Check EMA records for the problems that actually occur

Runs the integrity checks a single-case dataset needs before analysis: values outside their declared range, duplicated timestamps, missing values in required columns, records falling outside the study period, and timestamps that failed to parse.

Usage

validate_ema(data, ranges = NULL, required = "timestamp", n_days = NULL)

Arguments

data

A data frame from read_ema().

ranges

Named list of c(min, max) giving the valid range of each measured variable, e.g. list(mood = c(0, 100)). Columns not listed are not range-checked.

required

Character vector of columns that must not be missing.

n_days

Optional study length. When given, records with a study_day outside 1:n_days are flagged.

Value

An object of class nof1_validation: a list with n_records, n_issues, and a data frame issues with columns check, row, column, and detail. n_issues == 0 means every check passed.

Details

The result is returned as data, not printed. Reports are one way to consume it; a preregistered pipeline that halts on failure is another.

Examples

d <- data.frame(
  timestamp = as.POSIXct(c("2026-02-18 09:00:00", "2026-02-18 09:00:00"), tz = "UTC"),
  mood = c(72, 140),
  study_day = c(1L, 1L)
)
v <- validate_ema(d, ranges = list(mood = c(0, 100)))
v
#> EMA validation: 2 records
#> 2issues found
#> 
#>   duplicate_timestamp    1
#>   out_of_range           1
#> 
#> See $issues for row-level detail.