|  | ggcorrplot<-function(df, var){ | 
						
						
							|  |   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) | 
						
						
							|  | 
 | 
						
						
							|  |   order<- mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>% clustsort | 
						
						
							|  | 
 | 
						
						
							|  |   ggplot(df, aes(Var1, Var2, fill=Value))+ | 
						
						
							|  |     scale_x_discrete(limits=order$x)+ | 
						
						
							|  |     scale_y_discrete(limits=order$y)+ | 
						
						
							|  |     theme_heatmap(line.color="black")+ | 
						
						
							|  |     geom_text(data=df.pval, aes(label=round(Value, 2))) | 
						
						
							|  | }
 |