Friends don't let friends make bad graphs
github.com
github.com
On the other hand, I think a lot of these "bad graphs" are very intentionally chosen precisely in order to hide the small number of data points, or an underlying distribution that looks suspicious, etc.
So it's not so much "friends don't let friends", but more "when you see a graph that chooses to obfuscate rather than clarify, suspect it might be intentional".
Plots often fail to clarify for the precise reason that clarification takes time and effort and those things are lacking in academia in spades. Are people intentionally hiding ugly details, definitely on occasion? But I don't think it's the primary source of such bad figures.
I've had four decades of crunching numbers in a variety of Engineering, Geophysics, and science applications with a hefty amount of public consulting on a variety of applications and of the large population of those good at gathering and recording data perhaps only 20% had that extra talent for good visualisation to convey meaning without distortion.
[1] Can't remember the exact wording but that one it's like number 6 or 7.
We were talking about a graph that shows global temperature rise due to climate change. They claimed the graph was misleading because the Y axis (temperature) didn't start from 0 (fahrenheit? celsius? fucking kelvin?).
This person also quipped, "maybe if you can't see if with 0 at the bottom, it's not such a significant change?". That put a dent in my faith in humanity for a while. I'm just glad to see us operating at a higher level. I guess 2016-2020 was a different time.
Alternatively Fermi Mission Elapsed Time is also an acceptably cool zero point, which puts the zero in January 2001. The zero of the unix time is tolerable only in truly desperate circumstances.
Completely different things.
I mean yes, if you want the ratios of different temperatures to be meaningful, then that's where you'd need to set the zero point. You could argue that a graph that makes 25C look "25% hotter" than 20C is misleading in this sense. (Not that this justifies global warming denialism.)
> You could argue that a graph that makes 25C look "25% hotter" than 20C is misleading in this sense.
That’s meaningless in any sense. The origin of the Celsius scale is arbitrary, “25% hotter” has no meaning whatsoever.
True – but the origin of the Kelvin scale is not.
>“25% hotter” has no meaning whatsoever.
It absolutely does have a physical meaning. It means that the system has 25% more energy at the microscopic level. (Or, you know, substitute in a more precise physical definition of temperature – it will be some kind of measure of energy, even if it's not exactly that.)
It's not necessarily wrong to suppress the zero in a graph of temperature changes, but by doing so you are making bars in the graph proportionally larger or smaller relative to other bars by an arbitrary amount. That could potentially be misleading, depending on what point you are making using the graph.
No, not really. Heat is a poorly defined concept to which we are saddled for historical reasons. For example:
> It means that the system has 25% more energy at the microscopic level.
It does not. This definition only works in a frame of reference at rest compared to the thing you are observing. Imagine a piece of matter that is travelling at a velocity v in your implicit frame of reference. Its temperature does not depend on v, even though it’s kinetic energy does. We are back to the choice of scale.
And then there are negative absolute temperatures, which do not make any sense at all if heat is kinetic energy.
The actual definition of thermodynamic temperature is the inverse of the derivative of the energy with respect to the entropy. This is highly non-intuitive and we cannot extrapolate our intuitive concept of heat too much.
> It's not necessarily wrong to suppress the zero in a graph of temperature changes, but by doing so you are making bars in the graph proportionally larger or smaller relative to other bars by an arbitrary amount.
Right. This is the point that was made in the story and I entirely agree with that. A bar chart communicates a surface area. Changing the scale artificially changes the surface area and is misleading. The logical conclusion is that bar graph make no sense for temperatures, or to show the relative change of a variable.
Personally I would go further and say that bar graphs are inappropriate in the vast majority of cases, but that’s just my opinion.
> That could potentially be misleading, depending on what point you are making using the graph.
Indeed.
And I did say:
>substitute in a more precise physical definition of temperature
I partly agree with him.
Take this example, first graph I could find:
https://religionnews.com/wp-content/uploads/2014/08/61Years-...
For me it looks on a first look like a 2/3 decline and this is misleading. Often this decling graphs give an optical picture that does not reflect real decline.
It is not helpful in general. Magnitude and relative changes are different things. Sometimes you need one, and sometimes you need the other.
