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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
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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

Arranging data is useful to create a rank, making the dataset ordinated. The orders can be from low to high values, also known as increasing order, as well as from high to low or decreasing order. In RStudio, visualizing a dataset using the software’s viewer pane already allows the analyst to arrange the data with the click of a button. Just like many dynamic tables, if you click on a column name, that variable becomes ordered. For simply eyeballing it, the feature is terrific, but for programming purposes, it won’t have any effect. You will have to take advantage of the arrange() function from dplyr.

The most basic ways to arrange a dataset are by running the succeeding pieces of. First, let's try arranging by increasing order:

# Arrange data in increasing order
df_no_na %>% arrange(native_country)

Next, arranging in decreasing order:

# Arrange data in decreasing order
df_no_na %>% arrange( desc(native_country) )

Notice that adding...

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