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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

A workflow for data exploration

Now that you are familiar with the different ways to acquire and load data into Rstudio, let’s go over a basic workflow that I regularly use for data exploration. Naturally, the steps presented here are flexible and should be understood as a guide to begin understanding the dataset. It can and should be changed to adapt to your project’s needs.

When you start a data exploration, it is important to have in mind your final goal. What problem are you trying to solve? Then, you look to understand the variables, look for errors and missing data, understand the distributions, and create a couple of visualizations that will help you to extract good insights to help you along the way. Let’s explore the steps that can be performed:

  1. Load and view: Every Data Science project starts with data. Load a dataset to RStudio and take a first look at it, making sure the data types are correctly inferred and that the dataset is completely...
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