Description
Q1 [45 pts] Collecting and visualizing Twitter data
1. [30 pts] You will use the Twitter REST API to retrieve (1) followers, (2) followers of
followers, (3) friends and (4) friends of friends of a user on Twitter (a Twitter friend is
someone you follow and a Twitter follower is someone who follows you).
a. The Twitter REST API allows developers to retrieve data from Twitter. It uses the OAuth
mechanism to authenticate developers who request access to data. Here’s how you can set
up your own developer account to get started:
● Twitter : Create a Twitter account, if you don’t already have one.
● Authentication: You need to get API keys and access tokens that uniquely
authenticate you. Sign into Twitter Apps with your Twitter account credentials. Click
‘Create New App’. While requesting access keys and tokens, enter:
Name dva_hw1_<your-student-id (eg: dva_hw1_jdoe3)
Description “For CSE 6242 at Georgia Tech”
Website https://poloclub.gatech.edu/cse6242/2017spring/
Callback URL field should be left empty as we will not need it
Check the developer agreement checkbox and click on ‘Create your Twitter
application’. Once your request is approved, you can click ‘Keys and Access
Tokens’ to view your ‘API key’ and ‘API secret’. Generate your access token by
clicking the ‘Create my access token’ button. Now, you are ready to mak
You will use Python 2.7.x (not Python 3.0+) and the tweepy library to modify parts of the
boilerplate script (script.py). If you are new to Python, here are few useful links to help you
get started: tutorialspoint , file reading and writing methods
b. [15 pts] Search for followers of the Twitter username “PoloChau”. Use the API to retrieve
the first 10 followers. Further, for each of them, use the API to find their 10 followers.
● Read the documentation for getting followers of a Twitter user. Note that in tweepy,
the ‘screen_name’ parameter represents the Twitter username.
● You code will write the results to followers.csv.
Each line in the file should describe one relationship in the format:
follower-username, username
● Grading distribution is given in the boilerplate code.
Note: follower-username represents the Source and username represents the
Target for an edge in a directed graph, which you will use in a later question.
c. [15 pts] Search for friends of the Twitter screen name “PoloChau”. Use the API to retrieve
the first 10 friends. Further, for each of the 10 friends, use the API to find their 10 friends.
● Read the documentation for getting friends of a Twitter user.
● You code will write the results to friends.csv.
Each line in the file should describe one pair of relationship in the format:
username, friend-username
● Grading distribution is given in the boilerplate code.
Note: username represents the Source and friend-username represents the Target
for an edge in a directed graph.
If a user has fewer than 10 followers or friends, the API will return as many as it can find.
Your code should be flexible to work with whatever data the API endpoint returns.
Note: Some users may be protected, which means you won’t be able to fetch their
followers or friends. Such users can be ignored in the secondary list in the code. However,
they should be present in the primary list.
Deliverables: Create a directory called Q1 to store all the files listed below.
Note: Do NOT submit your API credentials (keys.json). They should not be shared. We will
use our own keys and tokens to grade your work.
● script.py: The boilerplate code modified by you. The submitted code should run as
is. That is, no extra installation or configuration should be required other than the
specified libraries. Also specify the python version in the code.
● followers.csv and friends.csv produced in step b and c respectively. Please note
that these files will be modified in task 2b shortly.
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2. [15 pts] Visualize the network of friends and followers obtained using Gephi, which you can
download here. Ensure your system fulfils all requirements for running Gephi.
a. Go through the Gephi quick-start guide.
b. [2 pts] Insert Source,Target as the first line in both followers.csv and friends.csv.
Each line in both files now represents a directed edge with the format Source,Target .
Import all the edges contained in these files using Data Laboratory.
Note: Remember to check the “create missing nodes” option while importing since we do
not have an explicit nodes file.
c. [8 pts] Using the following guidelines, create a visually meaningful graph:
● Keep edge crossing to a minimum, and avoid as much node overlap as possible.
● Keep the graph compact and symmetric if possible.
