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Extending Excel with Python and R

You're reading from  Extending Excel with Python and R

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781804610695
Pages 344 pages
Edition 1st Edition
Languages
Authors (2):
Steven Sanderson Steven Sanderson
Profile icon Steven Sanderson
David Kun David Kun
Profile icon David Kun
View More author details

Table of Contents (20) Chapters

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