Package {carwatch}

Title: Processing of 'CARWatch' Sampling Logs and Saliva Data
Version: 1.0.1
Description: Import and reconstruct saliva-sampling studies recorded by the 'CARWatch' application. Registration metadata and raw barcode events are converted into auditable study days and scheduled sample positions using a two-pass issue-review workflow. Functions assess sampling-time compliance, merge laboratory saliva measurements, calculate response features, and create quality-control visualizations. The application is described by Richer et al. (2023) <doi:10.1016/j.psyneuen.2023.106073>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-GB
Depends: R (≥ 4.3)
Imports: boot, clock (≥ 0.7.0), digest (≥ 0.6.0), dplyr (≥ 1.1.0), fs (≥ 1.6.0), ggplot2 (≥ 3.5.0), grid, jsonlite (≥ 1.8.0), readr (≥ 2.1.0), rlang (≥ 1.1.0), tibble (≥ 3.2.0), vctrs (≥ 0.6.0), withr (≥ 3.0.0)
Suggests: covr, DT, knitr, pkgdown, rmarkdown, roxygen2, shiny, shinytest2, testthat (≥ 3.2.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://carwatch-tools.github.io/carwatch-r/, https://github.com/carwatch-tools/carwatch-r
BugReports: https://github.com/carwatch-tools/carwatch-r/issues
Config/roxygen2/version: 8.0.0
NeedsCompilation: no
Packaged: 2026-09-09 12:43:54 UTC; richer
Author: Robert Richer ORCID iD [aut, cre]
Maintainer: Robert Richer <robert.richer@fau.de>
Repository: CRAN
Date/Publication: 2026-09-17 11:50:08 UTC

Package namespace declarations

Description

Import and reconstruct saliva-sampling studies recorded by the 'CARWatch' application. Registration metadata and raw barcode events are converted into auditable study days and scheduled sample positions using a two-pass issue-review workflow. Functions assess sampling-time compliance, merge laboratory saliva measurements, calculate response features, and create quality-control visualizations. The application is described by Richer et al. (2023) doi:10.1016/j.psyneuen.2023.106073.

Author(s)

Maintainer: Robert Richer robert.richer@fau.de (ORCID)

Authors:

See Also

Useful links:

Extract the explicit sample-level table

Description

Extract the explicit sample-level table

Usage

as_sample_events(data)

Arguments

data

Complete canonical Study Results.

Value

A tibble with one row per participant, day, and scheduled sample.

Extract the explicit day-level table

Description

Extract the explicit day-level table

Usage

as_study_days(data)

Arguments

data

Complete canonical Study Results.

Value

A tibble with one row per participant and canonical day.

Compute area under the curve with respect to ground and increase

Description

Compute area under the curve with respect to ground and increase

Usage

auc(
  data,
  saliva_type = "cortisol",
  remove_s0 = FALSE,
  compute_auc_post = FALSE,
  sample_times = NULL
)

Arguments

data

Saliva data or canonical results.

saliva_type

Measurement column name(s).

remove_s0

Whether to remove baseline sample S0.

compute_auc_post

Whether to add post-baseline AUCi.

sample_times

Sampling-time vector or column name.

Value

Group-level AUC values.

Compute the standard CARWatch saliva response feature set

Description

Compute the standard CARWatch saliva response feature set

Usage

compute_features(
  data,
  saliva_type = "cortisol",
  group_levels = NULL,
  sample_level = NULL,
  sample_times = NULL,
  slope_pairs = NULL,
  remove_s0 = FALSE
)

Arguments

data

Saliva data or canonical results.

saliva_type

Measurement column name(s).

group_levels

Grouping column names.

sample_level

Sample-position column name.

sample_times

Sampling-time vector or column name.

slope_pairs

Sample pairs for slopes.

remove_s0

Whether to remove baseline sample S0.

Value

Group-level response features.

