Basic Tips for Using Visualizations With ggplot2 in R Programming

Learn How to Plan a Basic Data Visualization Features In ggplot2

Pierre DeBois

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No matter who you are in data science, if you have heard of R programming, you likely have come across ggplot2, the visualization library created by Hadley Wickham. ggplot2 provides a means to display complex. Like many other popular libraries in R, its adoption among practitioners has spawned a subset of libraries. All are meant to make visualizations easier to customize. But for most visualization ggplot2 is the most useful.

The library ggplot2 is named for a visualization concept called The Grammar of Graphics, a system that treats graphic elements as individual layers that are called together to create a plot. This means elements in the ggplot() function take a set of data and applies a plot feature as arguments representing each graph part, such as adding an axis line, setting a color, and so on, as a layer. This layering system complements the function syntax of R while adding advanced plotting capability that allows for complex graphs that can not be made with the standard R plot function.

ggplot2 can create a unique workflow “sidebar” for new programmers in that there are a lot of arguments available. Moreover, there are additional libraries that can increase the visualization planning required to display your code.

But understanding ggplot2 enhances how information is displayed. Many graph examples in R programming articles and presentations rely on ggplot2 rather than the basic plot features in R to visualize results from advanced data techniques such as regressions and machine learning models.

The Basic Arrangement of Code for ggplot

A plot in ggplot2 is constructed with three minimal argument layers — Data, Aesthetics and Geometry. They are required in a line of code to form a graph.

1. Data refers to the data that is sourced for your intended graph. This data is treated as data frame object.

2. Aesthetics indicates how data are displayed in a plot, e.g., color, size, and the role of points, such as which data is x and y in a line chart.

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Pierre DeBois

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