-: Okay, so we've also talked
about scales a little bit before, right?
When we looked at this masterclass
and three charts changing the scales made a huge difference.
We also changed charts, we changed a bunch
of different things but the scales was sort
of the major difference between this chart, trillions
of dollars in time, this chart share of wealth
and median age, two very, very important changes,
and this one share of population share of wealth.
So very important decisions
that made these charts tell dramatically different stories,
enabled the audience to understand certain things.
Okay, good.
So we know we wanna make smart decisions about scales.
I'll point out a couple of other little things.
So in this chart, very, very simple chart.
There's no scale, right?
The bars have numbers on them, they're labeled,
so that essentially establishes the scale.
You can do it this way, right?
If you just have a bar or two bars you don't have
to literally put in a box where you have scales
on the actual lines or whatever.
The bars themselves act as the scale.
That's okay, as long as I know what I'm looking at
and I can make whatever comparison
or understanding I need to make, that's all right.
So don't think you have
to literally create a standardized chart
with a traditional scale the way you think of it.
Also, when we're looking at decisions
about scales we can make smart decisions.
For instance, here we have a line
that's going up and coming down.
Here is an example where we're showing plus
and minus, a percentage change, right?
That's a thoughtful decision to show what happened
to this value, not as actual values, but as rates.
Like we've talked about a hundred times,
how important it is to think about
whether the values are useful or the rates are useful.
Whether we want to show the actual numbers or in this case
how much did it go up or down?
We're not even showing the numbers themselves,
we're just showing only showing change.
That's a valid decision for what we put in the scale.
Now, on top of that, if we wanted to, we could use color
to emphasize what's going on with that change, right?
So the gray line means it only went up by a certain amount.
Blue means it went up by a lot, it's steeper
so we're sort of, it deserves a color now.
Orange means it came down by a lot again, more so
than the other down portion.
We're emphasizing that scale, that the change
and the value by adding color to it.
We can even use other things here.
So we're showing data.
Same exact data that we saw before.
Made the background a little bit brighter, whatever bolder
but now the number, the final value is popping
off the page a little bit more.
So it's not really necessarily a scale conversation
at this point, it's more of a design tweak
to highlight the important stuff.
This is a typography decision
but of course it's in the scale.
The scale is a place where I can emphasize things.
I could label that data point itself
or I can literally put it on the scale itself.
There's a million different ways to do these things.
There's no one right or wrong way
for just about any decision in data visualization.
Last but not least, there was this great thread
in the New York Times couple years ago, this is
from 2020 I believe during the pandemic,
so early in the pandemic and the New York Times had changed
how they were mapping Coronavirus data.
And it's a very subtle change
and this guy wrote this thread about it.
Essentially, the basic idea was this,
we have county level data, but the data, the map
on the right, this one over here is different.
And the reason is because there's a footnote
at the bottom of the map that says, "Parts of a county
with a population density lower than 10 people
per square mile are not shaded".
So the point is, sometimes when you have a choropleth map,
that's where you have the shape of the region is colored in
to represent the value of whatever is being measured
like we see in election maps.
And as we described with those election maps
that very first map that was all red and blue,
there's something misleading about that.
Montana a giant state looks like it's really important
and has a huge Republican influence
but it's actually less important
than Rhode Island, which is tiny, you can barely see it.
On top of that, or sort of related to that,
I suppose is the fact that it's about population density.
So the New York Times, not wanting to alarm people unfairly
where this state over here giant state might be doing poorly
their Coronavirus numbers are going up
but at the same time, hardly anybody lives there.
So maybe by essentially not coloring
in counties that have very low population density
or portions of counties there, it sort of de-emphasizes it.
It's sort of saying that yes, the numbers may be going
up over here, but the overall impact isn't as large.
So similar to those maps that we saw Kenneth Field did
where different ways of treating that election data
so the land wasn't as emphasized, it maybe was more
about the number of people, et cetera.
Just more ways of thinking about that.
This is a scale decision, right?
How do we set the scales for how the colors behave on a map?
And also do we even include them?
Is there a threshold at
which we don't even bother doing anything?
So it's a scale conversation, but it goes beyond that.
This is just how are we communicating the stuff
our audience needs to hear.