Syllabus - Kosuke Imai's Homepage - Princeton University

POL 245: Visualizing Data
Syllabus
Summer 2014
Jonathan Olmsted (Instructor)
Bella Wang (Preceptor)
Kosuke Imai (Course Head)
Department of Politics, Princeton University
In this course, we consider ways to illustrate compelling stories hidden in a blizzard of data. Equal parts
art, programming, and statistical reasoning, data visualization is a critical tool for anyone doing analysis.
In recent years, data analysis skills have become essential for those pursuing careers in policy advocacy and
evaluation, business consulting and management, or academic research in the fields of education, health,
medicine, and social science. This course introduces students to the powerful R programming language and
the basics of creating data-analytic graphics in R. From there, we use real datasets to explore topics ranging
from network data (like social interactions on Facebook or trade between counties) to geographical data
(like county-level election returns in the US or the spatial distribution of insurgent attacks in Afghanistan).
No prior background in statistics or programming is required or expected.
Contact Information
Name Jonathan Olmsted Bella Wang Kosuke Imai
Office Corwin 029 Corwin 026 Corwin 036
Email jolmsted@princeton.edu bxw@princeton.edu kimai@princeton.edu
Office Hours Fri: 2:00pm – 3:30pm Mon: 2:00pm – 3:30pm by appointment
During our office hours we may be in either the office that is listed above or in Corwin 023 (just across
the hall) which has space for more students. If these office hours do not fit your schedule, you should feel
free to contact us directly via email. Also, do not forget about Piazza and QuantLab (see below) where
you can ask questions and receive answers back immediately.
Logistics
Lectures. Monday and Wednesday, 9:00am–9:50am, Sherrerd Hall 101. Lecture slides will be posted on
Blackboard by 10:00pm the night before the lecture. Students should bring their own printed copy of the
slides to the lecture. The schedule during the first week deviates from this, details are below in the Course
Outline section.
Precepts. Tuesday and Thursday, 9:00am–10:20am, Friend Center 005 and 007. These are computer
labs, and so there is no need to bring a personal laptop (although it is fine for you to do so). The schedule
during the first week deviates from this, details are below in the Course Outline section.
1

Review Sessions/Guest Speakers. Friday, 9:00am–10:20am, Visualization Laboratory in Lewis Library
346. These sessions occur during the second week of FSI through the final week. The first four of
these five sessions involve guest speakers from various industries where data visualization is used. The
schedule during the first week deviates from this, details are below in the Course Outline section.
Lunch with Guest Speaker. Friday, 12:00pm–1:30pm, Prospect House. Students will sign up to have
lunch with one of the four guest speakers at the beginning of the course. During the selected week, students
and the course team will meet with the guest speaker during a casual, catered lunch. The schedule during
the first week deviates from this, details are below in the Course Outline section.
Course Requirements
• Precept participation (15%): There will be twelve precepts in the computer lab. Students must
actively participate in these precepts. During each of the six weeks, there will be an assignment due
before the first precept of that week. This assignment asks students to work on a small set of Precept
Questions and submit their answers the night before the first precept. Details on these assignments
are below. This is an individual assessment with limited collaboration.
• Problem sets (40%): There will be four problem sets during the semester. These problem sets
provide an opportunity for students to conduct analysis and visualization of a particular kind according
to the week’s material. Each problem set will be equally weighted. Details on these assignments
are below. This is an individual assessment with no collaboration.
• Midterm Exam (25%): There will be a take-home midterm exam after the first three weeks of
the course. This exam will provide students an opportunity to demonstrate proficiency of the introductory
material that the subsequent topical weeks will build on. This is an individual assessment
with no collaboration.
• Final Project (20%): The final evaluation is a group data analysis project. Students will be
assigned to groups. Each group will be assigned a dataset from those provided by the four guest
speakers throughout the course. Using the assigned dataset, students will give a 5 minute presentation
to the class focused on a compelling relationship or story from the dataset using no more than 3
figures. A full set of details regarding the final project will be provided later in the course. This is
a group assessment with collaboration allowed only within the assigned groups.
Submission of Computer Code via Blackboard Folders
For the Precept Questions on the Precept Handouts, answers to Problem Sets, and answers to
the Midterm exam, students are required to turn in their computer code via assignment folders on
Blackboard. For each assignment, you will submit your code to the appropriate folder as a single file
named xxxHandoutX.R or xxxPSetX.R where xxx is your NetID and X is the handout/problem set number.
For example, it might be kimaiHandout2.R or jolmstedPSet3.R for handout 2 or problem set 3.
Precept Materials, Format, and Policy
Precept Handouts. We will distribute a Precept Handout as preparation for the first precept of
each week. It contains all the materials we expect you to learn before that session. Mastery of these
materials is required for Problem Sets, Precept Worksheets, and the Midterm Exam. These
2

