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

Facet grids

Facet grids create a figure in the form of a matrix of rows and columns to plot multiple graphics side by side. Those graphics are subplots of one or more variables, facilitating the visualization of the relationship of a variable with others separately. In summary, facet grids show small plots representing a subgroup of the data.

We can see what a facet grid looks like using the diamonds dataset, which is built into ggplot2 (type ?diamonds into R’s console for the documentation). This data has the cuts, dimensions, colors, prices, and other attributes of 54,000 diamonds. If we want to see a scatterplot of the prices by carat, the graphic will look busy, as we can see in Figure 11.1. Notice that it is difficult to see the trends and relationships for each cut type, such as Fair or Good. They will be hidden under other points. What we see is the general trend and relationship for the entire dataset.

Figure 11.1 – Scatterplot of...