Re: [R] data.frame: temporal complexity
I did not test this way, I 'll do it later, I think that df$diffP <- c(NA, NA, diff(df$P,2)) is the best way to compute the difference !! I 'll post the result here -- View this message in context: http://r.789695.n4.nabble.com/data-frame-temporal-complexity-tp4269585p4270256.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] data.frame: temporal complexity
Something like this (not tested)?
df$diffP <- c(NA, NA, diff(df$P,2))
-Don
--
Don MacQueen
Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062
On 1/6/12 6:39 AM, "ikuzar" wrote:
>Hello,
>
>I created a data.frame which contains two columns: df$P (Power) et
>df$DateTime (time). I'd like to add a new column df$diffP (difference of
>Power between T and T-2).
>
>I made a loop :
>
>for (i in 3:length(df$DateTime)){
>df$diffP[i] = df$P[i] - df$P[i-2]
>}
>execution time result is unaceptable: 24s !!
>
>Is there any way to reduce complexity about O(n) ? for example 2 or 3s
>(10s
>maxi)
>
>Does anybody find better than ~24s ?
>
>thanks for your help
>
>--
>View this message in context:
>http://r.789695.n4.nabble.com/data-frame-temporal-complexity-tp4269585p426
>9585.html
>Sent from the R help mailing list archive at Nabble.com.
>
>__
>R-help@r-project.org mailing list
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide
>http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] data.frame: temporal complexity
Ok, thanks, it works ! -- View this message in context: http://r.789695.n4.nabble.com/data-frame-temporal-complexity-tp4269585p4270073.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] data.frame: temporal complexity
Try this:
> df <- data.frame(power = runif(1))
> # add difference
> diffGap <- 2
> df$diff <- c(rep(NA, diffGap), head(df$power, -diffGap) - tail(df$power,
> -diffGap))
> head(df, 10)
powerdiff
1 0.86170585 NA
2 0.90672473 NA
3 0.96868367 -0.10697782
4 0.44199262 0.46473211
5 0.48593923 0.48274443
6 0.99409592 -0.55210330
7 0.72790728 -0.24196804
8 0.38070013 0.61339579
9 0.69913680 0.02877047
10 0.07902925 0.30167088
>
On Fri, Jan 6, 2012 at 9:39 AM, ikuzar wrote:
> Hello,
>
> I created a data.frame which contains two columns: df$P (Power) et
> df$DateTime (time). I'd like to add a new column df$diffP (difference of
> Power between T and T-2).
>
> I made a loop :
>
> for (i in 3:length(df$DateTime)){
> df$diffP[i] = df$P[i] - df$P[i-2]
> }
> execution time result is unaceptable: 24s !!
>
> Is there any way to reduce complexity about O(n) ? for example 2 or 3s (10s
> maxi)
>
> Does anybody find better than ~24s ?
>
> thanks for your help
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/data-frame-temporal-complexity-tp4269585p4269585.html
> Sent from the R help mailing list archive at Nabble.com.
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] data.frame: temporal complexity
1. You are doing exactly what one is recommended to avoid in R. Have
you read an Introduction to R? -- especially about "vectorization"?
2. To answer your question: ?diff
It will probably be an order or 2 of magnitude faster.
-- Bert
On Fri, Jan 6, 2012 at 6:39 AM, ikuzar wrote:
> Hello,
>
> I created a data.frame which contains two columns: df$P (Power) et
> df$DateTime (time). I'd like to add a new column df$diffP (difference of
> Power between T and T-2).
>
> I made a loop :
>
> for (i in 3:length(df$DateTime)){
> df$diffP[i] = df$P[i] - df$P[i-2]
> }
> execution time result is unaceptable: 24s !!
>
> Is there any way to reduce complexity about O(n) ? for example 2 or 3s (10s
> maxi)
>
> Does anybody find better than ~24s ?
>
> thanks for your help
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/data-frame-temporal-complexity-tp4269585p4269585.html
> Sent from the R help mailing list archive at Nabble.com.
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] data.frame: temporal complexity
Hello,
I created a data.frame which contains two columns: df$P (Power) et
df$DateTime (time). I'd like to add a new column df$diffP (difference of
Power between T and T-2).
I made a loop :
for (i in 3:length(df$DateTime)){
df$diffP[i] = df$P[i] - df$P[i-2]
}
execution time result is unaceptable: 24s !!
Is there any way to reduce complexity about O(n) ? for example 2 or 3s (10s
maxi)
Does anybody find better than ~24s ?
thanks for your help
--
View this message in context:
http://r.789695.n4.nabble.com/data-frame-temporal-complexity-tp4269585p4269585.html
Sent from the R help mailing list archive at Nabble.com.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
