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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

Generating random time series objects in R

We are going to generate some random time series objects in base R. Doing this is very simple as base R comes with some distribution functions already packed in. We will make use of the random normal distribution by making calls to the rnorm() function. This function has three parameters to provide arguments to:

  • n: The number of points to be generated
  • mean: The mean of the distribution, with a default of 0
  • sd: The standard deviation of the distribution, with the default being 1

Let’s go ahead and generate our first random vector. We will call it x:

# Generate a Random Time Series
# Set seed to make results reproducible
set.seed(123)
# Generate Random Points using a gaussian distribution with mean 0 and sd = 1
n <- 25
x <- rnorm(n)
head(x)
[1] -0.56047565 -0.23017749  1.55870831  0.07050839  0.12928774  1.71506499

In the preceding code, we did the following:

...

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