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.
