Plot basicsAll ggplot2 plots with a call to |
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Create a new ggplot |
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Construct aesthetic mappings |
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Add components to a plot |
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Save a ggplot (or other grid object) with sensible defaults |
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Quick plot |
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Layer: geomsA layer combines data, aesthetic mapping, a geom (geometric object), a stat (statistical transformation), and a position adjustment. Typically, you will create layers using a |
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Reference lines: horizontal, vertical, and diagonal |
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Bar charts |
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Heatmap of 2d bin counts |
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Draw nothing |
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A box and whiskers plot (in the style of Tukey) |
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2d contours of a 3d surface |
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Count overlapping points |
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Smoothed density estimates |
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Contours of a 2d density estimate |
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Dot plot |
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Horizontal error bars |
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Hexagonal heatmap of 2d bin counts |
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Histograms and frequency polygons |
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Jittered points |
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Vertical intervals: lines, crossbars & errorbars |
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Polygons from a reference map |
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Connect observations |
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Points |
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Polygons |
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A quantile-quantile plot |
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Quantile regression |
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Ribbons and area plots |
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Rug plots in the margins |
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Line segments and curves |
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Smoothed conditional means |
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Line segments parameterised by location, direction and distance |
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Text |
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Rectangles |
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Violin plot |
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Visualise sf objects |
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Layer: statsA handful of layers are more easily specified with a |
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Compute empirical cumulative distribution |
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Compute normal confidence ellipses |
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Compute function for each x value |
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Leave data as is |
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Bin and summarise in 2d (rectangle & hexagons) |
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Summarise y values at unique/binned x |
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Remove duplicates |
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Layer: position adjustmentAll layers have a position adjustment that resolves overlapping geoms. Override the default by using the |
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Dodge overlapping objects side-to-side |
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Don't adjust position |
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Jitter points to avoid overplotting |
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Simultaneously dodge and jitter |
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Nudge points a fixed distance |
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Stack overlapping objects on top of each another |
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Layer: annotationsAnnotation are special types of layer than don’t inherit global settings from the plot. They are used to add fixed reference data to plot. |
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Reference lines: horizontal, vertical, and diagonal |
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Create an annotation layer |
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Annotation: Custom grob |
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Annotation: log tick marks |
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Annotation: a maps |
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Annotation: high-performance rectangular tiling |
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Create a layer of map borders |
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AestheticsThe following help topics give a broad overview of some of the ways you can use each aesthetic |
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Colour related aesthetics: colour, fill and alpha |
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Aesthetics: grouping |
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Differentiation related aesthetics: linetype, size, shape |
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Position related aesthetics: x, y, xmin, xmax, ymin, ymax, xend, yend |
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ScalesScales control the details of how data values are translated to visual properties. Override the default scales to tweak details like the axis labels or legend keys, or to use a completely different translation from data to aesthetic. |
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Modify axis, legend, and plot labels |
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Set scale limits |
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Expand the plot limits, using data |
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Alpha transparency scales |
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Sequential, diverging and qualitative colour scales from colorbrewer.org |
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Continuous colour scales |
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Position scales for continuous data (x & y) |
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Position scales for date/time data |
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Position scales for discrete data |
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Gradient colour scales |
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Sequential grey colour scales |
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Evenly spaced colours for discrete data |
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Use values without scaling |
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Scale for line patterns |
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Create your own discrete scale |
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Scales for shapes, aka glyphs |
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Scales for area or radius |
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Viridis colour scales from viridisLite |
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Guides: axes and legendsThe guides (the axes and legends) help readers interpret your plots. Guides are mostly controlled via the scale (e.g. with the |
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Continuous colour bar guide |
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Legend guide |
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Set guides for each scale |
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Specify a secondary axis |
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FacettingFacetting generates small multiples, each displaying a different subset of the data. Facets are an alternative to aesthetics for displaying additional discrete variables. |
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Lay out panels in a grid |
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Wrap a 1d ribbon of panels into 2d |
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Facetting: labelsThese functions provide a flexible toolkit for controlling the display of the “strip” labels on facets. |
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Construct labelling specification |
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Useful labeller functions |
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Label with mathematical expressions |
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Coordinate systemsThe coordinate system determines how the |
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Cartesian coordinates |
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Cartesian coordinates with fixed "aspect ratio" |
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Cartesian coordinates with x and y flipped |
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Map projections |
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Polar coordinates |
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Transformed Cartesian coordinate system |
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ThemesThemes control the display of all non-data elements of the plot. You can override all settings with a complete theme like |
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Modify components of a theme |
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Complete themes |
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Get, set, and modify the active theme |
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Theme elements |
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Programming with ggplot2These functions provides tools to help you program with ggplot2, creating functions and for-loops that generate plots for you. |
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Define aesthetic mappings programatically |
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Explicitly draw plot |
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Extending ggplot2To create your own geoms, stats, scales, and facets, you’ll need to learn a bit about the object oriented system that ggplot2 uses. Start by reading |
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Create a new ggproto object |
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Format or print a ggproto object |
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Vector helpersggplot2 also provides a handful of helpers that are useful for creating visualisations. |
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Discretise numeric data into categorical |
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A selection of summary functions from Hmisc |
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Calculate mean and standard error |
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Compute the "resolution" of a numeric vector |
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Dataggplot2 comes with a selection of built-in datasets that are used in examples to illustrate various visualisation challenges. |
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Prices of 50,000 round cut diamonds |
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US economic time series |
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2d density estimate of Old Faithful data |
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Midwest demographics |
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Fuel economy data from 1999 and 2008 for 38 popular models of car |
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An updated and expanded version of the mammals sleep dataset |
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Terms of 11 presidents from Eisenhower to Obama |
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Vector field of seal movements |
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Housing sales in TX |
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Autoplot and fortify
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Create a complete ggplot appropriate to a particular data type |
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Create a ggplot layer appropriate to a particular data type |
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Fortify a model with data. |
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Create a data frame of map data |