For the following exercises in this chapter, we use the Auto MPG dataset from the UCI Machine Learning repository (references 5 and 6 in the *References* section of this chapter). The dataset can be downloaded from https://archive.ics.uci.edu/ml/datasets.html. The dataset contains the fuel consumption of cars in the US measured during 1970-1982. Along with consumption values, there are attribute variables, such as the number of cylinders, displacement, horse power, weight, acceleration, year, origin, and the name of the car:

Load the dataset into R using the

`read.table()`

function.Produce a box plot of mpg values for each car name.

Write a function that will compute the scaled value (subtract the mean and divide by standard deviation) of a column whose name is given as an argument of the function.

Use the

`lapply()`

function to compute scaled values for all variables.Produce a scatter plot of mgp versus acceleration for each car name using

`coplot()`

. Use legends to annotate the graph.