● Whenever possible, show node labels. If showing all node labels create too much
visual complexity, try showing those for the “important” nodes.
● Using nodes’ spatial positions to convey information (e.g., “clusters” or groups).
Experiment with Gephi’s features, such as graph layouts, changing node size and color,
edge thickness, etc. The objective of this task is to familiarize yourself with Gephi and
hence is a fairly open ended task.
We (course staff) will select some of the most visually meaningful and beautiful graphs
from you all and share them with the class on Piazza.
d. [5 pts] Using Gephi’s built-in functions, compute the following metrics for your graph:
● Average node degree
● Diameter of the graph
● Average path length
Briefly explain the intuitive meaning of each metric in your own words.
You will learn about these metrics in the “graphs” lectures.
Deliverables: Place all the files listed below in the Q1 folder.
● For part b: followers.csv and friends.csv (with Source,Target as their first
lines).
● For part c: an image file named “graph.png” (or “graph.svg” ) containing your
visualization and a text file named “graph_explanation.txt” describing your design
choices, using no more than 50 words.
● For part d: a text file named “metrics.txt” containing the three metrics and your
intuitive explanation for each of them, using no more than 100 words.
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Q2 [35 pt] SQLite
The following questions help refresh your memory about SQL and get you started with SQLite — a
lightweight, serverless embedded database that can easily handle up to multiple GBs of data. As
mentioned in class, SQLite is the world’s most popular embedded database. It is convenient to
share data stored in an SQLite database — just one cross-platform file, and no need to parse
(unlike CSV files).
You will modify the given Q2.SQL.txt file to add SQL statements and SQLite commands to it.
We will test the correctness of your answers by running your modified Q2.SQL.txt against
olympics.db, which generates Q2.OUT.txt (assuming the data files are present in the current
directory).
$ sqlite3 olympics.db < Q2.SQL.txt Q2.OUT.txt
We will generate the Q2.OUT.txt using above command. You may not receive any points i f
we are unable to generate the file, or if you do not strictly follow the output formats specified in
each question.
We have added some additional lines of code in the Q2.SQL.txt file as follows:
● .headers off. : After each question, an output format has been given with a list of
column names/headers.This command ensures that the headers are not displayed in the
output.
● .separator ‘,’ : To specify that the input file and the output are
comma-separated.
● select ‘’: This command prints a blank line. After each question’s query, this
command ensures that there is new line between each result in the output file.
WARNING: Do not copy and paste any code/command from this PDF to the sqlite command
prompt, because PDFs sometimes introduce hidden/special characters, causing SQL error.
Manually type out the commands instead.
a. [2 pt] Import data. Create an SQLite database called olympics.db and provide the SQL
code (and SQLite commands) used to create the following tables. Use SQLite’s dot
commands (.separator STRING and .import FILE TABLE) to import data from files. Data
used in this question was derived from https://www.kaggle.com/rio2016/olympic-games.
Import the olympic athlete data from athletes.csv (in the Q2 Folder) into a new table (in
olympic.db) called athletes with the schema:
athletes (
id integer
name text,
nationality text,
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gender text,
dob numeric,
height real,
weight integer,
sport text,
gold integer,
silver integer,
bronze integer
)
Import the olympic countries data from countries.csv (in the Q2 Folder) into a new table (in
olympic.db) called countries with the schema:
countries (
country text,
code text,
population integer,
gdp_per_capita real
)
b. [2 pt] Build indexes. Create two indexes that will speed up subsequent join operations:
● An index called athletes_country_index in the athletes table for the nationality
column.
● An index called countries_country_index in countries t able for the code column.
c. [2 pt] Quick computations. Find the total number of unique female athletes who won gold
medals. Then find the total number of unique male athletes who won silver medals.
Output format (i.e., each line contains one number):
count_female_athletes_won_who_gold
count_male_athletes_won_who_silver
d. [4 pt] Who won the most medals? Find the top ten athletes who won the most total medals.
