CIS XXXXXXXXXXData Visualization
Summer 2020
Exam # 1 – Part 2
Due: July 3rd mid night (11:59 PM)
No late submits allowed
Maximum points: 20 or 20% of the course grade
Note: Each question is worth 1 point. You must include R code along with the output graph(s)
for your answer. The simplest way is to copy the R code along with the plot within a word
document and submit it. You must use the ggplot function for the ggplot2 package to write
the R code for visualizing data. Your work must involve using co
ect variables for the
dataset of your choice to plot meaningful visualizations. Just producing visual plots which
are inco
ect will not receive any credit. Partial credit will be assigned for work
demonstrating significant efforts in the right direction. You are allowed to use the code from
the text book.
1. How do you install a package in R environment? Write the R command as an example.
You may use any package of your choice to demonstrate the installation of a package.
2. How do you call a package within your cu
ent R session? You may use any installed
package of your choice to demonstrate this.
3. How do you load data from an excel file (with xlsx or csv extension) in R environment?
Demonstrate by using any excel file on your local computer and loading it into the R
environment.
4. Plot a scatter plot with numerical or quantitative variable on the y-axis and categorical or
qualitative variable on the x-axis. You may use any dataset within the R environment. You
must use the ggplot function from the ggplot2 package. Why such a plot with categorical
variable on the x-axis and quantitative variable on the y-axis are not very useful. Which
plot is used to plot categorical variable on the x-axis and numerical variable on the y-axis?
5. Now plot a scatter plot with numerical or quantitative variable on both the x-axis as well
as the y-axis. You may use any dataset within the R environment. You must use the ggplot
function from the ggplot2 package.
6. Plot a simple line graph using any dataset available within the R environment. You must
use the ggplot function from the ggplot2 package.
7. Which plot is used to plot a bell curve or normal curve? Plot a bell curve or normal curve
using any dataset present within the R environment. You must use the ggplot function from
the ggplot2 package.
8. Write the R code to create the following boxplot for the mpg dataset. This dataset is
available within the R environment:
Now interpret the above box plot as to what insights can be drawn from it.
9. Write the R code to create the following bar plot for the mtcars dataset. This dataset is
available within the R environment:
10. Write the R code to create the following grouped bar plot for the mpg dataset. This dataset
is available within the R environment:
Why there’s no multiple bars for cylinder = 5 in the above bar plot?
11. How do you change the default colors for the bars in question 10 above? Write the R
command to change the default colors to red, green and blue respectively for the three drv
levels in the above plot co
esponding to question 10. Your plot must look as shown below:
12. Which plot will you use to compare the diamond prices across categories of diamonds
ased on their cut quality for the diamonds dataset? This dataset is available within the R
environment. (Hint: use cut on the x-axis and price on the y-axis)
13. Write R code to create the following plot for the ca
age_exp dataset from the gccokbook
package.
14. Write the R code to create the following stacked bar plot for the mpg dataset. This dataset
is available within the R environment.
15. Write the R code to create the following proportional stacked bar graph for the mpg dataset.
This dataset is available within the R environment.
16. Demonstrate the use of xlim() and ylim() functions for the line plot for any dataset available
within the R environment. Write the co
esponding R command using ggplot function of
the ggplot2 package. Also show your output graph.
17. Write the R code to create the following multi-line line plot for the Orange dataset. This
dataset is available within the R environment.
18. Demonstrate the use of size, shape, color and fill attributes of geom_point() within a
multiline line graph for any dataset available within the R environment.
19. Demonstrate the use of linetype, size and color attributes of geom_line() within a multiline
line graph for any dataset available within the R environment.
20. Demonstrate the use of geom_area() to plot stacked area or proportional stacked area graph
for any meaningful dataset which can be plotted using geom_area() geometric method
within the R environment.
storytelling with data: a data visualization guide for business professionals
storytelling with data
storytelling
with data
a data visualization guide
for business professionals
cole nussbaumer knaflic
Cover image: Cole Nussbaumer Knaflic
Cover design: Wiley
Copyright © 2015 by Cole Nussbaumer Knaflic. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
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To Randolph
vii
contents
foreword ix
acknowledgments xi
about the author xiii
introduction 1
chapter 1 the importance of context 19
chapter 2 choosing an effective visual 35
chapter 3 clutter is your enemy! 71
chapter 4 focus your audience’s attention 99
chapter 5 think like a designer 127
chapter 6 dissecting model visuals 151
chapter 7 lessons in storytelling 165
chapter 8 pulling it all together 187
chapter 9 case studies 207
chapter 10 final thoughts 241
ibliography 257
index 261
ix
foreword
“Power Co
upts. PowerPoint Co
upts Absolutely.”
—Edward Tufte, Yale Professor Emeritus1
We’ve all been victims of bad slideware. Hit‐and‐run presentations
that leave us staggering from a maelstrom of fonts, colors, bullets,
and highlights. Infographics that fail to be informative and are only
graphic in the same sense that violence can be graphic. Charts and
tables in the press that mislead and confuse.
It’s too easy today to generate tables, charts, graphs. I can imagine
some old‐timer (maybe it’s me?) ha
umphing over my shoulder that
in his day they’d do illustrations by hand, which meant you had to
think before committing pen to paper.
Having all the information in the world at our fingertips doesn’t make
it easier to communicate: it makes it harder. The more information
you’re dealing with, the more difficult it is to filter down to the most
important bits.
Enter Cole Nussbaumer Knaflic.
I met Cole in late 2007. I’d been recruited by Google the year before
to create the “People Operations” team, responsible for finding, keep-
ing, and delighting the folks at Google. Shortly after joining I decided
1 Tufte, Edward R. ‘PowerPoint Is Evil.’ Wired Magazine, www.wired.com/wired
archive/11.09/ppt2.html, September 2003.
http:
www.wired.com/wired/archive/11.09/ppt2.html
http:
www.wired.com/wired/archive/11.09/ppt2.html
x foreword
we needed a People Analytics team, with a mandate to make sure
we innovated as much on the people side as we did on the product
side. Cole became an early and critical member of that team, acting
as a conduit between the Analytics team and other parts of Google.
Cole always had a knack for clarity.
She was given some of our messiest messages—such as what exactly
makes one manager great and another crummy—and distilled them into
crisp, pleasing imagery that told an i
efutable story. Her messages of
“don’t be a data fashion victim” (i.e., lose the fancy clipart, graphics and
fonts—focus on the message) and “simple beats sexy” (i.e., the point is
to clearly tell a story, not to make a pretty chart)