-: All right, picking the right chart.
And by the way, I should probably start this off
by saying that's a terrible title.
That's not what we should be calling this
'cause we don't just pick charts.
I mean, we do.
We do just pick charts.
Meaning that we're in our software,
we gotta get this stupid presentation done
or whatever it is we're creating.
We just gotta pick a chart
and move on with our lives, right?
That's how we actually do tend to do it.
But what we're doing, what we should be doing,
is thinking about our data, the context,
what we're trying to accomplish,
and selecting the best visual to accomplish that.
So, you know, maybe a little bit of semantics here
but I don't like that phrasing.
But I use it because, you know,
obviously everybody knows what I mean when I say it.
So, picking charts, yes, let's talk about it.
How should we do that?
Well, what we have to realize and acknowledge
is that to do it properly,
we have to consider a whole bunch of different questions
and we gotta focus on the right questions
that we should be asking ourselves.
Starting off with the foundational questions
which we've already talked about.
We have to focus in on our KWYs, right?
I keep saying it.
What's the data saying?
What type of data do I have?
What's the shape of the data?
How many variables are there?
That's gonna affect different charts being possible, right?
If I have 14 variables to express,
I'm probably not gonna use a bar chart
which is all really only good at one or two
at the most, okay?
What does my audience need to hear?
Obviously gonna affect how I choose charts,
the type of task I'm enabling them to do.
We'll talk about that.
And what do I really want to say,
which is closely aligned with that.
So these are the foundational questions.
When we are visualizing data, we need to have tunnel vision
on those questions and others.
So, when we're specifically identifying the chart,
which chart is gonna do the job,
one of the key questions that we can focus in on
very, very tightly is,
"What do we want our audience to see?"
And that's how we tend to think about it.
"What am I showing them?"
Yeah, that's part of it.
But it's also very critically about,
"What do I want my audience to do?"
It's about that action, that task, excuse me,
that you're gonna enable them to perform.
Now it's a subtle difference and, you know,
maybe you don't always think about it that way
or maybe you always think about it as tasks, which is great.
The fact is, those are different.
I want my audience to see something
is different from I want them to do something.
So what kinds of things do we want our audience
to do and or see?
They kind of come in these broad categories.
And this probably isn't a complete list.
There are probably others that I'm not thinking of
at the moment, but for the most part
these are the things, okay?
So then you might ask yourself,
"All right when would I choose to do one of those?
When is distribution the right thing to show them?
Or correlation or whatever, whatever,
if those are gonna be the guides for the charts I select?"
And by the way, we'll talk about which charts you can use
in those circumstances, later on.
Some of these are simpler than others
but why don't we walk through them one by one, okay?
So the universe of categories, broadly, are these,
starting off with a comparison.
Comparison is not always the job.
It's not always the task at hand, but sometimes it is.
Sometimes I need you to understand that this is bigger
than this and it's about two times the size, right?
So like in the Olympics example that I used
during the "So What?" module, you know, a couple,
few weeks ago, right?
It was all about comparing the average age
of different sports,
different competitors in different sports.
The comparison was part of it.
But there are other tasks where comparison
is not part of it.
For instance, maybe it's really important
to talk about trend or correlation.
These are different tasks.
Sometimes you're doing more than one at the same time.
Okay, fair enough.
Now if comparison is my task,
if that's the number one goal for my audience,
then I'm gonna give 'em a chart that enables comparison.
Bar charts are a chart, not the only one,
they are a chart that enables comparison.
We are very good at doing distinct value comparison
with good precision by when we compare those bars, okay?
So I'm not gonna use a bar chart for a task
it does not perform well, but this is one where it does.
And again, there are alternatives.
We'll talk more about that.
Sometimes I really want my audience to understand
the trend over time, right?
So it's not about comparing where we ended
and where we started, it's more about that flow over time.
When that's the job, I'm gonna pick a chart
that does that well.
For instance, a line chart.
I could visualize that same data with bars, right?
If this is like monthly data,
I could do a bunch of monthly bars.
But we know from research, if I show bars,
I'm more likely to think about comparison.
If I see a line I'm more likely to think about
trend and flow.
So we're choosing charts for a reason.
Next one up is composition.
Sometimes, not always, it's really important
for my audience to understand
the proportional share of something.
The part to whole relationship, the composition.
