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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

In this chapter, we created an end-to-end machine learning project. We started by studying some basic machine learning concepts to put us in sync. Then, we understood what was needed for the main goal of the project. First, we must understand the problem and know where we want to go so that the solution becomes clearer. In this case, our client was a digital marketing company that wanted to reduce the risk of their messages ending up in their spam filter, so we had to create a classification model to predict the probability of a message being marked as spam or not spam.

We loaded a dataset from UCI, which brought up some words and characters associated with spam messages and their percentage in the email. Then, we studied the data and created some visualizations to learn which elements were more likely to be classified as spam. Out of those, we created a new dataset with just six explanatory variables, reducing it from the original 57 columns.

Next, we trained and tested...

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