Compute geometric centers and spreads for ordination factors

stat_center(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  fun.data = NULL,
  fun.center = NULL,
  fun.min = NULL,
  fun.max = NULL,
  fun.args = list()
)

stat_star(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  fun.data = NULL,
  fun.center = NULL,
  fun.args = list()
)

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, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

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.

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().

...

Additional arguments passed to ggplot2::layer().

fun.data, fun.center, fun.min, fun.max, fun.args

Functions and arguments treated as in ggplot2::stat_summary(), with fun.center, fun.min, and fun.max behaving as fun.y, fun.ymin, and fun.ymax.

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.

Ordination aesthetics

The convenience function ord_aes() can be used to incorporate all coordinates of the ordination model into a statistical transformation. It maps the coordinates to the custom aesthetics ..coord1, ..coord2, etc.

Some transformations, e.g. stat_center(), are commutative with projection to the 'x' and 'y' coordinates. If they detect aesthetics of the form ..coord[0-9]+, then ..coord1 and ..coord2 are converted to x and y while any remaining are ignored.

Other transformations, e.g. stat_spantree(), yield different results in a planar biplot when they are computer before or after projection. If such a stat layer detects these aesthetics, then the lot of them are used in the transformation.

In either case, the stat layer returns a data frame with position aesthetics x and y.

Examples

# scaled PCA of Anderson iris measurements
iris[, -5] %>%
  princomp(cor = TRUE) %>%
  as_tbl_ord() %>%
  mutate_rows(species = iris$Species) %>%
  print() -> iris_pca
#> # 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  -2.26  0.480  0.128      | 1 setosa 
#> 2  -2.08 -0.674  0.235  ... | 2 setosa 
#> 3  -2.36 -0.342 -0.0442     | 3 setosa 
#> 4  -2.30 -0.597 -0.0913     | 4 setosa 
#> 5  -2.39  0.647 -0.0157     | 5 setosa 
#> # … with 145 more rows
#> # 
#> # Columns (standard): [ 4 x 4 | 0 ]
#>   Comp.1 Comp.2 Comp.3 ... | 
#>                            | 
#> 1  0.521 0.377   0.720     | 
#> 2 -0.269 0.923  -0.244 ... | 
#> 3  0.580 0.0245 -0.142     | 
#> 4  0.565 0.0669 -0.634     | 

# row-principal biplot with centroid-based stars
iris_pca %>%
  ggbiplot(aes(color = species)) +
  theme_bw() +
  scale_color_brewer(type = "qual", palette = 2) +
  stat_rows_star(alpha = .5, fun.center = "mean") +
  geom_rows_point(alpha = .5) +
  stat_rows_center(fun.center = "mean", size = 4, shape = 1L) +
  ggtitle(
    "Row-principal PCA biplot of Anderson iris measurements",
    "Segments connect each observation to its within-species centroid"
  )