Data Visualization Exercises Due Wednesday Oct. 17 at 3pm .docx

Data Visualization Exercises
Due: Wednesday Oct. 17 at 3pm
Submit your assignment as a pdf file via the compass course
space.
Exercise 1
The following tree map provides information about land area
and population density for states in regions of the
United States of America.
For this visualization do the following:
• Consider the Dos and Don’ts of good data visualization
discussed in class. Comment on at least 3 ways in
which this visualization should be improved to make the
information displayed more informative and
understandable.
• Comment on the fundamental comparisons being made in this
plot. What questions of interest can be
easily answered with this plot?
• Summarize the key takeaways from the graphic. What can we
say about population and land distribution
across and within regions of the USA based on this
visualization?

Exercise 2
The following is a chord diagram illustrating migration patterns
across regions of the United States. In the
diagram, each region has its own axis, and chords represent
migration from one region to another. The color of
the chord is for the region individuals migrated from, and there
is a small white strip on the starting side of the
chord.
For this visualization do the following:
• Consider the Dos and Don’ts of good data visualization
discussed in class. Comment on at least 3 ways in
which this visualization should be improved to make the
information displayed more informative and
understandable.
• Comment on how the 5 Basic Tactics discussed in class (size,
position, shape, color, and connectedness)
are used in this visualization. Note: It is possible that not all 5
are used.
• Summarize the key takeaways from the graphic. What can we
say about population migration across and
within the specified regions of the USA based on this
visualization?
Exercises 3 and 4
Using software of your choice, create the graphics described
from the data in the MigrationData.csv file attached
to this assignment. This is the region to region migration data
from the chord diagram in Exercise 2. The data is
based on the State-to-State Migration Flows: 2015 data set from
the United States Census Bureaui. In

MigrationData.csv, the rows are the regions of origin (the
region migrants started out in) and the columns are the
destination regions (where they moved to).
Be sure to use tactics discussed in class to clearly establish
what data is being compared and to make the desired
comparison as clear as possible.
Exercise 3
The chord diagram is very complete, but a similar diagram may
be useful for comparing proportions for a given
region and making a simple comparison of proportions across
regions. Create a graphic or set of graphics that
allows for easy comparison of absolute number of migrants to
each region for each of the four region.
Exercise 4
Create a graphic that allows for easy comparison of the ratio of
immigrants (those moving into a region from a
different region) to emigrants (those moving out of the region)
for the 4 regions. For this graphic we are focused
on exits from and entrances into regions, so migration counts
within region (i.e. same origin and destination
value) will be omitted entirely.
i The original data is available through the State-to-State
Migration Flows: 2015 link as
State_to_State_Migrations_Table_2015.xls on
https://www.census.gov/data/tables/time-
series/demo/geographic-
mobility/state-to-state-migration.html

series/demo/geographic-mobility/state-to-state-migration.html
series/demo/geographic-mobility/state-to-state-
migration.htmlExercise 1Exercise 2Exercises 3 and 4Exercise
3Exercise 4
Data Visualization
Theory & Best Practices
Developed by Michael Nute
Data Analysis and the
Goal of Visualizing Data
• The goal of Data Analysis, generally, is to answer some kind
of question or
get some kind of significant insight about the outside world
based on the
data.
– If you say: “The average sales during the month was $500 per
day...” that is not
analysis.
– If you say “The average sales during the month was $500 per
day, which
represented a decrease from the prior month and which is
alarming because
sales increased between the same months one year earlier...”
that is analysis.

• Analysis is the act of saying something interesting about the
data.
Data Analysis and the
Goal of Visualizing Data
• The FUNDAMENTAL UNIT of Data Analysis is the
comparison of two
numbers.
– It is the building block. The cell.
– In the previous example, we have two key comparisons: 1)
sales this month vs.
sales the previous month, and 2) this year’s change in sales vs.
last year’s
change in sales.
• When you create a chart, graph or other visualization, your
only goal
should be to make the comparisons of the numbers easier for the
user.
Otherwise you can just give him the numbers, no need for a
picture!
Data Visualization
Basic Manners
DO
• Have scales available wherever needed. Ensure
the units of your scale are intuitive and are clearly

