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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

Time series plotting

In this section, we will cover plotting time series objects, along with plotting some diagnostics such as decomposition. These plots include time series plots themselves, autocorrelation function (ACF) plots, and partial autocorrelation function (PACF) plots. We will start by using the AirPassengers dataset, which we will read in via the readxl package:

# Read the airpassengers.xlsx file in and convert to a ts object starting at 1949
ap_ts <- read_xlsx("./Chapter 10/airpassengers.xlsx")  |>
  ts(start = 1949, frequency = 12)
# Plot the ts object
plot(ap_ts)

This produces the following chart:

Figure 10.2 – Visualizing the AirPassengers time series dataset

Figure 10.2 – Visualizing the AirPassengers time series dataset

From here, it is easy to see that the data has a trend and a seasonal cycle component. This observation will lead us to our next visual. We will decompose the data into its parts and visualize the decomposition. The decomposition of the...

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