Avoiding quantitative scales that make graphs hard to read

[This post was originally published in May 2016 on Stephen Few's Perceptual Edge blog.] 

Every so often, I come across a graph with a quantitative scale that is confusing or unnecessarily difficult to use when decoding values. Consider the graph below from a popular currency exchange website:

Source: www.xe.com

Source: www.xe.com

Let’s say that you were interested in knowing the actual numerical value of the most recent (i.e., right-most) point on the line in this graph. Well, let’s see, it’s a little less than halfway between 1.25 and 1.40, so a little less than half of… 0.15, so about… 0.06, plus 1.25 is… 1.31. That feels like more mental work than one should have to perform to simply “eyeball” the numerical value of a point on a line, and it most certainly is. The issue here is that the algorithm used by the graph rendering software generated stops for the quantitative scale (0.95, 1.10, 1.25, etc.) that made perceiving values in the graph harder than it should be. This is frustrating since writing an algorithm that generates good quantitative scales is actually relatively straightforward. I had to develop such an algorithm in a previous role as a software developer and derived a few simple constraints that consistently yielded nice, cognitively fluent linear scales, which I’ve listed below:

1. All intervals on the scale should be equal.

Each interval (the quantitative “distance” between value labels along the scale) should be the same. If they’re not equal, it’s more difficult to accurately perceive values in the graph, since we have to gauge portions of different quantitative ranges depending on which part of the graph we’re looking at (see example below).

Source: www.MyXcelsius.com

Source: www.MyXcelsius.com

2. The scale interval should be a power of 10 or a power of 10 multiplied by 2 or 5.

Powers of 10 include 10 itself, 10 multiplied by itself any number of times (10 × 10 = 100, 10 × 10 × 10 = 1,000, etc.), and 10 divided by itself any number of times (10 ÷ 10 = 1, 10 ÷ 10 ÷ 10 = 0.1, 10 ÷ 10 ÷ 10 ÷ 10 = 0.01, etc.). We find it easy to think in powers of 10 because our system of numbers is based on 10. We also find it easy to think in powers of 10 multiplied by 2 or 5, the two numbers other than itself and 1 by which 10 can be divided to produce a whole number (i.e., 10 ÷ 2 = 5 and 10 ÷ 5 = 2). Here are a few examples of intervals that can be produced in this manner:


Here are a few examples of good scales:


Here are a few examples of bad scales:


After this post was originally published, astute readers pointed out that there are some types of measures for which the “power of 10 multiplied by 1, 2 or 5” constraint wouldn’t be appropriate, specifically, measures that the graph’s audience think of as occurring in groups of something other than 10. Such measures would include months (3 or 12), seconds (60), RAM in Gigabytes (4 or 16) and ounces (16). For example, a scale of months of 0, 5, 10, 15, 20 would be less cognitively fluent than 0, 3, 6, 9, 12, 15, 18 because virtually everyone is used to thinking of months as occurring in groups of 12 and many business people are used to thinking of them in groups of 3 (i.e., quarters). If, however, the audience is not used to thinking of a given measure as occurring in groups of any particular size or in groups that number a power of 10, then the “power of 10 multiplied by 1, 2 or 5” constraint would apply.

3. The scale should be anchored at zero.

This doesn’t mean that the scale needs to include zero but, instead, that if the scale were extended to zero, one of the value labels along the scale would be zero. Put another way, if the scale were extended to zero, it wouldn’t “skip over” zero as it passed it. In the graph below, if the scale were extended to zero, there would be no value label for zero, making it more difficult to perceive values in the graph:

Source: www.xe.com, with modifications by author.

Source: www.xe.com, with modifications by author.


In terms of determining how many intervals to include and what quantitative range the scale should span, most graph rendering applications seem to get this right, but I’ll mention some guidelines here for good measure.

Regarding the actual number of intervals to include on the scale, this is a little more difficult to capture in a simple set of rules. The goal should be to provide as many intervals as are needed to allow for the precision that you think your audience will require, but not so many that the scale will look cluttered, or that you’d need to resort to an uncomfortably small font size in order to fit all of the intervals onto the scale. For horizontal quantitative scales, there should be as many value labels as possible that still allow for enough space between labels for them to be visually distinct from one another.

When determining the upper and lower bounds of a quantitative scale, the goal should be for the scale to extend as little as possible above the highest value and below the lowest value while still respecting the three constraints defined above. There are two exceptions to this rule, however:

  1. When encoding data using bars, the scale must always include zero, even if this means having a scale that extends far above or below the data being featured.
  2. If zero is within two intervals of the value in the data that’s closest to zero, the scale should include zero.

It should be noted that these rules apply only to linear quantitative scales (e.g., 70, 75, 80, 85), and not to other scale types such as logarithmic scales (e.g., 1, 10, 100, 1,000), for which different rules would apply.

In my experience, these seem to be the constraints that major data visualization applications respect, although Excel 2011 for Mac (and possibly other versions and applications) happily recommends scale ranges for bar graphs that don’t include zero, and seems to avoid scale intervals that are powers of 10 multiplied by 2, preferring to use only powers of 10 or powers of 10 multiplied by 5. I seem to be coming across poorly designed scales more often, however, which is probably due to the proliferation of small vendor, open-source and home-brewed graph rendering engines in recent years.