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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

Creating pivot tables with tidyquant

The pivot_table() function from the tidyquant library is a useful tool for creating summary tables from data frames in R. It allows you to specify the rows, columns, values, and aggregation functions for your table and to employ other options such as sorting, formatting, and filtering.

To use the pivot_table() function, you need to load the tidyquant library first by using the library(tidyquant) command. Then, you can pass your data frame as the first argument to the function, followed by the other arguments that define your table. For example, if you want to create a table that shows the average sepal length and sepal width of different iris species, you can use the following code:

# Load the tidyquant library
library(tidyquant)
library(purrr)
# Create a pivot table
pivot_table(.data = iris,
            .rows = ~ Species,
          ...

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