displayed.
• Label your axes and add a succinct and
descriptive title to the chart.
• Label anything that is not obvious. E.g. if data
points are in two different colors, there had
better be a difference between them and a label
saying what it is.
• Note sample size in small print on the margin of
the graph, unless it is obvious in context.
• Annotate your graph with any important
additional information. For example, on a time-
series graph, denoting important concurrent
events is key.
• Cite your sources in footnotes where necessary.
Also include any explanatory footnotes for
methodology that may not be intuitive.
DON’T
• DON’T BE SLOPPY! Don’t have labels or other stuff
overlapping each other. Don’t have titles off-
center. Don’t let a single outlier dominate the
scale.
• Don’t use gridlines unless absolutely necessary.
They are a waste of ink and create visual clutter. If
there is an important threshold associated with
your data (i.e. a point where above/below is
important in context) then add a single gridline
for that threshold manually.

• If you absolutely must use gridlines, use the
lightest shade possible, and use as few lines as
possible (the largest unit possible between each
line).
• Don’t use obtrusive typeface. Stick to the basics:
TNR, Calibri, TeX fonts, etc... Try to keep it
consistent with the rest of your document.
• Don’t put the data values on the graph unless it’s
truly necessary (more on this later).
Data Visualization
Basic Manners (Dos & Don’ts)
DO: Have scales available wherever needed. Ensure the units of
your scale are intuitive
and clearly displayed.
Temp (C °)
-10.0
0.0
10.0
20.0
30.0
50 -40
-40
-30 -30
-20

-20
-10 0
10
10
10
20
20
30
30
Global Temperature Measurements But wait: which
one is hot and
which is cold? And
how much hotter
or colder?
It turns out the data was gathered in December, so
the Southern Hemisphere is warmer.
Data Visualization
DO: Label your axes and add a succinct and descriptive title to
the chart.
Ja

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Mike's Expenditures by Month
on Alcohol
BORING!!
Expenditures by
Who?
And for what?
And how
much did they
Spend?
Oh, well that’s more
interesting...
(R
ed
ac
te
d
to
P
ro
te
ct
M
ik

e)
Data Visualization
DO: Annotate your graph with any important additional
information. For example, on a
time-series graph, denoting important concurrent events is key.
Ja
n-
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A
pr
-0
7
Ju
l-0
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ct
-0
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Ja
n-

08
A
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Mike's Expenditures by Month
on Alcohol
(R
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to
P
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te
ct
M
ik
e)

July 2009: Trip to
Nantucket with buddies
for the 4th of July
May 2010: Two-week trip to
brother’s graduation & a
Canadian wedding
Ok Let’s Admit it: Summer is
the time for partying.
July 2010 & Beyond:
girlfriend moves to
Minneapolis; weekend
travel curtails partying...
Data Visualization
DO: Label anything that is not obvious. E.g. if data points are
in two different colors,
there had better be a difference between them and a label saying
what it is.
DO: Note sample size in small print on the margin of the graph,
unless it is obvious in
context.
DO: Cite your sources in footnotes where necessary. Also
include any explanatory
footnotes for methodology that may not be intuitive.
N = 30
Source: Fox News

Data Visualization
DON’T: DON’T BE SLOPPY! Don’t have labels or other stuff
overlapping each other.
Don’t have titles off-center. Don’t let a single outlier dominate
the scale.
Barry Bonds
Adrian BeltreAlbert PujolsScott Rolen
Jim Edmonds
J.D. DrewLance Berkman
Roger Clemens
Mark Loretta
Aramis RamirezCarlos BeltranJeff KentMoises Alou
Steve Finley
Todd Helton
.000
.100
.200
.300
.400

.500
.600
.700
.800
.900
.000 .100 .200 .300 .400 .500 .600 .700
Sl
u
gg
in
g
Pc
t.
On-Base Pct.
2004 Baseball MVP Top Vote-Getters
OBP vs. Slugging
How many things can you find that are wrong with this?
• Title centered over data, not whole picture.
• Labels are a mess
• Roger Clemens is an outlier because he is a pitcher,
doesn’t belong on the graph
• Vivid blue is obtrusive.

