sdplot: The Standard Deviation Plot
In AUMath-AdvancedR2018/StatEngPlots: PPCC Plot

sdplot() takes a vector of data, a list or a data frame x and finds the standard deviations for the different parts in the dataset. sdplot() is able to divide a vector of data into a self-chosen number of parts. The function sdplot() returns a plot of the standard deviations and the median of those. One more function is to find kind of a continous standard deviation plot, where the first nr numbers of elements are evaluated and then continues through the whole dataset with steps 1.

1	sdplot(x, nr = 0, divide = TRUE, divided = TRUE, return = FALSE)

`x`	The data to be analyzed. `x` needs to be a vector, a list or a data frame.
`nr`	If `divide = TRUE`: The total number of parts, the datasat has to be divided in, if `x` is a vector.\n If `divide = FALSE`: The number of elements which has to be evaluated at the same time while using the continous method.
`divide`	If set to `FALSE`, the continous method is used to find standard deviations. Default set to `TRUE`.
`divided`	Needs to be defined to tell the function whether the dataset `x` is already divided into parts (list or data frame) or not divided into parts (vector). If `divided` is set to `TRUE` the function divides the dataset into `nr` parts which are "as equal as possible". Default set to `TRUE`.
`return`	If set to `TRUE`, the function `sdplot()` returns a vector of the estimated standard deviations. Default set to `FALSE`.

# Simulate a dataset from a Poisson distribution:
x = rpois(1000, 3)
# Use sdplot() to divide x into 20 parts and to find the different standard deviations:
sdplot(x, divided = FALSE, nr = 20)
# Also return the estimated standard deviations:
sdplot(x, divided = FALSE, nr = 20, return = TRUE)

# Define a list (or data frame) of data:
x = list(c(1:4), c(4:10), c(1,6,1,8,0,0,98,6), c(10,11,11,11,11,6,3))
# Use sdplot() to find the standard deviations for the different parts:
sdplot(x)