-: All right, a little bit more about chart selection here.
I wanna talk about some specific like bits of research
that can guide some of our decisions
as we're picking charts, okay?
Number one, this is a good thing to be aware of,
and it's not just about line charts which it says here.
That was what the study was about, but it actually,
it applies to line charts, scatter plots
and probably other charts as well.
It is a good best practice that the independent variable,
the cause of something always goes on the x axis,
the horizontal axis,
and that the effect, the result, goes on the Y axis.
So that's why time always flows left to right.
Time happens, and as time goes by, I lose more hair, right?
The cause is the time, not the other way around.
It's not that my hair disappears,
and then therefore time happens, right?
So cause down here, effect going up and down.
Good, but this research found there's an exception.
You would never do it that way
if the cause was about highness, bigness, more-ness
you would never say
the higher I go on the mountain horizontally,
the colder it gets vertically.
That's just weird.
And this research proved it.
People are confused, disoriented by that.
So this also aligns
with what we've talked about a couple of times already here.
That's another reason why low numbers go down here.
High numbers go up here.
We always go low to high that way.
And the same thing left to right
because time flows left to right.
Okay, so that's a good one to know.
This one I mentioned just in the last video I believe.
When we see, or maybe in the prior video,
when we see bars, we instinctively compare them,
research has shown.
So if you want your audience to compare
those month to month values, sure, maybe you can pick bars.
If you want them to think about the overall trend,
show them a line.
Simple.
I also mentioned this one.
Correlation, you cannot beat a scatter plot
to show correlation.
So not that there aren't other charts.
There's one called parallel sets that also works,
parallel coordinates, sorry, but that's a weird chart type.
If you're gonna show correlation,
you're probably gonna use a scatter plot.
There are others, like I said.
You can actually do,
essentially a map can show the correlation
between geography and another variable obviously.
But, whatever.
Scatter plot's great for just numeric correlation.
Okay, this one.
We talked about small multiples in the last video,
the idea that you definitely can, research has shown,
and this being one of the research studies,
show people small multiples,
and they'll be very accurate at comparing
those different little charts.
What's interesting about this one
is they compared small multiples to animation
and to essentially traced static graphics.
And as you can see here, the traced static graphics,
especially when they're all piled on top of each other,
not very effective.
Animation, people loved it.
And that's worth something.
Engagement matters.
I use animation all the time,
but if I really want my audience
to very accurately compare two things to each other
as they've changed over time or whatever,
I might use small multiples instead.
So you know, be cautious, and again, try it.
Create an animation if you have time.
Create the animation
and create the the small multiples
and throw it past somebody.
Ask them what they think.
Aesthetics matter.
This is related sort of to what we were just talking about.
Engagement matters, okay?
I'm gonna make sexy eye candy anytime I can
because that does matter.
It's gonna attract my audience and keep them engaged.
Very important.
Also, aesthetics.
Very important.
This research, they took a bunch of hierarchical data,
you know, parents, children
and children of children type of data,
and they visualized it a whole bunch of different ways.
They used adeno dendrogram tree
which is like your typical org chart.
This one performed very well, no surprise.
Typical way of seeing hierarchical data.
They also visualized it in a whole bunch of other ways
including this crazy weird 3D cylinder thing.
I don't know what that is, but whatever, they tried it,
and the dendrogram tree performed really well, the best.
Okay, and by the way, performance in data vis research
is usually speed to understanding, accuracy of understanding
and accuracy of memory of the data, okay?
The other chart in this entire group of options
that performed pretty much exactly as well
as the dendrogram tree, the sunburst diagram.
Remember a couple videos ago
I whined about circular shapes
and how badly they performed for a while.
Turns out, I mean that's true,
but it also turns out that it's not always true.
In this research, they also ask people, what do you like?
Guess which one they loved the most?
The sunburst.
So turns out if it's pretty,
people will spend more time with it.
They will learn how to read it,
and it does not affect performance.
Okay, or it doesn't degrade performance.
In fact, I guess you could say it helps performance
cause the sunburst shouldn't perform well on paper.
So aesthetics matter.
Make pretty stuff.
All right.
I mentioned this one
towards the beginning of the course already.
I'll say it again.
Titles matter.
People do fixate on the title,
and if the title actually says something about your data,
your audience is more likely
to accurately remember your data.
So boy, in this course you will definitely be critiqued
if you don't have good titles on your slides
and for your overall story, et cetera.
It's really important, especially your slide titles, okay?
And by the way, we don't need to have redundant titles.
I don't need to have a slide title that says one thing
and then a chart title that says kind of the same thing
or worst case, like it just says nothing
like survey results.
Just skip the chart titles.
The slide title can essentially act as your chart title,
but say something with it.
Also, we should use semantically resonant colors
when relevant.
What does that mean?
Well, look at this.
We have apples, bananas, and blueberries.
It's hard for me to say that
cause the colors are wrong, right?
The right colors would be apples, bananas
and blueberries, right?
So there's a measurable cognitive load
when the colors don't match the thing.
So we should use the right color when relevant,
and I would add when appropriate, right?
So we're not gonna do that with gender,
blue and pink, yay!
No, we're not gonna do it with ethnicity, right?
But when appropriate and relevant
make the color match a thing.
And I think I talked about that earlier
a couple modules ago.
You know, remember when I said purple is
something other than a purple thing?
Instead of saying purple is eggplant right?
Okay, also colorblindness.
Last but not least here today,
colorblindness affects up to 8% of white males,
which is why we talk about it
cause there's a little bit of bias in the industry
and has been for generations obviously,
but it is up to 3% of the population overall.
So it is a not insignificant number of people.
So the most common form of colorblindness
is called red green colorblindness.
Those people have difficulty distinguishing
between shades of red and green.
As an example, this was run through
three different colorblindness filters.
This is the red green filter.
And clearly this shade of orange
and this shade of green are pretty much indistinguishable.
And by the way, even with a key, that's the idea.
Even if I had a legend, I'm not gonna be able to tell
these apart very easily.
So when you create, let's say, a scatter plot
with two categories of things, and you use red and green,
good and bad, it's gonna be literally impossible
to understand what they are, even with a key.
Now if you dual encoded it with
up and down arrows in addition to the color, okay?
Now the red and green doesn't matter.
And you could also, what we do in data vis,
professionals now, we use red and blue instead.
So don't use red and green.
Use red and blue.
That'll be safe to avoid this problem.