Assignment 4: Joins (Relational Data) and Visualization

CptS 475/575: Data Science
Assignment 4: Joins (Relational Data) and Visualization

General instruction: This assignment has three problems. The first problem is on Joins (relational
data from the data wrangling series of topics), and the second and third problems are on
visualization.
Your solution will be submitted as a PDF (or HTML) file. You are encouraged to use R
Markdown or a similar tool (like Jupyter) to prepare your file.
Problem 1 (50 pts). This problem will involve the nycflights13 dataset (including tables flights,
airlines, airports, planes and weather), which we saw in class. It is available in both R and Python,
however R is recommended for at least the visualization portion of the question. You can get more
information about this package on github at
https://github.com/tidyverse/nycflights13
The data tables can be found in the data-raw folder of the above-mentioned github repository.
Additionally, the flights.csv file which was used in assignment 3 is available in the Datasets
module on Canvas. Start by installing and importing the dataset to your chosen platform. We will
first use joins to search and manipulate the dataset, then we will produce a flight count
visualization.
a. (10 pts) Filter the dataset (using a left join) to display the tail number, year, month,
day, hour, origin, and humidity for all flights heading to Tampa International
Airport (TPA) after 12pm on November 1, 2013.
b. (10 pts) What is the difference between the following two joins?
anti_join(flights, airports, by = c("dest" = "faa"))
anti_join(airports, flights, by = c("faa" = "dest"))
c. (10 pts) Filter the table flights to only show flights with planes that have flown at
least 100 flights. Hint: tailnum is used to identify planes. (suggested functions: R:
semi_join(), count(), filter(); Python: merge(), value_counts(), filter())
d. (10 pts) What weather conditions make it more likely to see a delay? Briefly discuss
any relations/patterns you found.
e. (10 pts) Produce a map that sizes each destination airport by the number of
incoming flights. You may use a continuous scale for the size. Here is a code
snippet to draw a map of all flight destinations, which you can use as a starting
point. You may need to install the maps packages if you have not already. Adjust
the title, axis labels and aesthetics to make this visualization as clear as possible.
flights %>%
 left_join(airports, c("dest" = "faa")) %>%
 ggplot(aes(lon, lat)) +
 borders("state") +
 geom_point() +
 coord_quickmap()
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Problem 2 (30 pts). The goal of this problem to create a visualization of the US map showing the
states/territories and the number of vaccine doses administered in each state/territory. For this task,
you will work with the data table for COVID-19 vaccinations in the US provided by CDC (Centers
for Disease Control and prevention). The dataset can be found at
https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total
However, we are interested only in 4 columns in this dataset: State/Territory/Federal Entity, Total
number of Janssen doses administered, Total number of Moderna doses administered, Total
number of Pfizer doses administered
A subset of the data (covid19_vaccinations_USA.csv) containing only these 4 columns has already
been made available on Canvas, in the Datasets module. The dataset contains 63 observations of
4 variables:
 Name Description
state State/ Territory/Federal Entity
Janssen_doses Total number of Janssen doses administered
Moderna_doses Total number of Moderna doses administered
Pfizer_doses Total number of Pfizer doses administered
Create visualizations of the US map coloring the states or sizing the point/marker for the states
according to the number of doses for each vaccine (one map per vaccine). Compare the
administration of vaccines in different states using the maps you generated (we recommend that
you maintain a constant scale for showing the number of doses in all the three maps; hint: check
min and max values in the dataset for the scale).
You are free to choose any mapping tool you wish to produce this visualization. Try to make your
visualization as nice looking as possible. You can use the state column directly to visualize the
observations or you could get the coordinates for each state (depending on the tool and your
visualization). Research how this can be done and use what you find. The dataplusscience.com
website has some blogs about mapping that you may find useful. After you have coordinates you
can use different methods for mapping. You can use packages available in R or Python. Another
simple method is probably through https://batchgeo.com/features/map-coordinates/ . However,
you can also use d3 to map the locations, if you want to learn something that you could use for
other projects later.
Problem 3 (20 pts). Create a word cloud for an interesting (relatively short, say a couple of pages)
document of your own choice. Examples of suitable documents include: summary of a recent
project you are working or have worked on; your own recent Statement of Purpose or Research
Statement or some other similar document.
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You can create the word clouds in R using the package called wordcloud or you can use another
tool outside of R such as Wordle. If you do this in R, you will first need to install wordcloud
(using install.packages("wordcloud")) and then load it (using library(wordcloud)). Then look up
the documentation for the function called wordcloud in the package with the same name to create
your cloud. Note that this function takes many arguments, but you would be mostly fine with the
default settings. Only providing the text of your words may suffice for a minimalist purpose.
You are welcome (and encouraged) to take the generated word cloud and manipulate it using
another software to enhance its aesthetic. If you have used Wordle instead of R, Wordle gives you
functionalities to play with the look of the word cloud you get. Experiment till you get something
you like most.
Your submission for this would include the figure (cloud) and a brief caption that describes the
text for the cloud. For example, it could be something like ``Jenneth Joe's Essay on Life During
Pandemic, written in June 2021."