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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

Making a table with the Base R xtabs function

Before we move onto the core of the topic, let us understand a few of the important components.

Here is a list of some key components of a pivot table:

  • Rows and columns: Pivot tables typically involve two primary components – rows and columns. The data rows contain individual records or observations, while the columns contain the attributes or variables that define those records.
  • Values: Pivot tables allow users to aggregate and summarize data by calculating values based on specific metrics, such as sum, average, count, or percentage.
  • Filters and slicers: Filters and slicers enable users to focus on specific subsets of data within the pivot table, enhancing the granularity of analysis. These tools are especially useful when dealing with large datasets.
  • Row and column labels: Pivot tables allow users to drag and drop attributes into row and column labels, defining the layout and structure of the table dynamically...
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