| idl and R [message #59661] |
Fri, 04 April 2008 02:26  |
rlayberry
Messages: 33
Registered: November 2004
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Hi
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?
Thanks
Russ
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| Re: idl and R [message #59699 is a reply to message #59661] |
Mon, 07 April 2008 03:25  |
rlayberry
Messages: 33
Registered: November 2004
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Member |
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On 5 Apr, 17:19, "George N. White III" <aa...@chebucto.ns.ca> wrote:
> On Fri, 4 Apr 2008, rlaybe...@hotmail.com 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
> supports 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 <aa...@chebucto.ns.ca>
Thanks very much for both responses. I have found them very useful.
Russ
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| Re: idl and R [message #59711 is a reply to message #59661] |
Sat, 05 April 2008 09:19 |
George N. White III
Messages: 56
Registered: September 2000
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Member |
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On Fri, 4 Apr 2008, rlayberry@hotmail.com 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
supports 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@chebucto.ns.ca>
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