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I have a distribution of data that is shown below in image 1. My goal is to show the likelihood that a variable is below a particular value for both X and for Y. For instance, I'd like to have a good way to show that ~95% of values are below 8000 on X-axis and below 6500 on the Y-axis. I am confident that there is a simple answer to this. I apologize if this has been asked many times before.

plot1 <- df %>% ggplot(mapping = aes(x = FLUID_TOT)) + stat_ecdf() + theme_bw()
plot2 <- df %>% ggplot(mapping = aes(x = FLUID_TOT, y = y)) + geom_point() + theme_bw()

Continuous data in X and Y

Cumulative distribution of the X variable

Miha
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Brent B
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  • Please provide a [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) of your data (eg, via `dput(head)`). Without it, I can only supply a general approach, such as plotting a labelled `geom_hline` with a value of 0.95 – Punintended May 30 '18 at 20:14
  • `ggstatsplot` looks to have some useful functions for your purpose https://github.com/IndrajeetPatil/ggstatsplot – Chris May 31 '18 at 10:07

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