Re: [R] Using split() several times in a row?

From: Stephen Tucker <brown_emu_at_yahoo.com>
Date: Sat 31 Mar 2007 - 01:41:39 GMT


Hi Sergey,

I believe the code below should get you close to want you want.

For dates, I usually store them as "POSIXct" classes in data frames, but according to Gabor Grothendieck and Thomas Petzoldt's R Help Desk article <http://cran.r-project.org/doc/Rnews/Rnews_2004-1.pdf>, I should probably be using "chron" date and times...

Nonetheless, POSIXct casses are what I know so I can show you that to get the month out of your column (replace "8.29.97" with your variable), you can do the following:

month = format(strptime("8.29.97",format="%m.%d.%y"),format="%m")

Or,
month = as.data.frame(strsplit("8.29.97","\\."))[1,]

In any case, here is a code, in which I follow a series of function application and definitions (which effectively includes successive application of split() and lapply().

Best regards,

ST

# define data (I just made this up)
df <-

data.frame(month=as.character(rep(1:3,each=30)),fac=factor(rep(1:2,each=15)),
            data1=round(runif(90),2),
            data2=round(runif(90),2))

# define functions to split the data and another # to get statistics
doSplits <- function(df) {
  unlist(lapply(split(df,df$month),function(x) split(x,x$fac)),recursive=FALSE)
}
getStats <- function(x,f) {
  return(as.data.frame(lapply(x[unlist(lapply(x,mode))=="numeric" &

                                unlist(lapply(x,class))!="factor"],f)))
}
# create a matrix of data, means, and standard deviations listMatrix <- cbind(Data=doSplits(df),
           Means=lapply(doSplits(df),getStats,mean),
           SDs=lapply(doSplits(df),getStats,sd))

# function to subtract means and divide by standard deviations transformData <- function(x) {
  newdata <- x$Data
  matchedNames <- match(names(x$Means),names(x$Data))   newdata[matchedNames] <-
    sweep(sweep(data.matrix(x$Data[matchedNames]),2,unlist(x$Means),"-"),

          2,unlist(x$SDs),"/")
  return(newdata)
}
# apply to data
newDF <- lapply(as.data.frame(t(listMatrix)),transformData)

# Defind Fold function
Fold <- function(f, x, L) for(e in L) x <- f(x, e) # Apply this to the data
finalData <- Fold(rbind,vector(),newDF)

> Hi, fellow R users.
>
> I have a question about sapply and split combination.
>
> I have a big dataframe (40000 observations, 21 variables). First
> variable (factor) is "date" and it is in format "8.29.97", that is, I
> have monthly data. Second variable (also factor) has levels 1 to 6
> (fractiles 1 to 5 and missing value with code 6). The other 19
> variables are numeric.
> For each month I have several hunder observations of 19 numeric and 1
> factor.
>
> I am normalizing the numeric variables by dividing val1 by val2, where:
>
> val1: (for each month, for each numeric variable) difference between
> mean of ith numeric variable in fractile 1, and mean of ith numeric
> variable in fractile 5.
>
> val2: (for each month, for each numeric variable) standard deviation
> for ith numeric variable.
>
> Basically, as far as I understand, I need to use split() function several
> times.
> To calculate val1 I need to use split() twice - first to split by
> month and then split by fractile. Is this even possible to do (since
> after first application of split() I get a list)??
>
> Is there a smart way to perform this normalization computation?
>
> My knowledge of R is not so advanced, but I need to know an efficient
> way to perform calculations of this kind.
>
> Would really appreciate some help from experienced R users!
>
> Regards,
> S
>
> --
> Laziness is nothing more than the habit of resting before you get tired.
> - Jules Renard (writer)
>
> Experience is one thing you can't get for nothing.
> - Oscar Wilde (writer)
>
> When you are finished changing, you're finished.
> - Benjamin Franklin (Diplomat)
>
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>



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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. Received on Sat Mar 31 11:46:44 2007

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