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Extending Excel with Python and R

You're reading from   Extending Excel with Python and R Unlock the potential of analytics languages for advanced data manipulation and visualization

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804610695
Length 344 pages
Edition 1st Edition
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Authors (2):
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Steven Sanderson Steven Sanderson
Author Profile Icon Steven Sanderson
Steven Sanderson
David Kun David Kun
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David Kun
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Table of Contents (20) Chapters Close

Preface 1. Part 1:The Basics – Reading and Writing Excel Files from R and Python
2. Chapter 1: Reading Excel Spreadsheets FREE CHAPTER 3. Chapter 2: Writing Excel Spreadsheets 4. Chapter 3: Executing VBA Code from R and Python 5. Chapter 4: Automating Further – Task Scheduling and Email 6. Part 2: Making It Pretty – Formatting, Graphs, and More
7. Chapter 5: Formatting Your Excel Sheet 8. Chapter 6: Inserting ggplot2/matplotlib Graphs 9. Chapter 7: Pivot Tables and Summary Tables 10. Part 3: EDA, Statistical Analysis, and Time Series Analysis
11. Chapter 8: Exploratory Data Analysis with R and Python 12. Chapter 9: Statistical Analysis: Linear and Logistic Regression 13. Chapter 10: Time Series Analysis: Statistics, Plots, and Forecasting 14. Part 4: The Other Way Around – Calling R and Python from Excel
15. Chapter 11: Calling R/Python Locally from Excel Directly or via an API 16. Part 5: Data Analysis and Visualization with R and Python for Excel Data – A Case Study
17. Chapter 12: Data Analysis and Visualization with R and Python in Excel – A Case Study 18. Index 19. Other Books You May Enjoy

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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