Stack Overflow is a great source of answers to common ggplot2 questions. It is also a great place to get help, once you have created a reproducible example that illustrates your problem.
ggplot2 builds charts through layers using geom_ functions. Here is a list of the different available geoms. Click one to see an example using it. Annotation is a key step in data visualization.
You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. Please use the canonical form https://CRAN.R-project.org/package=ggplot2 to link to this page.
This hub brings together 35 step-by-step ggplot2 tutorials that solve the most common visualization challenges. Whether you want to make titles bold, rotate axis labels, customize legends, or annotate plots with p-values and arrows, you’ll find practical examples here.
However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), faceting specifications (like facet_wrap()) and coordinate systems (like coord_flip()).
With ggplot2, you can create engaging and informative plots effortlessly. Whether you're a beginner or an experienced programmer, ggplot2's popularity and versatility make it an essential skill to have in your R toolkit. If you are new to ggplot2, this cheat sheet will help you get started.
ggplot2 is an R package for producing visualizations of data. Unlike many graphics packages, ggplot2 uses a conceptual framework based on the grammar of graphics. This allows you to ‘speak’ a graph from composable elements, instead of being limited to a predefined set of charts.
Building a ggplot2 plot is similar to building a sentence with a specified form, like “determiner noun verb” (e.g., “The cat slept.”). Just like each “determiner noun verb” sentence is composed of three parts of speech, each ggplot2plot is composed of various plot elements. Take a look at the code for the faceted plot that we made above.
It is based on the Grammar of Graphics and its main advantage is its flexibility, as you can create and customize the graphics adding more layers to it. This library allows creating ready-to-publish charts easily. The ggplot2 package allows customizing the charts with themes.
Previously we saw a brief tutorial of making charts with ggplot2 package. It quickly touched upon the various aspects of making ggplot. Now, this is a complete and full fledged tutorial. I start from scratch and discuss how to construct and customize almost any ggplot.
Stack Overflow is a great source of answers to common ggplot2 questions. It is also a great place to get help, once you have created a reproducible example that illustrates your problem.