Constructing Effective Pareto Charts
The following are recommendations for improving the
visual clarity of Pareto charts:
- Decide carefully how the bars should be scaled.
The default percent scale is not always the best choice.
For instance, a count scale may be more appropriate in a
comparative Pareto chart where the total count per cell
varies widely from cell to cell and where you want to
compare Pareto distributions on an absolute scale
rather than a relative scale. You can request a
count scale by specifying SCALE=COUNT.
In other situations, it may be more appropriate to use
a weighted percent scale or a weighted count scale
(specify a WEIGHT= variable and either SCALE=PERCENT
or SCALE=WEIGHT).
- Use a weight variable if the
counts are dependent on a factor such as exposure or
opportunity that varies from one category to another.
For instance, suppose that you are creating a Pareto
chart for the number of medical claims submitted by
company employees categorized by job title.
The counts can be weighted to adjust for the fact
that there are more individuals in some jobs than in
others and for the fact that some jobs may be
associated with greater health risks than others.
- Use the NOCURVE option to eliminate the cumulative
percent curve in situations where the curve
reveals little information about the data.
In general, the bars should be more
prominent than the curve.
- Maximize the space used for the bars by eliminating
unnecessary labels and visual clutter. This is
particularly important for comparative Pareto charts.
The NOHLABEL and NOVLABEL options are useful for this purpose.
You can also use the NOVLABEL2, NOVTICK, and NOVTICK2 options
with a VBAR statement or the NOHLABEL2, NOHTICK and NOHTICK2
options with an HBAR statement.
- Make legends more informative by specifying legend
labels.
- Avoid filling bars with multiple types of cross-hatched
patterns; solid color fills are less distracting. Use
color sparingly to emphasize important features (such
as the "vital few" categories), and choose bar
colors that provide good visual discrimination.
- If you are working with a large data set involving
many categories, limit the number displayed
to achieve visual clarity.
- If your application involves classification effects,
construct more than one Pareto chart for the
data using various combinations of classification
variables (this approach is illustrated in
Example 29.2.
- Provide reference lines on comparative Pareto charts to
aid visual comparison.
Refer to Chapter 2 of Cleveland (1985)
for a general discussion of the principles of statistical
graphics.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
