R: Create key on a data table

setkey {data.table} | R Documentation

## 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.

`setindex()` creates an index (or indices) on provided columns. This index is simply an order of the dataset's according to the provided columns. This order is stored as a `data.table` attribute, and the dataset retains the original order in memory. See the Secondary indices and auto indexing vignette for more details.

`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

    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)
    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 Do not quote the column names.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 of column names. For `setindexv`, this can be a `list` of character vectors, in which case each element will be applied as an index.
`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 is 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

    # 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

[Package data.table version 1.11.8 Index]