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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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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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Table of Contents (11) Chapters Close

Preface 1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

R data analysis of the 2016 US election demographics


To get a flavor of the resources available to R developers, we can look at the 2016 election data. In this case, I am drawing from Wikipedia (https://en.wikipedia.org/wiki/United_States_presidential_election,_2016), specifically the table named 2016 presidential vote by demographic subgroup. We have the following coding below.

Define a helper function so we can print out values easily. The new printf function takes any arguments passed (...) and passes them along to sprintf:

printf <- function(...)print(sprintf(...))

I have stored the separate demographic statistics into different TSV (tab-separated value) files, which can be read in using the following coding. For each table, we use the read.csv function and specify the field separator as a tab instead of the default comma. We then use the head function to display information about the data frame that was loaded:

age <- read.csv("Documents/B05238_05_age.tsv", sep="\t")head(age)education...
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