handouts will be posted on Friday afternoon and due the night before Tuesday’s precept. You will want
paper copies of Precept Handouts to use as reference.
Before coming to Tuesday’s precept. Before coming to Tuesday’s precept, you should work through
the Precept Handout and answer the small set of programming questions, called Precept Questions
that are included in the handout. You are allowed to collaborate in only a limited fashion with others. See
below for details. These questions are designed to check whether you understood the materials covered in
the Precept Handout and facilitate problem-solving during precept sessions. You must turn in your
computer code by 10:00 pm the night before precept (according to Submission of Computer Code).
The assessment is graded only on a submitted or not basis, but only “good faith” submissions will count
for full credit.
During precept. The precept will begin by briefly reviewing the handout. Come to precept with
questions if you have any. We will give special attention to issues that seemed problematic for students in
the precept questions. The remainder of the precept time will then be spent working on the new material
and solving Precept Exercises if time permits. Precept Worksheets will be distributed via Blackboard.
You should work through these exercises on your own whether we work on them in the precept or
not. These exercises are a good model for the kinds of problems asked on the Problem Sets and the
Midterm Exam. Solutions to the Precept Exercises will be posted to Blackboard before the following
precept. You will want paper copies of the Precept Worksheets as a reference.
Problem Set/Midterm Exam Format
Each week will end with the posting of either a problem set or the midterm exam. These assignments will
be posted on Thursday afternoons via Blackboard.
Submission. Hard copies of your problem sets must be turned in at the beginning of the Tuesday precept
that they are due. Electronic submission via Blackboard of your computer code must also be done by the
beginning of the Tuesday precept at 9:00am.
Collaboration Policy
The assignments in this course are designated as individual or group assessments. The degree of permissible
collaboration depends on the kind of assignment:
1. Ungraded problems/exercises
2. Precept Questions from Precept Handouts
3. Problem Sets and the Midterm Exam
4. Final Project
Assignment Types
Ungraded problems. For ungraded problems, like those on the Precept Worksheets or old problem
set questions, students are encouraged to interact with each other, the instruction team, and QuantLab tutors
in discussing their approaches and solutions. This includes conceptual discussion and actual computer
code. However, for all other assignments, this degree of collaboration is not appropriate!
3

