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Practical Data Wrangling

You're reading from   Practical Data Wrangling Expert techniques for transforming your raw data into a valuable source for analytics

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286139
Length 204 pages
Edition 1st Edition
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Author (1):
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Allan Visochek Allan Visochek
Author Profile Icon Allan Visochek
Allan Visochek
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Table of Contents (10) Chapters Close

Preface 1. Programming with Data FREE CHAPTER 2. Introduction to Programming in Python 3. Reading, Exploring, and Modifying Data - Part I 4. Reading, Exploring, and Modifying Data - Part II 5. Manipulating Text Data - An Introduction to Regular Expressions 6. Cleaning Numerical Data - An Introduction to R and RStudio 7. Simplifying Data Manipulation with dplyr 8. Getting Data from the Web 9. Working with Large Datasets

Chaining operations together


Part of the expressive power of dplyr comes from its ability to chain data processing operations one after the other. The %>% symbol can be used with dplyr functions to chain together operations. The way it works is that the result of all of the expression before the %>% symbol is used as the first argument of the function that comes after the %>% symbol. In the following demonstration, I use the %>% symbol to put together the select and arrange operations:

vehicles.product.arranged <- as_tibble(
  vehicles %>% ## start with the original data
  select(make,model,year,cylinders) %>% ## select the columns
  arrange(make,model,year) ## arrange the rows
)

The previous chain of operations starts with the vehicles dataframe containing the original data. The vehicles dataframe is then followed by the %>% symbol, so it is passed as the first argument to the following function, which is the select() function. As such, the first argument of the select...

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