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Mastering Data analysis with R

You're reading from   Mastering Data analysis with R Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783982028
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Gergely Daróczi Gergely Daróczi
Author Profile Icon Gergely Daróczi
Gergely Daróczi
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Toc

Table of Contents (17) Chapters Close

Preface 1. Hello, Data! FREE CHAPTER 2. Getting Data from the Web 3. Filtering and Summarizing Data 4. Restructuring Data 5. Building Models (authored by Renata Nemeth and Gergely Toth) 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) 7. Unstructured Data 8. Polishing Data 9. From Big to Small Data 10. Classification and Clustering 11. Social Network Analysis of the R Ecosystem 12. Analyzing Time-series 13. Data Around Us 14. Analyzing the R Community A. References Index

Loading datasets from the Internet


The most obvious task is to download datasets from the Web and load those into our R session in two manual steps:

  1. Save the datasets to disk.

  2. Read those with standard functions, such as read.table or for example foreign::read.spss, to import sav files.

But we can often save some time by skipping the first step and loading the flat text data files directly from the URL. The following example fetches a comma-separated file from the Americas Open Geocode (AOG) database at http://opengeocode.org, which contains the government, national statistics, geological information, and post office websites for the countries of the world:

> str(read.csv('http://opengeocode.org/download/CCurls.txt'))
'data.frame':  249 obs. of  5 variables:
 $ ISO.3166.1.A2                  : Factor w/ 248 levels "AD" ...
 $ Government.URL                 : Factor w/ 232 levels ""  ...
 $ National.Statistics.Census..URL: Factor w/ 213 levels ""  ...
 $ Geological.Information.URL     : Factor...
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