Count the number of missing values that are denoted by NA. Count the number of missing values

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

In a .Rmd file, write the code and include written answers to the following steps:

1a. Read the data.  Tell R to treat admission_type_id, discharge_disposition_id, and admission_source_id as categorical.

1b. Clean the variables `diag_1`, `diag_2`, and `diag_3` using the code you wrote in the Lesson 11 Homework.  You should end up with 3 new variables, `diagnosis1`, `diagnosis2`, and `diagnosis3`, containing human-readable interpretations of the ICD-9 billing codes.

1c. Remove the old variables `diag_1`, `diag_2`, `diag_3`.  If you created any intermediate variables (such as `injury_cause1`), remove those also.

1d. Use code to determine which columns have no variability (i.e., all rows contain the same value).  Print the names of the columns, and remove those columns.

2a. Count the number of missing values that are denoted by NA.  Count the number of missing values that are denoted by "?".  (In a factor/character variable, R will not automatically ignore rows containing "?".)  Which column has the most missing values?

2b. Replace the "?" in the `race` column with NAs.  Then use mode imputation to impute the NAs in the data.  (Leave the "?" in other columns alone.)

3a. Use linear regression to model the number of diagnoses a patient has as a function of `age` and `diagnosis1`.

3b. As age increases from [0-10) to [90-100), what happens to the number of diagnoses?  Explain your answer based on the linear regression.  Illustrate your answer with a graph using `ggformula`.

3c. Compared to people whose diagnosis1 is a blood issue (the default value), which types of diagnosis1 are significantly associated with an increased number of diagnoses?  Which types are significantly associated with a decreased number of diagnoses?  Explain.  Illustrate your answer with a graph using `ggformula`.

4a. Under the Hospital Readmission Reduction Program, hospitals where large proportions of patients are readmitted within 30 days may be penalized in their Medicare reimbursements.  Create a binary variable that tells whether `readmitted` is `<30` or not.

4b. Make a bar graph and a conditional bar graph of the relationship between `diabetesMed` and whether a patient was readmitted within 30 days.  Also make a bar graph and a conditional bar graph of the relationship between `gender` and whether a patient was readmitted within 30 days.  

4c. If we want to predict whether a patient will be readmitted within 30 days, which variable is more informative:  `diabetesMed` or `gender`?  Explain your choice, referring to specific aspects of the graphs you made.

4d. Use logistic regression to model whether a patient will be readmitted within 30 days, based on `number_inpatient` and *one* of the variables `diabetesMed` or `gender` (choose the one that is more informative).

4e. Write 2-3 sentences describing the direction of association between the predictor variables and the probability that the patient will be readmitted within 30 days.

4f. Make *one* graph showing the predicted probability of readmission as a function of both predictor variables.

Now, knit your .Rmd file containing all the steps above into a LastName_FirstName.pdf file. You will be submitting the knitted file along with the pdf file for the R portion of the project. 

Your score for the R portion of the project will be based on the following:

Criterion	Excellent	Average	Weak
.Rmd file 5 pts	Knitted .docx or .pdf No huge data dumps Easy to read, with appropriate formatting (e.g., headers) 5 pts	Knitted .docx or .pdf No huge data dumps 3 pts	Not knitted, or contains a huge data dump (e.g., displaying the entire data set) 0 pts
Code completeness and efficiency 30 pts	Followed all instructions, with clean and well-organized code 30 pts	Code is disorganized or has obvious inefficiencies 23-28 pts	Did not follow all instructions 0-21 pts
Accuracy 15 pts	Results in problems 1d and 2a are correct.  Other output accurately reflects the data source. 15 pts	Small or subtle errors.  Counting NAs but not “?” in problem 2a falls into this category. 11-14 pts	Problem 1d is incomplete, or major errors in accuracy elsewhere. 0-10 pts
Graphs 25 pts	Selects appropriate graph types for problems 3b, 3c, 4b, 4f and implements them correctly 25 pts	Graphs are acceptable but sub-optimal 18-23 pts	Missing or inappropriate graphs 0-17 pts
Explanations 25 pts	Accurate, complete explanations for problems 3b, 3c, 4c, 4e 25 pts	Explanations are accurate but could be more thorough 18-23 pts	Errors in explanations 0-17 pts

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