2. Summary Statistics: With any dataset, one of the first forms of exploratory analysis involves calculating summary statistics from the data like the five number summary: the minimum, median, third quartile, maximum and mean. We are also interested in the variation of a variable as measured by its standard deviation. One can use the summary() function to calculate a five number summary. The apply() function can be used to find the standard deviation of each row or column of a data frame or matrix structure. We note that the apply() function is very general as it can apply any other R command across rows or columns of a dataset. Here, we apply the sd() function across the columns (and then rows) of the iris dataset. Calculate these summary statistics of the iris dataset using the code below. #calculate the 5-number summary of the {\it iris} dataset five.num.summary <- summary(iris) #calculate the standard deviation across the first four columns of the dataset st.devs.cols <- apply(iris[,1:4], 2, sd) #calculate the standard deviation across the samples of the dataset st.devs.rows <- apply(iris[, 1:4], 1, sd) Now, we can vizualize the quantitative summary of the 4 variables by building a boxplot using the boxplot() command. Plot a boxplot using the following command: boxplot(iris[, 1:4], main = "Boxplot of Iris Variables") Questions (a) (5 points) How many of each species are there in the dataset? (b) (5 points) Which two variables have a median that is smaller than their corresponding mean? (c) (5 points) What is the standard deviation of the sepal length measurements? (d) (5 points) Calculate the 5-number summary and standard deviations of the subset you extracted earlier (subset.data). What is the standard deviation of the sepal length measurements for this subset