Use R instead of Python (R is a language used in the field of Data Science) to do these questions:

The SUPPORT Study (Study to Understand Prognoses Preferences Outcomes and Risks of Treatments) was a five-hospital study of 10000 critically ill hospitalized adults. Patients were followed for in-hospital outcomes and for long-term survival. We analyze 35 variables and a random sample of 1000 patients from the study. To get the data, load the Hmisc package and run the command getHdata(support). (Use R Studio)

Explore the variables and patterns of missing data in the SUPPORT dataset.

(a) Print univariable summaries of all variables.

(b) Make a plot showing the extent of missing data and tendencies for some variables to be missing on the same patients. (Weve done this before, but alternate techniques from functions in the Hmisc package could be useful.)

(c) Total hospital costs (variable totcst) were estimated from hospital-specific Medicare cost- to-charge ratios. Characterize what kind of patients have missing totcst. For this charac- terization use the following patient descriptors: age,sex, dzgroup, num.co, edu, income, scoma, meanbp, hrt, resp, temp.

(d) Use predictive mean matching to multiply impute cost 10 times per missing observation. De- scribe graphically the distributions of imputed values and briefly compare these to distributions of non-imputed values.

(e) Using the multiple imputed values, develop an overall least squares model for total cost (using the log transformation). The model should use the predictors in Problem 1 and should not assume linearity in any predictor but should assume additivity.