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- ggcorrplot<-function(df, var, color="#FFFFFF00", stat="signif"){
- 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))$P
-
- df<-mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>%
- gather(Var2, Value, -Var1)
- df.pval<-mpval %>% as.data.frame() %>% add_column(Var1=rownames(mpval),.before=1) %>%
- gather(Var2, Value, -Var1)
-
- df.pval$Value<-round(df.pval$Value, 3)
-
- if (stat=="signif"){
- df.pval$Value<-gtools::stars.pval(df.pval$Value)
- }
-
- order<- mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>% clustsort
-
- ggplot(df, aes(Var1, Var2))+
- scale_x_discrete(limits=order$x)+
- scale_y_discrete(limits=order$y)+
- geom_tile(aes(fill=Value), color=color)+
- geom_text(data=df.pval, aes(label=Value))+
- scale_fill_gradientn(colors=col2(200))+
- theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5),
- panel.background = element_blank(),
- axis.ticks = element_blank())
- }
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