`frank.Rd`

Similar to `base::rank`

but *much faster*. And it accepts vectors, lists, `data.frame`

s or `data.table`

s as input. In addition to the `ties.method`

possibilities provided by `base::rank`

, it also provides `ties.method="dense"`

.

Like `forder`

, sorting is done in "C-locale"; in particular, this may affect how capital/lowercase letters are ranked. See Details on `forder`

for more.

`bit64::integer64`

type is also supported.

frank(x, ..., na.last=TRUE, ties.method=c("average", "first", "last", "random", "max", "min", "dense")) frankv(x, cols=seq_along(x), order=1L, na.last=TRUE, ties.method=c("average", "first", "last", "random", "max", "min", "dense"))

x | A vector, or list with all its elements identical in length or |
---|---|

... | Only for |

cols | A |

order | An |

na.last | Control treatment of |

ties.method | A character string specifying how ties are treated, see |

To be consistent with other `data.table`

operations, `NA`

s are considered identical to other `NA`

s (and `NaN`

s to other `NaN`

s), unlike `base::rank`

. Therefore, for `na.last=TRUE`

and `na.last=FALSE`

, `NA`

s (and `NaN`

s) are given identical ranks, unlike `rank`

.

`frank`

is not limited to vectors. It accepts `data.table`

s (and `list`

s and `data.frame`

s) as well. It accepts unquoted column names (with names preceded with a `-`

sign for descending order, even on character vectors), for e.g., `frank(DT, a, -b, c, ties.method="first")`

where `a,b,c`

are columns in `DT`

. The equivalent in `frankv`

is the `order`

argument.

In addition to the `ties.method`

values possible using base's `rank`

, it also provides another additional argument `"dense"`

which returns the ranks without any gaps in the ranking. See examples.

A numeric vector of length equal to `NROW(x)`

(unless `na.last = NA`

, when missing values are removed). The vector is of integer type unless `ties.method = "average"`

when it is of double type (irrespective of ties).

`data.table`

, `setkey`

, `setorder`

# on vectors x = c(4, 1, 4, NA, 1, NA, 4) # NAs are considered identical (unlike base R) # default is average frankv(x) # na.last=TRUE#> [1] 4.0 1.5 4.0 6.5 1.5 6.5 4.0frankv(x, na.last=FALSE)#> [1] 6.0 3.5 6.0 1.5 3.5 1.5 6.0# ties.method = min frankv(x, ties.method="min")#> [1] 3 1 3 6 1 6 3# ties.method = dense frankv(x, ties.method="dense")#> [1] 2 1 2 3 1 3 2# on data.table DT = data.table(x, y=c(1, 1, 1, 0, NA, 0, 2)) frankv(DT, cols="x") # same as frankv(x) from before#> [1] 4.0 1.5 4.0 6.5 1.5 6.5 4.0frankv(DT, cols="x", na.last="keep")#> [1] 4.0 1.5 4.0 NA 1.5 NA 4.0frankv(DT, cols="x", ties.method="dense", na.last=NA)#> [1] 2 1 2 1 2frank(DT, x, ties.method="dense", na.last=NA) # equivalent of above using frank#> [1] 2 1 2 1 2# on both columns frankv(DT, ties.method="first", na.last="keep")#> [1] 2 1 3 NA NA NA 4frank(DT, ties.method="first", na.last="keep") # equivalent of above using frank#> [1] 2 1 3 NA NA NA 4# order argument frank(DT, x, -y, ties.method="first")#> [1] 4 1 5 6 2 7 3#> [1] 4 1 5 6 2 7 3