Lapply with nested list
I have a nested list and I would like lapply
as.data.frame
at the deepest nesting level and then rbindlist
(from data.table
) everything. This is what my data looks like:
a <- list(date="2017-01-01",ret=1:5)
b <- list(date="2017-01-02",ret=7:9)
lvl3 <- list(a,b)
lvl2 <- list(lvl3,lvl3)
lvl1 <- list(lvl2,lvl2,lvl2)
If I only had lvl3 I would do this to transform the data data.frame
and rbind
:
rbindlist(lapply(lvl3,as.data.frame))
date ret
1: 2017-01-01 1
2: 2017-01-01 2
3: 2017-01-01 3
4: 2017-01-01 4
5: 2017-01-01 5
6: 2017-01-02 7
7: 2017-01-02 8
8: 2017-01-02 9
How do I do this from lvl1 and rbind
all nested ones data.frames
? This does not work:
rbindlist(lapply(lvl1,as.data.frame))
The desired output contains 48 lines:
date ret
1: 2017-01-01 1
2: 2017-01-01 2
3: 2017-01-01 3
4: 2017-01-01 4
5: 2017-01-01 5
6: 2017-01-02 7
7: 2017-01-02 8
8: 2017-01-02 9
9: 2017-01-01 1
10: 2017-01-01 2
11: 2017-01-01 3
12: 2017-01-01 4
13: 2017-01-01 5
14: 2017-01-02 7
15: 2017-01-02 8
16: 2017-01-02 9
17: 2017-01-01 1
18: 2017-01-01 2
19: 2017-01-01 3
20: 2017-01-01 4
21: 2017-01-01 5
22: 2017-01-02 7
23: 2017-01-02 8
24: 2017-01-02 9
25: 2017-01-01 1
26: 2017-01-01 2
27: 2017-01-01 3
28: 2017-01-01 4
29: 2017-01-01 5
30: 2017-01-02 7
31: 2017-01-02 8
32: 2017-01-02 9
33: 2017-01-01 1
34: 2017-01-01 2
35: 2017-01-01 3
36: 2017-01-01 4
37: 2017-01-01 5
38: 2017-01-02 7
39: 2017-01-02 8
40: 2017-01-02 9
41: 2017-01-01 1
42: 2017-01-01 2
43: 2017-01-01 3
44: 2017-01-01 4
45: 2017-01-01 5
46: 2017-01-02 7
47: 2017-01-02 8
48: 2017-01-02 9
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@docendo's general solution is best in my opinion, but if you know it's only nested in two depths ...
library(magrittr)
lvl1 %>%
unlist(recursive=FALSE) %>%
unlist(recursive=FALSE) %>%
lapply(as.data.table) %>%
rbindlist
From @lmo, there's no analogue possible here (which doesn't require magrittr):
do.call(
rbind,
lapply(
unlist(unlist(lvl1, recursive=FALSE), recursive=FALSE),
as.data.frame
)
)
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You can create your own recursive function, à la
f <- function(l) {
data.table::rbindlist(lapply(l, function(x) {
if(all(sapply(x, is.atomic))) as.data.table(x) else f(x)
}))
}
f(lvl1)
This returns a normal data table of 48 rows and 2 columns.
Also note that this works with lvl1
, lvl2
and lvl3
without change.
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There's probably more elegant ways, but combining .table data with nested foreach loops:
library(foreach)
library(data.table)
a <- list(date="2017-01-01",ret=1:5)
b <- list(date="2017-01-02",ret=7:9)
lvl3 <- list(a,b)
lvl2 <- list(lvl3,lvl3)
lvl1 <- list(lvl2,lvl2,lvl2)
o.3 <- foreach(i=1:length(lvl1)) %do% {
o.2 <- foreach(j=1:length(lvl1[[i]])) %do% {
o.1 <- foreach(k=1:length(lvl1[[i]][[j]])) %do% {
as.data.table(lvl1[[i]][[j]][[k]])
}
rbindlist(o.1)
}
rbindlist(o.2)
}
dat.final <- rbindlist(o.3)
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I would go with a wicked package purrr
. In particular:
library(purrr)
(rbindlist(lapply(simplify_all((rbindlist((lvl1 %>% at_depth(3,data.frame))))),rbindlist)))
date ret
1: 2017-01-01 1
2: 2017-01-01 2
3: 2017-01-01 3
4: 2017-01-01 4
5: 2017-01-01 5
-----
44: 2017-01-01 4
45: 2017-01-01 5
46: 2017-01-02 7
47: 2017-01-02 8
48: 2017-01-02 9
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An ugly nested call lapply
with do.call
will do the trick:
do.call(rbind,do.call(rbind,lapply(lvl1,function(x) lapply(x,function(y) do.call(rbind,lapply(y, function(z) as.data.frame(z)))))))
Output:
> do.call(rbind,do.call(rbind,lapply(lvl1,function(x) lapply(x,function(y) do.call(rbind,lapply(y, function(z) as.data.frame(z)))))))
date ret
1 2017-01-01 1
2 2017-01-01 2
3 2017-01-01 3
4 2017-01-01 4
5 2017-01-01 5
6 2017-01-02 7
7 2017-01-02 8
8 2017-01-02 9
9 2017-01-01 1
10 2017-01-01 2
11 2017-01-01 3
12 2017-01-01 4
13 2017-01-01 5
14 2017-01-02 7
15 2017-01-02 8
16 2017-01-02 9
17 2017-01-01 1
18 2017-01-01 2
19 2017-01-01 3
20 2017-01-01 4
21 2017-01-01 5
22 2017-01-02 7
23 2017-01-02 8
24 2017-01-02 9
25 2017-01-01 1
26 2017-01-01 2
27 2017-01-01 3
28 2017-01-01 4
29 2017-01-01 5
30 2017-01-02 7
31 2017-01-02 8
32 2017-01-02 9
33 2017-01-01 1
34 2017-01-01 2
35 2017-01-01 3
36 2017-01-01 4
37 2017-01-01 5
38 2017-01-02 7
39 2017-01-02 8
40 2017-01-02 9
41 2017-01-01 1
42 2017-01-01 2
43 2017-01-01 3
44 2017-01-01 4
45 2017-01-01 5
46 2017-01-02 7
47 2017-01-02 8
48 2017-01-02 9
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