geom_vector | R Documentation |
geom_vector()
renders arrows from the origin to points.
geom_vector( mapping = NULL, data = NULL, stat = "identity", position = "identity", arrow = default_arrow, ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data for this
layer, either as a |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
arrow |
Specification for arrows, as created by |
... |
Additional arguments passed to |
na.rm |
Passed to |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
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_vector()
understands the following aesthetics (required aesthetics
are in bold):
x
y
alpha
colour
linetype
size
group
Other geom layers:
geom_axis()
,
geom_isoline()
,
geom_lineranges()
,
geom_origin()
,
geom_text_radiate()
,
geom_unit_circle()
# compute unscaled row-principal components of scaled measurements (iris_pca <- ordinate(iris, cols = 1:4, princomp)) # row-principal biplot with coordinate-wise standard deviations iris_pca %>% ggbiplot(aes(color = Species)) + theme_bw() + scale_color_brewer(type = "qual", palette = 2) + geom_unit_circle() + geom_rows_point(alpha = .5) + geom_cols_vector(color = "#444444") + geom_cols_text_radiate(aes(label = name), color = "#444444") + ggtitle("Row-principal unscaled PCA biplot of Anderson iris measurements") + expand_limits(y = c(NA, 2))
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