This learning module supplies the example
used in demonstrations, so that you can follow
along with the video instructor.
Click the download button and extract the
demonstration example assets from the resulting
zip archive file.
In this learning module, we will consider
the ways in which Spotfire pie charts may
be used to visualize data, including the fact
that pie sectors, or wedges, are defined by
categorical color values and continuous size
values.
In addition, a pie chart which is trellised
into separate panels affords the option to
relate a secondary comparison by changing
the size of the overall pies.
Spotfire pie charts are useful whenever you
need to make relative comparisons of how categorical
components contribute to the total, generally
expressed as a percentage.
As you create pie charts, the two most important
properties are the Color-by property, where
the number of unique values determines the
number of pie wedges or sectors, and the Sector-size-by
property, which can be set to represent aggregate
values or counts.
A secondary variable can be compared in pie
chart visualizations which are split into
multiple trellis panels.
The pie in each trellis panel can be sized
differently in order to represent that measure.
For example, here we are comparing relative
purchase totals in each department across
states and territories.
And here we have added a Size-by component
in order to compare the total item price between
states and territories.
Let’s take a look at a demonstration where
several pie charts have already been configured.
Use Open File to navigate to the example with
Interpreting Pie Charts in the title.
Click Open, and agree to the terms and conditions
of use.
The visualizations in this analysis document
are based upon data from the Mega Mart chain
of department stores.
On this page, a basic Pie Chart visualization
has been configured.
The most important variables for you to know
in order to interpret this pie chart are the
Color-by values and the Sector-size-by values.
In this example the two unique values in the
Gender column result in two pie sectors.
The size of each sector is defined by the
total Shoe purchase amount made by customers
of each gender.
It appears that female customers contribute
a much larger percentage of shoe purchases
to the total than do our male customers.
On the next page, the pie chart has been trellised
into separate panels based upon both store
setting and gender.
Each pie allows us to compare the relative
electronics purchase totals made at each major
store location for the gender and setting
defined by that panel in the trellis matrix.
For example suburban males in Seattle contribute
40% to the total electronics purchases made
by suburban males across all store locations.
Due to the separation of this pie chart into
trellis panels, it was possible to configure
a secondary variable for comparison.
In this example the size of the overall pie
in each trellis panel relates the number of
customers assigned to that demographic.
So, you can see that rural males make up the
smallest number of customers (which the legend
reveals to be 82), and suburban females make
up the largest number of customers (which
the legend reveals to be 194).
Each of the remaining pie sizes is scaled
between these minimum and maximum values.
You may also notice that the percentage label
for this Los Angeles sector is not displayed.
This is because the sector percentage threshold
for labels has been configured to a limit
of 10%, in order to keep big labels from overlapping
on adjacent small pie sectors.
If we hover our mouse over that sector, the
tooltip information box indicates that this
sum of electronics purchases, made by these
customers, contributes only 9% to the total
represented by this pie – just under the
current label threshold.
On the final page of this analysis document,
the two pie charts configured on the left
are identical, except for one feature.
The sectors in this pie chart are sorted alphabetically,
based upon the values in the Color-by property
variable.
That sorting starts at 12:00, with Boston,
and proceeds clockwise through Los Angeles,
New York, and Seattle until 12:00 is reached
again.
This pie chart has been configured to sort
sectors by size.
Therefore, the largest sector percentage starts
at 12:00 and sector percentages become smaller
as you proceed clockwise until 12:00 is reached
again.
The pie chart on the right is designed to
show you what happens when more than one categorical
variable is selected as the color by property
for a pie chart.
Note that, for this example, each sector is
sized by the count of customers.
And, both Store Setting and Gender define
the color by property.
Therefore, a separate sector has been defined
for each combination of store setting and
gender.
With three store setting values and two gender
values, the result is a total of six sectors
displayed in this pie chart.
Therefore, if you wanted to know, “What
is the percentage of suburban female customers?”,
this pie chart would indicate that the answer
is 26%.
