-: Like we talked about in the last video,
picking charts is all about asking questions, right?
The why's, not the ones we keep talking about of course,
also what tasks you want to enable in your audience.
And there are other questions
and considerations you should be taking into account.
So, for instance, we should always be asking ourselves
the question compared to what?
Now we talked about comparison as being a specific task,
which you may or may not be really wanting your audience
to perform that task of comparison.
Literally compare this to this,
and understand how much the difference is.
Okay, that's a specific task.
But even when that's not the task,
maybe you're showing a trend over time,
but you still in sort of a more subtle way,
have to ask yourself well what is this compared to?
Not because that's the task you're enabling
in your audience,
but because we always need that context
to understand the data.
So as an example of that
there's this data story from Reuters,
which was looking at the use of plastic bottles, okay?
You know, individual use, single use plastic bottles.
And this visualization was part of that story.
And the basic idea was,
if I remember correctly this is the annual,
the number of individual single use plastic bottles
produced annually, maybe globally.
I can't remember.
Long story short it was like 1.3 trillion
or some crazy number,
which is like well what does that mean?
Well compared to what?
Well, one way of comparison,
one way of making that number relatable,
is to show us visually what a giant pile of them
would look like.
And yes, it would be a mountain
that would dwarf Manhattan okay?
So compared to what is something we should ask ourselves
simply to allow ourselves to make the numbers relatable
to our audience, even if the task is not comparison.
Okay, another question we should always be asking
is are these numbers good or bad?
Now, we ask ourselves that for a variety of reasons.
One of which is that I'm gonna be communicating them
with somebody and should I be helping them understand
whether they're good or bad?
Well, that's one question.
Sometimes you don't need to emphasize good or bad.
Other times you do want to.
And then also how would I do that?
So this is an example of great visualization
from the Wall Street Journal looking at polio over time.
And so when we use what's called a matrix heat map,
this is a matrix diagram, but it's a heat map
because we're using intensity of color to display the data.
It's a really cool visualization type
that you should definitely add to your repertoire
if you're not always using them because it's,
it has the beautiful effect of allowing us to see
all the data, tons of data points here, right?
In fact, in fact we have essentially 50 states,
40 years of data.
We have 2000 data points here.
And yet I don't lose the big picture story, okay?
Now in terms of the good bad question,
I'm using color here obviously to indicate good and bad.
We're using red for really bad, yellow for pretty bad right?
And then blue,
or actually I think in this case white for good.
In other words it disappears to show goodness,
a lack of polio.
So that's an interesting distinction too by the way.
So sometimes good, you know we've got the typical red green
right thing going.
Because of color blindness,
which I think we'll talk about a little bit later on,
you can avoid red green, instead use red blue.
But we're not actually using blue
as the opposite end of the spectrum for this visual.
We're using white because we're,
we're communicating a lack of.
And so it's sort of a different thing.
More of like fading into the background of the chart,
of the slide rather.
Long story short, we should ask ourselves is it good or bad?
And how are we gonna depict that if we need to depict that?
Another question we should ask is
about big versus small numbers.
And this brings up a couple different points.
I'm gonna show you the actual data story here.
So this is Roger Federer, 20 years, 20 titles,
and it starts off with just a very brief visual
showing a timeline.
And by the way, time is flowing down,
which is a weird choice.
Time usually flows left to right.
I'll try to remember to talk about that at some point.
But what we have here is rank going from left to right.
And you may remember when we talked about
pre-attentive processing and we talked about the idea
that the mountains are down here and the sky is up above,
and that low numbers should be down
and high numbers should be up.
And also that low numbers should be to the left
and high numbers should be to the right usually.
But it is also a best practice,
that bad should be left and down,
and good should be up and to the right.
So we have competing best practices here,
because a high number with rank, he was ranked 500th is bad,
and a low number, number one, is good.
So this was a very intentional decision
to change this scale so it goes high number to low number
left to right.
Because they wanted to show bad to good, left to right.
So you gotta be thoughtful about this stuff, right?
Is this a, first of all is this a big
or small number, right?
Compared to what conversation?
And how am I gonna depict that?
Be very thoughtful
and intentional about decisions like this.
And by the way, the reason time flows down in this case
and not left to right is because they use this
as a mechanism in the navigation.
As you scroll down, the story sort of scrolls down,
you know, and it would take too much horizontal space
to do it that way.
