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Add parentheses (#4059)
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R/aes.r

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#' evaluation to capture the variable names. `aes_` and `aes_string`
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#' require you to explicitly quote the inputs either with `""` for
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#' `aes_string()`, or with `quote` or `~` for `aes_()`.
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#' (`aes_q` is an alias to `aes_`). This makes `aes_` and
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#' `aes_string` easy to program with.
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#' (`aes_q()` is an alias to `aes_()`). This makes `aes_()` and
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#' `aes_string()` easy to program with.
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#'
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#' `aes_string` and `aes_` are particularly useful when writing
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#' `aes_string()` and `aes_()` are particularly useful when writing
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#' functions that create plots because you can use strings or quoted
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#' names/calls to define the aesthetic mappings, rather than having to use
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#' [substitute()] to generate a call to `aes()`.

R/autolayer.r

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#' Create a ggplot layer appropriate to a particular data type
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#'
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#' `autolayer` uses ggplot2 to draw a particular layer for an object of a
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#' `autolayer()` uses ggplot2 to draw a particular layer for an object of a
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#' particular class in a single command. This defines the S3 generic that
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#' other classes and packages can extend.
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#'

R/autoplot.r

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#' Create a complete ggplot appropriate to a particular data type
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#'
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#' `autoplot` uses ggplot2 to draw a particular plot for an object of a
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#' `autoplot()` uses ggplot2 to draw a particular plot for an object of a
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#' particular class in a single command. This defines the S3 generic that
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#' other classes and packages can extend.
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#'

R/axis-secondary.R

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#' the axis on the plot. Usually this is [guide_axis()].
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#'
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#' @details
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#' `sec_axis` is used to create the specifications for a secondary axis.
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#' `sec_axis()` is used to create the specifications for a secondary axis.
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#' Except for the `trans` argument any of the arguments can be set to
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#' `derive()` which would result in the secondary axis inheriting the
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#' settings from the primary axis.
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#'
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#' `dup_axis` is provide as a shorthand for creating a secondary axis that
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#' `dup_axis()` is provide as a shorthand for creating a secondary axis that
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#' is a duplication of the primary axis, effectively mirroring the primary axis.
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#'
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#' As of v3.1, date and datetime scales have limited secondary axis capabilities.

R/coord-map.r

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#' Map projections
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#'
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#' `coord_map` projects a portion of the earth, which is approximately
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#' `coord_map()` projects a portion of the earth, which is approximately
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#' spherical, onto a flat 2D plane using any projection defined by the
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#' `mapproj` package. Map projections do not, in general, preserve straight
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#' lines, so this requires considerable computation. `coord_quickmap` is a
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#' 0. For regions that span only a few degrees and are not too close to the
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#' poles, setting the aspect ratio of the plot to the appropriate lat/lon ratio
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#' approximates the usual mercator projection. This is what
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#' `coord_quickmap` does, and is much faster (particularly for complex
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#' `coord_quickmap()` does, and is much faster (particularly for complex
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#' plots like [geom_tile()]) at the expense of correctness.
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#'
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#' @param projection projection to use, see

R/coord-transform.r

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#' Transformed Cartesian coordinate system
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#'
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#' `coord_trans` is different to scale transformations in that it occurs after
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#' `coord_trans()` is different to scale transformations in that it occurs after
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#' statistical transformation and will affect the visual appearance of geoms - there is
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#' no guarantee that straight lines will continue to be straight.
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#'

R/facet-wrap.r

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#' Wrap a 1d ribbon of panels into 2d
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#'
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#' `facet_wrap` wraps a 1d sequence of panels into 2d. This is generally
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#' `facet_wrap()` wraps a 1d sequence of panels into 2d. This is generally
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#' a better use of screen space than [facet_grid()] because most
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#' displays are roughly rectangular.
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#'

R/geom-.r

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#' Compared to `Stat` and `Position`, `Geom` is a little
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#' different because the execution of the setup and compute functions is
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#' split up. `setup_data` runs before position adjustments, and
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#' `draw_layer` is not run until render time, much later. This
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#' `draw_layer()` is not run until render time, much later. This
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#' means there is no `setup_params` because it's hard to communicate
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#' the changes.
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#'

