Map Phenotype Values Using Conditional Rules

Efficiently transforms phenotype values based on user-defined conditional rules. Similar to dplyr::case_when but optimized for performance on large datasets.

Usage

PhenoMap(data, ..., .default = NA)

Arguments

data

A vector, data frame, or data.table containing the phenotype data to transform.

...

Named or unnamed formulas of the form condition ~ value. Conditions are evaluated sequentially; the first matching condition determines the output value. Example: col > 10 ~ "High", col <= 10 ~ "Low"

.default

Value to use when no conditions match. Default: NA.

Value

A data.table with the transformed column. If input is a vector, returns a vector.

See also

fcase, case_when

Other input_preprocess: BulkPreProcess(), PhenoPreProcess(), SCPreProcess()

Examples

if (FALSE) { # \dontrun{
# Example 1: Discretize a continuous phenotype
scores <- rnorm(100, mean = 50, sd = 10)
scores <- PhenoMap(scores, scores > 60 ~ "High", scores > 40 ~ "Medium", .default = "Low")

# Example 2: Transform a column in a data frame
df <- data.frame(
  age = c(25, 35, 45, 55, 65),
  gender = c("M", "F", "M", "F", "M")
)
df <- PhenoMap(df, age < 30 ~ "Young", age < 50 ~ "Middle", .default = "Senior")

# Example 3: Multiple conditions with complex logic
df <- data.frame(value = c(5, 15, 25, 35, 45))
df <- PhenoMap(
  df,
  value < 10 ~ "Very Low",
  value < 20 ~ "Low",
  value < 30 ~ "Medium",
  value < 40 ~ "High",
  .default = "Very High"
)
} # }