Precept Questions. Some collaboration on the Precept Questions from Precept Handouts is allowed.
Students may discuss approaches to solving these problems with other students. They may also ask
questions of QuantLab tutors, the instruction team, and on Piazza. However, when it comes time to write
the code that is submitted for precept participation credit, all code must be each student’s own work.
Problem Sets and the Midterm Exam. Problem Sets and the Midterm Exam do not allow
for any collaboration with other students whatsoever. Students may ask clarifying questions regarding
problem set and midterm questions to the instruction team (Jonathan Olmsted, Bella Wang, and Kosuke
Imai) through Piazza. This allows all students to benefit from clarifications equally. Clarifying questions
about the problem sets may not be asked of QuantLab tutors, however.
Final Project. The Final Project is a group visualization project. Students may fully collaborate within
their assigned groups, but may not discuss their group’s work or another group’s work with other students
in the course.
Plagiarism Policy
Violations of the above collaboration policy will be treated as instances of plagiarism. This course will
follow a modified version of the guidelines used for computer science classes here at Princeton. Please
take this guideline seriously. In the past, plagiarism cases typically result in one-year suspension from
Princeton.
Programming necessitates that you reach your own understanding of the problem and discover a path
to its solution.
Do not, under any circumstances, copy another person’s code. Incorporating someone
else’s code into your program in any form is a violation of academic regulations. Abetting plagiarism or
unauthorized collaboration by sharing your code is also prohibited. Sharing code in digital form is an
especially egregious violation: do not e-mail your code to anyone.
Novices often have the misconception that copying and mechanically transforming a program (by
rearranging independent code, renaming variables, or similar operations) makes it something different.
Actually, identifying plagiarized source code is easier than you might think. For example, there exists
computer software that can detect plagiarism.
This policy supplements the University’s academic regulations, making explicit what constitutes a
violation for this course. Princeton Rights, Rules, Responsibilities handbook asserts:
The only adequate defense for a student accused of an academic violation is that the work
in question does not, in fact, constitute a violation. Neither the defense that the student was
ignorant of the regulations concerning academic violations nor the defense that the student was
under pressure at the time the violation was committed is considered an adequate defense.
If you have any questions about these matters, please consult a member of the instruction team.
Textbooks
In addition to the Precept Handout and Precept Worksheets which serve as an introduction to,
discussion of, and reference for the material in this course, there will be assigned readings each week. Some
readings may be distributed (whether as a hard copy or via Blackboard) as appropriate. In the first few
weeks as we learn statistical concepts, readings will come from:
• Freedman, David, Robert Pisani, and Roger Purves. (2007). Statistics. W. W. Norton & Company.
4

This book will be referred to as FPP hereafter.
In addition, the following two books may be helpful references although they are completely optional
for this self-contained course.
• Verzani, John. (2005). Using R for Introductory Statistics. Chapman & Hall.
• Verzani, John. (2011). Getting Started with RStudio. Safari Books Online (Series).
Verzani (2005) is available on reserve at Firestone library: http://goo.gl/tYj5B. Verzani (2011)
is available online through the Princeton University Library website: http://goo.gl/zKLqzj. You can
access it from any computer on the Princeton network.
Finally, at the beginning of the Precept Handouts and Precept Worksheets there will be
references to any corresponding Verzani (2005) sections. In addition, throughout the materials, there will
be pointers to specific sections in Verzani (2011). When you see these Verzani (2011) references, you should
read the corresponding (brief) section online.
Statistical Software
In this course, we use the open-source statistical software R (http://www.r-project.org). Recently, a
New York Times article (“Data Analysts Captivated by R’s Power” January 6, 2009) featured R as,
a popular programming language used by a growing number of data analysts inside corporations
and academia. It is becoming their lingua franca [...] whether being used to set ad prices, find
new drugs more quickly or fine-tune financial models. Companies as diverse as Google, Pfizer,
Merck, Bank of America, the InterContinental Hotels Group and Shell use it. [...] “The great
beauty of R is that you can modify it to do all sorts of things,” said Hal Varian, chief economist
at Google. “And you have a lot of prepackaged stuff that’s already available, so you’re standing
on the shoulders of giants.”
R can be more powerful than other statistical software such as SPSS, STATA and SAS, but it can also
be more difficult to learn. A variety of resources will be made available for POL 245 students in order to
learn R as efficiently as possible.
To help make using R easier, we’ll be using RStudio (http://www.rstudio.com/)—a user-interface
that simplifies many common operations.
Computing Resources
In the event that you need to install R and RStudio on your own personal laptop, this will be covered
in Precept Handout 0 which is due before the first lecture. However, many students may find the
additional computer clusters around campus convenient to use. In these cases, the R and RStudio software
is already installed on the machines in several computer clusters.
• Frist 200-level computer clusters
• Butler D033
Be sure to check the hours of the building’s availability before making plans to use a specific location.
5

