Plot basics All ggplot2 plots with a call to ggplot(), supplying default data and aesthethic mappings, specified by aes(). You then add layers, scales, coords and facets with +. To save a plot to disk, use ggsave().
ggplot	Create a new ggplot
aes	Construct aesthetic mappings
+.gg	Add components to a plot
ggsave	Save a ggplot (or other grid object) with sensible defaults
qplot quickplot	Quick plot
Layer: geoms A layer combines data, aesthetic mapping, a geom (geometric object), a stat (statistical transformation), and a position adjustment. Typically, you will create layers using a geom_ function, overriding the default position and stat if needed.
geom_abline geom_hline geom_vline	Reference lines: horizontal, vertical, and diagonal
geom_bar geom_col stat_count	Bars charts
geom_bin2d stat_bin_2d	Heatmap of 2d bin counts
geom_blank	Draw nothing
geom_boxplot stat_boxplot	A box and whiskers plot (in the style of Tukey)
geom_contour stat_contour	2d contours of a 3d surface
geom_count stat_sum	Count overlapping points
geom_density_2d stat_density_2d	Contours of a 2d density estimate
geom_density stat_density	Smoothed density estimates
geom_dotplot	Dot plot
geom_errorbarh	Horizontal error bars
geom_hex stat_bin_hex	Hexagonal heatmap of 2d bin counts
geom_freqpoly geom_histogram stat_bin	Histograms and frequency polygons
geom_jitter	Jittered points
geom_crossbar geom_errorbar geom_linerange geom_pointrange	Vertical intervals: lines, crossbars & errorbars
geom_map	Polygons from a reference map
geom_path geom_line geom_step	Connect observations
geom_point	Points
geom_polygon	Polygons
geom_qq stat_qq	A quantile-quantile plot
geom_quantile stat_quantile	Quantile regression
geom_ribbon geom_area	Ribbons and area plots
geom_rug	Rug plots in the margins
geom_segment geom_curve	Line segments and curves
geom_smooth stat_smooth	Smoothed conditional means
geom_spoke	Line segments parameterised by location, direction and distance
geom_label geom_text	Text
geom_raster geom_rect geom_tile	Rectangles
geom_violin stat_ydensity	Violin plot
stat_sf geom_sf coord_sf	Visualise sf objects
Layer: stats A handful of layers are more easily specified with a stat_ function, drawing attention to the statistical transformation rather than the visual appearance.
stat_ecdf	Compute empirical cumulative distribution
stat_ellipse	Compute normal confidence ellipses
stat_function	Compute function for each x value
stat_identity	Leave data as is
stat_summary_2d stat_summary_hex	Bin and summarise in 2d (rectangle & hexagons)
stat_summary_bin stat_summary	Summarise y values at unique/binned x
stat_unique	Remove duplicates
Layer: position adjustment All layers have a position adjustment that resolves overlapping geoms. Override the default by using the position argument to the geom_ or stat_ function.
position_dodge	Dodge overlapping objects side-to-side
position_identity	Don't adjust position
position_jitter	Jitter points to avoid overplotting
position_jitterdodge	Simultaneously dodge and jitter
position_nudge	Nudge points a fixed distance
position_stack position_fill	Stack overlapping objects on top of each another
Layer: annotations Annotation are special types of layer than don’t inherit global settings from the plot. Rhey are used to add fixed reference data to plot.
geom_abline geom_hline geom_vline	Reference lines: horizontal, vertical, and diagonal
annotate	Create an annotation layer
annotation_custom	Annotation: Custom grob
annotation_logticks	Annotation: log tick marks
annotation_map	Annotation: a maps
annotation_raster	Annotation: high-performance rectangular tiling
borders	Create a layer of map borders
Aesthetics The following help topics give a broad overview of some of the ways you can use each aesthetic
aes_colour_fill_alpha	Colour related aesthetics: colour, fill and alpha
aes_group_order	Aesthetics: grouping
aes_linetype_size_shape	Differentiation related aesthetics: linetype, size, shape
aes_position	Position related aesthetics: x, y, xmin, xmax, ymin, ymax, xend, yend
Scales Scales 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. labs() and lims() are convenient helpers for the most common adjustments to the labels and limits.
