[R] Competing with SPSS and SAS: improving code that loops throughrows (data manipulation)
Bert Gunter
gunter.berton at gene.com
Fri Mar 26 22:25:39 CET 2010
Dmitri:
If you follow the R posting guide you're more likely to get useful replies.
In particular it asks for **small** reproducible examples -- your example is
far more code then I care to spend time on anyway (others may be more
willing or more able to do so of course). I suggest you try (if you haven't
already):
1. Profiling the code using Rprof to isolate where the time is spent.And
then...
2. Writing a **small** reproducible example to exercise that portion of the
code and post it with your question to the list. If you need to...
Typically, if you do these things you'll figure out how to fix the
situation on your own.
Cheers,
Bert Gunter
Genentech Nonclinical Statistics
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Dimitri Liakhovitski
Sent: Friday, March 26, 2010 2:06 PM
To: r-help
Subject: [R] Competing with SPSS and SAS: improving code that loops
throughrows (data manipulation)
Dear R-ers,
In my question there are no statistics involved - it's all about data
manipulation in R.
I am trying to write a code that should replace what's currently being
done in SAS and SPSS. Or, at least, I am trying to show to my
colleagues R is not much worse than SAS/SPSS for the task at hand.
I've written a code that works but it's too slow. Probably because
it's looping through a lot of things. But I am not seeing how to
improve it. I've already written a different code but it's 5 times
slower than this one. The code below takes me slightly above 5 sec for
the tiny data set. I've tried using it with a real one - was not done
after hours.
Need help of the list! Maybe someone will have an idea on how to
increase the efficiency of my code (just one block of it - in the
"DATA TRANSFORMATION" Section below)?
Below - I am creating the data set whose structure is similar to the
data sets the code should be applied to. Also - I have desribed what's
actually being done - in comments.
Thanks a lot to anyone for any suggestion!
Dimitri
###### CREATING THE TEST DATA SET ################################
set.seed(123)
data<-data.frame(group=c(rep("first",10),rep("second",10)),week=c(1:10,1:10)
,a=abs(round(rnorm(20)*10,0)),
b=abs(round(rnorm(20)*100,0)))
data
dim(data)[1] # !!! In real life I might have up to 150 (!) rows
(weeks) within each subgroup
### Specifying parameters used in the code below:
vars<-names(data)[3:4] # names of variables to be transformed
nr.vars<-length(vars) # number of variables to be transformed; !!!
in real life I'll have to deal with up to 50-60 variables, not 2.
group.var<-names(data)[1] # name of the grouping variable
subgroups<-levels(data[[group.var]]) # names of subgroups; !!! in
real life I'll have up to 20-25 subgroups, not 2.
# For EACH subgroup: indexing variables a and b to their maximum in
that subgroup;
# Further, I'll have to use these indexed variables to build the new ones:
for(i in vars){
new.name<-paste(i,".ind.to.max",sep="")
data[[new.name]]<-NA
}
indexed.vars<-names(data)[grep("ind.to.max$", names(data))] #
variables indexed to subgroup max
for(subgroup in subgroups){
data[data[[group.var]] %in%
subgroup,indexed.vars]<-lapply(data[data[[group.var]] %in%
subgroup,vars],function(x){
y<-x/max(x)
return(y)
})
}
data
############# DATA TRANSFORMATION #########################################
# Objective: Create new variables based on the old ones (a and b ind.to.max)
# For each new variable, the value in a given row is a function of (a)
2 constants (that have several levels each),
# (b) the corresponding value of the original variable (e.g.,
a.ind.to.max"), and the value in the previous row on the same new
variable
# PLUS: - it has to be done by subgroup (variable "group")
constant1<-c(1:3) # constant 1 used for transformation -
has 3 levels; !!! in real life it will have up to 7 levels
constant2<-seq(.15,.45,.15) # constant 2 used for transformation -
has 3 levels; !!! in real life it will have up to 7 levels
# CODE THAT IS TOO SLOW (it uses parameters specified in the previous
code section):
start1<-Sys.time()
for(var in indexed.vars){ # looping through variables
for(c1 in 1:length(constant1)){ # looping through levels of constant1
for(c2 in 1:length(constant2)){ # looping through levels of
constant2
d=log(0.5)/constant1[c1]
l=-log(1-constant2[c2])
name<-paste(strsplit(var,".ind.to.max"),constant1[c1],constant2[c2]*100,"..t
ransf",sep=".")
data[[name]]<-NA
for(subgroup in subgroups){ # looping through subgroups
data[data[[group.var]] %in% subgroup, name][1] =
1-((1-0*exp(1)^d)/(exp(1)^(data[data[[group.var]] %in% subgroup,
var][1]*l*10))) # this is just the very first row of each subgroup
for(case in 2:nrow(data[data[[group.var]] %in% subgroup, ])){
# looping through the remaining rows of the subgroup
data[data[[group.var]] %in% subgroup, name][case]=
1-((1-data[data[[group.var]] %in% subgroup,
name][case-1]*exp(1)^d)/(exp(1)^(data[data[[group.var]] %in% subgroup,
var][case]*l*10)))
}
}
}
}
}
end1<-Sys.time()
print(end1-start1) # Takes me ~0.53 secs
names(data)
data
--
Dimitri Liakhovitski
Ninah.com
Dimitri.Liakhovitski at ninah.com
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