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

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781804610695

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Analysis

Authors (2):

Steven Sanderson

Kun

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Table of Contents (20) Chapters

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

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19. 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.