R Question

What relationships exist between the pre-election polling attributesand the 2020 election results that indicate that former President Trump would or could lose the election?

Use Table 1 and Table 2 to understand what information is available for the project. Download the RData files from this document.

Table 1
Data Dictionary for the Polling before the 2020 Presidential Election Data

Column

Description

State

The target location of the polling sample

Candidate Names

The candidate receiving the percentage of polling votes in pct

Start Date

The first day interviews were conducted for this poll

End Date

The last day interviews were conducted for this poll

Pollster

The name of the polling organization

Sample Size

The size of the sample

Target Population

Whether the population interviewed was adults a, registered voters rv, voters v, or likely voters lv

Percentage

The percentage of the vote the candidate in candidate_namereceived in the poll

Poll Identification Number

Specific poll the data is derived from

Note. The data dictionary of the polling data leading up to the 2020 presidential election is adapted from FiveThirtyEight (2020).

Table 2
Data Dictionary for the Results of the 2020 Presidential Election Data

Column

Description

State

The location of the voting results

Available Electoral Votes

The number of electoral votes allocated to the state in the statefield

Biden

The percentage of election votes Biden received

Trump

The percentage of election votes Trump received

Note. The data dictionary of the 2020 presidential election results is adapted from Wikipedia (n.d.).

Click here to download the data. (polls2020.RData)

Click here to download the data. (results2020.RData)

You will document the objective and results of visual analysis in an APA 7 formatted student paper. This document is not an analysis plan; your paper needs to describe the results of an executed visualization project. It may help to think of the paper as a brief business report for a busy executive, except that you will use APA 7 formatting.

You will analyze the data in R. You will submit an R script file with programming that demonstrates the collection, cleaning, exploration, and analysis of the data. When you are conducting your research, you will come across key findings. A key finding is a graph that is interesting and must specifically relate to the overall objective of the research project. Only key findings are included in the research paper. Investigating critical findings from multiple perspectives in the programming will demonstrate your understanding of the weaknesses of visual analysis and prevent false conclusions. If you have done this correctly, there should be numerous figures in the programming that are not in the paper. Lastly, make sure that you provide interpretations of the information learned from each figure included in the paper. The report shall also include how these findings relate to the research objective.

When you think you’ve finished your analysis, revisit the objective. Did you achieve the objective? Did you provide an answer or answers to the research question? Were you thorough? Did you make sure that you did not misinterpret or misrepresent the results due to false conclusions?

Good to know

When you initialize the R script file for the assignment, make sure that the file begins with the three leading comments required in every script file. After these leading comments, call the necessary libraries and load the data before documenting other programming code. Don’t forget to use inline comments.

Reported results not found in the script file will be interpreted as unsupported or erroneous findings because there is no evidence to support the information.

I encourage you to write your report in its entirety before you add any figures. After you’ve written and proofread your paper, then place tables and figures between complete paragraphs, limiting the amount of whitespace figures. The figure labels must be in this order, as well. For example, the first figure is Figure 1; the second is Figure 2, and so on.

A professionally written report will not include discussions regarding R, RStudio, or programming code. Use real words. Don’t use variable names in place of real words. For example, candidate_names is not a real word. However, you could refer to the candidates’ names because those are real words.

Submission Requirements

When you document this information, you will need to write it as a paper. This is not a blog, a discussion, or a short answer paper. You will need to include an introduction, a topic sentence, supporting paragraphs, and a conclusion. Not great at writing? Go to Start Here in the main menu and look at the resources available to you. Make sure that you document your work using the standards of APA 7. To help with formatting, use the APA 7 student paper template.

You must submit a paper and an R script file for this assignment.

This document does not have a minimum or maximum length. Typically, this submission is about two pages, not including the cover page or reference section. No external sources are required for this assignment, except for the sources of the data. For every reference listed, there must also be content cited that is derived from the references.

References

FiveThirtyEight. (2020, November 2). 2020 election forecast [data set and code book] https://data.fivethirtyeight.com/

Kirk, A. (2019). Data visualisation: A handbook for data driven design (2nd ed.). Sage.

