Gain insights into using ggplot2 in R to create compelling visualizations. Learn about scatter plots, bar charts, customizing themes, and building interactive maps to enhance data storytelling.

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Data visualization in R using ggplot2 is an essential skill for data professionals and enthusiasts to efficiently discover and convey insights from datasets. Mastering ggplot2 allows you to uncover hidden patterns and trends in data, boosting decision-making and storytelling for a wide range of audiences. 

In this comprehensive course, you’ll begin by learning data visualization principles and the grammar of graphics. Progressing to ggplot2’s core functionalities, you’ll create scatter plots, bar charts, line graphs, histograms, and more explore advanced visualization through customizations and themes to create visually engaging plots. You’ll also be building interactive visualizations and captivating maps. 

By the end of this course, you’ll confidently generate engaging data visualizations in R, transforming yourself into an effective data communicator. You’ll also be able to elevate your data visualization skills with ggplot2 and excel as a data storyteller while enhancing your data science career.

Data Visualizations with ggplot2 in R

The `facet_grid()` function creates a default single plot while the `facet_wrap()` function creates two-dimensional grid plots.

The `facet_grid()` function makes only 2D plots while the `facet_wrap()` function makes 3D plots.

The `facet_grid()` function makes all combinations of plots, even if some plots are empty, while the `facet_wrap()` function only makes all possible plots and doesn’t include empty plots.

The `facet_grid()` function makes plots in grid view while the `facet_wrap()` function makes plots in list view.

What is the main difference between the `facet_grid()` and `facet_wrap()` functions in `ggplot2`?

What is the main difference between the <code>facet_grid()</code> and <code>facet_wrap()</code> functions in <code>ggplot2</code>?

Which library in R is _not_ used for arranging multiple `ggplot2` plots?

Which library in R is not used for arranging multiple <code>ggplot2</code> plots?

Which two operators are used by the `patchwork` package to combine plots in a nested layout?

Which two operators are used by the <code>patchwork</code> package to combine plots in a nested layout?

Which function in the `cowplot` package allows for arranging plots in a grid layout and includes options for labeling individual plots?

Which function in the <code>cowplot</code> package allows for arranging plots in a grid layout and includes options for labeling individual plots?

Which argument in the `ggarrange()` function allows including a shared legend for multiple plots?

Which argument in the <code>ggarrange()</code> function allows including a shared legend for multiple plots?

Test your knowledge on what you have learned so far in this chapter.

Welcome to the Course

Introduction to ggplot2

Drawing Basic Plots Using ggplot2

Advanced Plots

Plot Customization

Creating Panel of Plots

Building Interactive Plots

Summing Up

Appendix

Final Assessment

Quiz on Creating Panel of Plots