Configuring categorical functions

Several categorical functions ship with standard mappings that you can override through options: racen() maps race strings to codes, while agec() and bmic() bin a number into codes using fixed bands.

Race codes (racen())

racen() maps race strings to numeric codes. There are six built-in categories:

racen(c(
  "WHITE",
  "BLACK",
  "ASIAN",
  "AMERICAN INDIAN OR ALASKA NATIVE",
  "PACIFIC ISLANDER",
  "OTHER"
))
#> [1] 1 2 3 4 5 6

Set scicalc.racen_config (a named numeric vector, names case-insensitive) to change them.

Override a built-in code

options(scicalc.racen_config = c(white = 10, black = 20))
racen(c(
  "WHITE",
  "BLACK",
  "ASIAN",
  "AMERICAN INDIAN OR ALASKA NATIVE",
  "PACIFIC ISLANDER",
  "OTHER"
))
#> [1] 10 20  3  4  5  6

WHITE and BLACK use the overrides; all others keeps their default.

Add a new category

Names that aren’t built-ins become new mappings, matched before the defaults.

options(scicalc.racen_config = c("multiple" = 7))
racen(c("WHITE", "MULTIPLE"))
#> [1] 1 7

Age and BMI bands (agec(), bmic())

agec() and bmic() bin a number into codes using fixed bands (FDA age bands, WHO BMI bands). By default:

agec(c(1, 10, 30, 70))
#> [1] 2 3 5 6
#> attr(,"category_standard")
#> [1] "FDA"

Set scicalc.agec_config to a data frame of bands — label, min (lower bound), and code — to replace them. Bands are half-open [min, next min), with the top band open-ended.

options(scicalc.agec_config = data.frame(
  label = c("child", "adult", "senior"),
  min   = c(0, 18, 65),
  code  = c(1, 2, 3)
))
agec(c(5, 30, 70))
#> [1] 1 2 3
#> attr(,"category_standard")
#> [1] "custom"

bmic() works the same way through scicalc.bmic_config:

options(scicalc.bmic_config = data.frame(
  label = c("lean", "high"),
  min   = c(0, 27),
  code  = c(1, 2)
))
bmic(c(22, 30), age = c(40, 40))
#> [1] 1 2
#> attr(,"category_standard")
#> [1] "custom"

With the options set, the package startup message reports the active configuration:

── scicalc options ─────────────────────────────────────────────────────────────

[32m✔
[39m scicalc.missing_value : -999
── Categorical Configurations ──────────────────────────────────────────────────

[36mℹ
[39m scicalc.racen_config:
    white -> 1
    black -> 2
    asian -> 3
    american native -> 4
    pacific islander -> 5
    other -> 6
    multiple -> 7

[36mℹ
[39m scicalc.agec_config:
    0 <= age < 18 -> child, [1]
    18 <= age < 65 -> adult, [2]
    65 <= age -> senior, [3]

[36mℹ
[39m scicalc.bmic_config:
    0 <= bmi < 27 -> lean, [1]
    27 <= bmi -> high, [2]