# setkey

##### Create key on a data table

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).

- Keywords
- data

##### Usage

```
setkey(x, ..., verbose=getOption("datatable.verbose"), physical = TRUE)
setkeyv(x, cols, verbose=getOption("datatable.verbose"), physical = TRUE)
setindex(...)
setindexv(...)
key(x)
indices(x, vectors = FALSE)
haskey(x)
key(x) <- value # DEPRECATED, please use setkey or setkeyv instead.
```

##### Arguments

- x
A

`data.table`

.- …
The columns to sort by. Do not quote the column names. If

`…`

is missing (i.e.`setkey(DT)`

), all the columns are used.`NULL`

removes the key.- cols
A character vector (only) of column names.

- value
In (deprecated)

`key<-`

, a character vector (only) of column names.- verbose
Output status and information.

- physical
TRUE changes the order of the data in RAM. FALSE adds a secondary key a.k.a. index.

- vectors
logical scalar default

`FALSE`

, when set to`TRUE`

then list of character vectors is returned, each vector refers to one 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) 4-pass radix sort for integers, implemented
based on Pierre Terdiman's and Michael Herf's code (see links below). Similarly,
a very fast 6-pass radix order for columns of type `double`

is also implemented.
This gives a speed-up of about 5-8x 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.r-project.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

```
# NOT RUN {
# 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) # re-orders 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 copy-on-write 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
DT = 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
indices(DT, vectors = TRUE) # get indices list
# }
```

*Documentation reproduced from package data.table, version 1.10.4, License: GPL-3 | file LICENSE*

### Community examples

**raf.v.asuncion@gmail.com**at Feb 27, 2018 data.table v1.10.4-2

DT = data.table(A=5:1,B=letters[5:1]) DT # before setkey(DT,B) # re-orders table and marks it sorted. DT # after