Hello
I need to plot a histogram, but insted of using bars, I'd like to plot
the data points. I've been doing it like this so far:
h <- hist(x, plot = F)
plot(y = x$counts / sum(x$counts),
x = x$breaks[2:length(x$breaks)],
type = "p", log = "xy")
Sometimes I want to have a look
Why not use the interactive histogram in iplots? ihist(x) Then you
can vary the binwidth interactively and get a very quick idea of the
structure of your data by looking at a range of plots with different
binwidths. Relying on a single plot to reveal everything about a
variable's distrib
take a look at
?stem
There is still a place for handtools in the age of integrated
circuits. Of course, avoiding binning isn't really desirable.
url:www.econ.uiuc.edu/~rogerRoger Koenker
email[EMAIL PROTECTED]Department of Economics
vox: 217-333-4558
On Tue, Feb 26, 2008 at 4:10 PM, Andre Nathan <[EMAIL PROTECTED]> wrote:
> Hello
>
> I need to plot a histogram, but insted of using bars, I'd like to plot
> the data points. I've been doing it like this so far:
>
> h <- hist(x, plot = F)
> plot(y = x$counts / sum(x$counts),
>x = x$br
I know about stem, but the data set has 1 million points, so it's not
very useful here. I want to avoid binning just to have an idea about the
shape of the distribution, before deciding how I'll bin it.
Andre
On Tue, 2008-02-26 at 16:20 -0600, roger koenker wrote:
> take a look at
>
> ?ste
: roger koenker
> Cc: r-help
> Subject: Re: [R] "Raw" histogram plots
>
> I know about stem, but the data set has 1 million points, so
> it's not very useful here. I want to avoid binning just to
> have an idea about the shape of the distribution, before
> decidi
>
> HTH ..
>
> Peter Alspach
>
>
>> -Original Message-
>> From: [EMAIL PROTECTED]
>> [mailto:[EMAIL PROTECTED] On Behalf Of Andre Nathan
>> Sent: Wednesday, 27 February 2008 1:34 p.m.
>> To: roger koenker
>> Cc: r-help
>> Su
On Tue, 26 Feb 2008, Andre Nathan wrote:
> I know about stem, but the data set has 1 million points, so it's not
> very useful here. I want to avoid binning just to have an idea about the
> shape of the distribution, before deciding how I'll bin it.
Ideas:
1) use a much smaller sample of the dat
If I understand:
x <- rnorm(1e6)
out <- tapply(x, ceiling(x), length)
plot(as.numeric(names(out)), out)
On 27/02/2008, Andre Nathan <[EMAIL PROTECTED]> wrote:
> On Wed, 2008-02-27 at 14:15 +1300, Peter Alspach wrote:
> > If I understand you correctly, you could try a barplot() on the result
> >
On Wed, 2008-02-27 at 14:15 +1300, Peter Alspach wrote:
> If I understand you correctly, you could try a barplot() on the result
> of table().
Hmm, table() does the counting exactly the way I want, i.e., just
counting individual values. Is there a way to extract the counts vs. the
values from a ta
On Feb 27, 2008, at 8:16 AM, Andre Nathan wrote:
> On Wed, 2008-02-27 at 14:15 +1300, Peter Alspach wrote:
>> If I understand you correctly, you could try a barplot() on the
>> result
>> of table().
>
> Hmm, table() does the counting exactly the way I want, i.e., just
> counting individual value
Andre Nathan wrote:
> On Wed, 2008-02-27 at 14:15 +1300, Peter Alspach wrote:
>> If I understand you correctly, you could try a barplot() on the result
>> of table().
>
> Hmm, table() does the counting exactly the way I want, i.e., just
> counting individual values. Is there a way to extract the c
On Wed, 2008-02-27 at 08:48 -0500, Charilaos Skiadas wrote:
> x <- table(rbinom(20,2,0.5))
> plot(names(x),x)
>
> should do it. You can also try just plot(x). Use prop.table on table
> if you want the relative frequencies instead.
Yes, names is what I needed :) Thanks for the prop.table hint. I
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