A system for declaratively creating graphics, based on "The Grammar of Graphics". You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

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. More complete information about how to use ggplot2 can be found in the book, but here you’ll …

All ggplot2 plots begin with a call to ggplot(), supplying default data and aesthetic mappings, specified by aes(). You then add layers, scales, coords and facets with +. To save a plot to disk, use ggsave().

ggplot() initializes a ggplot object. It can be used to declare the input data frame for a graphic and to specify the set of plot aesthetics intended to be common throughout all subsequent layers unless specifically overridden.

Customising how aesthetic specifications are represented on your plot.

In ggplot2, a plot is constructed by adding layers to it. In addition to geoms and stats, position adjustments are the third required part of a layer. The 'position' part of a layer is responsible for dodging, jittering and nudging groups of data to minimise their overlap, or otherwise tweaking their positions. For example if you add position = position_nudge(x = 1) to a layer, you can offset ...

The point geom is used to create scatterplots. The scatterplot is most useful for displaying the relationship between two continuous variables. It can be used to compare one continuous and one categorical variable, or two categorical variables, but a variation like geom_jitter(), geom_count(), or geom_bin_2d() is usually more appropriate. A bubblechart is a scatterplot …

Using ggplot2 to visualize an object ggplot2 is commonly used in packages to visualize objects (e.g., in a plot() -style function). For example, a package might define an S3 class that represents the probability of various discrete values:

Good labels are critical for making your plots accessible to a wider audience. Always ensure the axis and legend labels display the full variable name. Use the plot title and subtitle to explain the main findings. It's common to use the caption to provide information about the data source. tag can be used for adding identification tags to differentiate between multiple plots. get_labs ...

There are two types of bar charts: geom_bar() and geom_col(). geom_bar() makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use geom_col() instead. geom_bar() uses stat_count() by default: it counts the number of cases at …