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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

Math operations with variables

As part of a data wrangling process, there will be tasks involving mathematical operations with variables, where there will be a need to add, multiply, or even calculate the log of numbers, for example. Ergo, working with a data frame or a Tibble object is recommended, due to the facilities to perform those operations with variables.

The most common math operators in R are as follows:

Figure 5.5 – A table with the R language’s math operators

If we still use the data frame with names and grades, just created for the last exercise, let’s imagine that the professor offered one extra point for those who wrote a paper. Let’s suppose everyone delivered it; here is how we can add a new column with the extra point:

# Extra point
# Scenario: everyone delivered
df$new_grade = df$grade + 1

Figure 5.6 – One point added to all the students

If the professor wants to normalize...

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