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Extending Excel with Python and R

You're reading from   Extending Excel with Python and R Unlock the potential of analytics languages for advanced data manipulation and visualization

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804610695
Length 344 pages
Edition 1st Edition
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Authors (2):
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Steven Sanderson Steven Sanderson
Author Profile Icon Steven Sanderson
Steven Sanderson
David Kun David Kun
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David Kun
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Table of Contents (20) Chapters Close

Preface 1. Part 1:The Basics – Reading and Writing Excel Files from R and Python
2. Chapter 1: Reading Excel Spreadsheets FREE CHAPTER 3. Chapter 2: Writing Excel Spreadsheets 4. Chapter 3: Executing VBA Code from R and Python 5. Chapter 4: Automating Further – Task Scheduling and Email 6. Part 2: Making It Pretty – Formatting, Graphs, and More
7. Chapter 5: Formatting Your Excel Sheet 8. Chapter 6: Inserting ggplot2/matplotlib Graphs 9. Chapter 7: Pivot Tables and Summary Tables 10. Part 3: EDA, Statistical Analysis, and Time Series Analysis
11. Chapter 8: Exploratory Data Analysis with R and Python 12. Chapter 9: Statistical Analysis: Linear and Logistic Regression 13. Chapter 10: Time Series Analysis: Statistics, Plots, and Forecasting 14. Part 4: The Other Way Around – Calling R and Python from Excel
15. Chapter 11: Calling R/Python Locally from Excel Directly or via an API 16. Part 5: Data Analysis and Visualization with R and Python for Excel Data – A Case Study
17. Chapter 12: Data Analysis and Visualization with R and Python in Excel – A Case Study 18. Index 19. Other Books You May Enjoy

Creating pivot tables with tidyquant

The pivot_table() function from the tidyquant library is a useful tool for creating summary tables from data frames in R. It allows you to specify the rows, columns, values, and aggregation functions for your table and to employ other options such as sorting, formatting, and filtering.

To use the pivot_table() function, you need to load the tidyquant library first by using the library(tidyquant) command. Then, you can pass your data frame as the first argument to the function, followed by the other arguments that define your table. For example, if you want to create a table that shows the average sepal length and sepal width of different iris species, you can use the following code:

# Load the tidyquant library
library(tidyquant)
library(purrr)
# Create a pivot table
pivot_table(.data = iris,
            .rows = ~ Species,
          ...
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