Sort by descending order with respect to the total medal count, then sort by ascending
order with respect to the athletes’ names (this will sort the athletes who won the same
amount of medals alphabetically). Note country in the output should be the country name,
not the three letter code.
Output format:
athlete_name, country, total_medals
e. [4 pt] Worldwide medal leaderboard. List the top ten countries that won the most medals,
and their medal counts in each category (bronze, silver, and gold). Just like above, sort
descending by the countries that have won the most medals in total, then sort ascending by
country name. Note, also like before, country in the output should be the country name,
not the three letter code.
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Output format:
country, gold_medal_sum, silver_medal_sum, bronze_medal_sum
f. [6 pt] Performance leaderboard: Find the top 10 countries with the best performance ratio
(total number of medals*1000/number of athletes). Display the country (the country
name, not the three letter code), performance_ratio, gdp_per_capita and
avg_bmi (average body mass index of each country). Sort the results by best performance
ratio (high to low) and then by country name (alphabetical order).
Note: The body mass index of an athlete is calculated as weight/(height*height). avg_bmi
of a country is the average over all athletes of a country. The country with the highest
performance ratio is the best performing country.
Output format:
country, performance_ratio, gdp_per_capita, avg_bmi
[2pt] Write down the following questions’ answers in observation.txt.
1. The name of the country with the highest gdp_per_capita.
2. The name of the country with the lowest avg_bmi.
Example content of observation.txt (f or reference purposes only)
Greece
Austria
g. [7 pt] Creating view: Create a view (virtual table) called most_played_sports, where each
row contains a sport that has more than 500 athletes. One column stores the sport name,
and another the total number of medals for that sport.
The format of the view is:
most_played_sports(sport, total_medals)
Using this view, write a query to find all distinct pairs of sports such that the total medals of
sport_1 is strictly fewer than the total medals of sport_2. Ensure that you do not
include pairs where both sports have the same medal counts. Sort the results by sport_1
(alphabetical order) and then by sport_2 (alphabetical order).
Output format:
sport_1, sport_2
Note: Remember that creating a view will produce no output. Full points will only be
awarded for queries that use joins.
Optional reading: Why create views?
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h. [2 pt] Calculate the total number of such pairs created from the view made in part g.
Output format:
count_total_pairs
i. [4 pt] SQLite supports simple but powerful Full Text Search (FTS) for fast text-based
querying (FTS documentation).
Import the movie overview data from movie-overview.txt (in the Q2 folder) into a new FTS
table (in rt.db) called movie_overview with the schema:
movie_overview (
id integer,
name text,
year integer,
overview text,
popularity decimal
)
1. [2pt] Count the number of movies whose overview fields contain the word “love” but
not “hate”.
Output format:
count_overview_love_not_hate
2. [2pt] List, in ascending order, the ids of the movies that contain the terms “love” and
“war” in their overview fields with no fewer than 7 intervening terms in between.
Output format:
id
Deliverables: Place all the files listed below in the Q2 folder
● Q2.SQL.txt: Modified file additionally containing all the SQL statements and SQLite
commands you have used to answer questions a – i in the appropriate sequence.
● Q2.OUT.txt: Output of the questions above. See below for how to generate this file.
● observation.txt: contains the two lines of answers in part f.
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Q3 [15 pt] D3 Warmup and Tutorial
● Go through the D3 tutorial here.
● Complete steps 01-16 (Complete through “16. Axes”).
● This is a simple and important tutorial which lays the groundwork for Homework 2.
Note: We recommend using Mozilla Firefox or Google Chrome, since they have relatively robust
built-in developer tools.
Deliverables: Place all the files/folders listed below in the Q3 folder
● A folder named d3 containing file d3.v3.min.js (download)
● index.html : When run in a browser, it should display a scatterplot with the following
specifications:
a. [5 pt] There should be 50 points that are randomly generated and placed on the plot.
