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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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Table of Contents (17) Chapters Close

Preface 1. Hello, Data! 2. Getting Data from the Web FREE CHAPTER 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

Rendering polygons around points

Besides thematic maps, another really useful way of presenting spatial data is to draw artificial polygons around the data points based on the data values. This is especially useful if there is no available polygon shape file to be used to generate a thematic map.

A level plot, contour plot, or isopleths, might be an already familiar design from tourist maps, where the altitude of the mountains is represented by a line drawn around the center of the hill at the very same levels. This is a very smart approach having maps present the height of hills—projecting this third dimension onto a 2-dimensional image.

Now let's try to replicate this design by considering our data points as mountains on the otherwise flat map. We already know the heights and exact geo-coordinates of the geometric centers of these hills (airports); the only challenge here is to draw the actual shape of these objects. In other words:

  • Are these mountains connected?
  • How steep are...
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