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Practical Data Science Cookbook, Second Edition

You're reading from   Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787129627
Length 434 pages
Edition 2nd Edition
Languages
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Authors (5):
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Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Sean P Murphy Sean P Murphy
Author Profile Icon Sean P Murphy
Sean P Murphy
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
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Table of Contents (12) Chapters Close

Preface 1. Preparing Your Data Science Environment FREE CHAPTER 2. Driving Visual Analysis with Automobile Data with R 3. Creating Application-Oriented Analyses Using Tax Data and Python 4. Modeling Stock Market Data 5. Visually Exploring Employment Data 6. Driving Visual Analyses with Automobile Data 7. Working with Social Graphs 8. Recommending Movies at Scale (Python) 9. Harvesting and Geolocating Twitter Data (Python) 10. Forecasting New Zealand Overseas Visitors 11. German Credit Data Analysis

Importing employment data into R


Our first step in this project is to import the employment data into R so that we can start assessing the data and perform some preliminary analysis.

Getting ready

You should be ready to go ahead after completing the previous recipe.

How to do it...

The following steps will guide you through two different ways of importing the CSV file:

  1. We can directly load the data file into R (even from the compressed version) using the following command:
ann2012 <- read.csv(unz('2012_annual_singlefile.zip', 
 '2012.annual.singlefile.csv'), stringsAsFactors=F)

However, you will find that this takes a very long time with this file. There are better ways.

  1. We chose to import the data directly into R since there are further manipulations and merges that we will do later. We will use the fread function from the data.table package to do this:
library(data.table)
ann2012 <- fread('data/2012.annual.singlefile.csv')

That's it. Really! It is also many times faster than the other...

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