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R for Data Science

You're reading from   R for Data Science Learn and explore the fundamentals of data science with R

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781784390860
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Summary

In this chapter, we explored a variety of 3D plotting methods in R. We generated 3D graphics using the built-in persp function. We used Lattice Cloud to get a 3D scatter plot using the cloud function and scatterplot3d. We used the scatter3d function from the rgl package. We generated a 3D plot and a bar graph into a VRML file using the vrmlgen package. We used RgoogleMaps for the map data and corresponding map displays.

In the Big Data area, we used the pbdR tools to use Big Data and used several methods that allow you to access Big Data in R.

Lastly, we looked at several research areas that seem promising, such as Rcpp (to develop your own R methods using C++), parallel (for parallel processing of R commands), and microbenchmark (allowing detailed timing to occur in your R coding).

In the next chapter, we will cover machine learning.

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