[MUSIC]
So it's no secret that people don't
usually like reading long blocks of text.
So if you're writing a report or a
research paper, how might you break it up
with some ways to display data that
aren't textual or even solely numerical?
This video will teach you some
simple basics for visualizing data,
in things like charts, graphs,
tables, and infographics.
There'll be dos and don'ts and
some key considerations for
what to highlight, and what to
include in your data visualizations.
My name is Allegra Smith.
I'm an instructor in the English
department at Purdue, and
a PhD student in the rhetoric and
composition program.
And I'll teach you some key tools and
terms for
visualizing data in your writing projects.
To begin with, I have a few criteria for
getting started with data visualizations.
The first thing when you're thinking about
incorporating a chart, table, graph, or
other visualization into a written report
is to be clear on the question that
you're answering with your visualization.
How is the visual you're providing
going to help the reader?
Is it going to, say,
condense what would be a page or
a paragraph into one simple chart?
Is it going to make something more
concrete or understandable or
actionable for them?
This can also help you to zoom in
on the key variable you're showing,
to prevent what we call
comparing apples to oranges, or
mixing multiple variables,
which can get confusing for your reader.
Second, you should be
familiar with the data or
information that you're
trying to illustrate, and
start with basic visualizations before
you get into more complicated ones.
Consider, for instance, what variables
you're trying to plot and illustrate here.
If you have a graph,
what are the x-axis or horizontal axis and
y-axis or vertical axis going to refer to?
Will the size or color mean anything?
Are you trying to say identify trends over
time or correlations between variables?
And this again becomes important when
we're differentiating between different
types of charts and graphs.
So here's a simple data
visualization showing off how
women are more likely to attend morning
classes at a particular university,
whereas men are more likely
to attend evening classes.
Now, which is the more
appropriate visualization here,
the pie chart or the bar graph?
Well, pie charts are good at
showing off proportions, and so
they're more appropriate for
this particular visualization to
demonstrate the relative scale.
Right, we see in morning classes,
women make up almost 75%.
And it's a lot easier to visualize in
the pie chart than in the bar graph.
Step 3, think about the messages
of the visualization, and
generate the most informative indicator.
This means you need to know your
data set well, and what each item or
variable represents.
You might also need to highlight
relationships between different points in
the data, or
provide additional interpretation
to make this clear to your reader.
Fourth, you can finally get into
different types of visualization and
choose the right type of chart, graph, or
a combination thereof that best
fits your purpose and audience.
I've listed a few different
types of charts here.
We're probably most familiar with bar
chart, line charts, and pie charts,
as well as perhaps more common
visualizations like Venn diagrams or maps.
But in actuality, there are dozens if not
hundreds of different types of
visualizations available to you.
This is the periodic table of
visualization methods, and
it's available online and you can hover
over some of these different method types,
to see an example, and
learn more about them.
I bet you've never heard of,
say, a layer chart,
or a perspectives diagram before.
You can take a look at this.
It'll be linked with this video to
understand some more complex ways that
you might visualize your data.
And there are some visualizations
you might be familiar with
that you haven't even considered for
your projects.
Fifth, and finally, you're going
to want to finesse the smaller and
more finite design principles of your
data visualization to make sure you're
getting across your point accurately,
and in the most impactful way possible.
Try out color, size, scales, shapes, and
labels to direct attention to the key
messages of your data visualization.
Make them readable and
usable by your target audience by making
the important elements easy to identify.
Nothing's worse than a data
visualization where you can't quite
understand the scale, or interpret it
without additional help from someone.
This is a way of minimizing friction
between your reader and their task.
You don't wanna make them work
hard to get your message.
You don't wanna put
barriers between them and
the information you're trying to provide.
So I have a few examples of particularly
bad data visualizations that I'll
walk you through so that you can
consider how to convey your information
in the most accurate and
aesthetically pleasing way.
And again, reduce the barriers
between your reader, and
the task they're trying to complete or
the information they're trying to get.
At first glance, this seems like
a pretty dynamic data visualization.
