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Hands-On Exploratory Data Analysis with R

You're reading from   Hands-On Exploratory Data Analysis with R Become an expert in exploratory data analysis using R packages

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789804379
Length 266 pages
Edition 1st Edition
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Authors (2):
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Radhika Datar Radhika Datar
Author Profile Icon Radhika Datar
Radhika Datar
Harish Garg Harish Garg
Author Profile Icon Harish Garg
Harish Garg
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Setting Up Data Analysis Environment
2. Setting Up Our Data Analysis Environment FREE CHAPTER 3. Importing Diverse Datasets 4. Examining, Cleaning, and Filtering 5. Visualizing Data Graphically with ggplot2 6. Creating Aesthetically Pleasing Reports with knitr and R Markdown 7. Section 2: Univariate, Time Series, and Multivariate Data
8. Univariate and Control Datasets 9. Time Series Datasets 10. Multivariate Datasets 11. Section 3: Multifactor, Optimization, and Regression Data Problems
12. Multi-Factor Datasets 13. Handling Optimization and Regression Data Problems 14. Section 4: Conclusions
15. Next Steps 16. Other Books You May Enjoy

The Shapiro-Wilk test

shapiro.test tests the null hypothesis that the samples come from a normal distribution, vis-à-vis the alternative hypothesis, that the samples do not come from a normal distribution. Let's understand this in detail by executing the following command:

> ?shapiro.test

The R help page will be visible to users as follows:

We can see that it takes an argument of a numeric vector of data values from a specific range. Since we have implemented a data frame, it is mandatory to pass the desired column as input to this function. Consider creating a shapiro.test analysis for a balance attribute, as this is considered a critical attribute:

> shapiro.test(bank$balance[1:10])
Shapiro-Wilk normality test
data: bank$balance[1:10]
W = 0.80236, p-value = 0.01549
> shapiro.test(bank$balance[1:4000])
Shapiro-Wilk normality test
data: bank$balance[1:4000]
W = 0...
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