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Please help me solve the following questions in R Studio and provide the answer in R markdown pdf form. Thank you!
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Final Project Instructions
Each group is required to write a R Markdown PDF report for analyzing the COVID-19 data listed
below. Your report must provide the sections requested in the grading rubric below. You will
need to import and tidy the data, and then come up with your own research questions that you
would like to answer from the data by performing exploratory data analysis. If you would like to
perform a predictive model to answer your research questions that is fine but it is not required.
Try to be creative in your analysis and investigate the data in a way that your classmates most
likely will not. Each group can have at most 2 students, only one person in the group need to
submit their final project in Brightspace.
Your report is expected to tell a story with the data. Do not just report some statistics that you
find but, rather, to provide a coherent narrative of your findings. Some examples of the type of
report are as follows.
https://rpubs.com/bradleyboehmke/final_project_example
https://rpubs.com/murphm6/233993
https://rpubs.com/dyang9411/301598
Note that the above examples are presented in HTML format but you are required to submit the
PDF format of your own report.
Your final project must focus on at least one of the following datasets available at the Resources
tool on the course’s NYU Brightspace site:
• us.csv
• us-states.csv
• us-counties.csv
The above three datasets contain the US COVID-19 data till 01/13/2021, and were originally
obtained from The New York Times github repository: https://github.com/nytimes/covid-19data
See the github repository for the detailed data descriptions. Note that the github repository
tracks COVID-19 with daily updates. Thus, you are required to download the above three datasets
from the NYU Brightspace not the github repository. You may bring in some additional data
elsewhere to improve the analysis on the required data, but you need first try a submission with
the additional dataset files to the NYU Brightspace to ensure that the files are not too large to
upload. Note that you are allowed to resubmit your final project unlimited times before the
deadline.
You are required to submit the R Markdown file (.Rmd), its generated PDF output file (.pdf), and
any other files (except the above 3 CSV files), which will enable us to reproduce your report by
knitting your .Rmd file. Note that any missing file will result in a zero grade of your final project.
Your final individual project will be temporally graded on a 50-point scale according to the grading
rubric below, but will be eventually counted into your final grade with the weight as per the
syllabus. Your final project must be submitted via NYU Brightspace by 11:59 PM (US eastern time)
on January 20, 2023. Late submissions will receive a grade of zero.
Section
Standard
Possible
Points
Introduction/ Synopsis
1.1 Provide an introduction that explains the problem statement you are
addressing. Why should I be interested in this?
1.2 Provide a short explanation of how you plan to address this problem
statement (the data used and the methodology employed).
1.3 Discuss your current proposed approach/analytic technique you think
will address (fully or partially) this problem.
1.4 Explain how your analysis will help the reader of your analysis.
5
Packages Required
2.1 All packages used are loaded upfront so the reader knows which are
required to replicate the analysis.
2.2 Messages and warnings resulting from loading the package are
suppressed.
2.3 Explanation is provided regarding the purpose of each package.
5
Data Preparation
3.1 Original source where the data was obtained is cited and, if possible,
hyperlinked.
3.2 Source data is thoroughly explained (i.e. what was the original purpose
of the data, when was it collected, how many variables did the original
have, explain any peculiarities of the source data such as how missing
values are recorded, or how data was imputed, etc.).
3.3 Data importing and cleaning steps are explained in the text (tell me
why you are doing the data cleaning activities that you perform) and
follow a logical process.
3.4 Once your data is clean, show what the final data set looks like.
However, do not print off a data frame with 200+ rows; show me the data
in the most condensed form possible.
3.5 Provide summary information about the variables of concern in your
cleaned data set. Do not just print off a bunch of code chunks with str(),
summary(), etc. Rather, provide me with a consolidated explanation,
either with a table that provides summary info for each variable or a nicely
written summary paragraph with inline code.
10
Exploratory Data Analysis
4.1 Uncover new information in the data that is not self-evident (i.e. do not
just plot the data as it is; rather, slice and dice the data in different ways,
create new variables, or join separate data frames to create new summary
information).
