What do you find after these 2 lines of codes ? (2 points ) f. Use the summary () function to generate a numerical summary of the variable in the data set , draw a short statement of what you find . (3 points ) g. Create a new qualitative variable with name Elite, we are going to do some study for attribute Toploperc , We are going to divide universities into two groups based on whether or not the proportion of students coming from the top 10% of their high school classes exceeds 50%. Elite =rep ("No",nrow(college )) Elite [college$Top10perc >50]=" Yes" Elite =as.factor (Elite) college =data.frame(college ,Elite) (Here , as .factor () function converts quantitative variables into qualitative as . factor () variables .) Use summary () to see how many elite universities in datafram (4 points ) h. Use hist () function to plot the histogram for attributes : Top10perc , Top25perc , F. Undergrad , Outstate (4 points) Practice 2 Find the Auto .csy in Data Set folder on Canvas ; we will use this dataset to perform some practice . a. Read the data file into a dataframe . (2 points ) b. Find the dimension of the dataframe , how many data objects and attributes are in this data frame (3 points) Now use the following line to re-load the Auto .csy to the dataframe auto = read .cav ("Auto .csv ", header = TRUE , na . string = "?") Here , na .string defines that any time R sees a particular character or set of characters (such as a question mark ), it should be treated as a missing element of the data . c. Use R functions to remove data with missing values (2 points ) d. What is the range of each quantitative attribute ? You can answer this using the range() function . (4 points ) e. What is the mean and standard deviation of each quantitative attribute ? (4 points ) f. Now remove the 10th through 85th observations . What is the range , mean , and standard deviation of each attribute in the subset of the data that remains ? (4 points )