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ECON 216 Final Project
Objective
The objective of the final project for this class is for each group to produce a visualization-based data
analysis. The analysis will demonstrate the students’ familiarity with the concepts learned throughout the
course. The analysis will use data of interest to the group. Students will explore the data and see what can
be learned from it. The visualization-based analysis will report what the group has learned.
The final report will center on a persuasive argument that uses visualizations and accom-
panying text to establish a set of facts that can be learned from the data chosen. This
visualization-based argument is something you could share with others to explain your anal-
ysis and its findings. Note that most of my class presentations are structured around building and
visualizing datasets with this goal in mind. You are therefore being exposed to models for making this kind
of persuasive, visualization-based argument throughout the semester. Since every topic and dataset has its
own peculiarities, it isn’t possible to formulate general rules for how to make such an argument. Learning
by example is best.
In preparing for making that persuasive arugment, the students will conduct and present an exploratory
data analysis that will produce a larger set of visualizations than included in the argument that characterize
their dataset, explore different avenues of analysis, some of which may not pan out, and from which the
finished analysis will draw.
The end product to be handed in will be report whose format is described below. Along the way to producing
the report, groups will complete two check-in assignments and a presentation of their analysis.
Project Steps and Deliverables
There are four deliverables: two check-ins, a presentation, and a final report. These efforts build on each
other. The check-ins deliberately have low point value to allow for improvement toward the
final report. Because of the cumulative nature of the work, the quality of the final, high value outputs of
presentation and report depend a lot on the work done at the check-ins, so you should put more effort in
than the point value suggests.
Feedback
You will get feedback after each check-in to help you improve. Please read it. I expect corrections made at
these stages to be addressed. I will also make suggestions which may be helpful and I will indicate whether
these are things you must do or are optional.
1
Check-in 1: Topic and Dataset (3 points)
The first task each group has is to find a dataset to analyse. A first step might be to have a discussion of
topics that interest each group member to narrow the search. We will discuss places to look for data in a
later class. When looking at a dataset, pay attention to the unit of observation and the variables included
and think about what those variables might tell you, both on their own and in relation to each other. In
class and on DataCamp we learning techniques that will help you.
This is an important step since it determines the rest of the project so should be undertaken with some care.
The check-in is a simple paragraph submitted to Canvas that includes:
1. The name of the dataset and a link to it.
2. The general topic you propose to use the dataset to investigate. Be as specific as you can. You may
change your focus, but you will need to have identified at least one topic at this stage.
3. The unit of observation of the dataset and the variables within it that you think will be useful. Describe
what the variables measure.
This check-in is due February 24th.
Check-in 2: Exploratory Data Analysis (EDA) (6 points)
This is the most time-consuming and coding-intensive part of the project. It contributes material both to
this check-in and to the final report.
The goal of the EDA is to explore the dataset to develop an understanding of the variables of interest and
how they may be related. The findings that are most interesting and that can be used to establish some
facts about your topic will go on the contribute to the persuasive argument in the final report.
The EDA will also be part of the final report, so will be graded twice. You can think of this check-in as
the draft EDA on which you will get feedback and the EDA in the final report as the final version.
Format
Your EDA should have the following format. It should be constructed as an RMarkdown document. Both
the markdown and a PDF version should be submitted. All code used to conduct the analysis should be
included.
I. Introduction
What is the topic under study and what do you think you can learn about it using the data?
II. Background
What data are you using? What structure does it have, including the level of observation and variables
included? What contextual information about the topic we will need to understand the EDA?
III. Data Wrangling
Show the data wrangling code required to get the data into the format(s) you use in section IV.
Discuss the main steps in words. This includes any new variables that must be generated, pivots or joins
made, recoding of variable categories, selection of a subset of the observations, etc.