Not to mention you're scrolling this way.
So again, very intentional decision to do it that way,
but it sort of breaks some best practices
but they did it appropriately I believe.
Long story short, good and bad, up and down,
left and right, whatever,
time generally flows left to right,
but you can break that with intention okay?
Alright so, another consideration is time.
Not only is it available,
do I have data with time as a component, but does it matter?
Should I be showing that to my audience?
Not always, right?
I may have had, you know, a hundred years of data
about the Olympics for that data story,
but I could have said
just look at the most recent Olympics.
I could have just honed in on the most recent Olympics.
In fact I did end up honing in just on the most recent
40 years of Olympics.
I didn't use the entire time series,
and I didn't really show,
in fact I don't think I showed time at all, right?
I didn't show how it progressed over time.
I actually did have one hover effect
which I don't think I showed you all where I do show that,
but that wasn't the point of my data story.
I used time, a timed data set to explore.
But the data story was not about showing time.
But if it is obviously you're gonna choose charts
that enable that.
One of the most important and common questions
you should be asking when visualizing any data is
whether the numbers themselves tell the story,
the actual values, or if he should be converting those
values into a rate okay?
As an example, I did this story a few years back
looking at minimum wage.
What can you buy with minimum wage over time?
So 1990, minimum wage was whatever it was,
over time it went up and it sort of jumps up at it,
you know that's why it's sort of this stepwise diagram
where it's not like a diagonal line,
and it jumps up and it gets to this point, okay?
That's the minimum wage.
The question is how many eggs could I buy
over that same time period?
So now we're looking at the price of a dozen eggs.
In 1990 a dozen eggs cost 88 cents, or sorry, 1980.
And by 2012, a dozen eggs cost $1.94.
Okay, that's the value, that's the actual number.
But the question is for this data story,
what could I buy with minimum wage?
So I have to convert it into a rate,
a ratio of essentially the price of eggs
divided by minimum wage or vice versa.
In that case, the price of eggs actually only went up by 6%.
Right, the real number, price of eggs went up 221%.
But when I compare it to minimum wage, it barely went up.
Electricity went actually down.
You can buy more electricity today with minimum wage
then you could 40 years ago,
or in that case 32 years ago or whatever it was.
So converting numbers into rates right?
This is why we convert numbers into percentages, per capita,
per 1,000, per 100,000,
because the numbers themselves are useless, right?
Easiest example you can remember from your own lives.
Covid, right?
What is the rate of Covid infection?
You know, if you said well, you know this,
Rhode Island has a hundred thousand infections
and California has 150,000,
oh no California is a big trouble.
No they're not.
California is like 20 times the size of Rhode Island
or maybe even more, probably more.
So it's very different.
You have to convert it into a per 100,000 rate
in order to understand what the numbers really mean.
Okay.
Other considerations are the scales.
What are the scales of your chart and how do you set them?
How do you use them?
We're gonna talk more about that later on.
Also, very important, especially for those of you who are
in science or any discipline where your measurements
include uncertainty which you know, let's be honest,
almost all data analytics includes uncertainty.
Sometimes it's really important to communicate
that uncertainty to your audience.
As an example, we're looking at what's really warming
the world from Bloomberg.
Great data story.
They're showing us the observed temperature over time.
This is a real number.
So we don't need to show uncertainty here.
This is actually based on thermometers okay,
so we're good with that one.
But this one is aimed at climate skeptics, saying alright,
for you skeptics, you probably might say something like,
maybe it's all about the orbital changes of the earth.
Maybe that's what changed that observed temperature.
Well, in fact no, here is an average of orbital changes
based on our computer models
with what the effect that would have on earth temperature.
But they don't just show the line, which feels very precise.
They show the band.
This is the 95% confidence interval for that value.
And they do this again and again and again.
This is the effect of the sun
that we would expect it to have on climate.
It could have some effect,
or might be literally have a negative effect.
Long story short, this is the range of uncertainty.
And so this whole data story is like this.
And the thing about this is it shows,
it's transparent, right?
And allows people who are gonna nitpick the data
and say well, this didn't happen.
This number isn't real.
Yeah, of course not.
There's this range of possibilities.
Let's acknowledge that in our visuals.
It's also very helpful, very important sometimes to think
about whether our data can be compared side by side.
Again, using Covid as the example, New York City.
Their first infection happened in March
or whenever it was of 2020,
and then it went, skyrocketed, right?
