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

Summary

After reading this chapter, you should be able to make enhanced plots, such as facet grids, maps, and 3D plots.

We started by learning about facet grids, which are one of the grammatical elements of the grammar of graphics. With facet grids, a graphic can be divided into subplots, making the interpretation easier for the reader. The next topics were how to plot maps and time series in R using ggplot2. These are vast subjects that lie within geospatial data analysis and time series analysis in data science, so we just covered the basics, but that should be enough for you to create great visualizations.

3-dimensional plots are beautiful and impactful, no doubt. However, they are not well suited for big data or for visualizations where precision is a requirement. They are good, though, for plotting surfaces or viewing the separation of data points that is only visible with the addition of a third dimension.

Finally, we closed the chapter with a function that combines...