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Transformations with respect to reference data

Source: R/stat-referent.r
stat_referent.Rd

Compute statistics with respect to a reference data set with shared positional variables.

Usage

stat_referent(
  mapping = NULL,
  data = NULL,
  geom = "blank",
  position = "identity",
  referent = NULL,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

# S3 method for class 'LayerRef'
ggplot_add(object, plot, ...)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

The geometric object to use to display the data for this layer. When using a stat_*() function to construct a layer, the geom argument can be used to override the default coupling between stats and geoms. The geom argument accepts the following:

  • A Geom ggproto subclass, for example GeomPoint.

  • A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

  • For more information and other ways to specify the geom, see the layer geom documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

referent

The reference data set, admitting the same 3 options as data; see Details.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display. To include legend keys for all levels, even when no data exists, use TRUE. If NA, all levels are shown in legend, but unobserved levels are omitted.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. annotation_borders().

...

Additional arguments passed to ggplot2::layer().

object

An object to add to the plot

plot

The ggplot object to add object to

Value

A ggproto layer.

Details

Often in geometric data analysis a statistical transformation applied to data \(X\) will also depend on data \(Y\), for example when drawing the projections of vectors \(X\) onto vectors \(Y\). The stat layer stat_referent() accepts \(Y\) as an argument to the referent argument and pre-processes them using the existing positional aesthetic mappings to x and y.

If a function is passed to referent, then the reference data are obtained by evaluating the function at the primary data. Alongside borrowing the aesthetic mappings, the evaluation is done during addition via ggplot2::ggplot_add() of the layer of custom class LayerRef.

The ggproto can be used as a parent to more elaborate statistical transformations, or the stat can be paired with geoms that expect the referent argument and use it to position their transformations of \(X\). It pairs by default to ggplot2::geom_blank() so as to prevent possibly confusing output.

Examples

# simplify the Motor Trends data to two predictors legible at aspect ratio 1
mtcars %>%
  transform(hp00 = hp/100) %>%
  subset(select = c(mpg, hp00, wt)) ->
  subcars
# compute the gradient of `mpg` against these two predictors
lm(mpg ~ hp00 + wt, subcars) %>%
  coefficients() %>%
  as.list() %>% as.data.frame() ->
  grad
# use the gradient as a reference (to no effect in this basic ggproto)
ggplot(subcars, aes(x = hp00, y = wt)) +
  coord_equal() +
  geom_point() +
  stat_referent(referent = grad)

ggplot(subcars, aes(x = hp00, y = wt)) +
  coord_equal() +
  stat_referent(geom = "point", referent = grad)


# passing a function yields a transformation of the primary data
p <- ggplot(subcars, aes(x = hp00, y = wt)) +
  stat_referent(
    data = head,
    referent = function(d) as.data.frame(lapply(d, mean))
  )
b <- ggplot_build(p)
# original data
b@plot@data
#>                      mpg hp00    wt
#> Mazda RX4           21.0 1.10 2.620
#> Mazda RX4 Wag       21.0 1.10 2.875
#> Datsun 710          22.8 0.93 2.320
#> Hornet 4 Drive      21.4 1.10 3.215
#> Hornet Sportabout   18.7 1.75 3.440
#> Valiant             18.1 1.05 3.460
#> Duster 360          14.3 2.45 3.570
#> Merc 240D           24.4 0.62 3.190
#> Merc 230            22.8 0.95 3.150
#> Merc 280            19.2 1.23 3.440
#> Merc 280C           17.8 1.23 3.440
#> Merc 450SE          16.4 1.80 4.070
#> Merc 450SL          17.3 1.80 3.730
#> Merc 450SLC         15.2 1.80 3.780
#> Cadillac Fleetwood  10.4 2.05 5.250
#> Lincoln Continental 10.4 2.15 5.424
#> Chrysler Imperial   14.7 2.30 5.345
#> Fiat 128            32.4 0.66 2.200
#> Honda Civic         30.4 0.52 1.615
#> Toyota Corolla      33.9 0.65 1.835
#> Toyota Corona       21.5 0.97 2.465
#> Dodge Challenger    15.5 1.50 3.520
#> AMC Javelin         15.2 1.50 3.435
#> Camaro Z28          13.3 2.45 3.840
#> Pontiac Firebird    19.2 1.75 3.845
#> Fiat X1-9           27.3 0.66 1.935
#> Porsche 914-2       26.0 0.91 2.140
#> Lotus Europa        30.4 1.13 1.513
#> Ford Pantera L      15.8 2.64 3.170
#> Ferrari Dino        19.7 1.75 2.770
#> Maserati Bora       15.0 3.35 3.570
#> Volvo 142E          21.4 1.09 2.780
# head of original data
b@data[[1]]
#>      x     y PANEL group
#> 1 1.10 2.620     1    -1
#> 2 1.10 2.875     1    -1
#> 3 0.93 2.320     1    -1
#> 4 1.10 3.215     1    -1
#> 5 1.75 3.440     1    -1
#> 6 1.05 3.460     1    -1
# means of original data
b@plot@layers$stat_referent$stat_params$referent
#>        mpg     hp00      wt
#> 1 20.09062 1.466875 3.21725
# means of head of original data
as.data.frame(lapply(layer_data(p, 1), mean))
#> Warning: argument is not numeric or logical: returning NA
#>          x        y PANEL group
#> 1 1.171667 2.988333    NA    -1