On Fri, 4 Apr 2008, rlayberry@[EMAIL PROTECTED]
wrote:
> Does anybody have a summary of the major differences between R and
> idl? I have been using idl for years and have never used R. I
> understand R is statistics orientated, but does it do anything that
> idl can't or does it do it better?
I use both IDL and R. The biggest advantage of R in my work is that
it provides NA values that are distinct from NaN. Most operations
have an na.rm option, e.g.
mean(c(1,2,3,NA),na.rm=FALSE) returns NA, but
mean(c(1,2,3,NA),na.rm=TRUE) returns 2
I've never been a fan of overloading NaN as a missing data value:
a) it can't be applied to integer data, b) there are times when
you need to know the difference between a computational error
and missing inputs. I've spent way to much of my life coding tests for
missing value flags, so I really appreciate a language that properly
sup****ts NA values.
R has very solid plotting capabilities, but tends to bog down
when working with images. R (like Matlab, unlike IDL) tends to
coerce everything to doubles for calculations, but (unlike Matlab and
IDL) checks for NA add significant overhead for big calculations.
R is an implementation of the S-plus language. It has a large, active
user community. R is widely available and has a nice system to manage
packages (which are generally provided as binaries on Windows, sources
on *X).
In my work I often use IDL to extract data (e.g, time-series) from remote
sensing images and then use R to analyse the resulting data sets.
--
George N. White III <aa056@[EMAIL PROTECTED]
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