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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

3D plots

3-dimensional plots are beautiful. Very often, they create a good impression with their audience, but the truth is that they are not the best type of graphic to use. To plot a 3D graphic on a 2D space, such as on a computer screen or on paper, the third dimension will have to simulate depth that does not exist. It is not recommended that you plot in 3D very often, as in general, a good old 2D plot will be the simplest and best option.

Sometimes, though, looking at 3D plots can be useful. Cases such as surface graphics, which can represent the surface of a given place, such as a mountain, can be interesting.

3D graphics can be created using the plotly library in R (loaded with library(plotly)). Let’s create a random surface and plot it. The surface graphics require the input data to be a matrix, thus we are creating one and then using the plot_ly() function, passing the z= ~surface argument to it to indicate that we want a 3D graphic. Remember that x and y are...

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