Displaying 4 results from an estimated 4 matches for "slowkow".
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2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...tlier to these ellipses/contours is it
advisable to do something like this:
SNP$density <- get_density(SNP$mean, SNP$var)
> summary(SNP$density)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 383 696 738 1170 1789
where get_density() is function from here:
https://slowkow.com/notes/ggplot2-color-by-density/
and then do something like this:
a=SNP[SNP$density>400,]
and plot it again:
p <- ggplot(a, mapping = aes(x = mean, y = var))
p <- p + geom_density_2d() + geom_point() + my.theme + ggtitle("SNPS_red")
On Thu, Oct 8, 2020 at 3:52 PM Ana Mar...
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...by,
Thanks for getting back to me, yes I believe I did that by doing this:
SNP$density <- get_density(SNP$mean, SNP$var)
> summary(SNP$density)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 383 696 738 1170 1789
where get_density() is function from here:
https://slowkow.com/notes/ggplot2-color-by-density/
and keep only entries with density > 400
a=SNP[SNP$density>400,]
and plot it again:
p <- ggplot(a, mapping = aes(x = mean, y = var))
p <- p + geom_density_2d() + geom_point() + my.theme + ggtitle("SNPS_red")
and probably I can increase...
2020 Oct 08
2
2 D density plot interpretation and manipulating the data
Hello,
I have a data frame like this:
> head(SNP)
mean var sd
FQC.10090295 0.0327 0.002678 0.0517
FQC.10119363 0.0220 0.000978 0.0313
FQC.10132112 0.0275 0.002088 0.0457
FQC.10201128 0.0169 0.000289 0.0170
FQC.10208432 0.0443 0.004081 0.0639
FQC.10218466 0.0116 0.000131 0.0115
...
and I am creating plot like this:
s <- ggplot(SNP, mapping = aes(x = mean, y = var))
2020 Oct 09
3
2 D density plot interpretation and manipulating the data
You could assign a density value to each point.
Maybe you've done that already...?
Then trim the lowest n (number of) data points
Or trim the lowest p (proportion of) data points.
e.g.
Remove the data points with the 20 lowest density values.
Or remove the data points with the lowest 5% of density values.
I'll let you decide whether that is a good idea or a bad idea.
And if it's a