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RStudio for R Statistical Computing Cookbook

You're reading from   RStudio for R Statistical Computing Cookbook Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781784391034
Length 246 pages
Edition 1st Edition
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Author (1):
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Andrea Cirillo Andrea Cirillo
Author Profile Icon Andrea Cirillo
Andrea Cirillo
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Table of Contents (10) Chapters Close

Preface 1. Acquiring Data for Your Project 2. Preparing for Analysis – Data Cleansing and Manipulation FREE CHAPTER 3. Basic Visualization Techniques 4. Advanced and Interactive Visualization 5. Power Programming with R 6. Domain-specific Applications 7. Developing Static Reports 8. Dynamic Reporting and Web Application Development Index

Detecting and removing outliers

Outliers are usually dangerous values for data science activities, since they produce heavy distortions within models and algorithms.

Their detection and exclusion is, therefore, a really crucial task.

This recipe will show you how to easily perform this task.

We will compute the I and IV quartiles of a given population and detect values that far from these fixed limits.

You should note that this recipe is feasible only for univariate quantitative population, while different kind of data will require you to use other outlier-detection methods.

How to do it...

  1. Compute the quantiles using the quantile() function:
    quantiles <- quantile(tidy_gdp_complete$gdp, probs = c(.25, .75))
    
  2. Compute the range value using the IQR() function:
    range <- 1.5 * IQR(tidy_gdp_complete$gdp)
    
  3. Subset the original data by excluding the outliers:
    normal_gdp <- subset(tidy_gdp_complete,
    tidy_gdp_complete$gdp > (quantiles[1] - range) & tidy_gdp_complete$gdp < (quantiles[2] + range...
You have been reading a chapter from
RStudio for R Statistical Computing Cookbook
Published in: Apr 2016
Publisher:
ISBN-13: 9781784391034
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