Global average temperatures are a good example: where is the zero? Is it significant? The effects of an increase or decrease of 0.5°C are massive, so the appropriate way of presenting this is to show the temperature anomaly, not the absolute temperature. Also, this way the information is conveyed regardless of the temperature scale in use.
The religiosity graph is interesting. If you want to show a sudden change at some point, then showing the relative change is appropriate. If you want to show that people are not religious anymore using this graph, then you are dishonest. It is all about the narrative and the point you want to make.
On its face, “scales must go to zero” is not good advice, because you can always change the variable so you can make anything go to zero without changing the shape of the curve and our perception. However, when we see a graph, then we always need to understand why it goes to zero or not, what the author is trying to show, and whether they are being honest about it
I don't know.
"The average surface temperature on Earth is approximately 59 degrees Fahrenheit (15 degrees Celsius), according to NASA"
But I know if the average temperature increased 0.5 degree C and I show a graph with the scale 14 to 16 degrees over time and the headline "world average surface temperature exploding" then this is excellent clickbait and a nice graph, but it is misleading on the first look.
Tukey was one of Tufte's mentors.
But the central premise the text presents is "maximise the information to ink ratio", which sounds very reasonable but is fundamentally flawed. The problem is that quantity of ink (or, on a screen, number of black pixels) is not the same as visual complexity. By the time your brain is interpreting something visual, it has done edge detection, grouping, and other preprocessing.
He gives an example of shortening the axes on a scatter plot to show the range of data, rather than intersecting at the corner. This is win-win because it uses less ink but shows more information. But, when I look at the comparison, it's just obvious that the modified version is visually more complex. It would be especially worse on a complex page with text and a few plots next to each other – the fragments would all visually bleed together.
One mitigation to that sort of complexity is to put boxes around large district elements, like an entire plot. But boxes are Tufte's absolute nemesis, in that book and elsewhere. It's surprising that after so many years looking at visual displays he still has that attitude.
It would be nice to find someone like Tufte that gathered some actual evidence. Like, trying different plots on groups of people to see which find information faster, maybe using eye tracking. Even just subjective surveys might be an improvement on one person's opinion.
- The elements of graphing data
- Visualizing data
However I feel a lot of people miss the logic behind it all and just straight up copy the "Tuft style" which often is too stylized and iconoclastic. A good example are the plotting defaults in R's ggplot2
It’s very interesting and relevant, just not the final word on the subject.
... a whistle stop tour around the research basis for a lot of claims around data viz best practice (esp interesting re the dogma around not using pie charts which seems to be a consistent bugbear of designers going back to the 1930's but around which the research is inconclusive at best)
[edit: fixed a typo]
At a glance of the medium article, they seem to have the same perspective on these issues, so the one should complement the other.
why use colors at all in those examples?
That is to say they aren't examples of when you would use a colour scale at all.
EDIT: I thought Tufte did it, I was wrong. https://www.amazon.com/Grammar-Graphics-Statistics-Computing...
Tufte's whole like, thing is essentially that charts need to be intentionally designed to convey a specific concept to a specific audience via careful choice about what information to present. A library can help you with the shapes but can't automate the decisions.
I've seen your other talks, which were all fantastic, and highly recommend them others.
The part about three levels of estimation was really eye-opening. Even though it's obvious in hindsight, I didn't make the connection until it was spelled out for me.
Basically, Tufte used the idea of "ink", classified into two groups, data ink and useless ink. The goal is to have a graph with as little useless ink as possible; where every bit of information visible in a graph (or table) is relevant to the end output. To this extent, he recommended dumping axis lines where unneeded, labels, keys, gridlines, and many more things.
LaTeX tables, by default, tend to look like what Tufte proposed, which is probably why LaTeX tables look so damn good compared to the HTML defaults
It was a good exercise.
I really like that book.
But I'd add getting rid of heatmaps on large datasets. They are information dense and pretty, but I can't see how anyone interprets them. Better to do clustering and plot the data for each relevant cluster in a more meaningful way.
Just a thought
(bar graphs are typically more appropriate)
e.g. How many graphs have you seen on evening news programs that don't even have axes labels?
I'm not going to say people with glass houses shouldn't throw stones, but maybe you should walk outside first.