There are different words.
The segmentation.
Different ways of expressing this.
So part of data visualization is about translating
what it is that we're talking about
into the actual words that help us
identify the charts that perform those things.
We talked about that in week zero
during data analytics, right?
So there are a bunch of words for this
but the basic idea is if I really want you to understand
that it's about 50% or it's about two thirds or whatever,
I'm gonna use a chart that enables that,
such as a pie chart or a stacked column chart,
or, those aren't the only ones, right?
There are a bunch of them.
One of them that I love is the waffle plot or the grid plot.
This is where I have like a hundred items,
it doesn't have to be a hundred, but, you know,
a bunch of individual items, and I use color
and I could use icons, I can use various things
to show that segmentation.
The nice thing about waffle plots is you're not limited
to just three to five categories
like you are with pie charts.
You can also see the smaller segments, like 1%,
that 1% icon there for, what is it,
for institutionalized, I believe.
I can see that.
Whereas if that was a teeny little slice
in a pie chart, it's gonna practically disappear.
This is also called a unit diagram.
There are a bunch of different types of unit diagrams
but the advantage of unit diagrams
is you have this one object that means one,
or a hundred, or a thousand, or whatever it is,
which makes it really easy to just sort of see the math.
A wedge that's this big compared to the one
that's that big, I don't know what I'm looking at
or how to compare the two.
Here, I can see institutionalized,
actually it's active duty military that's 1%.
Active duty military, 1%, institutionalized, 2%.
I can literally see it, "Oh that's one icon.
The other one's two icons."
It's easy for me to figure that stuff out.
Okay, distribution.
Sometimes, not always, but sometimes it's really important
for me to show my audience that distribution.
And we talked about this also in week zero.
Distribution lets us see the shape of the data.
Is there clustering at one end or the other?
Are there weird outliers?
Is it very evenly distributed?
What's the range of values?
That's when I would want to use the distribution diagram.
And there are a whole bunch of those.
One of them is a box plot.
This is the weather page in "The Wall Street Journal."
Fantastic for that audience.
That audience craves this much detail.
And I think we might talk about this one
a little bit more later.
But long story short,
there are a bunch of distribution diagrams.
We talked about some of them in week zero.
Okay, so, correlation.
Also a very specific task.
Correlation is when I want you to know
that these two variables are moving in the same direction
or in opposite directions.
It's about the relationship between those two variables.
If that is what I need you to understand,
which is, by the way, sometimes I want to do that
when I want to sort of show,
"Hey I'm trying to figure out what goes with, you know
let's say winning athletes in the Olympics."
Well they tend to be younger or they tend to be older,
or they tend to be right-handed or left-handed,
or they like burritos versus tacos.
I would look for a correlation between winning this
and burrito consumption, as an example, right?
That's correlation.
That's why we would use correlation.
And when I'm gonna look for correlation, again,
I'm gonna use a chart that enables that.
For instance, the scatter plot.
The number one best way, proven by research,
to show correlation.
For analysis, by the way, as well as for communication.
Next up is deviation.
Sometimes it's not about the data value itself
and it's also not about the benchmark
or whatever it's being compared to.
It's actually about that space in between.
And I'll show an example of that later on in this module.
In that case you would use a deviation chart,
such as this deviating area chart.
It's about emphasizing that space in between the two things
because the size and the breadth of that space
is sort of where the insight is.
And like I said, I'll show an example of that.
Sometimes it's about flow.
This becomes this becomes this.
Or these two categories sort of swap in importance
and become something else.
Or in like accounting, when we add values
and then subtract values, this is called a waterfall chart.
We can sort of show how we get from zero to a total by,
you know, adding and subtracting
using this particular chart type.
There's also sometimes where spatial information
is really important, and the GIS students
will certainly recognize this.
Sometimes it's all about that geographic
spatial relationship, in which case obviously a map
is one of the options you might use.
And then finally we have networks.
It's about the interconnectedness of stuff.
So in those cases we use network diagrams,
node link diagrams being one of the examples.
Network diagramming, network visualization,
is particularly complex.
Sometimes you end up with these hairball diagrams
of just craziness, which can be informative,
but they can also be very weird.
It can be its own field of study on its own, I think.
I actually find myself very rarely using network diagrams
because they can be so fraught.
