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ggplot2 is a plotting system designed for R and uses graphics grammar. The system only takes the good parts of base and lattice graphics leaving out the bad parts.
Data Visualization Software Open Source
•Layer: geoms •Layer: stats •Layer: position adjustment •Layer: annotations •Aesthetics •Scales •Guides: axes and legends •Facetting •Autoplot and fortify •Vector helpers
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
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•Layer: geoms •Layer: stats •Layer: position adjustment •Layer: annotations •Aesthetics •Scales •Guides: axes and legends •Facetting
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Ggplot2 is a part of an ecosystem called tidyverse designed with common APIs and a shared philosophy and is used as a plotting system for R, based on the grammar of graphics.
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ggplot2 is a plotting system designed for R and uses graphics grammar. The system only takes the good parts of base and lattice graphics leaving out the bad parts. The system is a powerful model which provides an easy way of producing complex, multilayered graphics without plotting hassles such as drawing legends.ggplot2’s Recent version works well with a most recent version of R.
Ggplot2 has features that allow the user to plot a call to ggplot(),supply default data and aesthetic mappings for aes and then add layers, coords, scales and facets then save plot to disk using ggsave().ggplot2 has a layer or geom which combines data, aesthetic mapping, statistical information, a geom and a position adjustment. Layers are created using a geom function and statistical information if needed.
The statistical function specifies layers, drawing attention to statistical information rather than visual appearance. All layers have a feature for position adjustment which resolves overlapping geoms. For position adjustment, the user overrides default by using the position argument to the geom or stat function.
Annotations are types of layers used to add fixed referenced data to the plot. They do not inherit global settings from the plot. Other features of ggplot2 are aesthetics and scales. Scales control the translation of data values to visual properties.
There are guides, axis, and legends used to interpret plots. Faceting in the plot allows the generation of small multiples which display different subsets of data. Facets are alternatives to aesthetics for displaying additional discrete variables. There are labels in facets which provide a flexible toolkit for controlling the display of strip labels on facets. ggplot 2 has a selection of built in datasets used as illustrations of different types of visualization challenges.
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