CitFuns/R/ggcorrplot.R

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

  order<- mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>% clustsort

  mor<-mcor[order$y, order$x]
  mpval<-mpval[order$y, order$x]

  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)

  if (stat=="signif"){
    df.pval$Value<-gtools::stars.pval(df.pval$Value)
  }

  df$Var1<-factor(df$Var1, levels=order$x)
  df$Var2<-factor(df$Var2, levels=order$y)

  ggplot(df, aes(Var1, Var2))+
    geom_tile(aes(fill=Value), color=color)+
    geom_text(data=df.pval, aes(label=Value), color="white")+
    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())
}