R/geom-ribbon.r

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#'
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#' For each x value, `geom_ribbon()` displays a y interval defined
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#' by `ymin` and `ymax`. `geom_area()` is a special case of
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#' `geom_ribbon`, where the `ymin` is fixed to 0 and `y` is used instead
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#' `geom_ribbon()`, where the `ymin` is fixed to 0 and `y` is used instead
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#' of `ymax`.
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#'
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#' An area plot is the continuous analogue of a stacked bar chart (see

R/geom-segment.r

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#' Line segments and curves
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#'
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#' `geom_segment` draws a straight line between points (x, y) and
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#' `geom_segment()` draws a straight line between points (x, y) and
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#' (xend, yend). `geom_curve` draws a curved line. See the underlying
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#' drawing function [grid::curveGrob()] for the parameters that
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#' control the curve.

R/geom-tile.r

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#' Rectangles
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#'
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#' `geom_rect` and `geom_tile` do the same thing, but are
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#' parameterised differently: `geom_rect` uses the locations of the four
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#' `geom_rect()` and `geom_tile()` do the same thing, but are
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#' parameterised differently: `geom_rect()` uses the locations of the four
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#' corners (`xmin`, `xmax`, `ymin` and `ymax`), while
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#' `geom_tile` uses the center of the tile and its size (`x`,
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#' `geom_tile()` uses the center of the tile and its size (`x`,
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#' `y`, `width`, `height`). `geom_raster` is a high
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#' performance special case for when all the tiles are the same size.
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#'

R/position-dodge.r

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#' Dodge overlapping objects side-to-side
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#'
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#' Dodging preserves the vertical position of an geom while adjusting the
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#' horizontal position. `position_dodge` requires the grouping variable to be
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#' be specified in the global or `geom_*` layer. Unlike `position_dodge`,
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#' `position_dodge2` works without a grouping variable in a layer.
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#' `position_dodge2` works with bars and rectangles, but is
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#' horizontal position. `position_dodge()` requires the grouping variable to be
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#' be specified in the global or `geom_*` layer. Unlike `position_dodge()`,
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#' `position_dodge2()` works without a grouping variable in a layer.
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#' `position_dodge2()` works with bars and rectangles, but is
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#' particulary useful for arranging box plots, which
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#' can have variable widths.
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#'

R/position-nudge.R

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#' Nudge points a fixed distance
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#'
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#' `position_nudge` is generally useful for adjusting the position of
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#' `position_nudge()` is generally useful for adjusting the position of
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#' items on discrete scales by a small amount. Nudging is built in to
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#' [geom_text()] because it's so useful for moving labels a small
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#' distance from what they're labelling.

R/quick-plot.r

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#' Quick plot
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#'
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#' `qplot` is a shortcut designed to be familiar if you're used to base
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#' `qplot()` is a shortcut designed to be familiar if you're used to base
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#' [plot()]. It's a convenient wrapper for creating a number of
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#' different types of plots using a consistent calling scheme. It's great
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#' for allowing you to produce plots quickly, but I highly recommend

R/scale-alpha.r

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#'
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#' Alpha-transparency scales are not tremendously useful, but can be a
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#' convenient way to visually down-weight less important observations.
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#' `scale_alpha` is an alias for `scale_alpha_continuous` since
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#' `scale_alpha()` is an alias for `scale_alpha_continuous()` since
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#' that is the most common use of alpha, and it saves a bit of typing.
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#'
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#' @param ... Other arguments passed on to [continuous_scale()], [binned_scale],

R/scale-discrete-.r

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#' Position scales for discrete data
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#'
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#' `scale_x_discrete` and `scale_y_discrete` are used to set the values for
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#' `scale_x_discrete()` and `scale_y_discrete()` are used to set the values for
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#' discrete x and y scale aesthetics. For simple manipulation of scale labels
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#' and limits, you may wish to use [labs()] and [lims()] instead.
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#'

