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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 1. Part 1:The Basics – Reading and Writing Excel Files from R and Python
2. Chapter 1: Reading Excel Spreadsheets 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

Performing linear regression in R

For this section, we are going to perform linear regression in R, both in base R and by way of the tidymodels framework. In this section, you will learn how to do this on a dataset that has different groups in it. We will do this because if you can learn to do it this way, then doing it in a single group becomes simpler as there is no need to group data and perform actions by group. The thought process here is that by doing it on grouped data, we hope you can learn an extra skill.

Linear regression in base R

The first example we are going to show is using the lm() function to perform a linear regression in base R. Let’s dive right into it with the iris dataset.

We will break the code down into chunks and discuss what is happening at each step. The first step for us is to use the library command to bring in the necessary packages into our development environment:

library(readxl)

In this section, we’re loading a library called...

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