Wikipedia. (n.d.). 2020 United States election results [data set]. Retrieved May 8, 2020, from https://en.wikipedia.org/wiki/2020_United_States_presidential_election/

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R Question

What relationships exist between the pre-election polling attributesand the 2020 election results that indicate that former President Trump would or could lose the election?

Use Table 1 and Table 2 to understand what information is available for the project. Download the RData files from this document.

Table 1
Data Dictionary for the Polling before the 2020 Presidential Election Data

Column

Description

State

The target location of the polling sample

Candidate Names

The candidate receiving the percentage of polling votes in pct

Start Date

The first day interviews were conducted for this poll

End Date

The last day interviews were conducted for this poll

Pollster

The name of the polling organization

Sample Size

The size of the sample

Target Population

Whether the population interviewed was adults a, registered voters rv, voters v, or likely voters lv

Percentage

The percentage of the vote the candidate in candidate_namereceived in the poll

Poll Identification Number

Specific poll the data is derived from

Note. The data dictionary of the polling data leading up to the 2020 presidential election is adapted from FiveThirtyEight (2020).

Table 2
Data Dictionary for the Results of the 2020 Presidential Election Data

Column

Description

State

The location of the voting results

Available Electoral Votes

The number of electoral votes allocated to the state in the statefield

Biden

The percentage of election votes Biden received

Trump

The percentage of election votes Trump received

Note. The data dictionary of the 2020 presidential election results is adapted from Wikipedia (n.d.).

Click here to download the data. (polls2020.RData)

Click here to download the data. (results2020.RData)

You will document the objective and results of visual analysis in an APA 7 formatted student paper. This document is not an analysis plan; your paper needs to describe the results of an executed visualization project. It may help to think of the paper as a brief business report for a busy executive, except that you will use APA 7 formatting.

You will analyze the data in R. You will submit an R script file with programming that demonstrates the collection, cleaning, exploration, and analysis of the data. When you are conducting your research, you will come across key findings. A key finding is a graph that is interesting and must specifically relate to the overall objective of the research project. Only key findings are included in the research paper. Investigating critical findings from multiple perspectives in the programming will demonstrate your understanding of the weaknesses of visual analysis and prevent false conclusions. If you have done this correctly, there should be numerous figures in the programming that are not in the paper. Lastly, make sure that you provide interpretations of the information learned from each figure included in the paper. The report shall also include how these findings relate to the research objective.

When you think you’ve finished your analysis, revisit the objective. Did you achieve the objective? Did you provide an answer or answers to the research question? Were you thorough? Did you make sure that you did not misinterpret or misrepresent the results due to false conclusions?

Good to know

When you initialize the R script file for the assignment, make sure that the file begins with the three leading comments required in every script file. After these leading comments, call the necessary libraries and load the data before documenting other programming code. Don’t forget to use inline comments.

Reported results not found in the script file will be interpreted as unsupported or erroneous findings because there is no evidence to support the information.

I encourage you to write your report in its entirety before you add any figures. After you’ve written and proofread your paper, then place tables and figures between complete paragraphs, limiting the amount of whitespace figures. The figure labels must be in this order, as well. For example, the first figure is Figure 1; the second is Figure 2, and so on.

A professionally written report will not include discussions regarding R, RStudio, or programming code. Use real words. Don’t use variable names in place of real words. For example, candidate_names is not a real word. However, you could refer to the candidates’ names because those are real words.

Submission Requirements

When you document this information, you will need to write it as a paper. This is not a blog, a discussion, or a short answer paper. You will need to include an introduction, a topic sentence, supporting paragraphs, and a conclusion. Not great at writing? Go to Start Here in the main menu and look at the resources available to you. Make sure that you document your work using the standards of APA 7. To help with formatting, use the APA 7 student paper template.

You must submit a paper and an R script file for this assignment.

This document does not have a minimum or maximum length. Typically, this submission is about two pages, not including the cover page or reference section. No external sources are required for this assignment, except for the sources of the data. For every reference listed, there must also be content cited that is derived from the references.

References

FiveThirtyEight. (2020, November 2). 2020 election forecast [data set and code book] https://data.fivethirtyeight.com/

Kirk, A. (2019). Data visualisation: A handbook for data driven design (2nd ed.). Sage.

