[SOLVED] Cpts 475/575: data science assignment 4: joins (relational data) and visualization

Problem 1 (50 pts). This problem will involve the Lahman dataset (including the tables Batting,
Teams, Salaries, and Managers). It is available in R by loading the Lahman library using the
following command:
library(Lahman)Alternatively, you can download the csv files from the Modules page on Canvas. The files are
Batting.csv, Teams.csv, Salaries.csv, and Managers.csv. You can use Lahman_Desc.txt (also from
Modules) to check the column descriptions for each dataset.
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 playerID, yearID, teamID, stint,
G (games played), HR (home runs), and salary for all players who hit more than 30 home
runs in a single season and played for a team in New York (teamID “NYA” or “NYN”)
between 2010 and 2020. How many players match these criteria?b) (10 pts) What is the difference between the following two joins? Do not show the result of
these anti_joins in your submission.
anti_join(Salaries, Batting, by = c(“playerID” = “playerID”))
anti_join(Batting, Salaries, by = c(“playerID” = “playerID”))
What is the difference between semi_join and anti_join? Provide an example using the
Salaries and Batting tables.c) (10 pts) Select the teamID, yearID, and the total number of runs batted in (RBI) for each
team in the American League (AL) for the year 2015 (using one or more inner joins with
the Teams and Batting tables). How many total home runs were hit by American League
teams in 2015?d) (10 pts) Using the Managers and Teams tables, determine the number of seasons each
manager managed a team. Use group_by and count to get the number of unique
managerID and teamID combinations. How many unique combinations of managerID and
teamID are present? Are there any players with unusually high number of years as a
manager?e) (10 pts) Using the provided template as a start, produce a horizontal bar plot that shows the
number of wins for the top 10 teams in 2019. Adjust the axis labels to clearly represent the
teams and the number of wins. Add a meaningful title to the plot, and include the number
of wins as text on each bar for clarity.
Teams %>%
filter(yearID == 2019) %>%
select(teamID, W) %>%
ggplot(aes(x = reorder(teamID, W), y = W)) +
geom_bar(stat = “identity”, fill = “steelblue”) +
coord_flip()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 presidential votes received during an election year. For this
task, you will work with the us-presidents.csv dataset. The dataset can be found on the Modules
page on Canvas.There dataset consists of 612 observations of 4 variables: year, state, state_po, office, totalvotes.
For this question, you will create two visualizations of the US map for two presidential years of
your choice coloring the states or sizing the point/marker for the states according to the number of
total votes received from that state for the presidential election.Compare both maps and comment on any observations.
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.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.”