But they're pretty interesting when you can get 'em
to do powerful stuff.
So it's all about enabling tasks.
And we talked about this a little bit in week four
when we talked about maps, right?
And I showed these different maps of the election
and how each of these maps is valid.
Each one enables a different task.
Right here I can see which county won, you know,
for which candidate.
Here I can see which county won
and the intensity of that win plus what the actual impact is
because of population.
Here I can see statewide impact due to population
and which person won.
And this one of course more about the blending, you know,
enabling me to see how mixed and how we're not quite
as polarized as we might think.
Different tasks enables different insights,
tells different stories, right?
So, here's the thing.
When we're creating visualizations,
yes, it is about picking charts,
but it's about other stuff also, okay?
And we're gonna talk about that other stuff later.
But I do wanna say when it is time to pick charts
based on those tasks that we were just talking about,
you're not alone, okay?
I'm gonna share with you two cheat sheets
that you should absolutely download and check out.
Every time, in fact, you're doing data visualization
you should look at these and be inspired,
reminded about what's out there.
This one being the Visual Vocabulary
from "The Financial Times."
"Financial Times" does great data visualization work.
If you need to show change over time,
you can see the green column there,
those are some of the options for you.
This is not a complete list.
There is a bazillion charts out there,
but this is a really solid list of options
if you wanna show change over time.
If you wanna show distribution,
there are some good distribution diagrams, et cetera.
Now, one thing about the Visual Vocabulary
is you'll see the two columns, ranking and magnitude.
Those are separate things.
Sometimes I just want to show you,
"Look, this is number one, this is number two,
this is number three.
And listen, the score for number one might have been
800 times as high as the score for number two,
but I'm not emphasizing that.
I'm just emphasizing placement, right, ranking."
Some charts will do that.
And sometimes the same chart will also show magnitude,
the actual scores.
Some charts will just do rank and that's okay.
It's about your KWYs.
What are you trying to accomplish?
Just show rank or also show the magnitude,
or one or the other, whatever.
Long story short, ranking and magnitude,
even though they are different tasks,
and you can use different charts to accomplish them,
the task that you want your audience to perform
when you're showing them rank or magnitude,
is often comparison.
So, "The Financial Times" does not use comparison
as the word, as a descriptor of any of these columns.
But just a sort of a little hint,
when you're thinking about comparison as the task,
ranking and magnitude are probably the two columns
you wanna look at here.
Now there's another cheat sheet out there
called datavizproject.com that I like.
And this one's a website so I'm just gonna click into it.
And this one also, you can choose charts by function,
like I was just talking about.
Correlation, distribution, et cetera.
Here they have comparison as the function.
So this one has ranking and magnitude charts in there.
Now what I like about this website as opposed
to the Visual Vocabulary is this is a really big collection,
a much longer list of visualizations.
So that's super cool.
Now there's a downside to it 'cause it includes
visualizations like the convex tree map,
which is like, insane.
No human can accurately compare this triangle
to this trapezoidal, whatever.
Like, no.
This is not a chart I would recommend you use, okay?
How about this one?
The Taylor diagram.
Does anyone know what that's for?
I think it has a very specific purpose.
Even the Ternary plot,
which is actually a pretty neat chart type.
It shows the correlation between
three variables essentially.
It's pretty cool, but it's kind of hard
to wrap your head around, especially for normal humans.
So, you know, this is a great list.
I do look at both of these websites,
both of these tools, frequently as a reminder
of the options out there for me.
By the way, the bump chart, this is a ranking chart.
This shows the rank position of these different things
without actually showing the score.
So, just 'cause I mentioned that earlier.
Long story short, use both of these.
Be inspired, be reminded of your options,
and especially as you're learning, you know,
learn what your options are, right?
These are very, very useful tools to do that.
Okay, now I wanna say a couple more things here
before we move on to the next lesson, the next video.
So, one thing is this.
We love our round shapes.
Humans love a round shape, right?
We don't really like straight edges and harsh corners, okay?
But, and we love pie too, at least I do.
But there are problems with round shapes.
One of them being this.
If I were to ask you to tell me,
"Which is bigger? A or B?"
And I do this all the time when I do live sessions
with with workshop clients.
People usually say, "Oh I think it's B,"
or, "No, maybe it's A, or maybe they're the same."