Wikipedia. (n.d.). 2020 United States election results [data set]. Retrieved May 8, 2020, from https://en.wikipedia.org/wiki/2020_United_States_presidential_election/

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R Question

Review the attached file. Suzie has an issue. She can either move to NY or FL and needs to review some data that her agent gave her. The agent reviewed house prices and crime ratings for houses that Suzie would be interested in based on her selection criteria. She wants to live in an area with lower crime but wants to know a few things:

  1. Is it more expensive or less expensive to live in FL or NY?
  2. Is the crime rate higher in FL or NY (Note a low score in crime means lower crime)?
  3. Is the crime rate higher in lower or higher house price areas?

Using the R tool, show the data in the tool to answer each of the questions. Also, show the data visualization to go along with the summary.

  1. If you were Suzie, where would you move based on the questions above?
  2. After you gave Suzie the answer above (to #4), she gave you some additional information that you need to consider:
    1. She has $100,000 to put down for the house.
    2. If she moves to NY she will have a job earning $120,000 per year.
    3. If she moves to FL she will have a job earning $75,000 per year.
    4. She wants to know the following:
      1. On average what location will she be able to pay off her house first based on average housing prices and income she will receive?
      2. Where should she move and why? Please show graphics and thoroughly explain your answer here based on the new information provided above.

Note: The screenshots should be copied and pasted and must be legible. Only upload the word document. Be sure to answer all of the questions above and number the answers. Be sure to also explain the rational for each answer and also ensure that there are visuals for each question above. Use two peer reviewed articles to support your position.

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R Question

Attached Files:

Review the attached file. Suzie has an issue. She can either move to NY or FL and needs to review some data that her agent gave her. The agent reviewed house prices and crime ratings for houses that Suzie would be interested in based on her selection criteria. She wants to live in an area with lower crime but wants to know a few things:

  1. Is it more expensive or less expensive to live in FL or NY?
  2. Is the crime rate higher in FL or NY (Note a low score in crime means lower crime)?
  3. Is the crime rate higher in lower or higher house price areas?

Using the R tool, show the data in the tool to answer each of the questions. Also, show the data visualization to go along with the summary.

  1. If you were Suzie, where would you move based on the questions above?
  2. After you gave Suzie the answer above (to #4), she gave you some additional information that you need to consider:
    1. She has $100,000 to put down for the house.
    2. If she moves to NY she will have a job earning $120,000 per year.
    3. If she moves to FL she will have a job earning $75,000 per year.
    4. She wants to know the following:
      1. On average what location will she be able to pay off her house first based on average housing prices and income she will receive?
      2. Where should she move and why? Please show graphics and thoroughly explain your answer here based on the new information provided above.

Note: The screenshots should be copied and pasted and must be legible. Only upload the word document. Be sure to answer all of the questions above and number the answers. Be sure to also explain the rational for each answer and also ensure that there are visuals for each question above. Use two peer reviewed articles to support your position.

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R Question

Learning Goal: I’m working on a r exercise and need an explanation and answer to help me learn.

# CDC data set

#REMEMBER THE DATA IS NAMED cdc, don’t forget to run the link first!
#Don’t forget to use $ to separate the name of the dataset and the column for some questions below ex: mean(cdc$weight) OR attach data with the code attach()

#INSTRUCTIONS
#Type the codes for each question
#Include answers to ALL questions in the script as a comment (with a #).
#Questions with a * next to it will ALSO require you to input the answer in Canvas.
#Lastly, upload this R script AND input the corresponding answers to Canvas.
#If you are missing codes or answers in this script, points will be deducted.

source(“http://www.openintro.org/stat/data/cdc.R“)

# 1 View the data

# *2 How many observations and variables are there?

# 3 Find the names of the variables

#Use summary code to view a summary of the age variable *Don’t forget to use $ if you didn’t attach data
# *4 What is the median age? What is the age of the youngest person in the data?

