Book Image

R for Data Science

By : Toomey
Book Image

R for Data Science

By: Toomey

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.

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R for Data Science
Toomey
Dec, 2014 | 364 pages
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Table of Contents (14 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Data Mining Patterns
1. Data Mining Patterns
Cluster analysis
Anomaly detection
Association rules
Questions
Summary
2
2. Data Mining Sequences
2. Data Mining Sequences
Patterns
Questions
Summary
3
3. Text Mining
3. Text Mining
Packages
Questions
Summary
4
4. Data Analysis – Regression Analysis
4. Data Analysis – Regression Analysis
Packages
Questions
Summary
5
5. Data Analysis – Correlation
5. Data Analysis – Correlation
Packages
Questions
Summary
6
6. Data Analysis – Clustering
6. Data Analysis – Clustering
Packages
K-means clustering
Questions
Summary
7
7. Data Visualization – R Graphics
7. Data Visualization – R Graphics
Packages
Questions
Summary
8
8. Data Visualization – Plotting
8. Data Visualization – Plotting
Packages
Scatter plots
Bar charts and plots
Questions
Summary
9
9. Data Visualization – 3D
9. Data Visualization – 3D
Packages
Generating 3D graphics
Questions
Summary
10
10. Machine Learning in Action
10. Machine Learning in Action
Packages
Dataset
Questions
Summary
11
11. Predicting Events with Machine Learning
11. Predicting Events with Machine Learning
Automatic forecasting packages
Questions
Summary
12
12. Supervised and Unsupervised Learning
12. Supervised and Unsupervised Learning
Packages
Questions
Summary
13
Index
Index

Scatter plots

A scatter plot is a basic plotting device comparing datasets with two axes. A basic scatter plot is provided with the plot function built into the R system.

Note

Several objects available in R and packages have their own plot function in order to effectively portray the data associated.

The plot function looks as follows:

plot(x,
   y,
   type,
   main,
   sub,
   xlab,
   ylab,
   asp)

The various parameters of this function are described in the following table:

Parameter

Description

x

This is an independent variable.

y

This is a dependent variable.

type

This defines the type of plot. It should be one of the following types:

  • p for points

  • l for lines

  • b for both

  • c for the lines part alone of b

  • o for both overplotted

  • h for histogram-like (or high-density) vertical lines

  • s for stair steps

  • S for other steps, see details below

  • n for no plotting (not sure why this is a choice as it ends up with no information plotted)

main

This is the title of the plot.

sub

This...

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