CODEWITHSUNDEEP
rdata.tablepastesplit-apply-combine
Bryan
Bryan

Reputation: 933

Efficient way to paste multiple column pairs in R data.table

I'm looking for an efficient way to paste/combine multiple pairs of adjacent columns at once using data.table. My feeble attempt is slow and not so elegant:

library(data.table)
dt <- data.table(ids = 1:3,
                 x1 = c("A","B","C"),
                 x2 = 1:3,
                 y1 = c("D", "E", "F"),
                 y2 = 4:6,
                 z1 = c("G", "H", "I"),
                 z3 = 7:9)

paste.pairs <- function(x, sep = "-"){
  xx <- unlist(x)
  x.len <- length(x)

  r <- rep(NA, x.len/2)
  s <- seq(1, x.len, by = 2)

  for(i in 1:(x.len/2)) {
    r[i] <- paste(xx[i], xx[i+1], sep = sep)
  }
  return(as.list(r))
}

dt[, paste.pairs(.SD), by = "ids"]

Is there a better way?

Upvotes: 3

Views: 891

Answers (2)

akrun
akrun

Reputation: 886978

An option with Map by creating column index with seq

i1 <- seq(1, length(dt)-1, 2)
i2 <- seq(2, length(dt)-1, 2)
dt[, Map(paste,
         .SD[, i1, with = FALSE], .SD[, i2, with = FALSE], 
         MoreArgs = list(sep="-")), 
   by = "ids"]

Another option would be to split by the names of the dataset and then paste

data.frame(lapply(split.default(dt[, -1, with = FALSE],
    sub("\\d+$", "", names(dt)[-1])), function(x) do.call(paste, c(x, sep="-"))))
#  x   y   z
#1 A-1 D-4 G-7
#2 B-2 E-5 H-8
#3 C-3 F-6 I-9

Or another option is with melt/dcast

dcast(melt(dt, id.var = 'ids')[,  paste(value, collapse = "-"),
  .(grp = sub("\\d+", "", variable), ids)], ids ~ grp, value.var = 'V1')

Upvotes: 3

Wimpel
Wimpel

Reputation: 27732

a solution using matrices

#create matrices
#use the columns you want to paste together...
m1 <- as.matrix( dt[,c(2,4,6)] )
m2 <- as.matrix( dt[, c(3,5,7)] )
#paste the matrices element-by-element, and convert result back to data.table
as.data.table( matrix( paste( m1, m2, sep="-"), nrow=nrow(m1), dimnames=dimnames(m1) ) )

Should run pretty fast, and is very readable and easy to adapt.

output

#     x1  y1  z1
# 1: A-1 D-4 G-7
# 2: B-2 E-5 H-8
# 3: C-3 F-6 I-9

benchmarks

microbenchmark::microbenchmark(
  wimpel = {
    #create matrices
    m1 <- as.matrix( dt[,c(2,4,6)] )
    m2 <- as.matrix( dt[, c(3,5,7)] )
    #paste the matrices element-by-element, and comvert to data.table
    as.data.table( matrix( paste( m1, m2, sep="-"), nrow=nrow(m1), dimnames=dimnames(m1) ) )
  },
  akrun_df = {
    data.frame(lapply(split.default(dt[, -1, with = FALSE],
                                sub("\\d+$", "", names(dt)[-1])), function(x) do.call(paste, c(x, sep="-"))))
  },
  akrun_map = {
    i1 <- seq(2, length(dt), 2)
    i2 <- seq(3, length(dt), 2)
    dt[, Map(paste, .SD[, i1, with = FALSE], .SD[, i2, with = FALSE], MoreArgs = list(sep="-"))]
    }, 
  akrun_dcast = {
    dcast(melt(dt, id.var = 'ids')[,  paste(value, collapse = "-"),.(grp = sub("\\d+", "", variable), ids)], ids ~ grp, value.var = 'V1')
  },
  times = 10 )

# Unit: microseconds
#        expr      min       lq      mean    median       uq      max neval
#      wimpel  303.072  315.122  341.2417  319.1895  327.775  531.429    10
#    akrun_df 1022.790 1028.515 1251.7812 1069.1850 1172.519 2779.460    10
#   akrun_map  742.013  751.051  785.6059  778.1650  799.855  884.812    10
# akrun_dcast 4104.719 4175.215 4414.6596 4348.7430 4650.911 4939.221    10

Upvotes: 2

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