Hi Javad,
saving R output to jpeg depends on what you want to save. For example
saving an `lm` object to an image would be fun :)
But you could export that quite easily to e.g. docx after installing
Pandoc[1] and pander[2] package. You can find some examples in the
README[3].
Best,
Gergely
[1] h
You might try:
R --slave -e
'as.numeric(suppressWarnings(suppressPackageStartupMessages(require(ggplot2'
Best,
Gergely
On Tue, Sep 4, 2012 at 3:30 AM, Tom Roche wrote:
>
> https://stat.ethz.ch/pipermail/r-help/2012-September/322985.html
> >>> for RSERVER in 'foo' 'bar' 'baz' ; do
> >>> s
I would call something like this via ssh (please note: I used "ggplot2" in
the example):
Rscript -e
'as.numeric(suppressWarnings(suppressPackageStartupMessages(require(ggplot2'
You could easily extend this to loop through the required packages and
tweak the output for your needs.
Best,
Gerge
On Fri, Aug 10, 2012 at 10:23 AM, Rui Barradas wrote:
> Hello,
>
> The main critique, I think, is that we assume a certain type of model
> where the times can decrease until zero. And that they can do so linearly.
> I believe that records can allways be beaten but 40-50 years ago times were
> mea
Hi,
I am not sure about your data structure, but if you do not alter those
often from third-party programs, then storing the information in R's native
file formats (like RData) might give you some performance boost against
always querying MySQL. Of course this depends on data structure and
volume.
Hi,
Sure.
Check fitdistr from MASS or fitdist from fitdistrplus package.
Best,
Gergely
On Mon, Feb 21, 2011 at 3:29 PM, Jim Silverton wrote:
> Is there any R package that can fit a beta distribution in R?
>
> --
> Thanks,
> Jim.
>
>[[alternative HTML version deleted]]
>
> __
Hi,
instead of sub, use substr in R, also look for 'which' and 'factor' in the
manual.
E.g.:
?substr
?which
> ?factor
I hope this could help you to rewrite the SPSS syntax in R.
Good luck!
Best,
Gergely
On Sat, Feb 12, 2011 at 2:08 PM, beky wrote:
>
> There is a code from SPSS Syntax
>
>
Hi,
use the help files (e.g. ?wilcox.test) to see references. There you can see
articles and book parts where you can find the original formula (and
background theory).
Also, analyzing the source code of a function may help.
Regards,
Gergely
On Tue, Jan 18, 2011 at 5:23 PM, ilya wrote:
> Hello
Hello,
this little example my work for demo purposes:
x1 = runif(45)
x2 = runif(45)
x3 = runif(45)
df = data.frame(x1=x1,x2=x2,x3=x3)
test <- function(df, values, n) {
if (nrow(df) < n) {
df <- rbind(df, values)
} else {
df[1:(nrow(df)-1),] <- df[2:nrow(df),]
df[n,] <- values
}
return(df)
}
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