The Charts used here to demonstrate bad charting practices are taken from
the "Illinois Higher Education Annual Report" prepared by the Illinois Board
of Higher Education, July, 2002.
Problems with this chart:
Displaying the total enrollments on the same chart as the Community, Public
and Private doubles the scale, making it very difficult to see significant
differences in enrollment growth rates between the private and public
institutions. Nevertheless, because this is the first chart in the
section of the report profiling higher education in Illinois, total enrollment
figures are a key indicator that should be reported in the first table.
One solution would be to lengthen the vertical axis, as was done in an earlier
chart contained in the 2001 IBHE Data Book:
Note how the increasing private sector enrollments are much more obvious in
this chart. The second chart also avoids two minor shortcomings of the
first: the unnecessary gray background and the use of curved lines between the
data points. This chart is not as pretty as the first, but it shows a
more accurate depiction of the data. Among the problems that remain are
gridlines that are too thick and the "over 30 years" text in the legend.
Whereas the first chart reports total fall enrollments the next chart, from
the 2002 Annual Report, reports 12-month headcounts (a larger number,
representing the number of individual students enrolled at anytime during the
year). Although the data are very similar to those shown in the first
chart, the authors of the report chose to use a different chart type and make
some very bad chart formatting decisions. The result is a chart that
deserves at least honorable mention as one of the worst charts ever
The first obvious problem here is the use of the unnecessary 3-D effect.
It takes a couple minutes to figure out whether the Y-axis scale references
the front or the back of the bars: is the first "Community Colleges" bar
greater than or less than 700,000? Note also how much more ink is used
in the bar for the private institutions data than is used for the public
universities data. Placing the Community College bar in the middle of
the three sets of bars seems an unfortunate choice.
But wait! There's a less obvious but far more serious problem with
this chart. As you examine the trends in these data, the first
impression is that the Community College bars are increasing and that the
public university enrollments are catching up with private institution
enrollments. But this is just the opposite of what we saw in the first
chart. Look again -- at the tiny numbers representing the years at
the bottom of the chart. The chart is backwards! Always depict time from
left to right.
Interestingly, the IBHE did a better job -- but still not good -- of
presenting these data in their previous years' Annual Report:
This chart shows a better placement of the Community College bar and the
years are in the correct order. It still suffers from the unnecessary
3-D effect, but at least gridlines allow a better visual estimation of the
data. Note also the awkwardness of the white border around the black bar
and how the border does not show up in the legend. Note that in contrast
with the 12-month headcount chart, above, in order to estimate data points for
the bars, you compare the back of the 3-D bar with the gridlines.
effects of 3-D graphics used throughout the Annual Report are best illustrated
in the pie chart on the left. See how the angle and placement of the
pies serve to exaggerate the size of the public university slice and how the
white border around the community college slice reduces its size.
Generally, pies do
not provide for an accurate visualization of data, as can be seen in a
comparison with a bar chart representation of the same data (on the left).
The next chart illustrates the problems that frequently occur with stacked
bar charts when the ordering of the elements on the stacked bar is arbitrary.
Again, the 3-D effects only compound the difficulty of visually estimating the
relative size of the components of the bars.
The Importance of sorting data.
Sorting data by the most significant variable greatly aids in the
interpretation of graphical representations of data. Sorting data
alphabetically, as is done throughout the IBHE report, generally works to hide
significant facts about the data.
Problems with this chart:
- The legend at the bottom of the chart shortens the plot area.
- Unnecessary 3-D effect.
- Unnecessary shading of plot area.
- The white borders on the undergraduate bars make those bars appear
shorter; the white borders do not show up on the legend.
- The universities are sorted in alphabetical order.
Correcting these problems results in a chart, below, that says more about
While sorting data alphabetically is a common graphical mistake, it is
even worse to have the data sorted randomly, as is the case in the next chart,
depicting the number of degrees awarded by major. Here the data are
sorted not alphabetically, not by the 1980-81 data, nor (as would be best) by
the 2000-01 data.
What explains this unusual sequencing of data? The answer, it seems,
is "consistency", as in Ralph Waldo Emerson's observation, "A foolish
consistency is the hobgoblin of little minds". In this case, the
IBHE authors are maintaining a consistency with their previous years' report,
where the following chart appeared:
Here we see
the top majors sorted at least in some order, although the 3-D effect makes it
difficult to figure out whether Psychology's bar is larger than Bio/Life
sciences, we will assume that the Psychology bar is the larger of the two
(note again, with this 3-D effect, you have to use the back of the bar).
So the original chart displays the number of degrees for the top ten majors
in 2000-01 and 1980-01 sorted by the number of degrees in 1978-79. This,
however, raises an interesting question: In what years were these the top ten