4.2 Provide findings in the form of plots and tables. Show me you can
display findings in different ways.
4.3 Graph(s) are carefully tuned for desired purpose. One graph illustrates
one primary point and is appropriately formatted (plot and axis titles,
legend if necessary, scales are appropriate, appropriate geoms used, etc.).
4.4 Table(s) carefully constructed to make it easy to perform important
comparisons. Careful styling highlights important features. Size of table is
appropriate.
4.5 Insights obtained from the analysis are thoroughly, yet succinctly,
explained. Easy to see and understand the interesting findings that you
uncovered.
10
Modeling and
Functions/Loops (bonus)
5.1Perform modeling/multivariate and bivariate analyses on your outcome
of interest (provide the reasoning and rationale for the model/method
selected)
5.2 Provide interpretations of your model rather than just print out the
output
5.3 Provide well formatted tables
5
Summary
6.1 Summarize the problem statement you addressed.
6.2 Summarize how you addressed this problem statement (the data used
and the methodology employed).
6.3 Summarize the interesting insights that your analysis provided.
6.4 Summarize the implications to the reader of your analysis.
6.5 Discuss the limitations of your analysis and how you, or someone else,
could improve or build on it.
Section
Standard
Formatting & Other
Requirements (These are
requirements not a specific
section written in your
report.)
7.1 Proper coding style is followed and code is well commented (see
section regarding style).
7.2 Coding is systematic – complicated problem broken down into
subproblems that are individually much simpler. Code is efficient, correct,
and minimal. Code uses appropriate data structure (list, data frame,
vector/matrix/array). Code checks for common errors.
7.3 Achievement, mastery, cleverness, creativity: Tools and techniques
from the course are applied very competently and, perhaps, somewhat
creatively. Perhaps student has gone beyond what was expected and
required, e.g., extraordinary effort, additional tools not addressed by this
course, unusually sophisticated application of tools from course.
7.4 .Rmd fully executes without any errors and PDF produced matches the
PDF report submitted by student.
5
Possible
Points
15
Final Project Instructions
Each group is required to write a R Markdown PDF report for analyzing the COVID-19 data listed
below. Your report must provide the sections requested in the grading rubric below. You will
need to import and tidy the data, and then come up with your own research questions that you
would like to answer from the data by performing exploratory data analysis. If you would like to
perform a predictive model to answer your research questions that is fine but it is not required.
Try to be creative in your analysis and investigate the data in a way that your classmates most
likely will not. Each group can have at most 2 students, only one person in the group need to
submit their final project in Brightspace.
Your report is expected to tell a story with the data. Do not just report some statistics that you
find but, rather, to provide a coherent narrative of your findings. Some examples of the type of
report are as follows.
https://rpubs.com/bradleyboehmke/final_project_example
https://rpubs.com/murphm6/233993
https://rpubs.com/dyang9411/301598
Note that the above examples are presented in HTML format but you are required to submit the
PDF format of your own report.
Your final project must focus on at least one of the following datasets available at the Resources
tool on the course’s NYU Brightspace site:
• us.csv
• us-states.csv
• us-counties.csv
The above three datasets contain the US COVID-19 data till 01/13/2021, and were originally
obtained from The New York Times github repository: https://github.com/nytimes/covid-19data
See the github repository for the detailed data descriptions. Note that the github repository
tracks COVID-19 with daily updates. Thus, you are required to download the above three datasets
from the NYU Brightspace not the github repository. You may bring in some additional data
elsewhere to improve the analysis on the required data, but you need first try a submission with
the additional dataset files to the NYU Brightspace to ensure that the files are not too large to
upload. Note that you are allowed to resubmit your final project unlimited times before the
deadline.
You are required to submit the R Markdown file (.Rmd), its generated PDF output file (.pdf), and
any other files (except the above 3 CSV files), which will enable us to reproduce your report by
knitting your .Rmd file. Note that any missing file will result in a zero grade of your final project.
