On 08/08/13 05:11, Eduard Antonyan wrote:
This seems like a pretty natural interpretation of list->data.table to
me, although it would be nice to maybe get a warning I think here:
X = list(a = list(1,2), b = list(1,2,3))
as.data.table(X)
Good point, that should be recycle-remainder warning.
especially since this simply refuses to do anything:
data.table(a = c(1,2), b = c(1,2,3))
Ah but that's for consistency with data.frame ;)
> data.frame(1:2,1:3)
Error in data.frame(1:2, 1:3) :
arguments imply differing number of rows: 2, 3
>
Happy to recycle and change both above to recyle-remainder warning. If
there's no recycle remainder then no warning, right? Repeating
singletons is a common use case I wouldn't want a warning about, for
example. Could you file a request please?
On Wed, Aug 7, 2013 at 10:30 PM, Ricardo Saporta
<[email protected]
<mailto:[email protected]>> wrote:
Hey Frank,
Thanks for pointing out that SO link, I had missed it.
All,
I'm curious as to which used cases this functionality would be
used in (used for?)
thanks,
Rick
On Wed, Aug 7, 2013 at 8:14 PM, Frank Erickson <[email protected]
<mailto:[email protected]>> wrote:
Hi Rick,
I guess it's intentional: Matthew saw this SO question (since
he edited one of the answers):
http://stackoverflow.com/questions/9547518/creating-a-data-frame-where-a-column-is-a-list
Some musings: Of course, to reproduce as.data.frame-like
behavior, you can un-nest the list, so both functions treat it
the same way.
Z <- unlist(Y,recursive=FALSE)
identical(as.data.table(Z),as.data.table(as.data.frame(Z))) #
TRUE
# or, equivalently (?)
identical(do.call(data.table,Z),data.table(do.call(data.frame,Z)))
# TRUE
On the other hand, going back the other direction (getting
data.table-like behavior when data.frame's is the default) is
more awkward, as seen in that SO question (where they mention
protecting each sublist with the I() function). Besides, I'm
with @flodel, who asked the SO question, in expecting
data.table's behavior: one top-level item in the list mapping
to one column in the result...
--Frank
On Wed, Aug 7, 2013 at 4:56 PM, Ricardo Saporta
<[email protected]
<mailto:[email protected]>> wrote:
Hi all,
Note the following discrepancy in structure between
as.data.frame & as.data.table when called on a nested list.
as.data.frame converts the sublist into individual columns
whereas as.data.table stacks them into a single column and
creates additional rows.
Is this intentional?
-Rick
as.data.frame(X)
# start type end data.editDist data.second
# 1 start_node is_similar end_node 1 HelloWorld
as.data.table(X)
# start type end data
# 1: start_node is_similar end_node 1
# 2: start_node is_similar end_node HelloWorld
### Copy+Paste'able Below ###
# Example 1:
X <- structure(list(start = "start_node", type =
"is_similar", end = "end_node",
data = structure(list(editDist = 1, second =
"HelloWorld"), .Names = c("editDist",
"second"))), .Names = c("start", "type", "end", "data"))
as.data.frame(X)
as.data.table(X)
as.data.table(as.data.frame(X))
# Example 2, with more elements:
Y <- structure(list(start = c("start_node", "start_node"),
type = c("is_similar", "is_similar"), end = c("end_node",
"end_node"), data = structure(list(editDist = c(1, 1),
second = c("HelloWorld", "HelloWorld")), .Names =
c("editDist", "second"))), .Names = c("start", "type",
"end", "data"))
as.data.frame(Y)
as.data.table(Y)
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