geom_origin | R Documentation |
geom_origin()
renders a symbol, either a set of crosshairs or
a circle, at the origin. geom_unit_circle()
renders the unit circle,
centered at the origin with radius 1.
geom_origin(
mapping = NULL,
data = NULL,
marker = "crosshairs",
radius = unit(0.04, "snpc"),
...,
na.rm = FALSE,
show.legend = NA,
inherit.aes = FALSE
)
geom_unit_circle(
mapping = NULL,
data = NULL,
segments = 60,
scale.factor = 1,
...,
na.rm = FALSE,
show.legend = NA,
inherit.aes = FALSE
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
marker |
The symbol to be drawn at the origin; matched to |
radius |
A |
... |
Additional arguments passed to |
na.rm |
Passed to |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
segments |
The number of segments to be used in drawing the circle. |
scale.factor |
The circle radius; should remain at its default value 1
or passed the same value as |
A ggproto layer.
ggbiplot()
uses ggplot2::fortify()
internally to produce a single data
frame with a .matrix
column distinguishing the subjects ("rows"
) and
variables ("cols"
). The stat layers stat_rows()
and stat_cols()
simply
filter the data frame to one of these two.
The geom layers geom_rows_*()
and geom_cols_*()
call the corresponding
stat in order to render plot elements for the corresponding factor matrix.
geom_dims_*()
selects a default matrix based on common practice, e.g.
points for rows and arrows for columns.
geom_origin()
accepts no aesthetics.
geom_unit_circle()
understands the following aesthetics (none required):
linetype
linewidth
colour
alpha
Other geom layers:
geom_axis()
,
geom_bagplot()
,
geom_interpolation()
,
geom_isoline()
,
geom_lineranges()
,
geom_rule()
,
geom_text_radiate()
,
geom_vector()
ggplot(seals, aes(delta_long, delta_lat)) +
theme_void() +
geom_origin() +
geom_point(alpha = .25)
# center each group separately
iris |>
split(~ Species) |>
lapply(subset, select = -c(Species)) |>
lapply(scale, center = TRUE, scale = FALSE) |>
lapply(as.data.frame) |>
unsplit(iris$Species) |>
transform(Species = iris$Species) ->
iris_ctr
ggplot(iris_ctr, aes(Petal.Width, Petal.Length)) +
coord_equal() +
facet_wrap(vars(Species)) +
geom_unit_circle() +
geom_point()
# scale group mean differences uniformly
iris_ctr |>
subset(select = -c(Species)) |>
scale(center = FALSE, scale = TRUE) |>
transform(Species = iris$Species) |>
ggplot(aes(Petal.Width, Petal.Length)) +
coord_equal() +
facet_wrap(vars(Species)) +
geom_unit_circle() +
geom_point()
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