Hello,
This solves my problem in a horribly inelegant way that works:
df <- data.frame(n=newInput$n, iter=newInput$iter, Error=newInput$Error,
Duality_Gap=newInput$Duality, Runtime=newInput$Acc)
df_last <- aggregate(x=df$iter, by=list(df$n), FUN=max)
names(df_last)[names(df_last)=="Group.1"] <-
))
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
> Behalf
> Of Giovanni Azua
> Sent: Sunday, September 09, 2012 8:14 AM
> To: r-help@r-project.org
> Subject: [
Hi Jeff,
Thanks for your help, but this doesn't work, there are two problems. First and
most important I need to keep the last _per category_ where my category is n
and not the last globally. Second, there seems to be an issue with the subset
variation that ends up not filtering anything ... bu
dfthin <- df[ c(which(iter %% 500 == 0),nrow(df) ]
or
dfthin <- subset(df, (iter %% 500 == 0) | (seq.int(nrow(df)==nrow(df)))
N.B. You should avoid using the name "df" for your variables, because it is the
name of a built-in function that you are hiding by doing so. Others may be
confused, an
Hello,
I bumped into the following funny use-case. I have too much data for a given
plot. I have the following data frame df:
> str(df)
'data.frame': 5015 obs. of 5 variables:
$ n : Factor w/ 5 levels "1000","2000",..: 1 1 1 1 1 1 1 1 1 1 ...
$ iter : int 10 20 30 40 50 60
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