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Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
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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 (17) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 2. Linear Regression - The Blocking and Tackling of Machine Learning 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 and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

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 named 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...

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