The first project we will introduce in this book is an analysis of automobile fuel economy data. The primary tool that we will use to analyze this dataset is the R statistical programming language. R is often referred to as the lingua franca of data science since it is currently the most popular language for statistics and data analysis. As you'll see from the examples in this book, R is an excellent tool for data manipulation, analysis, modeling, visualization, and creating useful scripts to get analytical tasks done.

The recipes in this chapter will roughly follow these five steps in the data science pipeline:

• Acquisition
• Exploration and understanding
• Munging, wrangling, and manipulation
• Analysis and modeling
• Communication and operationalization

Process-wise, the backbone of data science is the data science pipeline, and in order to get good at data science...

Every data science project starts with data and this chapter begins in the same manner. For this recipe, we will dive into a dataset that contains fuel efficiency performance metrics, measured in Miles Per Gallon (MPG) over time, for most makes and models of automobiles available in the US since 1984. This data is courtesy of the U.S. Department of Energy and the US Environmental Protection Agency. In addition to fuel efficiency data, the dataset also contains several features and attributes of the automobiles listed, thereby providing the opportunity to summarize and group data to determine which groups tend to have better fuel efficiency historically and how this has changed over the years. The latest version of the dataset is available at http://www.fueleconomy.gov/feg/epadata/vehicles.csv.zip, and information about the variables in...

For the following recipes, you will need the R statistical programming language installed on your computer (either the base R or RStudio, but the authors strongly recommend using the excellent and free RStudio) and the automobile fuel efficiency dataset. This quick recipe will help you ensure that you have everything that you will need to complete this analysis project.

You will need an internet connection to complete this recipe, and we assume that you have installed RStudio for your particular platform, based on the instructions in the previous chapter.

Once you have downloaded and installed everything in the previous recipe, Preparing R for your first project, you can import the dataset into R to start doing some preliminary analysis and get a sense of what the data looks like.

Much of the analysis in this chapter is cumulative, and the efforts of the previous recipes will be used for subsequent recipes. Thus, if you completed the previous recipe, you should have everything you need to continue.

The following steps will walk you through the...

Now that we have imported the automobile fuel efficiency data into R and learned a little about the nuances of importing, the next step is to do some preliminary analysis of the dataset. The purpose of this analysis is to explore what the data looks like and get your feet wet with some of R's most basic commands.

If you have completed the previous recipe, you should have everything that you need to continue.

The following steps will lead you through the initial exploration of our dataset,...

We have now successfully imported the data and looked at some important high-level statistics that provided us with a basic understanding of what values are in the dataset and how frequently some features appear. With this recipe, we continue the exploration by looking at some of the fuel efficiency metrics over time and in relation to other data points.

If you completed the previous recipe, you should have everything you need to continue.

The following steps will use both plyr and the graphics...

With the first set of questions asked and answered about this dataset, let's move on to additional analyses.

If you completed the previous recipe, you should have everything you need to continue.

This recipe will investigate the makes and models of automobiles and how they have changed over time:

1. Let's look at how the makes and models of cars inform fuel efficiency over time. First, let's look at the frequency of the makes and models of cars available in the US over this time...