Most efficient list to data.frame method?

Just had a conversation with coworkers about this, and we thought it'd be worth seeing what people out in SO land had to say. Suppose I had a list with N elements, where each element was a vector of length X. Now suppose I wanted to transform that into a data.frame. As with most things in R, there are multiple ways of skinning the proverbial cat, such as as.dataframe, using the plyr package, comboing do.call with cbind, pre-allocating the DF and filling it in, and others.

The problem that was presented was what happens when either N or X (in our case it is X) becomes extremely large. Is there one cat skinning method that's notably superior when efficiency (particularly in terms of memory) is of the essence?

Answers


Since a data.frame is already a list and you know that each list element is the same length (X), the fastest thing would probably be to just update the class and row.names attributes:

set.seed(21)
n <- 1e6
x <- list(x=rnorm(n), y=rnorm(n), z=rnorm(n))
x <- c(x,x,x,x,x,x)

system.time(a <- as.data.frame(x))
system.time(b <- do.call(data.frame,x))
system.time({
  d <- x  # Skip 'c' so Joris doesn't down-vote me! ;-)
  class(d) <- "data.frame"
  rownames(d) <- 1:n
  names(d) <- make.unique(names(d))
})

identical(a, b)  # TRUE
identical(b, d)  # TRUE

Update - this is ~2x faster than creating d:

system.time({
  e <- x
  attr(e, "row.names") <- c(NA_integer_,n)
  attr(e, "class") <- "data.frame"
  attr(e, "names") <- make.names(names(e), unique=TRUE)
})

identical(d, e)  # TRUE

Update 2 - I forgot about memory consumption. The last update makes two copies of e. Using the attributes function reduces that to only one copy.

set.seed(21)
f <- list(x=rnorm(n), y=rnorm(n), z=rnorm(n))
f <- c(f,f,f,f,f,f)
tracemem(f)
system.time({  # makes 2 copies
  attr(f, "row.names") <- c(NA_integer_,n)
  attr(f, "class") <- "data.frame"
  attr(f, "names") <- make.names(names(f), unique=TRUE)
})

set.seed(21)
g <- list(x=rnorm(n), y=rnorm(n), z=rnorm(n))
g <- c(g,g,g,g,g,g)
tracemem(g)
system.time({  # only makes 1 copy
  attributes(g) <- list(row.names=c(NA_integer_,n),
    class="data.frame", names=make.names(names(g), unique=TRUE))
})

identical(f,g)  # TRUE

This appears to need a data.table suggestion given that efficiency for large datasets is required. Notably setattr sets by reference and does not copy

library(data.table)
set.seed(21)
n <- 1e6
h <- list(x=rnorm(n), y=rnorm(n), z=rnorm(n))
h <- c(h,h,h,h,h,h)
tracemem(h)

system.time({h <- as.data.table(h)
            setattr(h, 'names', make.names(names(h), unique=T))})

as.data.table, however does make a copy.


Edit - no copying version

Using @MatthewDowle's suggestion setattr(h,'class','data.frame') which will convert to data.frame by reference (no copies)

set.seed(21)
n <- 1e6
i <- list(x=rnorm(n), y=rnorm(n), z=rnorm(n))
i <- c(i,i,i,i,i,i)
tracemem(i)

system.time({  
  setattr(i, 'class', 'data.frame')
  setattr(i, "row.names", c(NA_integer_,n))

  setattr(i, "names", make.names(names(i), unique=TRUE))

})

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