Barry Bonds
Adrian Beltre
Albert Pujols
Scott Rolen
Jim Edmonds
J.D.
Drew
Lance Berkman
Mark Loretta
Aramis Ramirez
Carlos
BeltranJeff Kent
Moises Alou
Steve Finley
Todd Helton
.400
.450
.500
.550

.600
.650
.700
.750
.800
.850
.300 .350 .400 .450 .500 .550 .600 .650
Sl
ug
gi
ng
P
ct
.
On-Base Pct.
2004 Baseball MVP Top Vote-Getters
OBP vs. Slugging
Slight Improvement
Data Visualization

DON’T: Don’t use gridlines unless absolutely necessary. They
are a waste of ink and
create visual clutter. If there is an important threshold
associated with your
data (i.e. a point where above/below is important in context)
then add a single
gridline for that threshold manually.
If you absolutely must use gridlines, use the lightest shade
possible, and use as
few lines as possible (the largest unit possible between each
line).
DON’T: Don’t use obtrusive typeface. Stick to the basics: TNR,
Calibri, TeX fonts, etc...
Try to keep it consistent with the rest of your document.
DON’T: Don’t put the data values on the graph unless it’s truly
necessary (more on this
later).
Basic Tactics
Features of Quality Data Visualization
1. Representationally Faithful
– The data scales that your graphic
suggests should be accurate, or should
be explicitly noted as not to scale.
2. Simple
– Don’t make the user work hard to
figure out what the graphic is saying.

3. Comprehensive
– If more context is helpful and you have
the real estate to provide it, then do
so.
4. Interesting
– Don’t make a graphic that doesn’t have
something interesting to say about the
data. The Trifecta (at right) should be in
harmony.
What is the
Question Of
Interest?
What Does
the Data
Say?
What Does
the Graphic
Say?
The Data Visualization Trifecta3
3This is also from Kaiser Fung at Junk Charts. He also has a
book called Numbers Rule Your World. Consider this a plug,
though I have not read it.

Representationally Faithful: Bad Example
Stocks Bonds
Stocks Bonds
I asked my investment manager how much of my money was in
stocks vs. bonds, which of these
should he send me?
Remember that the goal is to help the viewer compare two
numbers. Otherwise it’s just fluff.
Simplicity: Bad Example
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Head of the Charles Regatta
Projected vs. Actual Fastest & Slowest Boats
StdDev of OfficialTime Rabbit_Proj Rabbit_Act Avg
Caboose_Act Caboose_Proj
Question: Where in the Schedule can we
save time by revising our projections of the
fastest and slowest boat?
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om
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[7
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Se
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or
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et
er
an
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om
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Ve
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le

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Yo
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ou
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en
Yo
ut
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om
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Yo
ut
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en
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en
StdDev of OfficialTime Rabbit_Proj Rabbit_Act Avg
Caboose_Act Caboose_Proj
Full name of each event is completely un-
necessary, abbreviations are fine. Visual aide
in grouping, such as lines or color coding,
would be helpful.
What good are the gridlines here?
Standard Deviation adds nothing to
the meaning of the graph, should
be removed, or at very least in a
light grey without a border to
blend in better.
The goal here is to understand where in the
schedule we can save time by revising our
projections of the fastest and slowest boat.
But here we have to work very hard to do
that.
Simplicity: Bad Example Improved
12:58
15:50

18:43
21:36
24:29
27:22
30:14
33:07
36:00
38:53
41:46
Positive Margin* Negative Margin Boats on Course Avg
* Note: For fastest boats, positive margin indicates projected is
faster than actual. For slowest boats, positive
margin indicates projected is slower than actual.
Comprehensive: Same Example
12:58
15:50

18:43
21:36
24:29
27:22
30:14
33:07
36:00
38:53
41:46
This graph shows:
1. Every event in the whole
regatta
2. How long each event takes in
the schedule
3. Whether we have room for

error or we are taking a risk
4. The average time for each
event
And all of that in the blink of an
eye.
Interesting: Same Example
12:58
15:50
18:43
21:36
24:29
27:22
30:14
33:07
36:00
38:53
41:46