It's showing off the sheer enormity
of the sales of some fast food chains
that are multinational, like McDonald's,
Burger King, Starbucks, and Taco Bell.
However, this scale on the right hand side
doesn't have a particular unit listed.
Right, the units are next to
the various companies and the country.
The country of Afghanistan is kind
of hidden behind all of these, but
the worst problem here is scale.
So you see that McDonald's
here has $41 billion in sales,
roughly ten times as much as Starbucks,
which has $4.1 billion in sales.
But while McDonald's is 10
times the height of Starbucks,
it's also several times the width of
Starbucks, leading you to believe that
instead of being 10 times as big,
it's more like 100 times as big.
So it's a bit misleading
in terms of its size.
Here's another one.
This is from a news story about lowering
the drinking age across Canada.
There was a vote to determine if
the drinking age would be lowered from
19 to 18 in the provinces of Saskatchewan,
and the vote failed.
So they provided this handy
dandy graph to demonstrate
the drinking age across different
provinces and territories.
What's wrong with this?
So many things.
First off, it's showing off in a graph
what you could really show off in
a sentence saying, other than Quebec,
Manitoba, and Alberta, the drinking
age across Canada is 19 consistently.
Done.
But also, the scale is kind of strange.
Humans don't really think in six-tenths
of a year, so it's a little confusing.
The rule lines as well,
since there are half rule
lines in addition to the whole rule lines,
across the graph.
Just generally
an unnecessary visualization
that also leads to some
significant confusion.
Here's one that's particularly terrible.
I think that this was probably just
a dummy graph put in by a newspaper editor
so that they would change the bar heights
later when they got the data, but
they never quite got around to it.
So this is data from
the National College Health Assessment and
the University of California,
Santa Barbara.
And it's showing the difference
between the perceived substance abuse
that folks thought their peers were
engaging in versus the actual amount of
substance abuse.
So the perceived numbers
are in red on the right, and
the actual numbers are in
white on the left.
But note that all of the bars
are exactly the same size.
There's no rule lines given
to indicate units or scale.
And also, the 0% bar, for
example, for opiates or
cocaine, is the same size as
the 56.9% bar for alcohol.
So do yourself a favor and proof your
graphs and charts before you publish them.
Here's one more that's
confusing with units and scale.
So this is, in general,
a good idea to begin with I think,
is using colors in city abbreviations
to demonstrate the differences
in commuting to work across major
metropolitan areas of the US.
And at first glance,
it seems pretty effective.
You can see that, without a doubt, more
people drive themselves to work in Houston
than any of these other
metropolitan areas.
And more people seek public
transport in New York City.
Couple of other noteworthy
things is that Seattle and
Atlanta are more bikable than
some of these other places.
And there's a very small proportion
of people who walk to work in
hotter places like Houston,
Atlanta, and LA.
But then when you take a closer look at
it, you see an attempt at scale in these
grid lines in the middle, but
no actual scale articulated.
So there's no real way to
compare these factors, or
to give an actual numerical
indicator of what all of this means.
The author needs to do some unpacking or
interpreting of this for
their reader to make it a truly
effective data visualization.
So now that we've looked at some
of the examples of the bad,
I wanna show you some good,
and some potential options for
visualizing data that you
may not have considered.
We think sometimes that
visualizations have to be complex or
statistical or scientific, but
they can also be creative or
whimsical and still get the point across.
So here are a few different
basic types of visualizations
you may think about employing some time.
Charts and ratios and percentages are a
great way of showing parts of a whole.
Just like this humorous pie chart
that shows the creative processes
mostly binge eating and
random Internet surfing with actually very
little inspiration or work getting done.
I know I feel this way about
writing quite frequently.
Infographics are diagrams that
show off parts of a thing or
a process can be helpful.
This is from a student who
went to college with me,
who introduced himself to the class by
creating an infographic of himself.
Showing off his average hours of sleep
per week, his usual daily activities,
the typical coffee consumption, and
different things that he cared about.
