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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Table of Contents (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Basic commands for subsetting

R allows data to be sliced or to get the subset using various methods.

How to do it...

Perform the following steps to see subsetting. It is assumed that the DataFrame d and matrix m exist from the previous exercise:

> d$No   # Slice the column 
Output: 
[1] 1 2 3 
> d$Name  # Slice the column 
Output: 
[1] A B C 
> d$Name[1] 
Output: 
[1] A 
> d[2,]  # get Row 
Output: 
      No       Name   Attendance
2 2 B FALSE > temp = c(1:100) # Creates a vector of 100 elements from 1 to 100 > temp[14:16] # Part from vector Output: [1] 14 15 16 > m[,2] # To access second column from matrix m Output: [1] 4 5 6 > m[3,] # To access third row from matrix m Output: [1] 3 6 > m[2,1] # To access single element from matrix m Output: [1] 2 > m[c(1,3), c(2)] # Access [1,2] and [3,2] Output: [1] 4 6

Data input

R provides various ways to read data for processing. It supports reading data from CSV files, Excel files, databases, other statistical tools, binary files, and websites. Apart from this, there are many datasets that come bundled with the R. Just execute the data() command on RStudio or R prompt it will list the datasets available. If you want to create quick dataset you can create a blank DataFrame and use edit command as shown here:

> temp = data.frame()
> edit(temp)  

This will open an Excel like screen for data manipulation as shown in the following screenshot:

You have been reading a chapter from
Machine Learning with R Cookbook, Second Edition - Second Edition
Published in: Oct 2017
Publisher: Packt
ISBN-13: 9781787284395
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