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Data Wrangling with R

You're reading from  Data Wrangling with R

Product type Book
Published in Feb 2023
Publisher Packt
ISBN-13 9781803235400
Pages 384 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Gustavo R Santos Gustavo R Santos
Profile icon Gustavo R Santos

Table of Contents (21) Chapters

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

What is tidy data

To tidy something means to arrange it, to put it in order. Consequently, tidy data means that our data has a specific order and should follow a set of rules to be considered ready to be worked.

A dataset can be arranged in different ways. For those that, like me, worked for many years with Microsoft Excel, at first sight, a tidy dataset may seem odd, as there will be plenty of repeated cells. Many datasets I worked with in MS Excel had the same measurement split among many columns. A classic example of that is the monthly reports that bring the first columns as the descriptive part of the data (for example, product, profit, and loss), and the values refering to them are shown in one column each month.

Figure 8.2 – Example of dataset not in Tidy format

The table from Figure 8.2 is comfortable to look at but not useful for an algorithm or a programming language. If you try to determine what is the best month for sales, it will require...

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