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Data Wrangling with R

Data Wrangling with R

By : Gustavo R Santos, Gustavo Santos
4.9 (7)
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Data Wrangling with R

Data Wrangling with R

4.9 (7)
By: Gustavo R Santos, Gustavo 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)
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1
Part 1: Load and Explore Data
5
Part 2: Data Wrangling
12
Part 3: Data Visualization
16
Part 4: Modeling

Summary

We have reached the end of this book, and we closed it by learning about a fantastic tool: the Shiny library for R. Shiny is a tool for building interactive applications, enabling R developers to deploy and show their work online.

In this chapter, we learned the basics of the Shiny library, how to get started, and the most common functions, and we then turned our efforts to build an application that wrapped the random forest model trained in the last chapter, enabling it to serve the purpose that it was built for: helping our digital marketing client to have fewer messages going to the spam box.

The project was based on patterns from an open dataset from the UCI repository; therefore, it will not be applicable to any specific emails or any messages, but more specifically to the patterns learned from that dataset, to solve the problem from our hypothetical client, which was our goal.

We ended the chapter by deploying the app online using the ShinyApps.io free version...

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