Idaho or Montana,
their infections didn't start until months later.
Or at least it didn't get bad until months later.
So if we looked at them on a time series
and then we had these two lines going like this
but they're months apart, it's hard to compare them.
So what the New York Times and a lot of others did,
Financial Times, many others,
is they realized of course no, we have to index these.
Instead, instead of having their charts have a scale
based on time,
they had all of their charts on a scale based on
here's day one of infection,
that's the leftmost part in the chart.
And then all of the lines start in the same place.
So even though this happened in March,
this happened in June,
we can put 'em side by side because it's all about
how many days since the first infection
and what do their infection rates look like.
It's not always appropriate to do it that way,
but it's very appropriate for those purposes.
Another great lesson proven by research.
You should feel free to use a bunch of small charts.
I showed this one maybe in the previous video I think.
This is the Wall Street Journal weather page.
Now what we have here is each city
and there's like 20 maybe more of 'em.
We have 10 days of weather.
We have the record high and low, the normal high and low,
the actual high and low, and or the forecast high and low.
And the forecast has a range.
So first of all, crazy awesome distribution diagrams,
and for financial, for Wall Street Journal readers,
you know, they'll appreciate this.
This would be terrible for USA Today right?
We have thousands of data points here, okay?
Thousands of them.
Now, if I tried to do this in one chart
and show you Atlanta on top of Boston on top of Chicago,
this would be a ridiculously, like you couldn't even do it.
But we know from research if you show people a bunch
of small charts, they're fine at that.
We're good at that.
So it's okay to use small multiples.
Now you may say in this case
well I don't need to compare Atlanta to Detroit
'cause I either live in one or the other.
That's all I care about.
Sure, fair enough.
Maybe this isn't the best example.
Let's look at this one.
This is looking at the over representation
or the under representation of different states in terms
of their voting power essentially.
And so here I probably do want to compare some
of these states.
You know, California is grossly underrepresented.
It's a very large population state,
but because of the way district between works
and a variety of other things,
they don't get as much power in Congress compared
to that population size as some other states.
Montana is overrepresented, okay?
So I can easily compare these two to each other,
even though it's a bunch of small charts.
Now obviously in this particular case
you all will appreciate this one, especially the GIS people.
Not only is this small multiples,
but this is essentially a map, right?
This is, this is essentially, Maine is in the right place,
Vermont, New Hampshire.
I can read this like a map,
but it's also a series of charts.
I love this example.
Okay, and last but not least,
in terms of these other considerations,
you should always think about whether you want to
build your charts over time.
Complex charts can be complex, hard to digest,
particularly for a less informed audience.
But you can build to a very complex chart
by stepping people through it one bit at a time.
So as an example that's sort of that,
this is a project that I created called, you know,
trends in US occupations over time.
And I was looking at, this is a bump chart.
We talked about that earlier, or just a rank position chart.
Retail salespersons was the number one job
for all 20 years in the data set.
Cashiers, number two job for most of the time,
then dropped to number three
for the last few years, et cetera.
So this is a fairly complex chart
and this is sort of like a build.
But essentially as I walk through the data story,
I highlight the different parts of the chart
to reveal to you visually what I'm talking about
in the text going by on the left hand side.
So this is slightly different than a build,
but very similar idea that as I'm telling this story,
I'm gonna visually walk you through
those parts of the story.
I could have literally turned off all the rest of the chart
and literally just built it bit by bit by bit.
Either way, I'm using the visual and the text.
I'm sort of combining the two to really make it easier
for you to follow along.
Listen, the most important thing I can tell you
about chart selection is that it's all about intentionality.
You have to have a reason to do every single thing
that you're doing.
We should not mindlessly pick charts
by clicking buttons in Excel.
We should pick charts for a reason
based on all these questions that we're asking,
based on the tasks we're trying to enable,
et cetera, et cetera.
So pick charts with intention and yes,
think about how you're gonna label
and annotate your chart to enable your audience
to do whatever you need them to do.
You want to declutter and focus,
get rid of all the junk so your audience can focus in
on the parts that they really should be focusing in on.
Then test it, like I said earlier, test the chart,
run it past somebody.
Start with to them and say hey,
what did you learn from this?
What do you understand?
What don't you understand?
Are you confused about anything?
They'll answer you like humans will,
and then you can use that information
to improve your design.