Getting Help
POL 245 will be a challenging course and several resources additional are available to facilitate efficient
learning about data analysis and data visualization. We encourage you to take advantage of them whenever
you have questions about the course materials and are struggling with problem sets.
• QuantLab: In addition to the instruction team, there are QuantLab tutors available who can
help you work through the course materials. The tutors are Politics graduate students who have
experience with R and statistics. Unless otherwise noted, QuantLab will be held on Sunday, Monday,
and Wednesday nights from 7:00pm – 10:00pm. Shorter sessions of QuantLab will also be held on
Tuesday and Thursday afternoons from 2:00pm – 3:30pm. Tutoring sessions will be held in Butler
College D026 and D027.
• Office hours: Each of us will hold weekly office hours, starting the first week. In addition to these
office hours, we have an open door policy—you can stop by our Corwin offices at any time without
an appointment and we will answer your questions so long as we are available. You may also e-mail
to set up an appointment with either of us outside of our office hours.
• Discussion board: In addition to office hours and individual appointments, we will be available
online to answer any questions you may have about the course materials and the problem sets. We
use the Piazza discussion forum that will be linked on Blackboard course page or accessible directly
at http://piazza.com. Before posting your question, please review previous posts to make sure that
a similar question has not been answered. In accordance with the collaboration policy described above,
you should not directly post your code for a problem set. You should frame your questions in general
terms rather than trying to have us debug your code directly. You may subscribe to the Discussion
Forum so that you receive your fellow students’ questions and answers to those questions. You
should also feel free to respond to questions that you can answer. Piazza also has a free smartphone
application if you are interested.
Additional Accommodations
Students must register with the Office of Disability Services (ODS) (odsprinceton.edu ; 609-258-8840) for
disability verification and determination of eligibility for reasonable academic accommodations. Requests
for academic accommodations for this course need to be made at the beginning of the semester, or as soon
as possible for newly approved students, and again at least two weeks in advance of any needed accommodations
in order to make arrangements to implement the accommodations. Please make an appointment
to meet with me in order to maintain confidentiality in addressing your needs. No accommodations will
be given without authorization from ODS, or without advance notice.
6

Course Outline
Week 1: July 14 – July 18, Causality
During Week 1, we will learn how to make causal claims from datasets. We learn the distinction between
randomized experiments and observational studies. In addition, we introduce R, programming with data,
and familiarize students with basic skills in data analysis by considering experimental data. These tools
will be applied to topics like evaluating strategies for increasing voter turnout and the effect of class size
on educational achievement.
Note, the first session on Tuesday, July 15 will be a lecture in Sherrerd Hall 101 and not a precept.
The second session on Wednesday, July 16 will be a precept in the Friend Center. The third session on
Thursday, July 17 will be a lecture in Sherrerd Hall. And, for Friday, July 18 we will hold a precept in the
Friend Center. These changes affect only the first week of the course.
Handout 0: posted Monday, Jul. 14; due Tuesday, Jul. 15 (no submission)
Handout 1-A: posted Tuesday, Jul. 15; due Wednesday, Jul. 16
Handout 2-A: posted Friday, Jul. 18
Problem Set 1: posted Thursday, Jul. 17
Required Readings:
FPP, Chapter 1: Controlled Experiments.
FPP, Chapter 2: Observational Studies.
FPP, Chapter 3: The Histogram.
FPP, Chapter 4: The Average and the Standard Deviation.
↩→ only Sections 4.1, 4.2, and 4.3 for this week
Week 2: July 21 – July 25, Clustering
During Week 2, we will further learn methods for describing data and how to identify “clusters” of data.
The main example we discuss is ideological polarization in the United States. In addition, we will look at
the distribution of income, democracy, and natural resource endowments across different countries.
Handout 2-A: due Monday, Jul. 21
Handout 3-A: posted Friday, Jul. 25
Problem Set 1: due Tuesday, Jul. 22
Problem Set 2: posted Thursday, Jul. 24
Required Readings:
FPP, Chapter 4: The Average and the Standard Deviation.
↩→ only Sections 4.4, 4.5, 4.6, and 4.7 for this week
FPP, Chapter 7: Plotting Points and Lines.
Week 3: July 28 – August 1, Prediction and Forecasting
In Week 3, we continue extending general R programming, data-analytic, and data visualization skills.
Forecasting federal and state-level electoral outcomes will be used as a substantive motivation for these
new tools.
7

