4/24/20 Assignment 1 KS MY x P a g e | 1
Tip: Read through this document in its entirety before you begin.
The assignment is to conduct research based on the information below, using R. After analyzing the data in R,
document the research and findings in a research paper in APA 7 format. Ask questions if you need to!
Topic: The Center for Disease Control and Prevention (CDC) uses the social vulnerability index (SVI)
to evaluate the impact of disasters on communities, weighting the damage with social factors in
the states of Kansas and Maryland (CDC, 2018a; CDC 2018b).
Problem: Each community requires independent evaluation to identify the vulnerability of the area. Two of
the contributing factors for this analysis are minority status and language limitations. The data
may lack credibility due to the fear of reprisal for persons that fall into these categories.
Exploring the social and physical characteristics, excluding these metrics can provide insight on
the overall impact on the SVI.
Question 1: What impact does the exclusion of the metrics that represent minorities and language limited
individuals have on the predictability of the CDC’s SVI, based on the 2018 data (CDC, 2018a;
Question 2: Does the CDC’s SVI have key characteristics that impact the preclude potential exclusion
without limiting the overall predictability of the SVI, based on the 2018 data (CDC, 2018a; CDC,
• The data and data dictionaries are online.
o Center for Disease Control and Prevention. (2018a). Social Vulnerability Index [data set].
o Center for Disease Control and Prevention. (2018b). Social Vulnerability Index [code book].
o Note: Your raw data must be this report in its original form. Use the data dictionary to
understand the data.
• Create a subset of the data. Consider the metrics that are used in creating the SVI. Use the data
dictionary to identify the state variable field, along with the appropriate fields that represent the SVI
metrics, considering the research questions. The SVI index’s variable name is RPL_THEMES, in
▪ Persons below the poverty estimate
▪ Civilian unemployed estimate
▪ Per capita income estimate
▪ Persons with no high school diploma
o Household and composition disability features
▪ Ages 65 and older
▪ Ages 17 and under
▪ Persons with a disability, over the age of 5
▪ Single-parent households
o Minority status and language limitations
▪ Persons with minority status
▪ Persons with no or minimal use of the English language
o Housing types and transportation
▪ Multi-unit dwellings (10 or more units)
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▪ Mobile homes
▪ Homes with more residents than a home is designed for
▪ Homes with no vehicle
▪ Group quarters or institutionalized quarters
Note: Do not use the columns that are follow-on calculations of these columns. Variable names
preceded with “E_” are actual measures, while “M_” represents the margin of error estimates. Do
not include the margin of error estimates at this time. Considering the research questions, after
subsetting and excluding the variable that houses the STATE field, there will be 13 columns with
relevant information for analysis. The state field can be utilized in data exploration.
• Only clean the elements of the data that are inaccurately represented, such as missing values and data
• Do not remove missing values during cleaning. If missing values need to be removed for analysis
method, do it during the preparation for analysis.
• When changing data values or types, ensure that you validate that the change occurred and the
change is what was expected.
• Conduct two types of analysis: exploratory data analysis and a random forest model. You will move
through the sub-stages of Analyze two times; profile, prepare, and apply is required for each method of
analysis in your program. It is consolidated in your research paper. Make sure that your analyses align
with the research questions.**
• During exploratory data analysis, exclude visualizations in your final program or research paper that do
not serve a purpose toward the objective of the research or represent meaningful information. Just
remember, the relationships that do not exist in the data are sometimes as significant as the
relationships that do exist.
o Define your plan for all analyses.
o Your plan for the random forest model will include splitting the data after seeding it so that
training and testing evaluations can be conducted; additionally, you will exclude missing values
from the model.
o Carry out actions on the data that are necessary to prepare the data for the analyses.
• Apply (or Analyze)
o When carrying out the random forest modeling, it may be imperative to understand how much
time your analyses may take.
o Ensure that you analyze the results and understand what the different outcomes of the model
represent. Provide interpretations of the overall model that is trained, how the trained model
performs with the test data, and what features are most important for the explained variance.
Results, Impact of the Results:
• Ensure that all analyses, visual or otherwise included in the final version of the program, include
interpretations of what these analyses indicate.
• Make sure that you do not speculate! Use evidence to
support any assertions that you make.
• You must also include recommendations for future analysis. An example might look something like this:
o An opportunity for further research, based on gaps found in the random forest modeling, is to
look at the ability to tune the parameters further, to improve the outcome.
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o Additionally, an opportunity for future research is exploration modeling to determine what other
variables, when eliminated, have little or no impact on the ability to predict the SVI based on the
supporting characteristics in the data.
• You will base your recommendations on your findings in the analysis you conduct.
Create a random forest model for each state you were assigned. Is there a difference in the models’ results?
What does that result mean in terms of the data? Make sure that you do not speculate! Use evidence to
support any assertions that you make.
Required files to submit:
1) Research paper in APA 7 format; MS Word document file type
2) R Script; final version
Good to know:
• When submitting in Blackboard, you may receive an error, because the R file type is not recognized.
That is okay. It is only indicating that SafeAssign cannot evaluate that part of your submission.
o The research paper will be written in a professional writing style, following APA 7 student paper
format; you can use the student paper template.
o The document shall be 3-5 pages or at least 800 words. The page count does include the cover
page or reference page.
o Ensure that every reference in your reference list is also cited in the text. Do not forget to cite
and reference the source of the data.
• When developing your research paper, you may modify the topic, problem, and research questions.
However, the minimum requirements for the method of analysis cannot be altered.
• Ensure that you make the research yours and complete this assignment independently.
• There are several different versions of this assignment. If you complete a version of this assignment
that is not available to you in Blackboard, you will violate your pledge.
• If you are concerned about how long the model is taking to run, you have a few options.
o With the library GuessCompx, you can use the function CompEst() to evaluate the complexity
and time it will take to process the function. Some things have to be done to the setup to use
this methodology, though.
If the original model is called
model <- train(outcome~.,data=d,subset=trainer,importance=T,method=”rf”)
You would rewrite this as
f <- function(d) train(outcome~.,data=d,subset=trainer,importance=F,method=”rf”)
o With the library doParallel, you can use the functions makePSOCKcluster() and
registerDoParallel() you can run models in parallel, to improve performance.
▪ Prior to processing the model, use the following as an example, use help or Google to
learn more. This will maximize the processor power.
cl <- makePSOCKcluster(4) # the value is the number of processor cores in your device registerDoParallel(cl) model <- train(outcome~.,data=d,subset=trainer,importance=T,method=”rf”,allowParallel=T)