Extending Excel with Python and R: Unlock the potential of analytics languages for advanced data manipulation and visualization

by Steven Sanderson, David Kun

Overview

Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity

Key Features

• Perform advanced data analysis and visualization techniques with R and Python on Excel data
• Use exploratory data analysis and pivot table analysis for deeper insights into your data
• Integrate R and Python code directly into Excel using VBA or API endpoints
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

– Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics. – This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. – Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. – Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level. – By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed.

What you will learn

• Read and write Excel files with R and Python libraries
• Automate Excel tasks with R and Python scripts
• Use R and Python to execute Excel VBA macros
• Format Excel sheets using R and Python packages
• Create graphs with ggplot2 and Matplotlib in Excel
• Analyze Excel data with statistical methods and time series analysis
• Explore various methods to call R and Python functions from Excel

Who this book is for

– If you’re a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you. – The comprehensive approach to the topics covered makes it suitable for both beginners and intermediate learners. – A basic understanding of Excel, Python, and R is all you need to get started.

Product Details

ISBN-13:	9781804610695
Publisher:	Packt Publishing
Publication date:	04/30/2024
Pages:	344
Product dimensions:	7.50(w) x 9.25(h) x 0.72(d)

About the Author

Steven Sanderson, MPH, is an applications manager for the patient accounts department at Stony Brook Medicine. He received his bachelor's degree in economics and his master's in public health from Stony Brook University. He has worked in healthcare in some capacity for just shy of 20 years. He is the author and maintainer of the healthyverse set of R packages. He likes to read material related to social and labor economics and has recently turned his efforts back to his guitar with the hope that his kids will follow suit as a hobby they can enjoy together.

David Kun is a mathematician and actuary who has always worked in the gray zone between quantitative teams and ICT, aiming to build a bridge. He is a co-founder and director of Functional Analytics and the creator of the ownR Infinity platform. As a data scientist, he also uses ownR for his daily work. His projects include time series analysis for demand forecasting, computer vision for design automation, and visualization.

Table of Contents

Table of Contents

1. Reading Excel Spreadsheets
2. Writing Excel Spreadsheets
3. Executing VBA Code from R and Python
4. Automating Further (Email Notifications and More)
5. Formatting Your Excel sheet
6. Inserting ggplot2/matplotlib Graphs
7. Pivot Tables (tidyquant in R and with win32com and pypiwin32 in Python)/Summary Table {gt}
8. Exploratory Data Analysis with R and Python
9. Statistical Analysis: Linear and Logistic Regression
10. Time Series Analysis: Statistics, Plots, and Forecasting
11. Calling R/Python Locally from Excel Directly or via an API
12. Data Analysis and Visualization with R and Python for Excel Data – A Case Study