Your final individual project will be temporally graded on a 50-point scale according to the grading
rubric below, but will be eventually counted into your final grade with the weight as per the
syllabus. Your final project must be submitted via NYU Brightspace by 11:59 PM (US eastern time)
on January 20, 2023. Late submissions will receive a grade of zero.
Section
Standard
Possible
Points
Introduction/ Synopsis
1.1 Provide an introduction that explains the problem statement you are
addressing. Why should I be interested in this?
1.2 Provide a short explanation of how you plan to address this problem
statement (the data used and the methodology employed).
1.3 Discuss your current proposed approach/analytic technique you think
will address (fully or partially) this problem.
1.4 Explain how your analysis will help the reader of your analysis.
5
Packages Required
2.1 All packages used are loaded upfront so the reader knows which are
required to replicate the analysis.
2.2 Messages and warnings resulting from loading the package are
suppressed.
2.3 Explanation is provided regarding the purpose of each package.
5
Data Preparation
3.1 Original source where the data was obtained is cited and, if possible,
hyperlinked.
3.2 Source data is thoroughly explained (i.e. what was the original purpose
of the data, when was it collected, how many variables did the original
have, explain any peculiarities of the source data such as how missing
values are recorded, or how data was imputed, etc.).
3.3 Data importing and cleaning steps are explained in the text (tell me
why you are doing the data cleaning activities that you perform) and
follow a logical process.
3.4 Once your data is clean, show what the final data set looks like.
However, do not print off a data frame with 200+ rows; show me the data
in the most condensed form possible.
3.5 Provide summary information about the variables of concern in your
cleaned data set. Do not just print off a bunch of code chunks with str(),
summary(), etc. Rather, provide me with a consolidated explanation,
either with a table that provides summary info for each variable or a nicely
written summary paragraph with inline code.
10
Exploratory Data Analysis
4.1 Uncover new information in the data that is not self-evident (i.e. do not
just plot the data as it is; rather, slice and dice the data in different ways,
create new variables, or join separate data frames to create new summary
information).
4.2 Provide findings in the form of plots and tables. Show me you can
display findings in different ways.
4.3 Graph(s) are carefully tuned for desired purpose. One graph illustrates
one primary point and is appropriately formatted (plot and axis titles,
legend if necessary, scales are appropriate, appropriate geoms used, etc.).
4.4 Table(s) carefully constructed to make it easy to perform important
comparisons. Careful styling highlights important features. Size of table is
appropriate.
4.5 Insights obtained from the analysis are thoroughly, yet succinctly,
explained. Easy to see and understand the interesting findings that you
uncovered.
10
Modeling and
Functions/Loops (bonus)
5.1Perform modeling/multivariate and bivariate analyses on your outcome
of interest (provide the reasoning and rationale for the model/method
selected)
5.2 Provide interpretations of your model rather than just print out the
output
5.3 Provide well formatted tables
5
Summary
6.1 Summarize the problem statement you addressed.
6.2 Summarize how you addressed this problem statement (the data used
and the methodology employed).
6.3 Summarize the interesting insights that your analysis provided.
6.4 Summarize the implications to the reader of your analysis.
6.5 Discuss the limitations of your analysis and how you, or someone else,
could improve or build on it.
Section
Standard
Formatting & Other
Requirements (These are
requirements not a specific
section written in your
report.)
7.1 Proper coding style is followed and code is well commented (see
section regarding style).
7.2 Coding is systematic – complicated problem broken down into
subproblems that are individually much simpler. Code is efficient, correct,
and minimal. Code uses appropriate data structure (list, data frame,
vector/matrix/array). Code checks for common errors.
7.3 Achievement, mastery, cleverness, creativity: Tools and techniques
from the course are applied very competently and, perhaps, somewhat
creatively. Perhaps student has gone beyond what was expected and
required, e.g., extraordinary effort, additional tools not addressed by this
course, unusually sophisticated application of tools from course.
7.4 .Rmd fully executes without any errors and PDF produced matches the
PDF report submitted by student.
5
Possible
Points
15
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