Book Image

Data Wrangling with R

By : Gustavo R Santos
Book Image

Data Wrangling with R

By: Gustavo R 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)
1
Part 1: Load and Explore Data
5
Part 2: Data Wrangling
12
Part 3: Data Visualization
16
Part 4: Modeling

Adding interactivity to graphics

Images are interpreted by our brains faster than words or numbers (https://tinyurl.com/nhtbw9jk). That makes graphics an interesting way to show data, as we have learned throughout this book. But there is still more enhancement to be done when working with data visualization, and one of these enhancements is interactivity.

The ggplot2 library creates static graphics. Hence, the plots will not show values at the tops of bars or names of points on a scatterplot, for example. If that is a requirement for a visualization, it must be added using an annotation or text. However, when you combine the graphic’s code with plotly, some interaction is added to the visualization, such as making values appear just by hovering over a data point or zooming in and out the graphic.

To create an interactive scatterplot out of the same code that generated in Figure 11.1, we only have to add the ggplotly() function around the entire ggplot code. See the following...