Each point’s x coordinate should be a random number between 0 and 100
inclusively (i.e., [0, 100]), and so is each point’s y coordinate. A point’s x and y
coordinates should be independently computed.)
b. [2 pt] The plot must have visible X and Y axes that scale according to the generated
points. The ticks on these axes should adjust automatically based on the randomly
generated scatterplot points.
c. [5 pt] Each point’s radius will be a value between 1 and 5 inclusively, determined by
the point’s x coordinate. Use a linear scale for the radius, to map the domain of X
values to the range of [1,5].
d. [3 pt] All points with radii greater than the average radius of all scatterplot points
should be colored blue. All other points should be colored green.
e. Your full name which can appear above or below the scatterplot.
Note: No external libraries should be used. The index.html file can only refer to d3.v3.min.js within
the d3 folder.
Q4 [10 pt] OpenRefine
a. Watch the videos on the OpenRefine’s homepage for an overview of its features.
Download OpenRefine (latest release : 2.6 r.c2)
b. Import Dataset:
● Launch OpenRefine. It opens in a browser (127.0.0.1:3333).
● Choose “Create Project” – This Computer – “menu.csv”. Click “Next”.
● You will now see a preview of the dataset. Click “Create Project” in the upper right
corner.
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c. Clean/Refine the data:
Note: OpenRefine maintains a log of all changes. You can undo changes. See the
“Undo/Redo” button on the upper left corner.
i. [2 pt] Clean the “Event” and “Venue” columns (Select the column to be a Text Facet, and
cluster the data. Note: You can choose different “methods” and “keying functions” while
clustering). A clean “Event” column should have no more than 1650 unique values, and a
clean “Venue” column should have no more 150 unique values. Record the number of
unique values for each column after they have been cleaned in your observations. Note:
The number of unique values for a column is shown in the facet box under the title.
ii. [2 pt] Use the Transform feature (under Edit Cells → Transform) and a General Refine
Evaluation Language (GREL) expression to represent the dates in column (“date”) in a
format such that “2000-12-31“ is converted to “Sunday, December 31, 2000”. Record the
GREL expression you used in the observations text file.
iii. [1 pt] List a column in the dataset that contains only nominal data, and another column
that contains only ordinal data. (Refer to their definitions here)
iv. [2 pt] The “Note” column records whether the menu is present in English, German, or
Both. Create a new column to record all the menus that are both in German and English as
a boolean value. Use the Add column feature (under Edit Columns → Add column based
on this column…) and a GREL expression to create the new column. Notice that the note
may specify the menu as “GERMAN AND ENGLISH” or “ENGLISH AND GERMAN”. The
column should record “true” if the menu is bilingual and “false” if the menu is not. Record
the GREL expression you use in the observations text file.
v. [1 pt] Some call numbers have “_wotm” appended to the end of the 8 digit number. Use
the Transform feature and a GREL expression to remove the text at the end of the number.
Record the GREL expression you used in the observations text file.
vi. [2 pt] Experiment with Open Refine, and list a feature (apart from the ones used above)
you could additionally use to clean/refine the data, and comment on how it would be
beneficial in fewer than 50 words. (Basic operations like editing a cell or deleting a row do
not count.)
Deliverables: Place all the files listed below in the Q4 folder
● Q4Menu.csv : Export the final table.
● changes.json : Submit a list of changes made to file in json format. Use the “Extract
Operation History” option under the Undo/Redo tab to create this file.
● Q4Observations.txt : A text file with answers to parts c(i), c(ii), c(iii), c(iv), c(v), and c(vi)
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Important Instructions on Folder structure
The directory structure must be:
HW1-LastName-FirstName/
|— Q1/
|—- followers.csv
|—- friends.csv
|—- graph.png / graph.svg
|—- graph_explanation.txt
|—- metrics.txt
|—- script.py
|— Q2/
|—- Q2.SQL.txt
|—- Q2.OUT.txt
|—- observation.txt
|— Q3/
|—- index.html
|—- d3/
|—- d3.v3.min.js
|— Q4/
|—- changes.json
|—- Q4Menu.csv
|—- Q4Observations.txt
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