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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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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

Grouping and summarizing data

Grouping and summarizing are two complementary functions. Generally, they will be used together, as there is not much use in grouping a dataset and not calculating anything or using the groups for a purpose. That is when summarizing plays the important role of transforming the data from each group into a summary or a number that we can understand.

In the business world, requests such as the average number of sales by store, the median number of customers by day, the standard deviation of a distribution, and many other examples, are part of the routine of a data scientist. These tasks can be performed using the group_by() and summarise()functions from dplyr.

Starting with the group_by() function, observe that it alone cannot bring much value:

# group by not summarized
df %>% group_by(workclass)

Here is the result.

Figure 8.9 – Dataset grouped but not summarized

We can see in Figure 8.9 that it worked because...

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