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Hands-On Data Science with R

You're reading from  Hands-On Data Science with R

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Pages 420 pages
Edition 1st Edition
Languages
Authors (4):
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Profile icon Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Profile icon Doug Ortiz
Nataraj Dasgupta Nataraj Dasgupta
Profile icon Nataraj Dasgupta
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Profile icon Ricardo Anjoleto Farias
View More author details
Toc

Table of Contents (16) Chapters close

Preface 1. Getting Started with Data Science and R 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

Approach for creating a data product from statistical modeling and web UI

In this section, we are going to build an app with a dataset. Before we start with the construction of the architecture of our app, we need some open data to work with. I'm going to use the computer dataset that can be found in the Ecdat package, so make sure to install it by running install.packages("Ecdat"). A documentation about its variables is found at https://vincentarelbundock.github.io/Rdatasets/doc/Ecdat/Computers.html.

Once it was installed, if you type class(Ecdat::Computers), you will see that it is a DataFrame. A lot of information is hidden inside this data, and our goal here is to present a couple of them in a Shiny application, publishing it in a web page. We are going to rearrange and group our dataset, so you'll need the dplyr package; make sure it is installed and run...

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