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Data Wrangling with R

You're reading from   Data Wrangling with R Load, explore, transform and visualize data for modeling with tidyverse libraries

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781803235400
Length 384 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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Gustavo Santos Gustavo Santos
Author Profile Icon Gustavo Santos
Gustavo Santos
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Load and Explore Data
2. Chapter 1: Fundamentals of Data Wrangling FREE CHAPTER 3. Chapter 2: Loading and Exploring Datasets 4. Chapter 3: Basic Data Visualization 5. Part 2: Data Wrangling
6. Chapter 4: Working with Strings 7. Chapter 5: Working with Numbers 8. Chapter 6: Working with Date and Time Objects 9. Chapter 7: Transformations with Base R 10. Chapter 8: Transformations with Tidyverse Libraries 11. Chapter 9: Exploratory Data Analysis 12. Part 3: Data Visualization
13. Chapter 10: Introduction to ggplot2 14. Chapter 11: Enhanced Visualizations with ggplot2 15. Chapter 12: Other Data Visualization Options 16. Part 4: Modeling
17. Chapter 13: Building a Model with R 18. Chapter 14: Build an Application with Shiny in R 19. Conclusion 20. Other Books You May Enjoy

Arranging

Data can be arranged in two common forms: low to high or high to low, also known as ascending and descending order. Arranging data is useful for ranking observations or groups in an order that makes it easier for us to understand. When I look at the top 5 most sold items, I know that they are what brings traffic to a store. Then, imagine that the third best-selling item in terms of count is, in fact, the product that makes the most revenue. That could change our strategy, couldn’t it?

When looking at the other side of the rank, the tail, the bottom 5 items in terms of the number of items sold could be potential candidates to remove from the shelves, as they probably won’t bring much revenue to the business.

This simple example explains why arranging data is important when exploring data. But arranging is also an important part of data wrangling when visualizing data because it quickly pulls our eyes to the maximum point, from where we can read the rest...

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