Examples

curve <- tibble::tibble(
  participant = "P01", sample = paste0("S", 1:4),
  time_min = c(0, 30, 45, 60), cortisol = c(5, 9, 8, 7)
)
compute_features(
  curve,
  group_levels = "participant",
  sample_level = "sample",
  sample_times = "time_min"
)

Compute CARWatch saliva features using sample position and actual time

Description

Compute CARWatch saliva features using sample position and actual time

Usage

compute_features_from_carwatch(
  data,
  saliva_type = "cortisol",
  slope_pairs = NULL,
  group_levels = NULL
)

Arguments

data

Canonical results or sample events.

saliva_type

Measurement column name.

slope_pairs

Sample pairs for slopes.

group_levels

Additional R column names that identify separate curves.

Value

Per participant-day response features.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
saliva <- read_saliva(file.path(fixture, "saliva.csv"))
merged <- merge_saliva(results, saliva)
compute_features_from_carwatch(merged)

Edit conversion decisions interactively

Description

The table is shown on the left and issue-specific decision controls on the right. The report remains immutable until the selected row passes the same validation used by convert_raw_logs(). When raw_logs is supplied, ⁠Refresh remaining issues⁠ applies the cumulative decisions to the original events with errors = "warn" and replaces the visible table with unresolved issues from the new conversion. A diary-backed decision that cannot be applied is reset to ⁠Leave unresolved⁠ while successfully applied decisions are hidden. Resolved upstream decisions remain in the history returned by Done. Mouse and arrow-key row selection both update the decision controls.

Usage

conversion_report_editor(
  report,
  launch = interactive(),
  host = "127.0.0.1",
  port = NULL,
  raw_logs = NULL,
  protocol_manifest = NULL,
  sampling_schedule = NULL,
  manual_diary = NULL,
  check_compliance = TRUE,
  compliance_checker = new_sampling_compliance_checker()
)

Arguments

report

A conversion report list or its editable issue table.

launch

Run the Shiny gadget immediately. Set FALSE to return a shiny.appobj for tests, embedding, or deployment.

host

Host passed to shiny::runApp().

port

Optional port passed to shiny::runApp().

raw_logs

Optional immutable raw events. Required to enable refreshing the conversion after decisions change.

protocol_manifest, sampling_schedule, manual_diary, check_compliance, compliance_checker

Conversion settings forwarded unchanged by ⁠Refresh remaining issues⁠.

Value

A validated issue tibble, NULL after cancellation, or a Shiny app object when launch = FALSE.

Convert immutable CARWatch raw logs to canonical Study Results

Description

The first pass only reports issues. Decisions passed through issue_decisions are the only allowed route for corrections; raw event rows are never changed.

Usage

convert_raw_logs(
  raw_logs,
  protocol_manifest = NULL,
  errors = c("raise", "warn", "error"),
  create_report = FALSE,
  issue_decisions = NULL,
  sampling_schedule = NULL,
  manual_diary = NULL,
  check_compliance = TRUE,
  compliance_checker = new_sampling_compliance_checker()
)

Arguments

raw_logs

Immutable events returned by read_raw_logs().

protocol_manifest

Optional ordered registration configuration.

errors

Unresolved-issue handling: "raise", "warn", or legacy "error".

create_report

Return a list containing results and the issue report.

issue_decisions

A prior editable issue report with decisions.

sampling_schedule

Optional fallback schedule for use_default sample decisions. Supply a position-indexed vector, a named sample list, or a named day list containing either form.

manual_diary

Optional normalized manual diary.

check_compliance

Whether to calculate timing compliance.

compliance_checker

Timing tolerance configuration.

Value

Canonical results, or a results/report list.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
raw_logs <- read_raw_logs(file.path(fixture, "raw", "VP01"))
converted <- convert_raw_logs(raw_logs, errors = "warn", create_report = TRUE)
converted$report$issues
as_sample_events(converted$results)

Remove non-compliant observations

Description

Remove non-compliant observations

Usage

drop_non_compliant_samples(
  data,
  drop_entire_day = TRUE,
  drop_unassessed = FALSE
)

Arguments

data

Canonical results or sample events.

drop_entire_day

Whether one failed sample removes its day.

drop_unassessed

Whether missing assessments are removed.

