October 2015
Beginner to intermediate
168 pages
4h 11m
English
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Exercises
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 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.