diff --git a/R/gglegend.R b/R/gglegend.R
new file mode 100644
index 0000000..aad009f
--- /dev/null
+++ b/R/gglegend.R
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+gglegend<-function(data, x, y, var, stat="median", color="black", ...){
+  if (stat == "median"){
+    data<-data %>% group_by({{var}}) %>% summarise("{{x}}":=median({{x}}, na.rm=T), "{{y}}":=median({{y}}, na.rm=T))
+  }
+  if (stat == "mean"){
+    data<-data %>% group_by({{var}}) %>% summarise("{{x}}":=mean({{x}}, na.rm=T), "{{y}}":=mean({{y}}, na.rm=T))
+  }
+  if (!is.null(color)){
+    return(geom_label(data = data, aes({{x}},{{y}}, label={{var}}), color=color, ...))
+  }else{
+    return(geom_label(data = data, aes({{x}},{{y}}, label={{var}}),...))
+  }
+}
diff --git a/man/gglegend.Rd b/man/gglegend.Rd
new file mode 100644
index 0000000..684403d
--- /dev/null
+++ b/man/gglegend.Rd
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+\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")
+
+}