setkey sorts a
data.table and marks it as sorted with an
"sorted". The sorted columns are the key. The key can be any
number of columns. The data is always sorted in ascending order with
(if any) always first. The table is changed by reference and there is
no memory used for the key (other than marking which columns the data is sorted by).
There are three reasons
setkey is desirable:
binary search and joins are faster when they detect they can use an existing key
grouping by a leading subset of the key columns is faster because the groups are already gathered contiguously in RAM
simpler shorter syntax; e.g.
DT["id",] finds the group "id" in the first column of
DT's key using binary search. It may be helpful to think of a key as super-charged rownames: multi-column and multi-type.
NAs are always first because:
NA is internally
INT_MIN (a large negative number) in R. Keys and indexes are always in increasing order so if
NAs are first, no special treatment or branch is needed in many
data.table internals involving binary search. It is not optional to place
NAs last for speed, simplicity and rubustness of internals at C level.
NAs are present then we believe it is better to display them up front (rather than hiding them at the end) to reduce the risk of not realizing
NAs are present.
data.table parlance, all
set* functions change their input
by reference. That is, no copy is made at all other than for temporary
working memory, which is as large as one column. The only other
operator that modifies input by reference is
:=. Check out the
See Also section below for other
setindex creates an index for the provided columns. This index is simply an
ordering vector of the dataset's rows according to the provided columns. This order vector
is stored as an attribute of the
data.table and the dataset retains the original order
of rows in memory. See the
vignette("datatable-secondary-indices-and-auto-indexing") for more details.
key returns the
data.table's key if it exists;
NULL if none exists.
FALSE if the
data.table has a key.
setkey(x, ..., verbose=getOption("datatable.verbose"), physical = TRUE) setkeyv(x, cols, verbose=getOption("datatable.verbose"), physical = TRUE) setindex(...) setindexv(x, cols, verbose=getOption("datatable.verbose")) key(x) indices(x, vectors = FALSE) haskey(x)
The columns to sort by. Do not quote the column names. If
A character vector of column names. For
Output status and information.
setkey reorders (i.e. sorts) the rows of a
data.table by the columns
provided. The sort method used has developed over the years and we have contributed
to base R too; see
sort. Generally speaking we avoid any type
of comparison sort (other than insert sort for very small input) preferring instead
counting sort and forwards radix. We also avoid hash tables.
setkey always uses "C-locale"; see the Details in the help for
setorder for more on why.
The sort is stable; i.e., the order of ties (if any) is preserved.
For character vectors,
data.table takes advantage of R's internal global string cache, also exported as
In general, it's good practice to use column names rather than numbers. This is
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
If you really wish to use column numbers, it is possible but
deliberately a little harder; e.g.,
If you wanted to use
grep to select key columns according to
a pattern, note that you can just set
value = TRUE to return a character vector instead of the default integer indices.
The input is modified by reference and returned (invisibly) so it can be used
in compound statements; e.g.,
setkey(DT,a)[.("foo")]. If you require a
copy, take a copy first (using
copy may also
sometimes be useful before
:= is used to subassign to a column by
# Type 'example(setkey)' to run these at the prompt and browse output DT = data.table(A=5:1,B=letters[5:1]) DT # before#> A B #> <int> <char> #> 1: 5 e #> 2: 4 d #> 3: 3 c #> 4: 2 b #> 5: 1 asetkey(DT,B) # re-orders table and marks it sorted. DT # after#> Key: <B> #> A B #> <int> <char> #> 1: 1 a #> 2: 2 b #> 3: 3 c #> 4: 4 d #> 5: 5 etables() # KEY column reports the key'd columns#> NAME NROW NCOL MB COLS KEY #> 1: DT 5 2 0 A,B B #> Total: 0MBkey(DT)#>  "B"keycols = c("A","B") setkeyv(DT,keycols) DT = data.table(A=5:1,B=letters[5:1]) DT2 = DT # does not copy setkey(DT2,B) # does not copy-on-write to DT2 identical(DT,DT2) # TRUE. DT and DT2 are two names for the same keyed table#>  TRUEDT = 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#>  FALSEDT = data.table(A=5:1,B=letters[5:1]) setindex(DT) # set indices setindex(DT, A) setindex(DT, B) indices(DT) # get indices single vector#>  "A__B" "A" "B"indices(DT, vectors = TRUE) # get indices list#> [] #>  "A" "B" #> #> [] #>  "A" #> #> [] #>  "B" #>