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The fastest way to split character vectors into newlines in a data frame

I was not sure how to say this correctly during the search, so sorry if this has a simple answer.

I have 58 data frames with ~ 25,000 rows that I get from .csv. They look something like this:

Probe.Id     Gene.Id             Score.d
1418126_at   6352                28.52578
145119_a_at  2192                24.87866
1423477_at   NA                  24.43532
1434193_at   100506144///9204    6.22395

Ideally, I want to split the identifiers into "///" and get them in new lines. For instance:

Probe.Id     Gene.Id             Score.d
1418126_at   6352                28.52578
145119_a_at  2192                24.87866
1423477_at   NA                  24.43532
1434193_at   100506144           6.22395
1434193_at   9204                6.22395

Using strsplit allows me to get Gene.Id as a list of character vectors, but as soon as I can, I'm not sure if the most efficient way is to get each of the individual identifiers in their own row with the correct values ​​from the other columns. Ideally, I don't want to just iterate over 25,000 lines.

If someone knows the right way to do this, I would really appreciate it.

EDIT: , , , :

333932///126961///653604///8350///8354///8355///8356///8968///8352///8358///835‌​1///8353///8357"

, .

+5
3

: OP. data.table:

df <- structure(list(Probe.Id = c("1418126_at", "145119_a_at", "1423477_at", 
        "1434193_at", "100_at"), Gene.Id = c("6352", "2192", NA, 
        "100506144///9204", "100506144///100506146///100506148///100506150"), 
         Score.d = c(28.52578, 24.87866, 24.43532, 6.22395, 6.22395)), 
        .Names = c("Probe.Id", "Gene.Id", "Score.d"), row.names = c(NA, 5L), 
        class = "data.frame")

require(data.table)
dt <- data.table(df)
dt.out <- dt[, list(Probe.Id = Probe.Id, 
          Gene.Id = unlist(strsplit(Gene.Id, "///")), 
          Score.d = Score.d), by=1:nrow(dt)]

> dt.out

#    nrow    Probe.Id   Gene.Id  Score.d
# 1:    1  1418126_at      6352 28.52578
# 2:    2 145119_a_at      2192 24.87866
# 3:    3  1423477_at        NA 24.43532
# 4:    4  1434193_at 100506144  6.22395
# 5:    4  1434193_at      9204  6.22395
# 6:    5      100_at 100506144  6.22395
# 7:    5      100_at 100506146  6.22395
# 8:    5      100_at 100506148  6.22395
# 9:    5      100_at 100506150  6.22395

fixed = TRUE strsplit , /// - .

data.table. , strsplit Gene.Id , 1 ( data.table , , 2 :

# first split using strsplit (data.table can hold list in its columns!!)
dt[, Gene.Id_split := strsplit(dt$Gene.Id, "///", fixed=TRUE)]
# then just unlist them
dt.2 <- dt[, list(Probe.Id = Probe.Id, 
                  Gene.Id = unlist(Gene.Id_split), 
                  Score.d = Score.d), by = 1:nrow(dt)]

data.table, , , 295245. rbenchmark:

# first function
DT1 <- function() {
    dt.1 <- dt[, list(Probe.Id = Probe.Id, 
             Gene.Id = unlist(strsplit(Gene.Id, "///", fixed = TRUE)), 
             Score.d = Score.d), by=1:nrow(dt)]
}

# expected to be faster function
DT2 <- function() {
    dt[, Gene.Id_split := strsplit(dt$Gene.Id, "///", fixed=TRUE)]
    # then just unlist them
    dt.2 <- dt[, list(Probe.Id = Probe.Id, Gene.Id = unlist(Gene.Id_split), Score.d = Score.d), by = 1:nrow(dt)]
}

require(rbenchmark)
benchmark(DT1(), DT2(), replications=10, order="elapsed")

#    test replications elapsed relative user.self sys.self
# 2 DT2()           10  15.708    1.000    14.390    0.391
# 1 DT1()           10  24.957    1.589    23.723    0.436

1,6 . ///. , .

OLD-: ( )

: 1) find the positions, ///, 2) extract, 3) duplicate, 4) sub 5) combine them.

df <- structure(list(Probe.Id = structure(c(1L, 4L, 2L, 3L), 
         .Label = c("1418126_at", "1423477_at", "1434193_at", "145119_a_at"), 
         class = "factor"), Gene.Id = structure(c(3L, 2L, NA, 1L), 
         .Label = c("100506144///9204", "2192", "6352"), class = "factor"), 
         Score.d = c(28.52578, 24.87866, 24.43532, 6.22395)), 
         .Names = c("Probe.Id", "Gene.Id", "Score.d"), 
         class = "data.frame", row.names = c(NA, -4L))

# 1) get the positions of "///"
idx <- grepl("[/]{3}", df$Gene.Id)

# 2) create 3 data.frames
df1 <- df[!idx, ] # don't touch this.
df2 <- df[idx, ] # we need to work on this

# 3) duplicate
df3 <- df2 # duplicate it.

4) sub    
df2$Gene.Id <- sub("[/]{3}.*$", "", df2$Gene.Id) # replace the end
df3$Gene.Id <- sub("^.*[/]{3}", "", df3$Gene.Id) # replace the beginning

# 5) combine/put them back
df.out <- rbind(df1, df2, df3)

# if necessary sort them here.
+6

strsplit merge

dat <- read.table(text ='Probe.Id     Gene.Id             Score.d
1418126_at   6352                28.52578
145119_a_at  2192                24.87866
1423477_at   NA                  24.43532
1434193_at   100506144///9204    6.22395',header=T,stringsAsFactors=F)
dat1 <- dat
xx <- do.call(rbind,strsplit(dat$Gene.Id,split='///'))
dat[which(xx[,1]!=xx[,2]),2]  <- xx[which(xx[,1]!=xx[,2]),1]
dat1[which(xx[,1]!=xx[,2]),2]  <- xx[which(xx[,1]!=xx[,2]),2]
  merge(dat,dat1,all.y=T,all.x=T)
     Probe.Id   Gene.Id  Score.d
1  1418126_at      6352 28.52578
2  1423477_at      <NA> 24.43532
3  1434193_at 100506144  6.22395
4  1434193_at      9204  6.22395
5 145119_a_at      2192 24.87866
+2

Here we use a method that uses the constructor to data.frame, using the "function", that it processes the input vectors without problems:

do.call(rbind, 
        apply(dat, 1, function(x) 
                         data.frame(Probe.ID=x['Probe.Id'], 
                                    Gene.Id=strsplit(x['Gene.Id'], '///'),
                                    Score.d=x['Score.d'],
                                    row.names=NULL
                                   )
             )
        )
##      Probe.ID   Gene.Id  Score.d
## 1  1418126_at      6352 28.52578
## 2 145119_a_at      2192 24.87866
## 3  1423477_at      <NA> 24.43532
## 4  1434193_at 100506144  6.22395
## 5  1434193_at      9204  6.22395
+2
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