diff --git a/R/ggcorrplot.R b/R/ggcorrplot.R
index c928eb5..1880ded 100644
--- a/R/ggcorrplot.R
+++ b/R/ggcorrplot.R
@@ -1,5 +1,6 @@
-ggcorrplot<-function(df, var, color="#FFFFFF00", stat="signif", tri="all", method="pearson"){
-  m.df<-df %>% spread(Cyt, Value) %>% select(-pats)
+ggcorrplot<-function(df, var, value,color="#FFFFFF00", stat="signif", tri="all", method="pearson"){
+  allnames<-colnames(df)
+  m.df<-df %>% spread(all_of(var), all_of(value)) %>% select(!any_of(allnames))
   mcor<-cor(m.df, m.df, use="pairwise.complete.obs", method = method) # Por defecto usa el método de Pearson.
   mpval<-Hmisc::rcorr(as.matrix(m.df), type=method)$P
 
diff --git a/man/ggcorrplot.Rd b/man/ggcorrplot.Rd
index b5ec687..e296641 100644
--- a/man/ggcorrplot.Rd
+++ b/man/ggcorrplot.Rd
@@ -2,11 +2,12 @@
 \alias{ggcorrplot}
 \title{ggcorrplot}
 \usage{
-ggcorrplot(df, var, color="#FFFFFF00", stat="signif", tri="all", method="pearson")
+ggcorrplot(df, var, value, color="#FFFFFF00", stat="signif", tri="all", method="pearson")
 }
 \arguments{
   \item{df}{A data frame in "long" format.}
   \item{var}{The column that will be used to analyze correlation all against all.}
+  \item{value}{The column that will be used as numeric data to analyze correlation.}
   \item{color}{The color of the lines of geom_tile (the border). By default, its transparent.}
   \item{stat}{The stats that will be on the tiles. They can be "signif", "pval", "none". By default, it presents "signif" (stars representing pvalue).}
   \item{tri}{It specifies which half of the correlation matrix is shown. Can be "all" (the default), "upper" or "lower".}
@@ -25,5 +26,5 @@ df<-data.frame("pats"=paste0("PAT", 1:20), "CytA"=rnorm(20,5), "CytB"=rnorm(20,5
 df<-gather(df, Cyt, Value,-pats)
 head(df)
 
-ggcorrplot(df, Cyt)
+ggcorrplot(df, Cyt, Value)
 }