Let’s Examine the Data Trifecta:
1. What is the Question? Do we
have need to improve our
projections of the fastest and
slowest boats?
2. What does the data say? Yes,
for some events, there is as
much as 3 minutes of fat. For
others, we will most likely run
over the scheduled time.
3. What does the graphic say?
Some big green lines, and some
big red lines, just like the data!
Basic Tactics
Ways to Represent Data Visually
1. Size
– Very common: the larger the item, the
larger the value being represented.
– E.g. a bar graph or pie chart, or in a

bubble chart.
2. Position
– Relative to a scale, can convey size (e.g.
in a scatter plot).
– Relative to other data points, can convey
order (e.g. rank order, time order)
– In a group or cluster, can convey
similarity
3. Shape
– Basic shapes can be used for data
markers to convey grouping
– Can be used to convey some feature of
physical shape if applicable
There are many different ways to use visual features of a
graphic to represent data. A good
graphic will use many of these at the same time. Here are some
of the most common:
4. Color
– Color scale is a very effective way of
showing magnitude of a data value;
easily combined with other methods
shown here.
– Can be used similar to shape to convey
groupings, but with a natural association
for the user (e.g. red for loss, black for

profit. Red for hot, blue for cold, etc...)
5. Connectedness
– Shows that two items are naturally
related or “connected” to each other. E.g.
for a time series.
– Can be used to convey order when
position is used for something else.
Example: Size & Position
Marimekko of U.S. Government Spending (N.Y. Times):
– Size indicates amount. Position within box indicates category.
– Interactive graphic, enables drill down into category detail.
Link
http://www.nytimes.com/packages/html/newsgraphics/2011/011
9-budget/
Example: Position
XKCD: Movie Narrative ChartsCitation: XKCDLink
http://xkcd.com/657/
Example: Shape

Citation: XKCD
Link
Example: Connectedness & Position
Citation: XKCD
Link
Example: Color & Position
• This is called a “Cloropleth” or a “Heat Map”
– Name two reasons why this is not a very interesting
informative.
1. No scale
2. No historical perspective (when is this data from?)
Source:
Flowing Data
Link
U.S. Unemployment by County
http://flowingdata.com/2009/11/12/how-to-make-a-us-county-
thematic-map-using-free-tools/

Example: Size & Position
Bubble Chart of U.S. Insurance Industry Financial Performance
ACE
TRV
CB
ALL
PGR
XL
CINF
WRB RNR
TRH
HCC
VRAWH
THG
RLI
SIGI
TWGP
HMN
SAFT

NAVG
STFC
NATL
AMSF
ASI
0.0x
0.5x
1.0x
1.5x
2.0x
2.5x
0.0 5.0 10.0 15.0 20.0
Va
lu
at
io
n
(P
ri
ce

/
T
an
gi
bl
e
Bo
ok
V
al
ue
p
er
S
ha
re
)
Projected 2013 Return on Equity (%)
U.S. Property & Casualty Insurance Industry
Financial Valuation vs. Performance
Bubble Size Indicates Market Cap
Is this graphic too cluttered to
be useful?

Pretty close, but debatable.
Basic Tactics
Guiding the Viewer’s Eye
• The viewer’s eye will go first toward the boldest lines and the
most vivid
colors, so use those to represent the most important items.
– Thicker lines = Bolder
– Bright Red, Blue, Green & Yellow are usually the most vivid,
don’t use those
unless you want to yell. THINK OF IT LIKE WRITING IN ALL
CAPS!!!
– Pastels are calmer and more relaxing. Darker colors are also
calm, but can be
difficult to tell apart.
• The viewer’s eye will follow lines. If you want the viewer to
see points 1, 2,
3 and 4 in that order, then a line connecting them in that order
will help.
• The viewer’s eye will usually follow the graphic in this order:
1. Look at the center (most vivid stuff first)
2. Look at the axes & title to figure out what the center means
(Pause to make sure it makes sense)
3. Look for explanations for anything not immediately clear
4. Form an opinion about the stuff in the center

Principles of Data Visualization
The Data/Ink Ratio1
• Rule of Thumb: higher is better
• The idea here is simple: ink draws the eye of the
viewer, and you don’t want the viewer’s eye to go
where there is no meaningful data. We don’t want
to make the viewer work to understand our point.
• Don’t clutter the page with garbage that is loud
and strictly decorative (such as a choo-choo-train).
• Arrange your data points in such a way that the
comparisons the user will be interested in are easy
and don’t require doing mental math.
• Another way of saying this: the KISS principle
(Keep It Simple, Stupid)
• Negative Examples:
– A bar chart with 2 bars on it
– Most pie charts
– USA Today Snapshots©
1This idea comes from Edward Tufte in The Visual Display of
Quantitative Information, which is sort of the bible of Data
Visualization
The Data/Ink Ratio - Compare & Contrast

