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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

Using data.table

The data.table library describes itself as an enhanced version of the data.frames in R. Using only base R, it is not easy to group data, for example. There are other small enhancements, such as not converting strings to factors during data import and in the visualization of printing datasets on R’s console.

The syntax for this library is very similar to data.frames, as you may have already seen during this chapter, but it is formally presented here:

Basic syntax
DT[i, j, by]
  • i is for the row selection or a condition for the rows to be displayed
  • j is for selecting variables or calculating a statistic based on them
  • by is used for grouping variables

Before using the syntax for data.table, it is necessary to make sure that the object is the correct type. That can be done using type(object). Conversion to a data.table object can be done using as.data.table(object).

Consider the following code snippet:

# Syntax
dt[dt$age > 50...
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