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Perform k-means

Source: R/8_k_clustering.R
k_clustering.Rd

k_clustering performs k-means clustering on the PC scores with the selected value of k

Usage

k_clustering(
  kmeans_data,
  standardise = TRUE,
  k,
  nstart = 50,
  polygon = FALSE,
  output = TRUE,
  output_folder = NULL,
  verbose = FALSE
)

Arguments

kmeans_data

Data frame containing the combined PC scores and depth statistics to perform k-means on. Output from the 'combine_data()' function.

standardise

TRUE or FALSE. Whether or not to standardise the data. Defaults to TRUE.

k

Numerical. Value of k to use for analysis.

nstart

Numerical. Value of nstart for k-means analysis.

polygon

TRUE or FALSE. Plot polygons for cluster with more than 3 data points. Defaults to FALSE.

output

TRUE or FALSE. Whether or not to output the results. Defaults to TRUE.

output_folder

Output folder path. If output = TRUE, output_folder must be provided. Defaults to NULL.

verbose

Logical. If TRUE, progress messages are shown. Defaults to FALSE.

Value

An object of class 'kmeans' containing the k-means clustering data for the data frame. Additionally plots a 3D cluster plot of the top three Principal Components.

Details

This function relies on random initialisation in k-means clustering. For reproducible results, users may wish to set a random seed prior to calling this function using set.seed().

Examples

# Set file path
filepath <- system.file("extdata", package = "FishDiveR")

# Load kmeans_data
kmeans_data <- readRDS(file.path(filepath, "data/5_k-means/combined_stats.rds"))

# \donttest{
# Full example using the complete dataset.
# Set output to TRUE for real use!

kmeans_result <- k_clustering(
  kmeans_data = kmeans_data,
  standardise = TRUE,
  k = 4,
  nstart = 50,
  polygon = FALSE,
  output = FALSE,
  output_folder = tempdir(),
  verbose = TRUE
)
#> Standardising k-means input.
#> Total of 10 days of data
# }