0.0
0.2
0.4
0.6
0.8
1.0
1.2
0
2
4
6
8
10
12
14
4Q
09
1Q
10

2Q
10
3Q
10
4Q
10
1Q
11
2Q
11
3Q
11
4Q
11
1Q
12
2Q
12
3Q
12
4Q
12
1Q
13

2Q
13
3Q
13
M
ic
ro
so
ft
G
oo
gl
e
Earnings Per Share Comparison:
Google vs. Microsoft
(figures in $/common share)
GOOG MSFT
Question:
How does the growth in earnings per
share of Microsoft compare to that of
Google over the last three years?
• Which company’s earnings are
growing faster?
• Which graph lets you draw that

conclusion faster?
Other things to note here:
• In the bottom graphic, if I want to compare
Microsoft’s earnings quarter over quarter, I
have to mentally remove a big red vertical bar
to do it. The comparison is not made easy.
• The key comparison facilitated in the bottom
graphic is “MSFT vs. GOOG in Quarter X”, but
that is a useless comparison here because the
scales are different.
• This data is a time series, which means that
almost always a line graph is going to be
better than a bar graph.
• If we added gridlines here, would they help?
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0

2
4
6
8
10
12
14
4Q
09
1Q
10
2Q
10
3Q
10
4Q
10
1Q
11
2Q
11
3Q

11
4Q
11
1Q
12
2Q
12
3Q
12
4Q
12
1Q
13
2Q
13
3Q
13
M
ic
ro
so
ft
G
oo

gl
e
Earnings Per Share Comparison:
Google vs. Microsoft
(figures in $/common share)
GOOG MSFT2
The Disappearing Baseline
• Be wary of making an graph with an axis that doesn’t start
from zero.
– If starting from zero obscures the relevant comparisons, then
it may be
acceptable.
– If there’s not a good reason, it will be seen as purposely
misleading.
3.95
3.96
3.97
3.98
3.99
4.00

Voon's GPA John's GPA
True Story: Mike’s friend Voon was always a good student, and
John wasn’t. So
when John went back to business school and had a good first
semester, he sent
this:
*This is also classic Tufte
The Self-Sufficiency Principle2
• The basic idea: your graphic should be valuable on its own
without having to have
the data behind it shown at the same time.
– If I have to refer back to the values in order to make sense of
the graphic, then what is the
point of putting up the graphic? Remember, it’s suppose to help
me compare and contrast
data points.
• This is not a hard rule, just something to bear in mind.
– It’s always helpful to be asking yourself “Do I really need to
be doing this?” If you’re not
sure if or why the answer is yes, then maybe you can be doing
something more useful.
• Sometimes the values themselves are helpful because they
provide additional
context beyond the comparisons of interest in the graphic.

– Example: a graph showing growth in profits over time for a
business may be effective in
showing the trend for the business, but for an investor
considering purchasing shares in
the company, it may help them to see exactly how much profit
that represents.
– One often useful option is to show not the values themselves
but some transformation of
the values. For example, instead of showing the value of profits
over time, the graph could
show the values for return on investment represented by the
profits. This number is
directly interesting to the investor, regardless of trend.
2This idea comes from Kaiser Fung at Junk Charts
(http://junkcharts.typepad.com)
The Self-Sufficiency Principle – Compare & Contrast
9.5
9.2
8.9 8.9 8.8 8.8 8.7 8.6 8.6 8.5 8.5 8.5 8.4
8.1 8.1
7.0
8.0
9.0

10.0
U
ne
m
pl
oy
m
en
t R
at
e
(%
)
Top 15 States by Unemployment - July 2013
Look at the graphic below. Do the values add anything here?
• Actually, they do a little. Without them all we have are ten
bars that say the same thing in the
middle, with a couple on each end that are a little different.
This is not a very informative
graphic.
Do we even need the graph—what about a slightly annotated
table?
• No, we really don’t. Because we’ve sorted the data, there are
only a few comparisons of
interest, namely the “break” points between IL/NC and IN/CT.

