write_gazepoint_ml_split_csv: Write group-aware split tables to CSV

In gp3ml: Governance-First Predictive Modelling for 'Gazepoint' Research

Write group-aware split tables to CSV

Description

Write group-aware split tables to CSV

Usage

write_gazepoint_ml_split_csv(
  x,
  directory,
  prefix = "gazepoint_ml_split",
  tables = c("analysis", "assessment", "excluded", "assignment", "summary",
    "group_counts", "checks", "issues"),
  overwrite = FALSE,
  na = ""
)

Arguments

x	A gazepoint_ml_split object.
directory	Output directory.
prefix	Filename prefix.
tables	Tables to export.
overwrite	Whether existing files may be replaced.
na	Character representation of missing values.

Value

A named character vector of normalized file paths, invisibly.

Examples

example_data <- expand.grid(
  participant_id = sprintf("P%02d", 1:6),
  stimulus_id = sprintf("S%02d", 1:4),
  repetition = 1:2,
  KEEP.OUT.ATTRS = FALSE,
  stringsAsFactors = FALSE
)
example_data$trial_id <- paste0(
  example_data$stimulus_id,
  "_T",
  example_data$repetition
)
participant_number <- as.integer(
  sub("P", "", example_data$participant_id)
)
stimulus_number <- as.integer(
  sub("S", "", example_data$stimulus_id)
)
example_data$outcome <- factor(
  ifelse(
    (participant_number + stimulus_number) %% 2L == 0L,
    "review",
    "pass"
  ),
  levels = c("pass", "review")
)
row_index <- seq_len(nrow(example_data))
example_data$fixation_duration <- 180 + row_index
example_data$pupil_change <- round(
  sin(row_index / 7),
  4
)
example_data$repetition <- NULL
manifest <- create_gazepoint_feature_manifest(
  features = c("fixation_duration", "pupil_change"),
  scientific_source = c(
    "Gazepoint fixation export",
    "Gazepoint pupil export"
  ),
  source_table = c("fixations", "pupil"),
  transformation = c(
    "Trial-level mean",
    "Trial-level change"
  ),
  availability_stage = "during_exposure",
  prediction_time_available = TRUE,
  preprocessing_scope = "none",
  fold_local_required = FALSE
)
split <- split_gazepoint_ml_data(
  data = example_data,
  outcome = "outcome",
  predictors = c("fixation_duration", "pupil_change"),
  feature_manifest = manifest,
  generalization_target = "new_participants",
  participant_id = "participant_id",
  trial_id = "trial_id",
  stimulus_id = "stimulus_id",
  assessment_prop = 1 / 3,
  seed = 101L
)
output_directory <- tempfile()
paths <- write_gazepoint_ml_split_csv(
  x = split,
  directory = output_directory,
  tables = c("summary", "group_counts")
)
basename(unname(paths))
unlink(output_directory, recursive = TRUE)

gp3ml documentation built on Aug. 23, 2026, 5:11 p.m.