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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
Concepts
Authors (2):
Steven Sanderson Steven Sanderson
Profile icon Steven Sanderson
LinkedIn
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.
See other products by Steven Sanderson
David Kun David Kun
Profile icon David Kun
LinkedIn
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.
See other products by 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

Technical requirements

There are a few technical requirements for this chapter. Note that the code for this chapter can be found at https://github.com/PacktPublishing/Extending-Excel-with-Python-and-R/tree/main/Chapter%2010.

Some of the packages that we will be using in this chapter are as follows:

  • healthyR.ts
  • forecast
  • timetk
  • Modeltime
  • prophet (for Python)
  • keras
  • tensorflow

We will start by creating time series objects in base R. The basic object class for a time series object in R is ts and objects can be coerced to that object by either using the ts() function directly or calling as.ts() on an object such as a vector.

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Authors (2)

Left arrow icon
Profile icon Steven Sanderson
LinkedIn
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.
Read more
See other products by Steven Sanderson
Profile icon David Kun
LinkedIn
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.
See other products by David Kun
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