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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

Understanding the project

When starting a project, we need a purpose – that is, a goal we want to reach at the end. After all, knowing the problem is part of the solution. Like Lewis Carrol wrote in his book Alice’s Adventures in Wonderland, the Bunny says to Alice that if she does not know where she wants to go, any path will lead her there.

So, let’s begin by understanding the project, or where we want to go.

The dataset

The input data for this project is the Spambase Data Set (https://tinyurl.com/23xwdcah), which can be found in the UCI datasets repository. See the citation information in the Further reading section at the end of this chapter for more.

It contains 4,601 observations and 57 explanatory variables. Out of those, 48 features are floating numbers representing the percentage value, from 0 to 100, of specific words associated with spam and their percentage present in the message. There are six other variables with special characters such...

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