Vectors from the origin

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
)

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)).

stat

The statistical transformation to use on the data for this layer, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

arrow

Specification for arrows, as created by grid::arrow(), or else NULL for no arrows.

...

Additional arguments passed to ggplot2::layer().

na.rm

Passed to ggplot2::layer().

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.

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. borders().

Value

A ggproto layer.

Biplot layers

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.

Aesthetics

geom_vector() understands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• linetype

• size

• group

See also

Other geom layers: geom_axis(), geom_isoline(), geom_lineranges(), geom_origin(), geom_text_radiate(), geom_unit_circle()

Examples

# compute unscaled row-principal components of scaled measurements
(iris_pca <- ordinate(iris, cols = 1:4, princomp))
#> # A tbl_ord of class 'princomp': (150 x 4) x (4 x 4)'
#> # 4 coordinates: Comp.1, Comp.2, ..., Comp.4
#> # 
#> # Rows (principal): [ 150 x 4 | 1 ]
#>   Comp.1 Comp.2  Comp.3 ... |   Species
#>                             |   <fct>  
#> 1  -2.68  0.319  0.0279     | 1 setosa 
#> 2  -2.71 -0.177  0.210  ... | 2 setosa 
#> 3  -2.89 -0.145 -0.0179     | 3 setosa 
#> 4  -2.75 -0.318 -0.0316     | 4 setosa 
#> 5  -2.73  0.327 -0.0901     | 5 setosa 
#> # … with 145 more rows
#> # 
#> # Columns (standard): [ 4 x 4 | 3 ]
#>    Comp.1  Comp.2  Comp.3 ... |   name         center scale
#>                               |   <chr>         <dbl> <dbl>
#> 1  0.361   0.657   0.582      | 1 Sepal.Length   5.84     1
#> 2 -0.0845  0.730  -0.598  ... | 2 Sepal.Width    3.06     1
#> 3  0.857  -0.173  -0.0762     | 3 Petal.Length   3.76     1
#> 4  0.358  -0.0755 -0.546      | 4 Petal.Width    1.20     1

# 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))