Same as do.call("rbind", l) on data.frames, but much faster.

rbindlist(l, use.names="check", fill=FALSE, idcol=NULL)
# rbind(..., use.names=TRUE, fill=FALSE, idcol=NULL)

Arguments

l

A list containing data.table, data.frame or list objects. ... is the same but you pass the objects by name separately.

use.names

TRUE binds by matching column name, FALSE by position. `check` (default) warns if all items don't have the same names in the same order and then currently proceeds as if `use.names=FALSE` for backwards compatibility (TRUE in future); see news for v1.12.2.

fill

TRUE fills missing columns with NAs. By default FALSE. When TRUE, use.names is set to TRUE.

idcol

Creates a column in the result showing which list item those rows came from. TRUE names this column ".id". idcol="file" names this column "file". If the input list has names, those names are the values placed in this id column, otherwise the values are an integer vector 1:length(l). See examples.

Details

Each item of l can be a data.table, data.frame or list, including NULL (skipped) or an empty object (0 rows). rbindlist is most useful when there are an unknown number of (potentially many) objects to stack, such as returned by lapply(fileNames, fread). rbind is most useful to stack two or three objects which you know in advance. ... should contain at least one data.table for rbind(...) to call the fast method and return a data.table, whereas rbindlist(l) always returns a data.table even when stacking a plain list with a data.frame, for example.

Columns with duplicate names are bound in the order of occurrence, similar to base. The position (column number) that each duplicate name occurs is also retained.

If column i does not have the same type in each of the list items; e.g, the column is integer in item 1 while others are numeric, they are coerced to the highest type.

If a column contains factors then a factor is created. If any of the factors are also ordered factors then the longest set of ordered levels are found (the first if this is tied). Then the ordered levels from each list item are checked to be an ordered subset of these longest levels. If any ambiguities are found (e.g. blue<green vs green<blue), or any ordered levels are missing from the longest, then a regular factor is created with warning. Any strings in regular factor and character columns which are missing from the longest ordered levels are added at the end.

Value

An unkeyed data.table containing a concatenation of all the items passed in.

See also

data.table, split.data.table

Examples

# default case DT1 = data.table(A=1:3,B=letters[1:3]) DT2 = data.table(A=4:5,B=letters[4:5]) l = list(DT1,DT2) rbindlist(l)
#> A B #> 1: 1 a #> 2: 2 b #> 3: 3 c #> 4: 4 d #> 5: 5 e
# bind correctly by names DT1 = data.table(A=1:3,B=letters[1:3]) DT2 = data.table(B=letters[4:5],A=4:5) l = list(DT1,DT2) rbindlist(l, use.names=TRUE)
#> A B #> 1: 1 a #> 2: 2 b #> 3: 3 c #> 4: 4 d #> 5: 5 e
# fill missing columns, and match by col names DT1 = data.table(A=1:3,B=letters[1:3]) DT2 = data.table(B=letters[4:5],C=factor(1:2)) l = list(DT1,DT2) rbindlist(l, use.names=TRUE, fill=TRUE)
#> A B C #> 1: 1 a <NA> #> 2: 2 b <NA> #> 3: 3 c <NA> #> 4: NA d 1 #> 5: NA e 2
# generate index column, auto generates indices rbindlist(l, use.names=TRUE, fill=TRUE, idcol=TRUE)
#> .id A B C #> 1: 1 1 a <NA> #> 2: 1 2 b <NA> #> 3: 1 3 c <NA> #> 4: 2 NA d 1 #> 5: 2 NA e 2
# let's name the list setattr(l, 'names', c("a", "b")) rbindlist(l, use.names=TRUE, fill=TRUE, idcol="ID")
#> ID A B C #> 1: a 1 a <NA> #> 2: a 2 b <NA> #> 3: a 3 c <NA> #> 4: b NA d 1 #> 5: b NA e 2