Hi Ana,
You seem to have a p-value at the top of the second plot. Do you just
want that p-value in a different place?
My first guess would be the "annotate" argument. Say you wanted your
p-value in the middle of the plot.
# your ggplot line
p+annotate("text",x=1.5,y=0.05,label="p = 1.6x10-16")
p
Well that means already a ratio. Could you show me how the data frame looks
like? If they are array of values then you compare the distribution of your
ratios like Ratio1 array from “ (#of EQTLs)/(#of genes)” vs Ratio 2 arrays
of the “same for the other, RG condition”.
ratio1 = c(ratio1Value1, rat
Awesome, thanks!
Yes those two numbers on y axis I calculated as (#of EQTLs)/(#of genes) and
the same for the other, RG condition. So implicitly I do have a spread just
that data was not used to plot this, only those ratios. Is in this case
still p value not necessary?
On Fri, 27 Sep 2019 at 23:4
Ah, this is a single observation and not pvalue calculation over a
distribution. You don’t seem to have a spread. Here your code seemed like
it was over all genes(more than 1) vs RG genes(also more than one). But it
is basically an observation of difference of 2 values. So it doesn’t need
to calcul
You will need to add stat_compare_means. Take a look at here.
http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/76-add-p-values-and-significance-levels-to-ggplots/
library(ggpubr)
p + stat_compare_means()
Should be fine.
Vivek
On Fri, Sep 27, 2019 at 7:29 PM Ana Marija
Hi,
I created a bar plot with this code:
library(ggplot2)
df <- data.frame("prop" = c(7.75,70.42), "Name" = c("All Genes","RG Genes"))
p<-ggplot(data=df, aes(x=Name, y=prop,fill=Name)) +
geom_bar(stat="identity")+ labs(x="", y = "Proportion of cis
EQTLs")+ scale_fill_brewer(palette="Greens") +
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