Fill missing depth values

Fix swapped upper/lower depth values and fill NAs using genus-level means. IUCN Red List assessments (see fetch_species_assessments()) sometimes record depth limits the wrong way around or omit them entirely. Designed for use inside dplyr::mutate() — returns a two-column tibble (upper_depth and lower_depth) that can be unpacked with mutate().

Usage

fill_missing_depths(upper, lower, genus, method = "genus_mean")

Arguments

upper

Numeric vector. Upper (shallower) depth limit values, possibly with NAs or swapped values.

lower

Numeric vector. Lower (deeper) depth limit values, possibly with NAs or swapped values.

genus

Character vector. Genus names, used to compute genus-level mean depths for filling NAs.

method

Character. Method for filling missing values. Currently only "genus_mean" is supported. Default "genus_mean".

Value

A tibble with columns upper_depth and lower_depth, suitable for use with dplyr::mutate().

Examples

# Depth limits as they can arrive from an IUCN assessment: the first row is
# swapped (upper deeper than lower) and the third is missing entirely.
assessments <- data.frame(
  genus_name = c("Carcharhinus", "Carcharhinus", "Carcharhinus", "Sphyrna"),
  upper_depth_limit = c(280, 0, NA, 0),
  lower_depth_limit = c(0, 100, NA, 512)
)

fill_missing_depths(
  assessments$upper_depth_limit,
  assessments$lower_depth_limit,
  assessments$genus_name
)
#>   upper_depth lower_depth
#> 1           0         280
#> 2           0         100
#> 3           0         190
#> 4           0         512

# A genus with no non-missing values anywhere has nothing to fill from:
fill_missing_depths(c(NA, NA), c(NA, NA), c("Raja", "Raja"))
#>   upper_depth lower_depth
#> 1         NaN         NaN
#> 2         NaN         NaN

# Typical use, unpacking both columns inside dplyr::mutate():
if (FALSE) { # \dontrun{
assessments <- assessments %>%
  mutate(fill_missing_depths(upper_depth_limit, lower_depth_limit, genus_name))
} # }