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:
http://www.sthda.com/english/wiki/clarifyingdistancemeasuresunsupervisedmachinelearning.
 get_dist():
Computes a distance matrix between the rows of a data matrix. Compared to
the standard
dist
() function, it supports
correlationbased distance measures including "pearson", "kendall" and
"spearman" methods.
 fviz_dist(): Visualizes a distance matrix
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"))
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.
 ...
 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().
 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.
Value
 get_dist(): returns an object of class "dist".

fviz_dist(): returns a ggplot2
See also
dist
Examples
data(USArrests)
res.dist < get_dist(USArrests, stand = TRUE, method = "pearson")
fviz_dist(res.dist,
gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))