In one of the comments you say you used "random data" but you don't say from what distribution. If you are talking about heights of humans, they are roughly normally distributed, but your data is not remotely appropriate for human heights - yours are fractions of a cm!

And your data is not remotely normal. I'm guessing you used a uniform distribution with bounds of 0 and 1. And you generated a very small sample. Let's try with a bigger sample:

set.seed(1234)  #Sets a seed
x <- runif(10000, 0 , 1)
sd(x)  #0.28

so, no data points are beyond 2 sd from the mean, because that is beyond the bounds of the data. And the portion within 1 sd will be approximately 0.56.

In one of the comments you say you used "random data" but you don't say from what distribution. If you are talking about heights of humans, they are roughly normally distributed, but your data are not remotely appropriate for human heights - yours are fractions of a cm!

And your data are not remotely normal. I'm guessing you used a uniform distribution with bounds of 0 and 1. And you generated a very small sample. Let's try with a bigger sample:

set.seed(1234)  #Sets a seed
x <- runif(10000, 0 , 1)
sd(x)  #0.28

so, none of the data is beyond 2 sd from the mean, because that is beyond the bounds of the data. And the portion within 1 sd will be approximately 0.56.

In one of the comments you say you used "random data" but you don't say from what distribution. If you are talking about heights of humans, they are roughly normally distributed, but your data are not remotely appropriate for human heights - yours are fractions of a cm!

And your data are not remotely normal. I'm guessing you used a uniform distribution with bounds of 0 and 1. And you generated a very small sample. Let's try with a bigger sample:

set.seed(1234)  #Sets a seed
x <- runif(10000, 0 , 1)
sd(x)  #0.28

so, none of the data is beyond 2 sd from the mean, because that is beyond the bounds of the data. And the portion within 1 sd will be approximately 0.56.

In one of the comments you say you used "random data" but you don't say from what distribution. If you are talking about heights of humans, they are roughly normally distributed, but your data is not remotely appropriate for human heights - yours are fractions of a cm!

And your data is not remotely normal. I'm guessing you used a uniform distribution with bounds of 0 and 1. And you generated a very small sample. Let's try with a bigger sample:

set.seed(1234)  #Sets a seed
x <- runif(10000, 0 , 1)
sd(x)  #0.28

so, no data points are beyond 2 sd from the mean, because that is beyond the bounds of the data. And the portion within 1 sd will be approximately 0.56.