R/scale-shape.r

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#' Scales for shapes, aka glyphs
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#'
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#' `scale_shape` maps discrete variables to six easily discernible shapes.
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#' `scale_shape()` maps discrete variables to six easily discernible shapes.
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#' If you have more than six levels, you will get a warning message, and the
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#' seventh and subsequence levels will not appear on the plot. Use
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#' [scale_shape_manual()] to supply your own values. You can not map
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#' a continuous variable to shape unless `scale_shape_binned()` is used. Still,
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#' as shape has no inherent order, this use is not advised..
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#' as shape has no inherent order, this use is not advised.
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#'
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#' @param solid Should the shapes be solid, `TRUE`, or hollow,
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#' `FALSE`?

R/scale-size.r

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#' Scales for area or radius
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#'
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#' `scale_size` scales area, `scale_radius` scales radius. The size
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#' `scale_size()` scales area, `scale_radius()` scales radius. The size
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#' aesthetic is most commonly used for points and text, and humans perceive
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#' the area of points (not their radius), so this provides for optimal
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#' perception. `scale_size_area` ensures that a value of 0 is mapped
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#' to a size of 0. `scale_size_binned` is a binned version of `scale_size` that
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#' perception. `scale_size_area()` ensures that a value of 0 is mapped
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#' to a size of 0. `scale_size_binned()` is a binned version of `scale_size()` that
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#' scales by area (but does not ensure 0 equals an area of zero). For a binned
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#' equivalent of `scale_size_area` use `scale_size_binned_area`.
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#' equivalent of `scale_size_area()` use `scale_size_binned_area()`.
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#'
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#' @name scale_size
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#' @inheritParams continuous_scale

R/stat-qq.r

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#' A quantile-quantile plot
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#'
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#' `geom_qq` and `stat_qq` produce quantile-quantile plots. `geom_qq_line` and
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#' `stat_qq_line` compute the slope and intercept of the line connecting the
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#' `geom_qq()` and `stat_qq()` produce quantile-quantile plots. `geom_qq_line()` and
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#' `stat_qq_line()` compute the slope and intercept of the line connecting the
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#' points at specified quartiles of the theoretical and sample distributions.
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#'
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#' @eval rd_aesthetics("stat", "qq")

R/stat-summary-2d.r

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#' Bin and summarise in 2d (rectangle & hexagons)
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#'
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#' `stat_summary_2d` is a 2d variation of [stat_summary()].
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#' `stat_summary_hex` is a hexagonal variation of
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#' `stat_summary_2d()` is a 2d variation of [stat_summary()].
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#' `stat_summary_hex()` is a hexagonal variation of
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#' [stat_summary_2d()]. The data are divided into bins defined
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#' by `x` and `y`, and then the values of `z` in each cell is
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#' are summarised with `fun`.

R/stat-summary.r

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#' Summarise y values at unique/binned x
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#'
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#' `stat_summary` operates on unique `x` or `y`; `stat_summary_bin`
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#' `stat_summary()` operates on unique `x` or `y`; `stat_summary_bin()`
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#' operates on binned `x` or `y`. They are more flexible versions of
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#' [stat_bin()]: instead of just counting, they can compute any
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#' aggregate.

R/utilities-break.r

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#' Discretise numeric data into categorical
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#'
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#' `cut_interval` makes `n` groups with equal range, `cut_number`
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#' `cut_interval()` makes `n` groups with equal range, `cut_number()`
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#' makes `n` groups with (approximately) equal numbers of observations;
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#' `cut_width` makes groups of width `width`.
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#' `cut_width()` makes groups of width `width`.
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#'
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#' @author Randall Prium contributed most of the implementation of
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#' `cut_width`.

man/aes_.Rd

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man/autolayer.Rd

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man/autoplot.Rd

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man/coord_map.Rd

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man/coord_trans.Rd

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man/cut_interval.Rd

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man/facet_wrap.Rd

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man/geom_qq.Rd

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man/geom_ribbon.Rd

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man/geom_segment.Rd

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man/geom_tile.Rd

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man/ggplot2-ggproto.Rd

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man/position_dodge.Rd

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man/position_nudge.Rd

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