# *5 What is the height of the tallest and shortest person *Don’t forget to use $ if you didn’t attach data

# 6 Show the first 3 observations in the dataset

# 7 Show the last 5 observations in the dataset

# 8 Use the mean code to find the mean of the wtdesire column and save it as av_wtdesire *Don’t forget to use $ if you didn’t attach data

# *9 Run your item, av_wtdesire that you just created

# *10 Find the standard deviation of age and the variance of height

# 11 View a histogram of height

# *12 What class is genhlth

# 13 Create a table of hlthplan

# *14 What is the 3rd quantile of height and what does it mean? Hint: Use summary code

# 15 Find the first 3 observations of the weight column

###Normal Distribution###
# *16 Let’s assume the age a person buys their first house is normally distributed with a mean of 28 years old and a standard deviation of 7 years.

#a What is the probability that a person buys their first home younger than 24 years old?

#b What is the probability that a person buys their first home older than 35?

#c What is the probability that a person buys their first home between 20 and 37 years old?

#d What age is the cuttoff value corresponding to the top 20%?

#e What age is the cutoff value corresponding to the bottom 30%?

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R Question

# CDC data set

#REMEMBER THE DATA IS NAMED cdc, don’t forget to run the link first!
#Don’t forget to use $ to separate the name of the dataset and the column for some questions below ex: mean(cdc$weight) OR attach data with the code attach()

#INSTRUCTIONS
#Type the codes for each question
#Include answers to ALL questions in the script as a comment (with a #).
#Questions with a * next to it will ALSO require you to input the answer in Canvas.
#Lastly, upload this R script AND input the corresponding answers to Canvas.
#If you are missing codes or answers in this script, points will be deducted.

source(“http://www.openintro.org/stat/data/cdc.R“)

# 1 View the data

# *2 How many observations and variables are there?

# 3 Find the names of the variables

#Use summary code to view a summary of the age variable *Don’t forget to use $ if you didn’t attach data
# *4 What is the median age? What is the age of the youngest person in the data?

# *5 What is the height of the tallest and shortest person *Don’t forget to use $ if you didn’t attach data

# 6 Show the first 3 observations in the dataset

# 7 Show the last 5 observations in the dataset

# 8 Use the mean code to find the mean of the wtdesire column and save it as av_wtdesire *Don’t forget to use $ if you didn’t attach data

# *9 Run your item, av_wtdesire that you just created

# *10 Find the standard deviation of age and the variance of height

# 11 View a histogram of height

# *12 What class is genhlth

# 13 Create a table of hlthplan

# *14 What is the 3rd quantile of height and what does it mean? Hint: Use summary code

# 15 Find the first 3 observations of the weight column

###Normal Distribution###
# *16 Let’s assume the age a person buys their first house is normally distributed with a mean of 28 years old and a standard deviation of 7 years.

#a What is the probability that a person buys their first home younger than 24 years old?

#b What is the probability that a person buys their first home older than 35?

#c What is the probability that a person buys their first home between 20 and 37 years old?

#d What age is the cuttoff value corresponding to the top 20%?

#e What age is the cutoff value corresponding to the bottom 30%?

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R Question

Review the attached file. Suzie has an issue. She can either move to NY or FL and needs to review some data that her agent gave her. The agent reviewed house prices and crime ratings for houses that Suzie would be interested in based on her selection criteria. She wants to live in an area with lower crime but wants to know a few things:

  1. Is it more expensive or less expensive to live in FL or NY?
  2. Is the crime rate higher in FL or NY (Note a low score in crime means lower crime)?
  3. Is the crime rate higher in lower or higher house price areas?

Using the R tool, show the data in the tool to answer each of the questions. Also, show the data visualization to go along with the summary.

  1. If you were Suzie, where would you move based on the questions above?
  2. After you gave Suzie the answer above (to #4), she gave you some additional information that you need to consider:
    1. She has $100,000 to put down for the house.
    2. If she moves to NY she will have a job earning $120,000 per year.
    3. If she moves to FL she will have a job earning $75,000 per year.
    4. She wants to know the following:
      1. On average what location will she be able to pay off her house first based on average housing prices and income she will receive?
      2. Where should she move and why? Please show graphics and thoroughly explain your answer here based on the new information provided above.

Note: The screenshots should be copied and pasted and must be legible. Only upload the word document. Be sure to answer all of the questions above and number the answers. Be sure to also explain the rational for each answer and also ensure that there are visuals for each question above. Use two peer reviewed articles to support your position.

Place this order or similar order and get an amazing discount. USE Discount code “GET20” for 20% discount

Posted in Uncategorized