Value

Filtered data preserving canonical results when supplied.

Extract the registration-aware protocol schedule

Description

Extract the registration-aware protocol schedule

Usage

extract_registration_schedule(
  raw_logs,
  protocol_manifest = NULL,
  errors = c("raise", "warn", "error")
)

Arguments

raw_logs

Immutable events returned by read_raw_logs().

protocol_manifest

Optional ordered registration configuration.

errors

Unresolved-issue handling: "raise", "warn", or legacy "error".

Value

A tibble with registration-aware sample positions.

Identify non-compliant samples

Description

Identify non-compliant samples

Usage

find_non_compliant_samples(data)

Arguments

data

Canonical results or sample events.

Value

Rows with failed compliance.

Identify tube and day mismatches

Description

Identify tube and day mismatches

Usage

find_sampling_anomalies(data)

Arguments

data

Canonical results or sample events.

Value

Rows with sampling anomalies.

Generate deterministic local CARWatch example data

Description

Generated anomalies are represented by ordinary raw-log omissions and a matching manual diary/decision report. The source events themselves are never patched. Study Manager snake-case, camelCase, and QR aliases are accepted, including multiple registration blocks and opaque saliva IDs.

Usage

generate_synthetic_study_data(
  output_dir,
  study_config = NULL,
  n_participants = NULL,
  non_compliant_sample_ratio = 0.1,
  missing_awakening_time_ratio = 0.01,
  missing_sampling_time_ratio = 0.02,
  random_state = 42L,
  create_cortisol_data = FALSE,
  overwrite = FALSE,
  validate = TRUE
)

Arguments

output_dir

Target directory.

study_config

Study Manager-style list, decoded CARWATCH; QR payload, or NULL for the four-day CAR default.

n_participants

Number of generated participants. When NULL, use the configured value or the Study Manager default of 80.

non_compliant_sample_ratio

Proportion of expected samples with timing deliberately outside the default compliance tolerance. Relative and fixed clock-time samples use their respective tolerance-aware deviation ranges.

missing_awakening_time_ratio

Proportion of participant-days with a missing awakening event and first sample.

missing_sampling_time_ratio

Total proportion of expected scans omitted.

random_state

Integer seed, or NULL for unseeded generation. Separate deterministic streams are used for timings, anomaly selection, and cortisol.

create_cortisol_data

Whether to create position-indexed cortisol.csv.

overwrite

Whether an existing target directory may be replaced.

validate

Run advisory and submitted-decision conversion after writing.

Value

The normalized output directory path.

Examples

output <- tempfile("carwatch-study-")
generate_synthetic_study_data(
  output,
  study_config = list(study_days = 1, saliva_distances = c(0, 30)),
  n_participants = 1,
  non_compliant_sample_ratio = 0,
  missing_awakening_time_ratio = 0,
  missing_sampling_time_ratio = 0,
  validate = FALSE
)
unlink(output, recursive = TRUE)

Return the initial value in each saliva curve

Description

Return the initial value in each saliva curve

Usage

initial_value(data, saliva_type = "cortisol", remove_s0 = FALSE)

Arguments

data

Saliva data or canonical results.

saliva_type

Measurement column name(s).

remove_s0

Whether to remove baseline sample S0.

Value

Group-level initial values.

Open an interactive participant-day sampling timeline

Description

This R-native replacement for the Python notebook widget uses Shiny. The app owns participant/day selectors and redraws plot_sampling_timeline().

Usage

interactive_sampling_timeline(
  data,
  show_expected = TRUE,
  launch = interactive(),
  host = "127.0.0.1",
  port = NULL
)

Arguments

data

Complete canonical results or a sample-event tibble.

show_expected

Whether registered target times are drawn.

launch

Run the app immediately. Set FALSE to return a shiny.appobj for testing, embedding, or deployment.

host

Host passed to shiny::runApp().

port

Optional port passed to shiny::runApp().

Value

A Shiny app object when launch = FALSE; otherwise the result of shiny::runApp().

