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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! 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

Satellite maps

There are many R packages on CRAN that can fetch data from Google Maps, Stamen, Bing, or OpenStreetMap—even some of the packages that we have previously used in this chapter, such as the ggmap package, can do this. Similarly, the dismo package also comes with both geo-coding and Google Maps API integration capabilities, and there are some other packages focused on that latter, such as the RgoogleMaps package.

Now we will use the OpenStreetMap package, mainly because it supports not only the awesome OpenStreetMap database back-end, but also a bunch of other formats as well. For example, we can render really nice terrain maps via Stamen:

> library(OpenStreetMap)
> map <- openmap(c(max(map_data$y, na.rm = TRUE),
+                  min(map_data$x, na.rm = TRUE)),
+                c(min(map_data$y, na.rm = TRUE),
+                  max(map_data$x, na.rm = TRUE)),
+                type = 'stamen-terrain')

So we defined the left upper and right lower corners...

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