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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

Alternative map designs

Besides being able to use third-party tools, another main reason why I tend to use R for all my data analysis tasks is that R is extremely powerful in creating custom data exploration, visualization, and modeling designs.

As an example, let's create a flow-map based on our data, where we will highlight the flights from Houston based on the number of actual and cancelled flights. We will use lines and circles to render these two variables on a 2-dimensional map, and we will also add a contour plot in the background based on the average time delay.

But, as usual, let's do some data transformations first! To keep the number of flows at a minimal level, let's get rid of the airports outside the USA at last:

> dt <- dt[point.in.polygon(dt$lon, dt$lat,
+                           usa_data$x, usa_data$y) == 1, ]

We will need the diagram package (to render curved arrows from Houston to the destination airports) and the scales package to create transparent...

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