Compute maximum increase from the initial sample

Description

Compute maximum increase from the initial sample

Usage

max_increase(
  data,
  saliva_type = "cortisol",
  remove_s0 = FALSE,
  percent = FALSE
)

Arguments

data

Saliva data or canonical results.

saliva_type

Measurement column name(s).

remove_s0

Whether to remove baseline sample S0.

percent

Whether to return the percentage increase.

Value

Group-level maximum increase.

Compute the maximum measured value in each saliva curve

Description

Compute the maximum measured value in each saliva curve

Usage

max_value(data, saliva_type = "cortisol", remove_s0 = FALSE)

Arguments

data

Saliva data or canonical results.

saliva_type

Measurement column name(s).

remove_s0

Whether to remove baseline sample S0.

Value

Group-level maximum values.

Compute mean and standard error by sample

Description

Compute mean and standard error by sample

Usage

mean_se(data, saliva_type = "cortisol", group_cols = NULL, remove_s0 = FALSE)

Arguments

data

Saliva data or canonical results.

saliva_type

Measurement column name(s).

group_cols

Grouping columns.

remove_s0

Whether to remove baseline sample S0.

Value

Mean and standard error by sample.

Merge laboratory saliva values into canonical Study Results

Description

Raw tube identifiers remain opaque. With match_on = "sample" values are matched to the recorded tube (falling back to the scheduled tube); with match_on = "position" they are matched to registration-aware positions.

Usage

merge_saliva(
  study_results,
  saliva,
  correct_swaps = TRUE,
  match_on = c("sample", "position", "physical_id"),
  missing_carwatch_data = c("ignore", "raise"),
  metadata_cols = NULL
)

Arguments

study_results

Complete canonical results.

saliva

Long laboratory tibble.

correct_swaps

Match physical tubes to their recorded position.

match_on

"sample"/"physical_id" or "position".

missing_carwatch_data

Whether unmatched laboratory rows are ignored or rejected.

metadata_cols

Optional laboratory columns to retain as metadata rather than numeric measurements. When omitted, non-numeric non-key columns are treated as metadata. This is the R equivalent of additional pandas index levels. Constant participant-day values are stored at the canonical day level; values varying within any day stay sample-level everywhere.

Value

Complete canonical results with laboratory and merge-provenance fields.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
saliva <- read_saliva(file.path(fixture, "saliva.csv"))
merged <- merge_saliva(results, saliva)
as_sample_events(merged)[c("participant", "sample", "cortisol")]

Construct canonical CARWatch results

Description

A carwatch_results object is an R-native representation of the Python package's participant-indexed, three-level wide table. Internally it is a tibble with a participant column and safe column names. The column_spec attribute maps every value column reversibly to day, sample, and variable; opaque sample IDs are never encoded into the internal names.

Usage

new_carwatch_results(data, column_spec, display_only = FALSE)

Arguments

data

Tibble containing participant and one column per result value.

column_spec

Tibble with name, day, sample, and variable.

display_only

Whether required provenance has intentionally been removed.

Value

A carwatch_results object: a tibble with one row per participant, a column_spec attribute mapping value columns to day, sample, and variable, and a carwatch_display_only attribute.

Configure CARWatch sampling-compliance tolerances

Description

Configure CARWatch sampling-compliance tolerances

Usage

new_sampling_compliance_checker(
  awakening_delay_tolerance_min = 5,
  sampling_delay_tolerance_min = 5,
  absolute_time_tolerance_min = 15,
  check_absolute_times = TRUE
)

Arguments

awakening_delay_tolerance_min

Tolerance for the first relative sample.

sampling_delay_tolerance_min

Tolerance for later relative samples.

absolute_time_tolerance_min

Tolerance for fixed-time samples.

check_absolute_times

Whether fixed-time samples are evaluated.

Value

A carwatch_compliance_checker list containing the configured tolerance values and the check_absolute_times flag.

Plot cohort sampling compliance

Description

Plot cohort sampling compliance

Usage

plot_compliance_overview(
  data,
  by = "sample_position",
  view = c("proportion", "heatmap")
)

Arguments

data

Canonical results or sample events.

by

Summary grouping variable.

view

Plot form.

