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_rows_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_cols_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()
)

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

stat_cols_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()` or `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 display the data
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
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`.

Format

An object of class StatCenter (inherits from Stat, ggproto, gg) of length 3.

An object of class StatRowsCenter (inherits from StatCenter, Stat, ggproto, gg) of length 2.

An object of class StatColsCenter (inherits from StatCenter, Stat, ggproto, gg) of length 2.

An object of class StatStar (inherits from StatCenter, Stat, ggproto, gg) of length 2.

An object of class StatRowsStar (inherits from StatStar, StatCenter, Stat, ggproto, gg) of length 2.

An object of class StatColsStar (inherits from StatStar, StatCenter, Stat, ggproto, gg) of length 2.

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.

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: [ 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: [ 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 centroids
iris_pca %>%
  ggbiplot(aes(color = species)) +
  theme_bw() +
  scale_color_brewer(type = "qual", palette = 2) +
  geom_rows_point(alpha = .5) +
  stat_rows_center(fun.center = "mean", size = 3, shape = "triangle") +
  ggtitle(
    "Row-principal PCA biplot of Anderson iris measurements",
    "Overlaid with centroids and 99% confidence ellipses"
  )
# 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) +
  ggtitle(
    "Row-principal PCA biplot of Anderson iris measurements",
    "Segments connect each observation to its within-species centroid"
  )