3

I have already found other versions of the same question but I was not able to adapt the answers given there for my problem. Here is an older link:

The op there had data consisting of two columns only - and the given answer handles this really nicely. But what about more than two columns? Is there a way to adapt the linked code snippet?

Here is an example:

ve <- rbind("4,2","3","1,2,3","5","6","7")
expl <- cbind(head(mtcars),ve)

    row.names           mpg     cyl     disp    hp      drat    wt      qsec    vs  am  gear carb   ve
1   Mazda RX4           21.0    6       160     110     3.90    2.620   16.46   0   1   4    4      4,2
2   Mazda RX4 Wag       21.0    6       160     110     3.90    2.875   17.02   0   1   4    4      3
3   Datsun 710          22.8    4       108     93      3.85    2.320   18.61   1   1   4    1      1,2,3
4   Hornet 4 Drive      21.4    6       258     110     3.08    3.215   19.44   1   0   3    1      5
5   Hornet Sportabout   18.7    8       360     175     3.15    3.440   17.02   0   0   3    2      6
6   Valiant             18.1    6       225     105     2.76    3.460   20.22   1   0   3    1      7

I would need:

    row.names           mpg     cyl     disp    hp      drat    wt      qsec    vs  am  gear carb   ve
1   Mazda RX4           21.0    6       160     110     3.90    2.620   16.46   0   1   4    4      4
2   Mazda RX4           21.0    6       160     110     3.90    2.620   16.46   0   1   4    4      2
3   Mazda RX4 Wag       21.0    6       160     110     3.90    2.875   17.02   0   1   4    4      3
4   Datsun 710          22.8    4       108     93      3.85    2.320   18.61   1   1   4    1      1
5   Datsun 710          22.8    4       108     93      3.85    2.320   18.61   1   1   4    1      2
6   Datsun 710          22.8    4       108     93      3.85    2.320   18.61   1   1   4    1      3
7   Hornet 4 Drive      21.4    6       258     110     3.08    3.215   19.44   1   0   3    1      5
8   Hornet Sportabout   18.7    8       360     175     3.15    3.440   17.02   0   0   3    2      6
9   Valiant             18.1    6       225     105     2.76    3.460   20.22   1   0   3    1      7

Thank you!

miken32
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MineSweeper
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3 Answers3

4

Try unnest from the tidyr package. My example uses dplyr, but you can also accomplish with base functions.

library(dplyr)
library(tidyr)

expl %>%
  mutate(ve = strsplit(as.character(ve), ",")) %>% 
  unnest(ve)
davechilders
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2

Here's an attempt using base R only (which also preserves the row names- in a way at least...)

ve <- strsplit(ve, ",")
Res <- expl[rep(seq_len(nrow(expl)), sapply(ve, length)), ]
Res$ve <- unlist(ve)
Res
#                    mpg cyl disp  hp drat    wt  qsec vs am gear carb ve
# Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  4
# Mazda RX4.1       21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  2
# Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  3
# Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  1
# Datsun 710.1      22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  2
# Datsun 710.2      22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  3
# Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1  5
# Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2  6
# Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  7

Or using data.table, one option is

library(data.table)
setDT(expl)[, 
            strsplit(as.character(ve), ","), 
            c(names(expl)[-length(expl)])
            ]

Another option would be

setkey(expl, ve)[setDT(expl)[, strsplit(as.character(ve), ","), ve]]
David Arenburg
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1

I would recommend cSplit from my "splitstackshape" package.

Since your example has rownames, I've converted your example data to a data.table with the keep.rownames = TRUE argument.

library(splitstackshape)
cSplit(as.data.table(expl, keep.rownames = TRUE), "ve", ",", "long")
#                   rn  mpg cyl disp  hp drat    wt  qsec vs am gear carb ve
# 1:         Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  4
# 2:         Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  2
# 3:     Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  3
# 4:        Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  1
# 5:        Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  2
# 6:        Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  3
# 7:    Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1  5
# 8: Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2  6
# 9:           Valiant 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  7
A5C1D2H2I1M1N2O1R2T1
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