Create key on a data table
Description
In data.table
parlance, all set*
functions change their input
by reference. That is, no copy is made at all, other than temporary
working memory, which is as large as one column.. The only other data.table
operator that modifies input by reference is :=
. Check out the
See Also
section below for other set*
function data.table
provides.
setkey()
sorts a data.table
and marks it as sorted (with an
attribute sorted
). The sorted columns are the key. The key can be any
columns in any order. The columns are sorted in ascending order always. The table
is changed by reference and is therefore very memory efficient.
key()
returns the data.table
's key if it exists, and NULL
if none exist.
haskey()
returns a logical TRUE
/FALSE
depending on whether
the data.table
has a key (or not).
Usage
1 2 3 4 5 6 7 8 
Arguments
x 
A 
... 
The columns to sort by. Do not quote the column names. If

cols 
A character vector (only) of column names. 
value 
In (deprecated) 
verbose 
Output status and information. 
physical 
TRUE changes the order of the data in RAM. FALSE adds a secondary key a.k.a. index. 
Details
setkey
reorders (or sorts) the rows of a data.table by the columns
provided. In versions 1.9+
, for integer
columns, a modified version
of base's counting sort is implemented, which allows negative values as well. It
is extremely fast, but is limited by the range of integer values being <= 1e5. If
that fails, it falls back to a (fast) 4pass radix sort for integers, implemented
based on Pierre Terdiman's and Michael Herf's code (see links below). Similarly,
a very fast 6pass radix order for columns of type double
is also implemented.
This gives a speedup of about 58x compared to 1.8.10
on setkey
and all internal order
/sort
operations. Fast radix sorting is also
implemented for character
and bit64::integer64
types.
The sort is stable; i.e., the order of ties (if any) is preserved, in both
versions  <=1.8.10
and >= 1.9.0
.
In data.table
versions <= 1.8.10
, for columns of type integer
,
the sort is attempted with the very fast "radix"
method in
sort.list
. If that fails, the sort reverts to the default
method in order
. For character vectors, data.table
takes advantage of R's internal global string cache and implements a very efficient
order, also exported as chorder
.
In v1.7.8, the key<
syntax was deprecated. The <
method copies
the whole table and we know of no way to avoid that copy without a change in
R itself. Please use the set
* functions instead, which make no copy at
all. setkey
accepts unquoted column names for convenience, whilst
setkeyv
accepts one vector of column names.
The problem (for data.table
) with the copy by key<
(other than
being slower) is that R doesn't maintain the over allocated truelength, but it
looks as though it has. Adding a column by reference using :=
after a
key<
was therefore a memory overwrite and eventually a segfault; the
over allocated memory wasn't really there after key<
's copy. data.table
s
now have an attribute .internal.selfref
to catch and warn about such copies.
This attribute has been implemented in a way that is friendly with
identical()
and object.size()
.
For the same reason, please use the other set*
functions which modify
objects by reference, rather than using the <
operator which results
in copying the entire object.
It isn't good programming practice, in general, to use column numbers rather
than names. This is why setkey
and setkeyv
only accept column names.
If you use column numbers then bugs (possibly silent) can more easily creep into
your code as time progresses if changes are made elsewhere in your code; e.g., if
you add, remove or reorder columns in a few months time, a setkey
by column
number will then refer to a different column, possibly returning incorrect results
with no warning. (A similar concept exists in SQL, where "select * from ..."
is considered poor programming style when a robust, maintainable system is
required.) If you really wish to use column numbers, it's possible but
deliberately a little harder; e.g., setkeyv(DT,colnames(DT)[1:2])
.
Value
The input is modified by reference, and returned (invisibly) so it can be used
in compound statements; e.g., setkey(DT,a)[J("foo")]
. If you require a
copy, take a copy first (using DT2=copy(DT)
). copy()
may also
sometimes be useful before :=
is used to subassign to a column by
reference. See ?copy
.
Note
Despite its name, base::sort.list(x,method="radix")
actually
invokes a counting sort in R, not a radix sort. See do_radixsort in
src/main/sort.c. A counting sort, however, is particularly suitable for
sorting integers and factors, and we like it. In fact we like it so much
that data.table
contains a counting sort algorithm for character vectors
using R's internal global string cache. This is particularly fast for character
vectors containing many duplicates, such as grouped data in a key column. This
means that character is often preferred to factor. Factors are still fully
supported, in particular ordered factors (where the levels are not in
alphabetic order).
References
http://en.wikipedia.org/wiki/Radix_sort
http://en.wikipedia.org/wiki/Counting_sort
http://cran.at.rproject.org/web/packages/bit/index.html
http://stereopsis.com/radix.html
See Also
data.table
, tables
, J
,
sort.list
, copy
, setDT
,
setDF
, set
:=
, setorder
,
setcolorder
, setattr
, setnames
,
chorder
, setNumericRounding
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  # Type 'example(setkey)' to run these at prompt and browse output
DT = data.table(A=5:1,B=letters[5:1])
DT # before
setkey(DT,B) # reorders table and marks it sorted.
DT # after
tables() # KEY column reports the key'd columns
key(DT)
keycols = c("A","B")
setkeyv(DT,keycols) # rather than key(DT)<keycols (which copies entire table)
DT = data.table(A=5:1,B=letters[5:1])
DT2 = DT # does not copy
setkey(DT2,B) # does not copyonwrite to DT2
identical(DT,DT2) # TRUE. DT and DT2 are two names for the same keyed table
DT = data.table(A=5:1,B=letters[5:1])
DT2 = copy(DT) # explicit copy() needed to copy a data.table
setkey(DT2,B) # now just changes DT2
identical(DT,DT2) # FALSE. DT and DT2 are now different tables
