CODEWITHSUNDEEP
rbigdataapplyff
Clarinetist
Clarinetist

Reputation: 1187

Using apply on large ffdfs

The basic idea is this: I have a large ffdf (about 5.5 million x 136 fields). I know for a fact that some of these columns in this data frame have columns which are all NA. How do I find out which ones and remove them appropriately?

My instinct is to do something like (assuming df is the ffdf):

apply(X=is.na(df[,1:136]), MARGIN = 2, FUN = sum)

which should give me a vector of the NA counts for each column, and then I could find which ones have ~5.5 million NA values, remove them using df <- df[,-c(vector of columns)], etc. Pretty straightforward.

However, apply gives me an error. 

Error: cannot allocate vector of size 21.6 Mb
In addition: Warning messages:
1: In `[.ff`(p, i2) :
  Reached total allocation of 3889Mb: see help(memory.size)
2: In `[.ff`(p, i2) :
  Reached total allocation of 3889Mb: see help(memory.size)
3: In `[.ff`(p, i2) :
  Reached total allocation of 3889Mb: see help(memory.size)
4: In `[.ff`(p, i2) :
  Reached total allocation of 3889Mb: see help(memory.size)

This tells me that apply can't handle a data frame of this size. Are there any alternatives I can use?

Upvotes: 3

Views: 156

Answers (2)

Jan van der Laan
Jan van der Laan

Reputation: 8105

It is easier to use all(is.na(column)). sapply/lapply donot work because and ffdf object is not a list.

You use df[, 1:136] in your code. This will cause ff to try to load all 136 columns into memory. This is what causes the memory issues. This does not happen when you do df[1:136]. The same happens when indexing for the final result: df <- df[,-c(vector of columns)] reads all selected columns into memory. 

na_cols <- logical(136)
for (i in seq_len(136)) {
  na_cols[i] <- all(is.na(df[[i]]))
}

res <- df[!na_cols]

Upvotes: 1

zx8754
zx8754

Reputation: 56169

Try this example:

#dummy data
df <- sample(1000000*5)
df <- data.frame( matrix(df,nrow = 1000000))
df$X3 <- NA
df$X6 <- NA

#list of col to remove or keep
colToRemove <- colnames(df)[ colSums(is.na(df[ ,1:6])) == nrow(df) ]
colToKeep <- setdiff(colnames(df), colToRemove)

#subset
res <- df[, colToKeep]

colnames(df)
#[1] "X1" "X2" "X3" "X4" "X5" "X6"
colnames(res)
#[1] "X1" "X2" "X4" "X5"

Upvotes: 0

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