Book Image

Data Wrangling with R

By : Gustavo R Santos
Book Image

Data Wrangling with R

By: Gustavo R Santos

Overview of this book

In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you’ll need plenty of tools that enable you to extract the most useful knowledge from data. Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization. The book begins by teaching you how to load and explore datasets. Then, you’ll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you’ll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards. By the end of this book, you’ll have learned how to create your first data model and build an application with Shiny in R.
Table of Contents (21 chapters)
1
Part 1: Load and Explore Data
5
Part 2: Data Wrangling
12
Part 3: Data Visualization
16
Part 4: Modeling

Build an Application with Shiny in R

We have finally reached the last chapter. We have learned so much during this book, and we have been able to consolidate our new knowledge with the machine learning project we created in the last chapter. Now, it is time to put that model in production, making it available for the final user.

Putting a model into production is nothing more than taking it from the environment where it was created and trained, usually an internal environment in a company or even a data scientist’s local machine, and making it available in an application, serving the purpose it was created for.

In this project, putting the model into production entails creating a web app with the Shiny library, embedding the model into it, and making it available to receive textual input from users. It will predict the probability of that text being spam or not.

This is our plan for this final chapter:

  • Learning the basics of Shiny
  • Creating an application...