partition (version 0.2.2)

plot_area_clusters: Plot partitions

Description

plot_stacked_area_clusters() and plot_area_clusters() plot the partition against a permuted partition. plot_ncluster() plots the number of variables per cluster. If .partition is the result of map_partition() or test_permutation(), plot_ncluster() facets the plot by each partition. plot_information() plots a histogram or density plot of the information of each variable in the partition. If .partition is the result of map_partition() or test_permutation(), plot_information() plots a scatterplot of the targeted vs. observed information with a 45 degree line indicating perfect alignment.

Usage

plot_area_clusters(
  .data,
  partitioner = part_icc(),
  information = seq(0.1, 0.5, length.out = 25),
  ...,
  obs_color = "#E69F00",
  perm_color = "#56B4E9"
)
plot_stacked_area_clusters(
  .data,
  partitioner = part_icc(),
  information = seq(0.1, 0.5, length.out = 25),
  ...,
  stack_colors = c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00")
)
plot_ncluster(
  .partition,
  show_n = 100,
  fill = "#0172B1",
  color = NA,
  labeller = "target information:"
)
plot_information(
  .partition,
  fill = "#0172B1",
  color = NA,
  geom = ggplot2::geom_density
)

Value

a ggplot

Arguments

.data

a data.frame to partition

partitioner

a partitioner. See the part_*() functions and as_partitioner().

information

a vector of minimum information to fit in partition()

...

arguments passed to partition()

obs_color

the color of the observed partition

perm_color

the color of the permuted partition

stack_colors

the colors of the cluster sizes

.partition

either a partition or a tibble, the result of map_partition() or test_permutation()

show_n

the number of reduced variables to plot

fill

the color of the fill for geom

color

the color of the geom

labeller

the facet label

geom

the geom to use. The default is geom_density.

Examples

set.seed(123)
df <- simulate_block_data(c(3, 4, 5), lower_corr = .4, upper_corr = .6, n = 100)

df %>%
  partition(.6, partitioner = part_pc1()) %>%
  plot_ncluster()