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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
Languages
Concepts
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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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Toc

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

Saving files

During data exploration or modeling, it becomes crucial to save our work so we can continue from a certain point without the need to rerun the code all over again. This becomes especially interesting when the dataset you are dealing with is particularly large. Large datasets can take much longer to run some operations, so you want to save your clean data in order to pick up from there the next time you revisit that work.

Saving files in R is simple. The utils library takes care of that. The following code saves the content of the df variable – which is the Monthly Treasury Statement (MTS) pulled from the API – to a CSV file in the same working directory of the script or Rproj file:

# Save a variable to csv
write.csv(df, "Monthly_Treasury_Statement.csv", row.names = FALSE)

The row.names parameter is used to omit the index column. You can do the same task using the readr version too, write_csv(df, "file_name.csv").

Furthermore...

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