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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

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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