Like essentially there's a good debate, okay?
Which is already a problem because there is
a real difference here and it is pretty noticeable.
B is bigger.
But then I ask, "How much bigger?" and universally,
almost without exception, people will say,
"Oh I don't know, 5%?
Certainly not more than 5%, no way."
Then I show them this.
And clearly B is bigger, not even close.
And it is much, much more than 5%.
It's more than 10%, easily more than 10, maybe close to 15%.
So, this is a problem, all right?
Now at the same time,
if I asked you to tell me what percentage
of the hole is this bar over here,
you wouldn't be able to answer it.
So the bar chart fails at the composition display,
the pie chart fails at distinctive value comparison.
Proves the point that I've been talking about.
But here's the thing, why are we so bad at this?
Because human beings are very bad
at pre-attentively measuring the areas of circles,
comparing the areas of circles,
and of partial circles, wedges.
And by the way, we're bad at doing it pre-attentively.
We're also even bad at doing it attentively.
You can stare at this thing for 10 minutes
and you're not gonna succeed, okay?
On top of that, you may say,
"All right, well maybe I'll compare the angle, right?
The angle inside and here."
Nope, you suck at that too.
"Maybe I'll compare the arc length."
Nope, failure, okay?
So, no matter what we do, we're not gonna do a good job
at precise comparison with a pie chart.
Now, at the same time, it's much bigger.
It's about two thirds.
Yeah, pie charts are fine at that.
So as long as what you're doing is a vague comparison
and precision doesn't matter at all, go for it.
Use a pie chart.
But again, only if the proportional share is the key point.
Okay?
"They're about the same.
Maybe I think one's a little bit bigger."
Fine at that.
Now, as part of the proof, sort of an argument
for the failure of our ability to compare circles,
this circle back here, this half circle,
is supposed to represent $1.5 billion.
This one is 2.1 billion, 40% bigger.
It's only a tiny bit wider, right?
Tiny bit bigger diameter,
but it's supposed to be 40% bigger.
Which brings up an important point, by the way.
So, we are always comparing the areas of these objects
and you remember pi r squared,
how you calculate the area of a circle?
That r, that radius or the diameter, expands a tiny bit
and because of pi r squared,
it massively increases the area of the object.
So, just keep that in mind.
All right.
Another problem with round shapes is something called
the Jastrow illusion.
You have two wooden train track pieces here.
They are identical.
They don't look it, but they are.
You could pick one of them up, put it on the other one
and you would find that they fit perfectly.
This is a photograph, this is not like manipulated, okay?
So when you have like a double donut chart,
the outer arc will look much smaller than the inner arc.
That's a problem.
If you're comparing these two things,
you're gonna say the bottom one is bigger than the top one.
So, you gotta be cautious about that, okay?
So, optical illusions abound with circular shapes.
And yet with all of that said,
and I'll add one more thing on top of it,
don't do 3D charts.
3D charts are abysmal, they fail, they're terrible always.
And yet this works.
So, it's a 3D pie chart,
like the worst offense in all of data visualization.
But I like it and I think it works.
So this is an example where all of the quote unquote rules,
which really aren't rules, they're just sort of guidance.
Some of which based, you know, on serious research for sure.
But the fact of the matter is,
sometimes things work even though they shouldn't.
Okay? This is where you can do what you want to do,
try it out, and test it.
If you showed this to somebody and said,
"Well what does this tell you?
What did you learn from this?"
And you want to ask open-ended questions like that.
And if they said, "Yeah, yeah, okay.
Of all the water on Earth,
the vast majority of it is saltwater
and only a tiny bit is freshwater.
The vast majority of which is frozen
and or deep in the ground, and only a tiny little sliver
of that is actually accessible to us as drinking water
in lakes or in the soil or whatever,"
then you know what, this succeeded.
And I can read this chart and understand what it means.
So, take it all with a grain of salt.
Most importantly, try different visuals.
Try a bunch of different things.
You have a data set, especially if you're in software
where you have a do have a bunch of buttons to click,
click all the buttons and see what you see,
and then test it, okay?
Test it and see if it works.
Find inspiration and tools like the Visual Vocabulary
and datavizproject.com to help you identify
charts to try.
And also don't be limited by the charts in your tools.
So, we're gonna talk more about that
as we go through the rest of this module.