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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Master machine learning techniques with R to deliver insights for complex projects

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783984527
Length 400 pages
Edition 1st Edition
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (15) Chapters Close

Preface 1. A Process for Success 2. Linear Regression – The Blocking and Tackling of Machine Learning FREE CHAPTER 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis and Recommendation Engines 11. Time Series and Causality 12. Text Mining A. R Fundamentals Index

Data frames and matrices

We will now create a data frame, which is a collection of variables (vectors). We will create a vector of 1, 2, and 3 and another vector of 1, 1.5, and 2.0. Once this is done, the rbind() function will allow us to combine the rows:

> p = seq(1:3)

> p
[1] 1 2 3

> q = seq(1,2, by=0.5)

> q
[1] 1.0 1.5 2.0

> r = rbind(p,q)

> r
  [,1] [,2] [,3]
p    1  2.0    3
q    1  1.5    2

The result is a list of two rows with three values each. You can always determine the structure of your data using the str() function, which in this case, shows us that we have two lists, one named p and the other, q:

> str(r)
 num [1:2, 1:3] 1 1 2 1.5 3 2
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:2] "p" "q"
  ..$ : NULL

Now, let's put them together as columns using cbind():

> s = cbind(p,q)

> s
     p   q
[1,] 1 1.0
[2,] 2 1.5
[3,] 3 2.0

To put this in a data frame, use the as.data.frame() function. After that, examine the...

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