Handout 3-A: due Monday, Jul. 28
Handout 4-A: posted Friday, Aug. 1
Problem Set 2: due Tuesday, Jul. 29
Midterm Exam: posted Thursday, Jul. 31
Required Readings:
FPP, Chapter 8: Correlation.
FPP, Chapter 9: More about Correlation .
FPP, Chapter 10: Regression.
FPP, Chapter 11: The R.M.S. Error for Regression.
FPP, Chapter 12: The Regression Line.
Week 4: Aug 3 – August 8, Text as Data
For Week 4, we start to cover how text can be analyzed as data. These methods allow researchers to process
large amounts of unstructured text and extract meaningful measures of politically important concepts like
partisanship. We use these methods to analyze the Federalist Papers and the press releases of legislators.
Handout 4-A: due Monday, Aug. 4
Handout 5-A: posted Friday, Aug. 8
Midterm Exam: due Tuesday, Aug. 5
Problem Set 3: posted Thursday, Aug. 7
Week 5: Aug 11 – August 15, Network Data
Week 5 focuses on analyzing data with a network structure. With the rise of social networks, network data
is increasingly common. We use algorithms to describe the networks generated by bill (co-)sponsorship in
Congress and the citation of Supreme Court opinions.
Handout 5-A: due Monday, Aug. 11
Handout 6-A: posted Friday, Aug. 15
Problem Set 3: due Tuesday, Aug. 12
Problem Set 4: posted Thursday, Aug. 14
Week 6: August 18 – August 22, Maps and Spatial Data
During the final week, Week 6, we introduce mapping and visualizing geo-coded data. With this, we will
be able to answer questions about how US voters separate themselves by ideology or to what extent the
country is split into “red” or “blue” states.
Handout 6-A: due Monday, Aug. 18
Problem Set 4: due Tuesday, Aug. 19
Final Project: posted Thursday, Aug. 21; due Wednesday Aug. 27
8

POL 245 Schedule
Due to the condensed nature of the course, the semester and weekly schedules (including assignments) can
seem very full. To help with this, we have a Google Calendar with events, due dates, and sessions. The
URL for this calendar is http://goo.gl/D3pKXL. We also outline a typical week of assignments in POL
245 below:
Sun Mon Tue Wed Thur Fri Sat
7:00pm-
10:00pm
QuantLab
9:00am-9:50am
Lecture
7:00pm-
10:00pm
QuantLab
10:00pm
Submit Handout
code
9:00am
Submit Problem
Set code
9:00am
Submit Problem
Set write-up
9:00am-
10:20am
Precept
2:00pm-
3:30pm
QuantLab
9:00am-9:50am
Lecture
7:00pm-
10:00pm
QuantLab
9:00am-
10:20am
Precept
12:00pm
New Problem Set
posted
2:00pm-
3:30pm
QuantLab
9:00am-
10:20am
Guest Speaker
12:00pm
New Handout
posted
Guest Speakers
For four of the six weeks during the course, we will have guest speakers during our Friday sessions. In
addition to their presentation from 9:00am – 10:20am in the Lewis Library Visualization Lab, they will be
on campus for lunch with a small group of students. Students will sign up for lunch with a specific speaker
at the beginning of the course.
FSI Week Date Guest Affiliation
2 7/25
Archie Tse
Deputy Graphics Director
Telling Stories with Data
New York Times
3 8/1
4 8/8
5 8/15
Eurry Kim
Advertising Researcher
Visualizing the Social TV Networks of
Facebook
Bruce Willsie
President/CEO
Big Data, High Speeds, New Insights—How
High Speed Data Visualization is Changing
Politics
Sondra Scott
Head of Macro and Industry Trends
Energy Fundamentals Analysis—How data
drives insight
Facebook
L2
Wood McKenzie Ltd.
9