On 15/08/07, Vladimir Eremeev <[EMAIL PROTECTED]> wrote:
Nick Chorley-3 wrote:
>
> I have a large amount of data that I would like to create a histogram of
> and
> plot and do things with in R. It is pretty much impossible to read the
> data
> into R, so I have written a program to bin the data and now have a list of
> counts in each bin. Is it possible to somehow import this into R and use
> hist(), so I can, for instance, plot the probability density? I have
> looked
> at the help page for hist(), but couldn't find anything related to this
> there.
>
Hi! And why do you think, its impossible to import the data in R?
It can handle rather large data volumes, especially in Linux. Just study
help("Memory-limits").
My data file is 4.8 GB!
You can plot something looking like a histogram using barplot() or plot(...
type="h").
The problem with those is that I can't plot the probability density.
You can create the "histogram" class object manually.
For example,
[ import bin counts... probably, it is a table of 2 columns, defining bin
borders and counts.
let's store it in ncounts. ]
Yes, that's what I have.
> hst<-hist(rnorm(nrow(ncounts)),plot=FALSE)
> str(hst) # actually I called hist(rnorm(100))
List of 7
$ breaks : num [1:10] -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
$ counts : int [1:9] 3 6 12 9 24 19 14 9 4
$ intensities: num [1:9] 0.06 0.12 0.24 0.18 0.48 ...
$ density : num [1:9] 0.06 0.12 0.24 0.18 0.48 ...
$ mids : num [1:9] -2.25 -1.75 -1.25 -0.75 -0.25 0.25 0.75 1.25 1.75
$ xname : chr "rnorm(100)"
$ equidist : logi TRUE
- attr(*, "class")= chr "histogram"
> hst$breaks <- [ bsdfgsdghsdghdfgh ]
> hst$counts <- [ asfd109,mnasdfkjhdsfl ]
> hst$intensities <-
My data isn't normally distributed, so I tried rexp() rather than rnorm(), but
it's not looking like it should
Studying the hist.default() sources will help you to understand, how every
list element is created.
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