% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dist.R
\name{dist}
\alias{dist}
\alias{get_dist}
\alias{fviz_dist}
\title{Enhanced Distance Matrix Computation and Visualization}
\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")
)
}
\arguments{
\item{x}{a numeric matrix or a data frame.}

\item{method}{the distance measure to be used. This must be one of 
"euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", 
"pearson", "spearman" or "kendall".}

\item{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.}

\item{...}{other arguments to be passed to the function dist() when using get_dist().}

\item{dist.obj}{an object of class "dist" as generated by the function dist() or get_dist().}

\item{order}{logical value. if TRUE the ordered dissimilarity image (ODI) is shown.}

\item{show_labels}{logical value. If TRUE, the labels are displayed.}

\item{lab_size}{the size of labels.}

\item{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{
\itemize{ \item get_dist(): returns an object of class "dist". \item 
  fviz_dist(): returns a ggplot2 }
}
\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: 
  \href{https://www.datanovia.com/en/lessons/clustering-distance-measures/}{STHDA
   website - clarifying distance measures.}. \itemize{ \item get_dist(): 
  Computes a distance matrix between the rows of a data matrix. Compared to
  the standard \code{\link[stats]{dist}}() function, it supports 
  correlation-based distance measures including "pearson", "kendall" and 
  "spearman" methods. \item fviz_dist(): Visualizes a distance matrix }
}
\examples{
data(USArrests)
res.dist <- get_dist(USArrests, stand = TRUE, method = "pearson")

fviz_dist(res.dist, 
   gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))
}
\seealso{
\code{\link[stats]{dist}}
}
\author{
Alboukadel Kassambara \email{alboukadel.kassambara@gmail.com}
}
