\name{gglegend}
\alias{gglegend}
\title{gglegend}
\usage{
gglegend(data, x, y, var, stat="median", color="black", ...)
}
\arguments{
  \item{data}{A data frame from where to take x and y axis, and the grouping variable.}
  \item{x}{The variable that will be used for X axis in the heatmap.}
  \item{y}{The variable that will be used for Y axis in the heatmap.}
  \item{value}{The variable that will be used for grouping the heatmap. The labels will be taken from it}
  \item{stat}{The statistical central test that will be used to calculate the label coordinates. "mean" or "median" are posible, defaulting to "median".}
  \item{color}{The color of the labels, "black" by default. If NULL, nothig will be passed to the geom_label so it may be taken from aesthetics.}
  \item{...}{Other arguments that will be passed to the geom_label function.}
}
\description{
Generates a geom_label object with labels in the center of each population or agrupation.
}
\examples{
library(tidyverse)
library(Rtsne)
library(Rphenograph)
library(igraph)

## We will generate an example using tSNE and Phenograph algorithms, but can be applied to any other situation.

iris_unique <- unique(iris) # Remove duplicates
iris_matrix <- as.matrix(iris_unique[,1:4])

# Set a seed if you want reproducible results
set.seed(42)
tsne_out <- Rtsne(iris_matrix,pca=FALSE,perplexity=30,theta=0.0) # Run TSNE

rpheno<-Rphenograph(tsne_out$Y)
df<-data.frame(as.data.frame(tsne_out$Y), "Clust"=factor(membership(rpheno[[2]])))
head(df)

ggplot(df, aes(V1,V2, color=Clust))+
  geom_point()+
  gglegend(df, V1, V2, Clust)+
  guides(color="none")

}