dist: Enhanced Distance Matrix Computation and Visualization

Description

Clustering methods classify data samples into groups of similar objects. This process requires some methods for measuring the distance or the (dis)similarity between the observations. Read more: Clustering Distance Measures in R.

get_dist(): Computes a distance matrix between the rows of a data matrix. Compared to the standard dist() function, it supports correlation-based distance measures including "pearson", "kendall" and "spearman" methods.
fviz_dist(): Visualizes a distance matrix

When stand = TRUE, scaling that produces non-finite values is rejected. fviz_dist() also validates that supplied distance objects contain only finite values before plotting.

Usage

get_dist(x, method = "euclidean", stand = FALSE, ...)
fviz_dist(
  dist.obj,
  order = TRUE,
  show_labels = TRUE,
  lab_size = NULL,
  gradient = list(low = "red", mid = "white", high = "blue")
)

Value

get_dist(): returns an object of class "dist".
fviz_dist(): returns a ggplot2

Arguments

x: a numeric matrix or a data frame.
method: the distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman" or "kendall".
stand: logical value; default is FALSE. If TRUE, then the data will be standardized using the function scale(). Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's standard deviation. If scaling produces NA values, get_dist() stops with a package-level error.
...: other arguments to be passed to the function dist() when using get_dist().
dist.obj: an object of class "dist" as generated by the function dist() or get_dist(), containing only finite dissimilarity values.
order: logical value. if TRUE the ordered dissimilarity image (ODI) is shown.
show_labels: logical value. If TRUE, the labels are displayed.
lab_size: the size of labels.
gradient: a list containing three elements specifying the colors for low, mid and high values in the ordered dissimilarity image. The element "mid" can take the value of NULL.

Author

Alboukadel Kassambara alboukadel.kassambara@gmail.com

Examples

Run this code

data(USArrests)
res.dist <- get_dist(USArrests, stand = TRUE, method = "pearson")

fviz_dist(res.dist, 
   gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))