Density values sum up 2 in histograms, can any one explain ...?

[QuijoteMx]

Hi!
I'm new to R. I have experience in other languages or statistical apps.
I'm doing a process analysis and finally I learn to do histograms, but when I selected the option for plotting densities instead of frequencies I noticed that individual values of the distribution sum up 2 instead of 1. I verified and the height of each bar is ok. Can anybody explain that behavior ...

Thanks ...

[joels]

Please provide the code you ran so that we can more easily help you.

For now, maybe the code below will help clarify what's happening.

# Generate two density distributions with densities on the same x-scale
set.seed(5)
x1 = rnorm(1000, 0, 1)
x2 = rnorm(1000, 3, 1)

x1d = density(x1, from=-5, to=8)
x2d = density(x2, from=-5, to=8)

# Area under each curve is 1
sum(x1d$y * median(diff(x1d$x)))
#> [1] 1.000979
sum(x2d$y * median(diff(x2d$x)))
#> [1] 1.000978

plot(x1d, ylim=c(0, 0.45))
lines(x2d)

# Area under curve has doubled to 2 when we add the densities
lines(x1d$x, x1d$y + x2d$y, col="red")

# Renormalize so area under curve is 1
lines(x1d$x, (x1d$y + x2d$y)/2, col="blue", lwd=3)

^{Created on 2020-11-14 by the reprex package (v0.3.0)}

[QuijoteMx]

This is no my real code. I'm using your sample data and the behavior is the same.
The chole example data was taken from another forum. It got worst. The sum of densities totals 0.02.
Sorry for not understanding the format for posting code

chol <- read.table(url("http://assets.datacamp.com/blog_assets/chol.txt"), header = TRUE)
x<-hist(chol$AGE,prob=T)
x
sum(x$counts)
sum(x$density)

x1 = rnorm(1000, 0, 1)
x2 = rnorm(1000, 3, 1)

Y1<-hist(x1,prob=T)
sum(Y1$density)

Y2<-hist(x2,prob=T)
sum(Y2$density)

[nirgrahamuk]

@QuijoteMx

Sorry for not understanding the format for posting code

Hi!
Here is a guide

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[FJCC]

The density values will not sum to one. The density is represented with point values, usually evenly spaced. If you increase the number of points and reduce their spacing, the sum of the density points will increase. What may sum to one, for the right kind of function, is the area under the curve. That is what @joels calculated:

@joels

x1d = density(x1, from=-5, to=8)
x2d = density(x2, from=-5, to=8)

# Area under each curve is 1
sum(x1d$y * median(diff(x1d$x)))
#> [1] 1.000979
sum(x2d$y * median(diff(x2d$x)))
#> [1] 1.000978

[QuijoteMx]

Thank you for your response.