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Jupyter Cookbook

You're reading from   Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839440
Length 238 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Toc

Table of Contents (12) Chapters Close

Preface 1. Installation and Setting up the Environment 2. Adding an Engine FREE CHAPTER 3. Accessing and Retrieving Data 4. Visualizing Your Analytics 5. Working with Widgets 6. Jupyter Dashboards 7. Sharing Your Code 8. Multiuser Jupyter 9. Interacting with Big Data 10. Jupyter Security 11. Jupyter Labs

Creating an R dashboard


In this section, we create a dashboard in an R Notebook.

How to do it...

We have the following script; it loads and analyzes the data, producing four graphical elements. They are the head of the dataset, regression analysis, comparing sales components as drivers to sales, and comparing ad effectiveness:

 # Load and display same of the data points
 #install.packages("s20x", repos='http://cran.us.r-project.org')
 #install.packages("car", repos='http://cran.us.r-project.org')

 # libraries used
 library(s20x)
 library(car)

 # load and display data - originally at http://www.dataapple.net/wp-content/uploads/2013/04/
 df <- read.csv("grapeJuice.csv",header=T)
 head(df) 
# Calculate ad effectiveness
#
#divide the dataset into two sub dataset by ad_type
 sales_ad_nature = subset(df,ad_type==0)
 sales_ad_family = subset(df,ad_type==1)

# graph the two
 par(mfrow = c(1,2))

 hist(sales_ad_nature$sales,main="",xlab="sales with nature production theme ad",prob=T)
 lines(density...
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