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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

Data Analysis and Visualization with R and Python in Excel – A Case Study

In this final chapter, we are going to perform an analysis—visualization and a simple model—built with data from Excel and place all those outcomes back into it. This can be useful when there is a lot of data, or the calculations themselves are best suited to being done outside of Excel.

First, we will start with importing our data and then performing some data exploration via visualizations. For this chapter, we are going to use the diamonds dataset from the R package called ggplot2. We will view the data where the price is the outcome and look at it via different facets of the diamond’s characteristics. After the visualizations are done, we will perform some simple modeling to predict the price of a diamond based on its characteristics.

In this chapter, we’re going to cover the following main topics:

  • Getting a visualization
  • Performing a simple machine learning...

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