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R Data Analysis Cookbook, Second Edition

You're reading from   R Data Analysis Cookbook, Second Edition Customizable R Recipes for data mining, data visualization and time series analysis

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Length 560 pages
Edition 2nd Edition
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Authors (3):
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Kuntal Ganguly Kuntal Ganguly
Author Profile Icon Kuntal Ganguly
Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Author Profile Icon Shanthi Viswanathan
Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Author Profile Icon Viswa Viswanathan
Viswa Viswanathan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data FREE CHAPTER 2. What's in There - Exploratory Data Analysis 3. Where Does It Belong? Classification 4. Give Me a Number - Regression 5. Can you Simplify That? Data Reduction Techniques 6. Lessons from History - Time Series Analysis 7. How does it look? - Advanced data visualization 8. This may also interest you - Building Recommendations 9. It's All About Your Connections - Social Network Analysis 10. Put Your Best Foot Forward - Document and Present Your Analysis 11. Work Smarter, Not Harder - Efficient and Elegant R Code 12. Where in the World? Geospatial Analysis 13. Playing Nice - Connecting to Other Systems

Removing duplicate cases

We sometimes end up with duplicate cases in our datasets and want to retain only one among them.

Getting ready

Create a sample data frame:

> salary <- c(20000, 30000, 25000, 40000, 30000, 34000, 30000) 
> family.size <- c(4,3,2,2,3,4,3)
> car <- c("Luxury", "Compact", "Midsize", "Luxury", "Compact", "Compact", "Compact")
> prospect <- data.frame(salary, family.size, car)

How to do it...

The unique() function can do the job. It takes a vector or data frame as an argument and returns an object of the same type as its argument, but with duplicates removed.

Remove duplicates to get unique values:

> prospect.cleaned <- unique(prospect) 
> nrow(prospect)
[1] 7
> nrow(prospect.cleaned)
[1] 5

How it works...

The unique() function takes a vector or data frame as an argument and returns a similar object with the duplicate eliminated. It returns the non-duplicated cases as is. For repeated cases, the unique() function includes one copy in the returned result.

There's more...

Sometimes we just want to identify the duplicated values without necessarily removing them.

Identifying duplicates without deleting them

For this, use the duplicated() function:

> duplicated(prospect) 
[1] FALSE FALSE FALSE FALSE TRUE FALSE TRUE

From the data, we know that cases 2, 5, and 7 are duplicates. Note that only cases 5 and 7 are shown as duplicates. In the first occurrence, case 2 is not flagged as a duplicate.

To list the duplicate cases, use the following code:

> prospect[duplicated(prospect), ] 

salary family.size car
5 30000 3 Compact
7 30000 3 Compact
You have been reading a chapter from
R Data Analysis Cookbook, Second Edition - Second Edition
Published in: Sep 2017
Publisher: Packt
ISBN-13: 9781787124479
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