Practice 2 Find the Auto.csv 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.csv to the dataframe auto = read.csv(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) Practice 2 Find the Auto.csv 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.csv to the dataframe auto = read.csv(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)