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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 logistic regression in R

As we did in the section on linear regression, in this section, we will also perform logistic regression in base R and with the tidymodels framework. We are going to only perform a simple binary classification regression problem using the Titanic dataset, where we will be deciding if someone is going to survive or not. Let’s dive right into it.

Logistic regression with base R

In order to get going, we are going to start with a base R implementation of logistic regression on the Titanic dataset where we will be modeling the response of Survived. So, let’s get straight into it.

The following is the code that will perform the data modeling along with explanations of what is happening:

library(tidyverse)
df <- Titanic |>
       as.data.frame() |>
       uncount(Freq)

This block of code starts by loading a library called tidyverse, which contains...

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