Hello,
With the confusion between bin size and width the OP started, I'll
repost my answer with a final line. Sorry for the repetition.
h <- hist(x, breaks=quantile(x, probs=seq(0, 1, by=1/20)))
h$counts
[1] 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
Hope this helps,
Rui Barradas
Em 05-07-2012 20:47, Sarah Goslee escreveu:
There's no reason you can't do that with normally-distributed data,
though I'm not sure why you'd want to. My point was rather that you
can't specify the bin width and size both. If you let the bin size
vary, this will work:
set.seed(1234)
mydata <- rnorm(1000, mean = 2, sd = 4)
mydata.hist <- hist(mydata, breaks=quantile(mydata, probs=seq(0, 1,
length.out = length(mydata)/50 + 1)))
mydata.hist$counts
Sarah
On Thu, Jul 5, 2012 at 3:37 PM, Jim Silverton <jim.silver...@gmail.com> wrote:
Thanks Sarah!!
Ok so if I have say x = runif(1000,0,1) say instead if the normal and I want
a histogram with bins that have an equal number of observations. For example
if I want each bin to have 50 observations, how do I do this?
On Thu, Jul 5, 2012 at 3:34 PM, Sarah Goslee <sarah.gos...@gmail.com> wrote:
Hi Jim,
You can't specify both number of bins and bin size. You can specify
breaks: either the number of bins or the location of breakpoints. A
histogram with 20 bins of 50 observations each must by definition come
from a uniform distribution.
What are you trying to accomplish?
Sarah
On Thu, Jul 5, 2012 at 3:29 PM, Jim Silverton <jim.silver...@gmail.com>
wrote:
I have a column of 1000 datapoints from the normal distribution with
mean 2
and variance 4. How can I get a histogram of these observations with 20
bins with each bin having 50 observations?
--
Thanks,
Jim.
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