His interests, his favorite food,
and the way he got work done.
You could easily use this to show off
a process in your major or field of study.
Or to give a sense of a broad overview
of a situation, problem, or phenomenon.
Networks, systems, and ecologies can
also show parts of a more complex thing.
Systems, biospheres, environments.
This is after a network mapping, which
is a way to understand how technologies,
people, and situations work together.
So here's an actor-network
map that shows how different
technologies are inputs into
a way to try and understand,
and map survivors and
casualties in a terrorist attack.
This is the way for researchers to
show off field-based research, and
the different actors in a system.
Complex visualizations can combine
a number of different ones to give
them more power than they
would have individually.
Things like infographics that
are printed in magazines, newspapers,
and online publications often do this.
Here's our visualization
infographic about feminism and
gender rights across the world.
Notice how we have lots of different
types of data visualizations here.
We've got a map,
we've got some line charts,
we have different scale in
graphs showing off, for example,
gender parity in legislatures
across the world as well as the pay
gap in the United States between white
men and women of different races.
We got lots of different figures here
as well as images representing and
symbolizing different gendered phenomena.
Note also how this author
cites their sources.
They use numbers, and then provide a
complete citation in the lower right-hand
corner, to give credit to
where the data came from.
Another example of a complex
visualization here, about earthquakes.
I like the consistency in colors,
in alignments, and
in contrast across this infographic
as well as how it uses maps
to demonstrate earthquake
hotspots globally.
A couple of smaller infographics
about biotechnology for
agriculture as well as renewable energy.
Again, showing off ways that you can
combine multiple visualizations in
a single cohesive package, making them
stronger than they would be individually.
So how might you go about creating
some of these more basic visuals for
your own purposes?
If you don't know about
the Microsoft Office SmartArt tool,
allow me to introduce it to you.
It can be extremely helpful for
integrating charts and
graphs into your work if you're
creating a report in PowerPoint,
Word, Publisher, or another
Microsoft Office Suite application.
So I'm showing it off here in PowerPoint,
but the navigation ribbon and
buttons look the same
across most Office apps.
So if you go to Insert > Chart,
you can create
a line graph, a bar graph,
a pie chart, and
all of these other interesting options.
Note this simple chart that I made
about my family's interest in
the presidential election in 2016.
It's a very easy way to demonstrate that
my father's interest gradually went up.
My mother's interest gradually went down.
And mine reached a low in September and
then sharply peaked leading up
to the election in November.
Of course, there are some issues
here like, what is the scale?
What does an n of 10 mean
here versus an n of 2?
How am I measuring interest?
But as you can see it's a nice way
to quickly visualize some data.
You can also use SmartArt, which is
typically close to the Charts function in
Word, in Office, excuse me, applications.
SmartArt are graphic visualizations
to communicate information.
They're often less numerically heavy,
so they're better for
representing, say, qualitative
data like processes or concepts.
You can see the different types
of SmartArt graphics here,
like cycles, hierarchies,
matrices, pyramids, and more.
I used the cycle option to create
a quick reduce, reuse, recycle graphic.
It's simple, but it's powerful.
You can supplement your text with
visuals using free online tools.
You can search for royalty free stock
photography on websites like Pexels or
Unsplash, or you could use my favorite
site, The Noun Project, to find
open-source, free icons that you can use
in documents, presentations, and projects.
Just don't forget to
credit the icons' creator.
Give credit where credit is due for
their creative work.
So why is it important for
us to consider data visualization?
Because we tell stories with both
numerical and quantitative data.
We're able to illustrate themes in
ways that are more powerful, and
interesting, and compelling than
if we said, then if we had,
say, that page or
two pages full of uninterrupted text.
This TED Talk video, Making Data Mean
More Through Storytelling, demonstrates
how you could take big data sets and tell
stories about places like New York City.
I hope you'll watch that video and learn
a little bit more about why it's important
for us to consider how we leverage
our data, and how we represent it for
particular audiences.
Thanks for watching, and
happy data visualization.
