The three fundamental elements of bad graphical display are these: Data
Ambiguity, Data Distortion, and Data Distraction.
Data ambiguity arises from the failure to precisely define just what the
data represent. Every dot on a scatterplot, every point on a time series
line, every bar on a bar chart represents a number (actually, in the case of a
scatterplot, two numbers). It is the job of the text on the chart to
tell us just what each of those numbers represents. If a number
represented in a chart is, say, 33½, the text in the graph -- in the title,
the axis labels, the data labels, the legend, and sometimes the footnote --
must answer question: "Thirty-three and a half what?".
The interpretation of the graph is as follows:
There is some evidence that the cost of higher education may not have
escalated so much... Figure 9-12 reflect the average cost for tuition, room,
and board as a percentage of median family income from 1964 to 1995.
While private institutions have increased costs substantially, public
university costs have remained constant. This indicates that the
increased costs associated with higher education may be quite reasonable
when compared to family income levels. (Cochran 346-7)
Note the ways in which the authors have understated the rising costs of
public university education. First, the costs are deflated not by
adjusting for the consumer price index but by median family income --
especially for the years after 1982, median family income rose much faster
than the consumer price index. Second, graphing both the private and
public data on the same graph enlarges the scale on which the public data is
displayed. It's hard to tell from the graph, but between 1980 and
1995 it appears that public university costs increased from around 11% of
family income to near 15% -- in effect the share of family income going to
public university costs has increased by a third. The third way of minimizing
the cost increases that have occurred since 1980 is to extend the time series
back to 1965.
different picture emerges if one were to compare the rate of increase in
public university costs to the rate of increases in other sectors of the
economy. On the left, we see that from 1981 to 1999 -- over the lifetime
of today's college student -- public university costs have risen faster than
any other sector of the economy. Faster even than rising medical care
costs. In addressing the topic of health care inflation, the same
authors note that: "Cost escalation in the medical field has been constant,"
and spend four pages of text addressing the reasons for the increases. (pp.
Examine this chart from the UNICEF
that purports to demonstrate that the gap between rich and poor countries is
increasing. We can see that the per capita GNP of the wealthiest
countries has slightly almost doubled (from about $12,000 to about $26,000),
but it is not clear that the GNP hasn't doubled or tripled among either the
middle or low income countries.
Here's an example from the same
source that seems to distort the data. Note the size of the two arrows,
but look carefully at the first arrow -- the negative $18 change is
represented not by the arrow, but by the little line below it.
Edward Tufte's fundamental rule of efficient graphical design is to
minimize the ratio of ink-to-data. This is essentially the same
advice offered by Strunk and White to would be writers:
"A sentence should contain no unnecessary words, a paragraph no
unnecessary sentences for the same reason that a drawing should contain no
unnecessary lines and a machine no unnecessary parts." (23)
The primary source of extraneous lines in charting graphics today are the
3-D options offered by conventional spreadsheet graphics. These 3-D
options serve no useful purpose; they add only ink to the chart, and more
often than not make it more difficult to estimate the values represented.
Even worse are the spreadsheet options that allow one to rotate the
perspective. For those who would take bad graphical display to even
higher levels, the Excel spreadsheet program offers the option of doughnut,
radar, cylinder, cone, bubble charts.
|2-D Pie Chart
||3-D Pie, Exploded
|3-D Column Bar
||Simple 2-D Bar
Pie charts should rarely be used. It is more difficult for the eye to
discern the relative size of pie slices than it is to assess relative bar
length. With a the pies, without looking at the numbers it is difficult
to figure out whether the Navy or Air Force is larger; from the bar charts it
is obvious. 3-D pie charts are even worse, as they also add a
visual distortion (in this case, making the Air Force appear much larger).
Note how much less ink the 2-D bar charts uses compared to the 3-D bar.
Using data labels rather than a y-axis scale in this case reduces the number
of numbers displayed from 6 to 4, and adds precision as well. Normally,
I would have sorted the data here, so that the Navy would be between the Army
and Air Force, but since the Marines are a part of the Navy (and the Air
Force, originally, a part of the Army), this order made more sense. A
strict application of the ink-to-data in this case, however, would eliminate
the bars altogether and simply present the data as a table.
Pies are even less effective when an additional variable is added and
comparisons between pies are required (sometimes by adjusting the relative
size of the pies).
Not content with
the distractions and distortions made possible by the use of 3-D effects,
charters sometimes feel the need to add all sorts of other Chartjunk to a
graph. In the graphics on the left, Kevin Phillips (1991, 9) is
trying to make the point that income is more inequitably distributed in
the United States than in other countries.
Note the extraneous features
of this in this graphic.
- A completely irrelevant map of the world.
- Two entirely different kinds of 3-D charts displayed at two
- Country names are repeated three times.
- To display 24 numeric data points, 28 numbers are used to define the
- The countries are sorted in no apparent order (not even
- Note the use of the letter " I "
to separate the countries on the bottom chart.
While it might be possible to display these data better graphically, a
table does the job quite nicely:
Two chart types that should always be avoided.
Two common charts easily produced by spreadsheet programs that should
almost always be avoided are the stacked bar chart and the pie chart.
The stacked bar chart, made even worse by the use of 3-D effects in figure 3,
makes it very difficult to estimate the values of the variables represented on
the top of the bars. Similar "stacking" can also been done with time series
area charts and should be avoided as well.
|Figure 3: Stacked 3-D bar chart
|source: Putnam, p. 227
Pie charts are fun to look at, but generally involve using a great deal of
ink to display very little data. In addition, the charts often make it
difficult to discern the exact magnitude of the size of the pie slices.
Using multiple pie charts to display more than one variable is also a bad
idea. All this is made even worse by exploiting the power of the
spreadsheet technology to produce 3-D pie charts and "exploding" 3-D pie
charts. If you think that you really must use a pie chart, make sure it
is for data that does indeed at up to a total (i.e., the percentages for the
slices add up to 100) and stay away from the fancy stuff.
|Bad Chart 2: Where do the lines cross?
|Phillips, p. 206
Clarke Cochran et. al. American Public Policy: An
Introduction (1999: St. Martin's Press)
Kevin Phillips, The Politics of Rich and Poor (1991:
Putnam, Robert D., Bowling Alone (Simon and Schuster,
Strunk, William Jr., and E. B. White, The Elements of
Style 3d edition (MacMillan publishing, 1976).