We can convey the same
information in a table with a lot less real estate.
Nevada 9.5
Il l i noi s 9.2
North Carol i na 8.9
Rhode Is l and 8.9
Georgi a 8.8
Mi chi gan 8.8
Cal i forni a 8.7
D.C. 8.6
New Jers ey 8.6
Kentucky 8.5
Mi s s i s s i ppi 8.5
Tennes s ee 8.5
Indi ana 8.4
Connecti cut 8.1
South Carol i na 8.1
Top 15 States by
Unemployment Rate - July
2013
Example
Histogram
Sometimes the relevant comparison for the data is to something
more abstract like a Normal
Distribution:
0
50

100
150
200
250
300
350
400
450
0.0 - 0.5
0.5 - 1.0
1.0 - 1.5
1.5 - 2.0
2.0 - 2.5
2.5 - 3.0
3.0 - 3.5
3.5 - 4.0
4.0 - 4.5
4.5 - 5.0

5.0 - 5.5
5.5 - 6.0
6.0 - 6.5
6.5 - 7.0
7.0 - 7.5
7.5 - 8.0
8.0 - 8.5
8.5 - 9.0
9.0 - 9.5
9.5 - 10.0
10.0 - 10.5
10.5 - 11.0
11.0 - 11.5
11.5 - 12.0
12.0 - 12.5
12.5 - 13.0
13.0 - 13.5
13.5 - 14.0

#
of
R
ec
or
ds
Histogram of Adjusted Passing Yards/Attempt
Based on Quarterback-Seasons, 2002-2012
Note: Passing averages of
0 are omitted, and
averages greater than 14
are not shown.
Examples
Table with Color
This graphic shows which
baseball teams made the
playoffs each year, how they
did in the playoffs and where
they ranked in player salary
budget.
The conclusion from this
table is quick and easy:
playoff teams tend to come
from the top of the ranks in
budget.

Source: Kaiser Fung
(www.junkcharts.typepad.co
m)
Link
http://junkcharts.typepad.com/junk_charts/2011/07/the-
meaning-of-pretty-pictures-and-the-case-of-15-scales.html
Examples
Line Chart with Color & Non-Uniform Time-Scale
This graphic shows the number of seats available on a United
flights from O’Hare to Boston on a
specific day, graphed over time as the date approached the
departure date.
6:55 AM
8:00 AM
9:21 AM
11:14 AM
1:12 PM
2:37 PM
4:36 PM
5:27 PM
7:08 PM
9:09 PM
-20

0
20
40
60
80
100
120
140
6/13 6/18 6/23 6/28 7/3 7/8 7/13 7/18
#
of
A
va
ila
bl
e
Se
at
s,
b
y

D
ep
ar
tu
re
As Of
Sunday 7/22: ORD-BOS Flights
6:55 AM
8:00 AM
9:21 AM
11:14 AM
1:12 PM
2:37 PM
4:36 PM
5:27 PM
7:08 PM
9:09 PM
Scheduled capacity can
change suddenly if the
scheduled aircraft changes
Early flights are often

more popular on Sundays,
as they were on this day
Note that this graphic
conveys four different data
points at the same time:
1) The number of seats
available (y-axis)
2) The as-of date for the
capacity (x-axis)
3) Which departures had
the most capacity
(grouping by line)
4) What time of day each
departure was (color)
Also note the annotation
for two odd features of the
chart.
Examples
Multiple Overlapping and Stacked Time Series (Yahoo!
Finance)
Note the use of the blue here to
make the major stock of interest
(THG) the most vivid. The other three
are there for comparison only.
This graphic compares the share price percent gain for a single

stock (THG) to three major
indices. At the bottom is the THG trading volume for each
trading day shown.
This construction of pairing a volume measure with a time
series of
interest is a common way to provide important context. Often
when the
metric is a ratio, the “volume” is the denominator.
Note that the gridlines, such
as they are, are light-toned.
The vertical gridlines also
convey how many trading
days were in the period.
Example
Basic Table
AAPL Fiscal Quarter 3Q2013 2Q2013 1Q2013 FY2012 4Q2012
3Q2012 2Q2012 1Q2012
Calendar Quarter 2Q2013 1Q2013 4Q2012 TFQ 3Q2012 2Q2012
1Q2012 4Q2011
Income Statement
Net sales 36,551 43,603 54,512 156,508 35,966 35,023 39,186
46,333
Cost of sales 23,210 27,254 33,452 87,846 21,565 20,029
20,622 25,630
Gross margin 13,341 16,349 21,060 68,662 14,401 14,994
18,564 20,703
Operating expenses:
Research and development 1,343 1,119 1,010 3,381 906 876 841

