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Practical Data Science Cookbook, Second Edition
Practical Data Science Cookbook, Second Edition

Practical Data Science Cookbook, Second Edition: Data pre-processing, analysis and visualization using R and Python , Second Edition

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Profile Icon Purushottam Joshi Profile Icon Tattar Profile Icon Anthony Ojeda Profile Icon ABHIJIT DASGUPTA Profile Icon Sean P Murphy +1 more Show less
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Profile Icon Purushottam Joshi Profile Icon Tattar Profile Icon Anthony Ojeda Profile Icon ABHIJIT DASGUPTA Profile Icon Sean P Murphy +1 more Show less
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$48.99
Paperback Jun 2017 434 pages 2nd Edition
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Practical Data Science Cookbook, Second Edition

Driving Visual Analysis with Automobile Data with R

In this chapter, we will cover the following:

  • Acquiring automobile fuel efficiency data
  • Preparing R for your first project
  • Importing automobile fuel efficiency data into R
  • Exploring and describing fuel efficiency data
  • Analyzing automobile fuel efficiency over time
  • Investigating the makes and models of automobiles

Introduction

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...

Acquiring automobile fuel efficiency data

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...

Preparing R for your first project

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.

Getting ready

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.

How to do it...

...

Importing automobile fuel efficiency data into R

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.

Getting ready

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.

How to do it...

The following steps will walk you through the...

Exploring and describing fuel efficiency data

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.

Getting ready

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

How to do it...

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

Analyzing automobile fuel efficiency over time

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.

Getting ready

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

How to do it...

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

Investigating the makes and models of automobiles

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

Getting ready

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

How to do it...

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...
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Key benefits

  • Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data
  • Get beyond the theory and implement real-world projects in data science using R and Python
  • Easy-to-follow recipes will help you understand and implement the numerical computing concepts

Description

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.

Who is this book for?

If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python.

What you will learn

  • Learn and understand the installation procedure and environment required for R and Python on various platforms
  • Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python
  • Build a predictive model and an exploratory model
  • Analyze the results of your model and create reports on the acquired data
  • Build various tree-based methods and Build random forest
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Table of Contents

11 Chapters
Preparing Your Data Science Environment Chevron down icon Chevron up icon
Driving Visual Analysis with Automobile Data with R Chevron down icon Chevron up icon
Creating Application-Oriented Analyses Using Tax Data and Python Chevron down icon Chevron up icon
Modeling Stock Market Data Chevron down icon Chevron up icon
Visually Exploring Employment Data Chevron down icon Chevron up icon
Driving Visual Analyses with Automobile Data Chevron down icon Chevron up icon
Working with Social Graphs Chevron down icon Chevron up icon
Recommending Movies at Scale (Python) Chevron down icon Chevron up icon
Harvesting and Geolocating Twitter Data (Python) Chevron down icon Chevron up icon
Forecasting New Zealand Overseas Visitors Chevron down icon Chevron up icon
German Credit Data Analysis Chevron down icon Chevron up icon
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