I have a data table and one of the columns is a bunch of 0's and 1's, just like `vec`

below.

```
vec = c(rep(1, times = 6), rep(0, times = 10), rep(1, times = 11), rep(0, times = 4))
> vec
[1] 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
```

What I want to do is to split the data everytime there's a change in that column from 0 to 1 or vice-versa. Here is what I have done so far:

```
b = c(vec[1],diff(vec))
rowby = numeric(0)
for (i in 2:(length(b))) {
if (b[i] != 0) {
rowby <- c(rowby, i-1)
}
}
splitted_data <- split(vec, cumsum(c(TRUE,(1:length(vec) %in% rowby)[-length(vec)])))
```

There must be some thing right under my nose I can't see. What is a correct way to do this? This works for the example above, but not generally.

Answer:

Try

```
split(vec,cumsum(c(1, abs(diff(vec)))))
#$`1`
#[1] 1 1 1 1 1 1
#$`2`
#[1] 0 0 0 0 0 0 0 0 0 0
#$`3`
#[1] 1 1 1 1 1 1 1 1 1 1 1
#$`4`
#[1] 0 0 0 0
```

Or use `rle`

```
split(vec,inverse.rle(within.list(rle(vec), values <- seq_along(values))))
```

If you have the devel version of `data.table`

, `rleid`

is one function which can be relied for this job

```
library(data.table)#v1.9.5+
split(vec,rleid(vec))
```

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