## ----setup, include = FALSE---------------------------------------------------
if (identical(Sys.getenv("IN_PKGDOWN"), "true")) {
  dpi <- 320
} else {
  dpi <- 72
}

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  fig.align = "center",
  fig.dpi = dpi,
  warning = FALSE,
  message = FALSE
)

options(tibble.max_extra_cols = 10)

## -----------------------------------------------------------------------------
library(partition)
library(ggplot2)
set.seed(1234)
# create a 100 x 15 data set with 3 blocks
df <- simulate_block_data(
  # create 3 correlated blocks of 5 features each
  block_sizes = rep(5, 3),
  lower_corr = .4,
  upper_corr = .6,
  n = 100
)

## -----------------------------------------------------------------------------
ggcorrplot::ggcorrplot(corr(df))

## -----------------------------------------------------------------------------
baxter_otu

## -----------------------------------------------------------------------------
correlation_subset <- corr(baxter_otu[, 1:200])
ggcorrplot::ggcorrplot(correlation_subset, hc.order = TRUE) + ggplot2::theme_void()

## -----------------------------------------------------------------------------
prt <- partition(baxter_otu, threshold = .5)

prt

## -----------------------------------------------------------------------------
partition_scores(prt)

## -----------------------------------------------------------------------------
pca <- prcomp(baxter_otu)

# print the results more neatly
tibble::as_tibble(pca$x)

## -----------------------------------------------------------------------------
plot_ncluster(prt, show_n = 20) +
  # plot_*() functions return ggplots, so they can be extended using ggplot2
  theme_minimal(14)

## -----------------------------------------------------------------------------
plot_information(prt, geom = geom_histogram) +
  theme_minimal(14)

## -----------------------------------------------------------------------------
mapping_key(prt)

## -----------------------------------------------------------------------------
unnest_mappings(prt)

## -----------------------------------------------------------------------------
part_icc()

## -----------------------------------------------------------------------------
prt_pc1 <- partition(baxter_otu, threshold = .5, partitioner = part_pc1())
prt_pc1

## -----------------------------------------------------------------------------
# create a data.frame of 10 independent features
ind_df <- purrr::map_dfc(1:10, ~ rnorm(30))
ind_part <- partition(ind_df, .5)
ind_part

identical(ind_df, partition_scores(ind_part))

## -----------------------------------------------------------------------------
plot_stacked_area_clusters(df) +
  theme_minimal(14)

## -----------------------------------------------------------------------------
perms <- test_permutation(df, nperm = 10)
perms

## ----fig.height = 7-----------------------------------------------------------
plot_permutation(perms, .plot = "nreduced") +
  theme_minimal(14)