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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 learned what data visualization is and understood its importance for any project. Our brain is designed to quickly capture images; thus, graphics are more likely to be absorbed by an audience than by a table or text.

We introduced the basic types of single-variable plots – that is, histograms, which are commonly used to view the distribution of variables, and boxplots, which are especially good at detecting outliers.

In the sequence, we learned about graphics with two variables, such as scatterplots, that can show us how x and y are related and whether that relationship is positive or negative. We also learned about bar plots, a good representation of categorical variables because they are one of the simplest types of visual and easily understandable. Finally, we looked at line plots and how they are a great fit for continuous data and time series plots.

The chapter concluded with some examples of plots with many variables, with the scatterplot...

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