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Machine Learning with R Cookbook, Second Edition - Second Edition

You're reading from  Machine Learning with R Cookbook, Second Edition - Second Edition

Product type Book
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Pages 572 pages
Edition 2nd Edition
Languages
Author (1):
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Profile icon Yu-Wei, Chiu (David Chiu)
Toc

Table of Contents (21) Chapters close

Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Practical Machine Learning with R 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)

Working with univariate descriptive statistics in R


Uni means one and dealing with one variable for looking into data in univariate descriptive statistics, describes a single variable for unit analysis, which is also the simplest form of quantitative analysis. To find the pattern or find the meaning in data in cases of univariate analysis use central tendencies such as mean, median, mode, range, variance, maximum, minimum, and standard deviation. Bar charts, histograms, pie charts, and frequency polygons are the best option for representing the univariate data. In this recipe, we introduce some basic functions used to describe a single variable.

Getting ready

We need to apply descriptive statistics to a sample data. Here, we use the built-in mtcars data as our example.

How to do it...

Perform the following steps:

  1. First, load the mtcars data into a DataFrame with a variable named mtcars:
> data(mtcars)
  1. To obtain the vector range, the range function will return the lower and upper bound of the...
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