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Extending Excel with Python and R

Extending Excel with Python and R

By : Steven Sanderson, Kun
5 (5)
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Extending Excel with Python and R

Extending Excel with Python and R

5 (5)
By: Steven Sanderson, Kun

Overview of this book

– 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.
Table of Contents (20 chapters)
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1
Part 1:The Basics – Reading and Writing Excel Files from R and Python
6
Part 2: Making It Pretty – Formatting, Graphs, and More
10
Part 3: EDA, Statistical Analysis, and Time Series Analysis
14
Part 4: The Other Way Around – Calling R and Python from Excel
16
Part 5: Data Analysis and Visualization with R and Python for Excel Data – A Case Study

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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