Efficiently locate group-wise constant columns in a data.frame

Question

How can I efficiently extract group-wise constant columns from a data frame? I've included an plyr implementation below to make precise what I'm trying to do, but it's slow. How can I do it as efficiently as possible? (Ideally without splitting the data frame at all).

base <- data.frame(group = 1:1000, a = sample(1000), b = sample(1000))
df <- data.frame(
  base[rep(seq_len(nrow(base)), length = 1e6), ], 
  c = runif(1e6), 
  d = runif(1e6)
)


is.constant <- function(x) length(unique(x)) == 1
constant_cols <- function(x) head(Filter(is.constant, x), 1)
system.time(constant <- ddply(df, "group", constant_cols))
#   user  system elapsed 
# 20.531   1.670  22.378 
stopifnot(identical(names(constant), c("group", "a", "b")))
stopifnot(nrow(constant) == 1000)

In my real use case (deep inside ggplot2) there may be an arbitrary number of constant and non-constant columns. The size of the data in the example is about the right order of magnitude.

Answer 1

(Edited to possibly address the issue of consecutive groups with the same value)

I'm tentatively submitting this answer, but I haven't completely convinced myself that it will correctly identify within group constant columns in all cases. But it's definitely faster (and can probably be improved):

constant_cols1 <- function(df,grp){
    df <- df[order(df[,grp]),]

    #Adjust values based on max diff in data
    rle_group <- rle(df[,grp])
    vec <- rep(rep(c(0,ceiling(diff(range(df)))),
               length.out = length(rle_group$lengths)),
               times = rle_group$lengths)
    m <- matrix(vec,nrow = length(vec),ncol = ncol(df)-1)
    df_new <- df
    df_new[,-1] <- df[,-1] + m

    rles <- lapply(df_new,FUN = rle)
    nms <- names(rles)
    tmp <- sapply(rles[nms != grp],
                  FUN = function(x){identical(x$lengths,rles[[grp]]$lengths)})
    return(tmp)
}

My basic idea was to use rle, obviously.