758
Selling, general and administrative 2,672 2,672 2,840 10,040
2,551 2,545 2,339 2,605
Total operating expenses 4,015 3,791 3,850 13,421 3,457 3,421
3,180 3,363
Operating income 9,326 12,558 17,210 55,241 10,944 11,573
15,384 17,340
Other income and expense 347 347 462 522 (51) 288 148 137
Income before provision for income taxes 8,979 12,905 17,672
55,763 10,893 11,861 15,532 17,477
Provision for income taxes 2,424 3,358 4,594 14,030 2,670
3,037 3,910 4,413
Net income 6,555 9,547 13,078 41,733 8,223 8,824 11,622
13,064
Earnings per common share:
Basic 10.16 13.93$ 44.64 8.77 9.42$ 12.45 14.03
Diluted 7.12$ 10.09 13.81$ 44.15 8.68 9.32$
12.30 13.87
Shares used in computing earnings per share:
Basic 939,629 938,916 934,818 938,053 936,596 933,582
931,041
Diluted 921,035 946,035 947,217 945,355 947,896 947,059
944,893 941,572
Shares Added During Period (1,182) (679) 837 2,166 3,321
1,900
Cash Dividend Declared Per Share 3.05 2.65 2.65 2.65
Select Balance Sheet Items 7,575 15,861 4,030 7,045 12,575
16,031

Cash on hand 138,433 144,687 137,112 121,251 121,251
117,221 110,176 97,601
Cash per share 150.30$ 152.94$ 144.75$ 127.92$
127.92$ 123.77$ 116.60$ 103.66$
This table is a summary income statement for AAPL for the
most recent 7 quarters. Projected
results are in blue, and important summary lines are in peach.
The table layout
allows the viewer to
follow the math
behind each
calculation,
essentially a different
use of position.
Note that the viewers
eye is guided toward
the most important
lines via the summary
underlines and the
bold type. Also the
peach color.
Exercise
What Works or Doesn’t Work About This Graphic?
Graphic from a previous Career Center in-class presentation
Exercise
What Works or Doesn’t Work About This Graphic?

Graphic pulled from here via Junk Charts.
http://dish.andrewsullivan.com/2013/08/29/households-shrink-
houses-dont/Data Visualization�Theory & Best PracticesData
Analysis and the �Goal of Visualizing DataData Analysis and
the �Goal of Visualizing DataData Visualization�Basic
MannersData Visualization�Basic Manners (Dos & Don’ts)Data
Visualization�Basic Manners (Dos & Don’ts)Data
Visualization�Basic Manners (Dos & Don’ts)Basic
Tactics�Features of Quality Data VisualizationFeatures of
Quality Data Visualization�Representationally Faithful: Bad
ExampleFeatures of Quality Data Visualization�Simplicity:
Bad ExampleFeatures of Quality Data
Visualization�Simplicity: Bad ExampleFeatures of Quality
Data Visualization�Simplicity: Bad Example ImprovedFeatures
of Quality Data Visualization�Comprehensive: Same
ExampleFeatures of Quality Data Visualization�Interesting:
Same ExampleBasic Tactics�Ways to Represent Data
VisuallyWays to Represent Data Visually�Example: Size &
PositionWays to Represent Data Visually�Example:
PositionWays to Represent Data Visually�Example: ShapeWays
to Represent Data Visually�Example: Connectedness &
PositionWays to Represent Data Visually�Example: Color &
PositionWays to Represent Data Visually�Example: Size &
PositionBasic Tactics�Guiding the Viewer’s EyePrinciples of
Data Visualization�The Data/Ink Ratio1Principles of Data
Visualization�The Data/Ink Ratio - Compare &
ContrastPrinciples of Data Visualization�The Disappearing
BaselinePrinciples of Data Visualization�The Self-Sufficiency
Principle2Principles of Data Visualization�The Self-
Sufficiency Principle – Compare &
ContrastExample�HistogramExamples�Table with

ColorExamples�Line Chart with Color & Non-Uniform Time-
ScaleExamples�Multiple Overlapping and Stacked Time Series
(Yahoo! Finance)Example�Basic TableExercise�What Works
or Doesn’t Work About This Graphic?Exercise�What Works or
Doesn’t Work About This Graphic?

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