Split text string in a data.table columns

I have a script that reads in data from a CSV file into a data.table and then splits the text in one column into several new columns. I am currently using the lapply and strsplit functions to do this. Here's an example:

library("data.table")
df = data.table(PREFIX = c("A_B","A_C","A_D","B_A","B_C","B_D"),
                VALUE  = 1:6)
dt = as.data.table(df)

# split PREFIX into new columns
dt$PX = as.character(lapply(strsplit(as.character(dt$PREFIX), split="_"), "[", 1))
dt$PY = as.character(lapply(strsplit(as.character(dt$PREFIX), split="_"), "[", 2))

dt 
#    PREFIX VALUE PX PY
# 1:    A_B     1  A  B
# 2:    A_C     2  A  C
# 3:    A_D     3  A  D
# 4:    B_A     4  B  A
# 5:    B_C     5  B  C
# 6:    B_D     6  B  D 

In the example above the column PREFIX is split into two new columns PX and PY on the "_" character.

Even though this works just fine, I was wondering if there is a better (more efficient) way to do this using data.table. My real datasets have >=10M+ rows, so time/memory efficiency becomes really important.

---

UPDATE:

Following @Frank's suggestion I created a larger test case and used the suggested commands, but the stringr::str_split_fixed takes a lot longer than the original method.

library("data.table")
library("stringr")
system.time ({
    df = data.table(PREFIX = rep(c("A_B","A_C","A_D","B_A","B_C","B_D"), 1000000),
                    VALUE  = rep(1:6, 1000000))
    dt = data.table(df)
})
#   user  system elapsed 
#  0.682   0.075   0.758 

system.time({ dt[, c("PX","PY") := data.table(str_split_fixed(PREFIX,"_",2))] })
#    user  system elapsed 
# 738.283   3.103 741.674 

rm(dt)
system.time ( {
    df = data.table(PREFIX = rep(c("A_B","A_C","A_D","B_A","B_C","B_D"), 1000000),
                     VALUE = rep(1:6, 1000000) )
    dt = as.data.table(df)
})
#    user  system elapsed 
#   0.123   0.000   0.123 

# split PREFIX into new columns
system.time ({
    dt$PX = as.character(lapply(strsplit(as.character(dt$PREFIX), split="_"), "[", 1))
    dt$PY = as.character(lapply(strsplit(as.character(dt$PREFIX), split="_"), "[", 2))
})
#    user  system elapsed 
#  33.185   0.000  33.191 

So the str_split_fixed method takes about 20X times longer.

Update: From version 1.9.6 (on CRAN as of Sep'15), we can use the function tstrsplit() to get the results directly (and in a much more efficient manner):

require(data.table) ## v1.9.6+
dt[, c("PX", "PY") := tstrsplit(PREFIX, "_", fixed=TRUE)]
#    PREFIX VALUE PX PY
# 1:    A_B     1  A  B
# 2:    A_C     2  A  C
# 3:    A_D     3  A  D
# 4:    B_A     4  B  A
# 5:    B_C     5  B  C
# 6:    B_D     6  B  D

tstrsplit() basically is a wrapper for transpose(strsplit()), where transpose() function, also recently implemented, transposes a list. Please see ?tstrsplit() and ?transpose() for examples.

See history for old answers.

I add answer for someone who do not use data.table v1.9.5 and also want an one line solution.

dt[, c('PX','PY') := do.call(Map, c(f = c, strsplit(PREFIX, '-'))) ]

Using splitstackshape package:

library(splitstackshape)
cSplit(df, splitCols = "PREFIX", sep = "_", direction = "wide", drop = FALSE)
#    PREFIX VALUE PREFIX_1 PREFIX_2
# 1:    A_B     1        A        B
# 2:    A_C     2        A        C
# 3:    A_D     3        A        D
# 4:    B_A     4        B        A
# 5:    B_C     5        B        C
# 6:    B_D     6        B        D

We could try:

library(data.table)  
cbind(dt, fread(text = dt$PREFIX, sep = "_", header = FALSE))
    #    PREFIX VALUE V1 V2
    # 1:    A_B     1  A  B
    # 2:    A_C     2  A  C
    # 3:    A_D     3  A  D
    # 4:    B_A     4  B  A
    # 5:    B_C     5  B  C
    # 6:    B_D     6  B  D

With tidyr the solution is:

separate(df,col = "PREFIX",into = c("PX", "PY"), sep = "_")