Rank a vector based on either alphabetical or frequency order

This function acts as a drop-in replacement for the base rank() function with the added option to:

Rank categorical factors based on frequency instead of alphabetically
Rank in descending or ascending order

Usage

smartrank(
  x,
  sort_by = c("alphabetical", "frequency"),
  desc = FALSE,
  ties.method = "average",
  na.last = TRUE,
  freq_tiebreak = c("match_desc", "asc", "desc"),
  verbose = TRUE
)

Arguments

x

A numeric, character, or factor vector

sort_by

Sort ranking either by "alphabetical" or "frequency" . Default is "alphabetical"

desc

A logical indicating whether the ranking should be in descending ( TRUE ) or ascending ( FALSE ) order. When input is numeric, ranking is always based on numeric order.

ties.method

a character string specifying how ties are treated, see ‘Details’; can be abbreviated.

na.last

a logical or character string controlling the treatment of NAs. If TRUE, missing values in the data are put last; if FALSE, they are put first; if NA, they are removed; if "keep" they are kept with rank NA.

freq_tiebreak

Controls how alphabetical tie-breaking works when sort_by = "frequency" and x is character/factor/logical. Must be one of:

"match_desc" (default): alphabetical tie-breaking direction follows desc (ascending when desc = FALSE, descending when desc = TRUE).
"asc": ties are always broken by ascending alphabetical order, regardless of desc.
"desc": ties are always broken by descending alphabetical order, regardless of desc.

verbose

verbose (flag)

Value

The ranked vector

Details

If x includes ‘ties’ (equal values), the ties.method argument determines how the rank value is decided. Must be one of:

average: replaces integer ranks of tied values with their average (default)
first: first-occurring value is assumed to be the lower rank (closer to one)
last: last-occurring value is assumed to be the lower rank (closer to one)
max or min: integer ranks of tied values are replaced with their maximum and minimum respectively (latter is typical in sports-ranking)
random which of the tied values are higher / lower rank is randomly decided.

NA values are never considered to be equal: for na.last = TRUE and na.last = FALSE they are given distinct ranks in the order in which they occur in x.

Note

When sort_by = "frequency", ties based on frequency are broken by alphabetical order of the terms. Use freq_tiebreak to control whether that alphabetical tie-breaking is ascending, descending, or follows desc.

When sort_by = "frequency" and input is character, ties.method is ignored. Each distinct element level gets its own rank, and each rank is 1 unit away from the next element, irrespective of how many duplicates

Examples


# ------------------
## CATEGORICAL INPUT
# ------------------

fruits <- c("Apple", "Orange", "Apple", "Pear", "Orange")

# rank alphabetically
smartrank(fruits)
#> [1] 1.5 3.5 1.5 5.0 3.5
#> [1] 1.5 3.5 1.5 5.0 3.5

# rank based on frequency
smartrank(fruits, sort_by = "frequency")
#> [1] 2.5 4.5 2.5 1.0 4.5
#> [1] 2.5 4.5 2.5 1.0 4.5

# rank based on descending order of frequency
smartrank(fruits, sort_by = "frequency", desc = TRUE)
#> [1] 3.5 1.5 3.5 5.0 1.5
#> [1] 1.5 3.5 1.5 5.0 3.5

# sort fruits vector based on rank
ranks <- smartrank(fruits,sort_by = "frequency", desc = TRUE)
fruits[order(ranks)]
#> [1] "Orange" "Orange" "Apple"  "Apple"  "Pear"  
#> [1] "Apple"  "Apple"  "Orange" "Orange" "Pear"


# ------------------
## NUMERICAL INPUT
# ------------------

# rank numerically
smartrank(c(1, 3, 2))
#> [1] 1 3 2
#> [1] 1 3 2

# rank numerically based on descending order
smartrank(c(1, 3, 2), desc = TRUE)
#> [1] 3 1 2
#> [1] 3 1 2

# always rank numeric vectors based on values, irrespective of sort_by
smartrank(c(1, 3, 2), sort_by = "frequency")
#> smartrank: Sorting a non-categorical variable. Ignoring `sort_by` and sorting numerically
#> [1] 1 3 2
#> smartrank: Sorting a non-categorical variable. Ignoring `sort_by` and sorting numerically
#> [1] 1 3 2