[R] how to improve this inefficient R code for imputing missing values

[Coen van Hasselt]

Hello all,

I have a big data.frame multiple studies, subjects and timepoints per
subject, i.e.

STUDY[,1] SUBJECT[,2] ...... WT[,16] HT[,17] TEMP[,18] BSA[,19]
1               1                            50          170       37
         1.90
1               1                            NA           NA      NA
        NA
1               1                            52          170       38
         1.94


In this dataset, three types of missing (demographic) values exist:

1) first value for a subject is missing:
ie. study 1, subject 1: mis X1 X2 X3.
Here I want to carry the first non-missing value backwards to the missing value.

2) last values for a subject is missing:
ie. study 1, subject 1: X1 X2 X3 mis.
Here I want to carry the last non-missing value forwards to the missing value

3) some "intermediate" value for a subject is missing (like example
data.frame above)
i.e. study 1, subject 1: X1 mis X2 X3.
Here I want to impute the missing value with the mean value between X1 and X2

The missing value is actually a subset of columns in the data frame,
ie. always the columns WT HT TEMP BSA (m[,16:19]) are missing
altogether.

I have written some R code that tries to do this, but it is incredibly
slow due to the many for-loops and the big dataset I have (and might
not even be completely correct yet).

QUESTION:
I would greatly appreciate it if somebody can be give me some
guidance/hints on what direction I should roughly think for coding the
above a little more efficient then the horribly inefficient code
pasted below.

Thank you in advance and best regards,

Coen


for(s in unique(m$Study)){ # for each study
 for(i in unique(m$Subject[m$Study==s & is.na(m$Wt)])){  # for each
subject with a missing value (if $Wt is missing, all 4 columns 16:19
are missing)
   vals<-which(m$Study==s & m$Subject==i & !is.na(m$Wt))  # values
with NO missing values
   for(w in which(m$Study==s & m$Subject==i & is.na(m$Wt))){  # for
each value that is missing for subject "i" and study "s"
     if(w < min(vals) ){              # FIRST VALUES MISSING ?       #
carry the backwards
       m[w,][16:19]<-m[min(vals),][16:19]
     } else if(w > max(vals) ) {      # LAST VALUES MISSING        #
carry forwards
       m[w,][16:19]<-m[max(vals),][16:19]
     } else {                         # INTERMEDIATE VALUES MISSING  #
impute missing with mean
       maxV<-min(vals[vals>w])
       minV<-max(vals[vals<w])
       m[w,][16:19]<- mean(m[c(maxV,minV),][16:19],na.rm=T)
     }
   }
 }
}