labs xlab ylab ggtitle	Modify axis, legend, and plot labels
lims xlim ylim	Set scale limits
expand_limits	Expand the plot limits, using data
scale_alpha scale_alpha_continuous scale_alpha_discrete	Alpha transparency scales
scale_colour_brewer scale_fill_brewer scale_colour_distiller scale_fill_distiller	Sequential, diverging and qualitative colour scales from colorbrewer.org
scale_x_continuous scale_y_continuous scale_x_log10 scale_y_log10 scale_x_reverse scale_y_reverse scale_x_sqrt scale_y_sqrt	Position scales for continuous data (x & y)
scale_x_date scale_y_date scale_x_datetime scale_y_datetime scale_x_time scale_y_time	Position scales for date/time data
scale_x_discrete scale_y_discrete	Position scales for discrete data
scale_colour_gradient scale_fill_gradient scale_colour_gradient2 scale_fill_gradient2 scale_colour_gradientn scale_fill_gradientn	Gradient colour scales
scale_colour_grey scale_fill_grey	Sequential grey colour scales
scale_colour_hue scale_fill_hue	Evenly spaced colours for discrete data
scale_colour_identity scale_fill_identity scale_shape_identity scale_linetype_identity scale_alpha_identity scale_size_identity	Use values without scaling
scale_linetype scale_linetype_continuous scale_linetype_discrete	Scale for line patterns
scale_colour_manual scale_fill_manual scale_size_manual scale_shape_manual scale_linetype_manual scale_alpha_manual	Create your own discrete scale
scale_shape	Scales for shapes, aka glyphs
scale_radius scale_size scale_size_area	Scales for area or radius
Guides: axes and legends The guides (the axes and legends) help readers interpret your plots. Guides are mostly controlled via the scale (e.g. with the limits, breaks, and labels arguments), but sometimes you will need additional cover over the guide apperance. Use guides() or the guide argument to individual scales along with guide_colourbar() or guide_legend().
guide_colourbar guide_colorbar	Continuous colour bar guide
guide_legend	Legend guide
guides	Set guides for each scale
sec_axis dup_axis derive	Specify a secondary axis
Facetting Facetting generates small multiples, each displaying a different subset of the data. Facets are an alternative to aesthetics for displaying additional discrete variables.
facet_grid	Lay out panels in a grid
facet_wrap	Wrap a 1d ribbon of panels into 2d
Facetting: labels These functions provide a flexible toolkit for controlling the display of the “strip” labels on facets.
labeller	Construct labelling specification
label_value label_both label_context label_parsed label_wrap_gen	Useful labeller functions
label_bquote	Label with mathematical expressions
Coordinate systems The coordinate system determines how the x and y aesthetics combine to position elements in the plot. The default coordinate system is Cartesian (coord_cartesian()), which can be tweaked with coord_map(), coord_fixed(), coord_flip(), and coord_trans(), or completely replaced with coord_polar().
coord_cartesian	Cartesian coordinates
coord_fixed	Cartesian coordinates with fixed "aspect ratio"
coord_flip	Cartesian coordinates with x and y flipped
coord_map coord_quickmap	Map projections
coord_polar	Polar coordinates
coord_trans	Transformed Cartesian coordinate system
Themes Themes control the display of all non-data elements of the plot. You can override all settings with a complete theme like theme_bw(), or choose to tweak individual settings by using theme() and the element_ functions. Use theme_set() to modify the active theme, affecting all future plots.
theme	Modify components of a theme
theme_grey theme_gray theme_bw theme_linedraw theme_light theme_dark theme_minimal theme_classic theme_void theme_test	Complete themes
theme_get theme_set theme_update theme_replace %+replace%	Get, set, and modify the active theme
margin element_blank element_rect element_line element_text rel	Theme elements
Programming with ggplot2 These functions provides tools to help you program with ggplot2, creating functions and for-loops that generate plots for you.
aes_ aes_string aes_q	Define aesthetic mappings programatically
print plot	Explicitly draw plot
Extending ggplot2 To 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 vignette("extending-ggplot2") then consult these functions for more details.
ggproto ggproto_parent is.ggproto	Create a new ggproto object
print format	Format or print a ggproto object
Vector helpers ggplot2 also provides a handful of helpers that are useful for creating visualisations.
cut_interval cut_number cut_width	Discretise numeric data into categorical
mean_cl_boot mean_cl_normal mean_sdl median_hilow	A selection of summary functions from Hmisc
mean_se	Calculate mean and standard error
resolution	Compute the "resolution" of a numeric vector
Data ggplot2 comes with a selection of built-in datasets that are used in examples to illustrate various visualisation challenges.
diamonds	Prices of 50,000 round cut diamonds
economics	US economic time series
faithfuld	2d density estimate of Old Faithful data
midwest	Midwest demographics
mpg	Fuel economy data from 1999 and 2008 for 38 popular models of car
msleep	An updated and expanded version of the mammals sleep dataset
presidential	Terms of 11 presidents from Eisenhower to Obama
seals	Vector field of seal movements
txhousing	Housing sales in TX
luv_colours	colors() in Luv space
Autoplot and fortify autoplot() is an extension mechansim for ggplot2 it provides a way for package authors to add methods that work like the base plot() function, generating useful default plots with little user interaction. fortify() turns objects into tidy data frames: it has largely been superceded by the broom package.
autoplot	Create a complete ggplot appropriate to a particular data type
autolayer	Create a ggplot layer appropriate to a particular data type
fortify	Fortify a model with data.
map_data	Create a data frame of map data