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ggcorrplot<-function(df, var){ |
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m.df<-df %>% spread(Cyt, Value) %>% select(-pats) |
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mcor<-cor(m.df, m.df, use="pairwise.complete.obs") # Por defecto usa el método de Pearson. |
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mpval<-Hmisc::rcorr(as.matrix(m.df))$P |
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df<-mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>% |
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gather(Var2, Value, -Var1) |
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df.pval<-mpval %>% as.data.frame() %>% add_column(Var1=rownames(mpval),.before=1) %>% |
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gather(Var2, Value, -Var1) |
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order<- mcor %>% as.data.frame() %>% add_column(Var1=rownames(mcor),.before=1) %>% clustsort |
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ggplot(df, aes(Var1, Var2, fill=Value))+ |
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scale_x_discrete(limits=order$x)+ |
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scale_y_discrete(limits=order$y)+ |
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theme_heatmap(line.color="black")+ |
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geom_text(data=df.pval, aes(label=round(Value, 2))) |
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} |