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

# What’s more in aesthetics?

The aesthetics in ```ggplot2``` are not limited to what we have discussed (color, fill, point shapes, alpha, etc.) in the previous lesson. Let’s look at what else `ggplot2` offers for aesthetics.

For example, we can introduce textures in our graphs using a supporting package called ```ggpattern```. These are optional aesthetics that can improve the visual appearance of any data visualization.


We’ll cover some well-known ```ggplot2``` supporting packages in this lesson. However, it is challenging to cover all such libraries in a single course. 

Ultimately, it depends on the project requirements. It is important to understand what data is to be plotted and how we perceive the plot to look. 

That being said, the collection of R libraries on [CRAN](https://cran.r-project.org/) boasts of several packages that you should be able to use on your own to extend the ```ggplot2``` functionality after completing this course.
 
# Recapping the basics of aesthetics

The ```aes()``` function in ```ggplot2``` is used along with other grammar of graphics elements to define the required aesthetics. This ```aes()``` function can be used either in a nested format, i.e., in a global manner (applies to the entire graph) or in an individual layer. To generate specific graph elements, the ```aes()``` function is nested in specific geom functions such as ```geom_point()```, ```geom_line()```, etc. In short, we can remember this by thinking of it as inside and outside the ```aes()``` function. 

## Inside `aes()` vs. outside `aes()`

We define some aesthetics within `aes()` in a nested format, while others are defined differently.



There are some aesthetics within `aes()` that we define with a nested format, while others are defined differently. How do we decide which type of ```aes()``` function to use and when? Let’s consider two situations:

1. We would like to define aesthetics that apply to a specific variable in our dataset. Pick _inside_ `aes()`.
2. We plan to have an aesthetic that applies to our entire visualization and holds a constant value, i.e., it does not change or depend on the dataset values. This one needs to be placed outside, so we pick _outside_ `aes()` and place the aesthetics within the `geom` element.

# Aesthetics vs. attributes

When discussing aesthetics in ```ggplot2```, another important concept to understand is ```attributes```. Aesthetics indicate the variable mapped to it more than the look of the plot. So, what are attributes, and how are they connected to aesthetics?



Understand how to use different annotations in ggplot2 for adding details to plots, both inside and outside the aesthetics.

Welcome to the Course

Introduction to ggplot2

Drawing Basic Plots Using ggplot2

Advanced Plots

Plot Customization

Creating Panel of Plots

Building Interactive Plots

Uber Data Analysis Using the R Language

Summing Up

Appendix

Final Assessment

Stock Data Analysis with R

Aesthetics and Annotation in ggplot2

What’s more in aesthetics?

Recapping the basics of aesthetics

Inside `aes()`