Say you want to plot some data. You could just plot it by itself:

Or you could put lines on the left and bottom:

Or you could put lines everywhere:

Or you could be weird:

Which is right? Many people treat this as an aesthetic choice. But I’d like to suggest an unambiguous rule.

## Principles

First, try to accept that all axis lines are optional. I promise you, your readers will recognize a plot even without lines around it.

So say you’re wondering if you should draw a line for the x-axis. Consider these plots:

Which is better? That depends on what you’re plotting. To answer, mentally picture these arrows:

Now, ask yourself, *are the lengths of these arrows meaningful*? When you draw that horizontal line, you invite people to compare those lengths.

You use the same principle for deciding if you should draw a y-axis. Mentally draw these lines and then ask yourself if people should be comparing the lengths:

## Example: Years vs. GDP

Suppose your data is how the GDP of some country changed over time, so the x-axis is years and the y-axis is GDP.

You could draw either axis or not. So which of these four plots is acceptable?

Got your answers? Here’s a key:

Why?

GDP is an absolute quantity. If GDP doubles, then that means something. So readers

*should*be thinking about the distance between the curve and the x-axis.But 1980 is arbitrary. When comparing 2020 to 2000, all that matters is that they’re 20 years apart. No one cares that “2020 is twice as far from 1980 as 2000” because time did not start in 1980.

## Example: Years vs. GDP again

Say you have years and GDP again, except all the GDP numbers are much larger—instead of varying between 0 and $3T, they vary between $50T and $53T.

What to do? In principle you could stretch the y-axis all the way down to zero.

But that doesn’t seem like a good idea—you can barely see anything.

Sometimes you need to start the y-axis at $50T. That’s fine. (As long as you’re not using a bar chart.) But then, the right answer changes.

The difference is that $50T isn’t a meaningful baseline. You don’t want people comparing things like (GDP in 1980 - $50T) vs. (GDP in 2000 - $50T) because that ratio doesn’t mean anything.

## Example: Years vs temperature

What if the y-axis were temperature? Should you draw a line along the x-axis at zero?

If the temperature is in Kelvin, then probably yes.

If the temperature is in Fahrenheit, then no. No one cares about the difference between the current temperature and the freezing point of some brine that Daniel Fahrenheit may or may not have made.

If the temperature is in Celsius, then maybe. Do it if the difference from the freezing point of water is important.

Of course, if the freezing point of water is critical and you’re using Fahrenheit, then draw a line at *32°F*. Zero and one are the most *common* useful baselines, but use whatever is meaningful.

(Rant about philosophical meaning of “0” and “1” and identity elements in mathematical rings redacted at strenuous insistence of test reader.)

## Example: Votes vs. homeowners

Sometimes you should put lines at the ends of axes, too. Say the x-axis is the fraction of homeowners in different counties, and the y-axis is support for legal cannabis:

Should you draw axis lines? Well, comparisons to 0% are meaningful along both axes. So it’s probably good to add these lines:

But comparisons to 100% are *also* meaningful. So in this case, you probably want a full box around the plot.

## Lines can also be used for evil

Lots of people hate the Myers-Briggs personality test—suggesting that you should use a created-by-academic-psychologists test like the Big Five instead. I’ve long held this was misguided and that if you take the Myers-Briggs *scores* (without discretizing them into categories) they’re almost equivalent to the Big Five without neuroticism or “Big Four”.

So I was excited to see some recent research that tests this. They had a bunch of people take various personality tests and then rate themselves on 40 life outcomes, e.g. how happy they were and how many friends they had. They then looked at the correlation between personality tests and life outcomes:

Here, the correlation is an R value—0 means a test tells you nothing about life outcomes, and 1 means the test allows you to predict life outcomes perfectly. So this suggests the Big Four is similar to Myers-Briggs scores, but maybe a little more informative.

But we’re here to talk about *figures*, not psychology. So look at how the above numbers were pictured in Scientific American:

That “small correlation” line is *genius*—your eye naturally compares the dots to it, rather than the “no correlation” line, giving the impression that the Big Four is *twice as good* as the MBTI. Of course, the difference between a correlation and a “small correlation” threshold is not something that anyone could conceivably care about.

A plot that follows the rules I laid out here is much less misleading:

## Related advice

Andrew Gelman advises, “If zero is in the neighborhood, invite it in”. The idea is that if your plot *almost* includes zero, and zero is meaningful, then extend the y-axis down to zero. He gives this example:

I agree with this advice, though I’m not sure about that vertical line. Is “time since the Slim Shady LP came out” paramount here? And if you *don’t* extend the y-axis to zero, then you shouldn’t draw a horizontal line either. So I think this would be a better demonstration of the principle:

## Axis lines ≠ tick marks

On a related note, see how there are tick marks in the previous plots, even when there are no axis lines? You can do that. It’s fine. Put tick marks wherever you want. Tick marks are no excuse for axis lines.

## Matplotlib’s tragic defaults

Matplotlib is a popular plotting library. Here’s an example of using it:

```
time = [0,1,2,3,4,5]
speed = [1,.3,.2,.1,.03,.01]
plt.plot(time,speed)
plt.xlabel('Time (s)')
plt.ylabel('Speed (m/s)')
```

I don’t want to quibble with the default of adding axis lines everywhere. After all, there has to be *some* default, and if the right choice depends on the semantics of the data, then you can’t expect the plotting library to guess that.

Except, I *can’t* not quibble because there’s a more serious problem. Do you see it? Here’s a little hint:

I literally cracked up just reading the TITLE. I love this newsletter so much.

Nothing to to with the charting advice, which mainly makes sense, but the Myers-Briggs data touched on a pet peeve of mine: Researchers who report R rather than R-squared in cases where anti-correlation (negative R) is not operative, and thus there's no good reason to use the signed R rather than the unsigned R-squared. This is especially prevalent in cases of weak correlation.

In this particular case, the largest correlation shown corresponds to an R-squared of 0.05. But that looks almost like a null result. So the researchers instead report R=0.23, which looks more impressive.