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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

Building a Model with R

The last part of this book is about modeling data. It has been a long learning journey so far. We started with the fundamentals of data wrangling while covering the concepts that surround the matter and going through techniques to munge each type of data. During practical projects, we had the opportunity to wrangle entire datasets, showing some transformations. In the previous part, we worked with plenty of material regarding data visualization while going over one of the most complete libraries for visualization.

Now, it is time to put all our knowledge to the test and work on a final project. This will involve end-to-end work, from loading the dataset into RStudio to deploying it in a production environment using Shiny, where anyone can interact with the application.

This project will be built during these last two chapters in Part 4. Let’s get to work.

We will be covering the following main topics:

  • Machine learning concepts
  • Understanding...

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