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R for Data Science

R for Data Science

By : Toomey
3.4 (5)
Buy this Book
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R for Data Science

R for Data Science

3.4 (5)
By: Toomey
Buy this Book

Overview of this book

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Data Mining Patterns
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1. Data Mining Patterns
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Cluster analysis
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Anomaly detection
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Association rules
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Questions
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Summary
2
2. Data Mining Sequences
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2. Data Mining Sequences
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Patterns
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Questions
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Summary
3
3. Text Mining
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3. Text Mining
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Packages
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Questions
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Summary
4
4. Data Analysis – Regression Analysis
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4. Data Analysis – Regression Analysis
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Packages
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Questions
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Summary
5
5. Data Analysis – Correlation
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5. Data Analysis – Correlation
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Packages
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Questions
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Summary
6
6. Data Analysis – Clustering
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6. Data Analysis – Clustering
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Packages
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K-means clustering
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Questions
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Summary
7
7. Data Visualization – R Graphics
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7. Data Visualization – R Graphics
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Packages
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Questions
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Summary
8
8. Data Visualization – Plotting
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8. Data Visualization – Plotting
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Packages
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Scatter plots
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Bar charts and plots
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Questions
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Summary
9
9. Data Visualization – 3D
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9. Data Visualization – 3D
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Packages
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Generating 3D graphics
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Questions
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Summary
10
10. Machine Learning in Action
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10. Machine Learning in Action
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Packages
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Dataset
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Questions
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Summary
11
11. Predicting Events with Machine Learning
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11. Predicting Events with Machine Learning
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Automatic forecasting packages
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Questions
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Summary
12
12. Supervised and Unsupervised Learning
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12. Supervised and Unsupervised Learning
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Packages
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Questions
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Summary
13
Index
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Index

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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Tech Concepts
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Programming languages
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R for Data Science
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