrary.
Finally, we'll look at a pie chart I made,
of the origins of the international students at MIT.
I made this chart with the default settings
in Google Sheets.
First of all, not all of the segments
are labeled, so that data is lost,
for the Middle East, Africa, Oceania,
and the unknown regions.
Second, again, we have colors that are arbitrary and almost
close enough to be confusing.
The difference between Asia and Africa's colors is subtle.
And of course, the 3D-effect on the pie
chart adds nothing, but does play a subtle trick on the eye.
Due to the 3D-effect, the blue and red segments
are actually larger looking, which at a glance,
may lead the viewer to overestimate their size.
What we are going to do now is, switch over to R
and plot this data more appropriately, using ggplot.
And then we'll return to the slides,
to discuss some more possibilities for this data.