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Assignment 6
PS 3780 Data Literacy & Visualization, Summer 2022
Due Date: Thursday, June 30, 2022 at 11:59 p.m.
Please save your visualizations and answers to these questions as one .pdf
�le (use the �save as� function in most word processors). Be sure to include your name,
your teammate's name if there is anyone, and the assignment number. Submit the �le
to Carmen by the due date. Remember we are looking for professional visualizations so
please include a meaningful title as well as axis labels and a legend.
Presidential Approval
Return to 538 (https:
projects.fivethirtyeight.com
iden-approval-rating/)
to download the presidential approval data for President Biden. Scroll to the bottom of
the page and click on �polls" to get the proper dataset. Subset the dataset to only
include polls where the `Subgroup' column has a value of �All polls".
We are going to explore Net Approval and Approval Ratio. Net Approval is the
di�erence between approval and disapproval. Approval Ratio is approval divided by
disapproval. You created these variables in Assignment 3; do it again for this updated
dataset using the same code as before.
Create two visualizations to address the theory of the presidential honeymoon e�ect;
one for Net Approval and one for Approval Ratio. Scholars suggest that new presidents
have a short period of time at the beginning of their presidency when they are not
overly criticized. The period of time may range from 30 to 100 days. After the period,
presidential approve quickly reacts to what was accomplished in that period and opinion
ecomes more re�ective of actual presidential action. This tends to lead to decreasing
approval as it is di�cult for presidents to make quick unilateral change. Select a time
length within the propsed period to visualize the honeymoon e�ect for President Biden.
With a plot and a paragraph for each plot, use polling data from the most common
polling agencies to suggest whether your chosen honeymoon length �ts the theory fo
each dependent variable. Make sure your visualization includes the following features. (6
pt)
ˆ Include only polling agencies that have reported more than 70 polls for the �All
polls" group (there should be 4).
ˆ Include a reference line at the date where you think the honeymoon may have ended
(how many days).
1
https:
projects.fivethirtyeight.com
iden-approval-rating
ˆ Include a reference line at a key value of the dependent variable (what is `even' fo
Net and Ratio).
ˆ Include separate trend lines for pre- and post-honeymoon polls from each agency
(a total of 8 trend lines).
ˆ Scatterplot points can be suppressed for clarity.
ˆ Include a title, axis labels, and a legend.
ˆ Your paragraphs should analyze your visualization and then make a conclusion
about the theory.
2
Answered Same Day Jun 29, 2022

Solution

Subhanbasha answered on Jun 30 2022
74 Votes
Document
There I have downloaded the data from the given link that is polls data of the president of US that is Biden. The data has many subgroups.
The following is the code to load the data into R
# Installing required package
install.packages('dplyr')
# Calling Required package
li
ary(dplyr)
# Reading the polls data into R
Polls_data <- read.csv('approval_polllist.csv')
As the requirement need to remove the data where the subgroup having only All Polls. So that I have created filtered data which is having subgroup only All Polls. The code as follows
# Filtering data only having All polls in subgroup
Polls_data <- Polls_data %>% filter(subgroup=="All polls")
In the next step we are going to use the data to create the two new columns that is net approval and approval ratio based on using approve and disapprove columns. The code as follows.
# Creating columns in the data that is Net Approval
Polls_data$Net_Approval <- Polls_data$approve-Polls_data$disapprove
# Creating columns in the data that is Approval Ratio
Polls_data$Approval_Ratio <- Polls_data$approve/Polls_data$disapprove
In this step we are going to filter data again to get the data only those poll agencies which are greater than 70 polls in the data of All polls category. For...
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