On Nov 22, 2009, at 7:07 AM, Duncan Murdoch wrote:
On 22/11/2009 1:07 AM, Marc Chiarini (Tufts) wrote:
Dear R Community:
Recently, I have managed to plot some really useful graphs of my
research data using persp(). I have even figured out how to
overplot rectangular regions (corresponding
On 11/21/09, frenchcr wrote:
> are there any more packages that help decribe and explore data sets
>
See numSummary() in Rcmdr.
Liviu
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PLEASE do read the posting
On Nov 22, 2009, at 6:26 AM, soeren.vo...@eawag.ch wrote:
I have created a function to do something:
i <- factor(sample(c("A", "B", "C", NA), 793, rep=T, prob=c(8, 7, 5,
1)))
k <- factor(sample(c("X", "Y", "Z", NA), 793, rep=T, prob=c(12, 7,
9, 1)))
mytable <- function(x){
xtb <- x
btx
A few more came to mind:
VIM package (for exploring missing data):
http://cran.r-project.org/web/packages/VIM/index.html
http://bm2.genes.nig.ac.jp/RGM2/index.php?scope=name&query=VIM
And the basic commands:
* edit (for seeing the dataframe as in a spreadsheet)
And the commands:
* head (and)
Here is one more function for the list:
"whatis"
from the package:
"YaleToolkit"
See:
http://cran.r-project.org/web/packages/YaleToolkit/
I also like using:
ls()
ls.str()
And sometimes (for just one variable):
stem (which can be viewd as an ascii histogram)
Wonderful question and list, I hope
On 22/11/2009 1:07 AM, Marc Chiarini (Tufts) wrote:
Dear R Community:
Recently, I have managed to plot some really useful graphs of my
research data using persp(). I have even figured out how to overplot
rectangular regions (corresponding to submatrices) with a different
color. This is acco
Thank you Gabor, Romain and Stefan.
Gabor this looks like really interesting for speeding up loops. I just have
to install it and add jit(1) before a loop ! Is the result faster than
Python ?
I have seen the name of L. Tierney among the contributors. I guess it is
good for MCMC :-)
Best,
Jean
200
On Sat, Nov 21, 2009 at 02:01:07PM -0800, frenchcr wrote:
>
> i just found the following list, i wondered if anybody could add to this as i
> have to characterize a large data set and am new to R...the list below was
> so helpfulcan you add to this???
>
> Just to forestall confusion amongst t
Sure, badly written R code does not perform as well as well written
python code or C code. On the other hand badly written python code
does not perform as well as well written R code.
What happens when you try one of these :
sum <- sum( 1:N )
R runs out of memory and crashes. :-) I didn't
hi,
Try making your last line
invisible( list(table=xtb, elbat=btx) )
HTH,
baptiste
2009/11/22 Soeren.Vogel :
> I have created a function to do something:
>
> i <- factor(sample(c("A", "B", "C", NA), 793, rep=T, prob=c(8, 7, 5, 1)))
> k <- factor(sample(c("X", "Y", "Z", NA), 793, rep=T, prob
I have created a function to do something:
i <- factor(sample(c("A", "B", "C", NA), 793, rep=T, prob=c(8, 7, 5,
1)))
k <- factor(sample(c("X", "Y", "Z", NA), 793, rep=T, prob=c(12, 7, 9,
1)))
mytable <- function(x){
xtb <- x
btx <- x
# do more with x, not relevant here
cat("The table
On 11/21/2009 11:32 PM, Stefan Evert wrote:
My hunch is that Python and R run at about the same speed, and both
use C libraries for speedups (Python primarily via the numpy package).
That's not necessarily true. There can be enormous differences between
interpreted languages, and R appears to
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