Description
Problem 1 (50 pts). This problem will involve the nycflights13 dataset (including the 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
You can access the data tables in R by loading the nycflights13 library using the following:
library(nycflights13)
Alternatively, you can download the csv files from the Modules page on Canvas. The files are
flights.csv, weather.csv, planes.csv, airports.csv, airlines.csv.
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. How many flights happened
during the given time frame that day?
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) Select the origin and destination airports and their latitude and longitude
for all flights in the dataset (using one or more inner joins). How many flights are
there in your result?
d. (10 pts) Use group_by and count to get the number of flights to each unique
carrier/dest combination. How many unique combination of carrier/dest are
present?
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()
Problem 2 (30 pts). The goal of this problem to create visualizations of the US map showing the
states/territories and the Gross Domestic Product (GDP) of each state/territory. For this task, you
will work with the data table for GDP by States in the US provided by BEA (Bureau of Economic
Analysis). The dataset can be found at
https://apps.bea.gov/regional/histdata/
However, we are interested only in 5 columns in this dataset: GeoName, 2019, 2020, 2021, 2022
A subset of the data (us_states_gdp.csv) containing only these 5 columns has already been made
available on Canvas, in the Datasets module. The dataset contains 51 observations of 5 variables:
Name Description
state GeoName (State/ Territory/Federal Entity)
gdp_2019 GDP in the year of 2019
gdp_2020 GDP in the year of 2020
gdp_2021 GDP in the year of 2021
gdp_2022 GDP in the year of 2022
Create visualizations of the US map coloring the states or sizing the point/marker for the states
according to the GDP for each state (one map per year). Compare the GDP of different states for
all the years using the maps you generated (we recommend that you maintain a constant scale for
showing the GDP in all the four 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. You can use
packages available in R or Python. For example, the usmap package in R is convenient for plotting
the US map. Alternatively, 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. 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.
Try to make your visualization as nice looking as possible.
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.
You can create the word cloud 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. An example of caption could be something like “Jenneth Joe’s Essay on Life
During Pandemic, written in June 2021.”
