Please follow the question and answer it, I need my code in python in rstudio

You must only analyse the specified data. No You should explain your decisions with other data is to be used for this assessment. respect to: All data manipulation and analyses must be - any manipulation of the data structure done within R. - handling of groups with low frequency \# Data structure counts The following variables are collected for a - any redundant variables sample of students on the history course: \#Question 2 status - If course is 'Complete' or 'Pending'. It is suggested that the average 'rating' for the rating - Student rating of course (higher history course is 5. Perform an appropriate ratings show higher student regard for the statistical test of this hypothesis and create a course). 95% confidence interval for the average class - Number of tutors run classes attended. rating. Your answer should include: a statement of the statistical hypotheses; online - Number of times accessed online relevant code and output for the statistical system. test; interpretation and summary of the library - Number of library visits. results. Test1 - Mark for Test 1. \#Question 3 Test2 - Mark for Test 2. Show and interpret the linear correlations between 'rating' and each of the four test Test3 - Mark for Test 3. results (i.e., 'Test 1', 'Test 2', 'Test 3' and 'Test Test4 - Mark for Test 4. 4'). Marks are awarded for well-designed output, and the interpretation of the output. gender - Gender. \#Question 4 " \{r libraries, include=FALSE } Produce relevant descriptive statistics and \# Load any required libraries/additional files_ exploratory graphics for 'rating' and each of here the four test results, with a particular focus on how the patterns differ according to 'gender'. " rr data } Marks are awarded for well-designed output, \# Load dataset here and the interpretation of the output. "\# QUESTIONS START HERE \# Question 1: Data Preparation Ensure that you have prepared your submission as per 'Instructions to Students'. Note that only observations with status = complete are considered to be valid, as such only status = complete should be included in any analyses. Select only observations where 'status' = 'complete', then implement any further required data preparation prior to analyses