He empezado a incorporar el triangular la matriz.

R/ggcorrplot.R

@@ -1,12 +1,21 @@
-ggcorrplot<-function(df, var, color="#FFFFFF00", stat="signif"){
+ggcorrplot<-function(df, var, color="#FFFFFF00", stat="signif", tri="all"){
   m.df<-df %>% spread(Cyt, Value) %>% select(-pats)
   mcor<-cor(m.df, m.df, use="pairwise.complete.obs") # Por defecto usa el método de Pearson.
   mpval<-Hmisc::rcorr(as.matrix(m.df))$P
 
-  df<-mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>%
-    gather(Var2, Value, -Var1)
-  df.pval<-mpval %>% as.data.frame() %>% add_column(Var1=rownames(mpval),.before=1) %>%
-    gather(Var2, Value, -Var1)
+  df<-mcor %>% as.data.frame()
+  if (tri == "lower"){
+    df[lower.tri(df, diag=T)]<-NA
+  }
+  df<-df %>% add_column(Var1=rownames(mcor),.before=1) %>%
+    gather(Var2, Value, -Var1) %>% filter(!is.na(Value))
+
+  df.pval<-mpval %>% as.data.frame()
+  if (tri == "lower"){
+    df.pval[lower.tri(df.pval, diag=T)]<-NA
+  }
+  df.pval<-df.pval %>% add_column(Var1=rownames(mpval),.before=1) %>%
+    gather(Var2, Value, -Var1) %>% filter(!is.na(Value))
 
   df.pval$Value<-round(df.pval$Value, 3)
 
@@ -20,8 +29,6 @@ ggcorrplot<-function(df, var, color="#FFFFFF00", stat="signif"){
   df$Var2<-factor(df$Var2, levels=order$y)
 
   ggplot(df, aes(Var1, Var2))+
-    # scale_x_discrete(limits=order$x)+
-    # scale_y_discrete(limits=order$y)+
     geom_tile(aes(fill=Value), color=color)+
     geom_text(data=df.pval, aes(label=Value), color="white")+
     scale_fill_gradientn(colors=col2(200))+