Value

A ggplot object.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
plot_compliance_overview(results)

Plot CARWatch-aligned saliva response curves

Description

Actual time_min values define the horizontal axis. Faint lines show participant-day curves. Thick lines connect group means calculated separately at each registered sample_position; shaded ribbons show BCa bootstrap confidence intervals when at least two measurements contribute.

Usage

plot_saliva_curve(
  data,
  value = "cortisol",
  participant = NULL,
  day = NULL,
  group_by = NULL,
  ci = 95,
  n_boot = 1000,
  seed = 0,
  show_individual = TRUE
)

Arguments

data

Canonical results or sample events.

value

Non-empty name of the saliva measurement column.

participant

Optional exact participant filter.

day

Optional exact canonical-day filter.

group_by

Optional character vector defining separate aggregate curves.

ci

Bootstrap confidence level between 0 and 100, or NULL for no band.

n_boot

Positive number of bootstrap resamples.

seed

Integer random seed, or NULL to use the current R RNG state.

show_individual

Whether individual participant-day curves are drawn.

Value

A ggplot object.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
saliva <- read_saliva(file.path(fixture, "saliva.csv"))
merged <- merge_saliva(results, saliva)
plot_saliva_curve(merged, ci = NULL)

Plot an individual sampling timeline

Description

Actual sampling events are coloured by compliance and shaped by timestamp provenance. When show_expected = TRUE, hollow grey circles show the static protocol targets, hollow blue diamonds show targets updated from preceding recorded relative samples, and arrows show the signed deviation from the app-updated target. Missing relative scans break the adaptive target chain. Missing events and scheduled/recorded sample mismatches remain visible.

Usage

plot_sampling_timeline(data, participant, day, show_expected = TRUE)

Arguments

data

Canonical results or sample events.

participant

Participant identifier.

day

Canonical day identifier.

show_expected

Whether expected sampling times are shown.

Value

A ggplot object.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
plot_sampling_timeline(results, participant = "VP01", day = "D1")

Plot signed sampling-time deviations

Description

Negative values indicate early collection and positive values late collection. Boxes summarize each group while jittered points retain every recorded observation.

Usage

plot_timing_deviation(data, by = "sample_position")

Arguments

data

Canonical results or sample events.

by

Grouping variable. Defaults to sample_position.

Value

A ggplot object.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
plot_timing_deviation(results)

Read a conversion-issue report CSV

Description

Read a conversion-issue report CSV

Usage

read_conversion_report(path)

Arguments

path

CSV path.

Value

A validated editable issue tibble.

Read a manual measurement diary

Description

Read a manual measurement diary

Usage

read_manual_diary(path)

Arguments

path

Wide CSV diary.

Value

A tibble keyed by participant and canonical day.

Read CARWatch raw log files

Description

Read CARWatch raw log files

Usage

read_raw_logs(
  path,
  tz = "Europe/Berlin",
  errors = c("raise", "warn", "ignore", "error")
)

Arguments

path

CSV file, ZIP archive, directory, or vector of paths.

tz

IANA timezone for Unix timestamps.

errors

Invalid-payload handling: "error", "warn", or "ignore".

Value

A tibble of immutable raw events.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
raw_logs <- read_raw_logs(file.path(fixture, "raw", "VP01"))
head(raw_logs)

Read raw logs from one explicitly mapped folder per participant

Description

Read raw logs from one explicitly mapped folder per participant

Usage

read_raw_logs_from_participant_dirs(
  participant_dirs,
  tz = "Europe/Berlin",
  errors = c("raise", "warn", "ignore", "error"),
  create_report = FALSE
)

Arguments

participant_dirs

Named paths, one folder per participant.

tz

IANA study timezone.

errors

Invalid-payload handling.

create_report

Whether to return the source audit.

Value

Raw events, or a raw-events/source-audit list.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
folders <- c(VP01 = file.path(fixture, "raw", "VP01"))
imported <- read_raw_logs_from_participant_dirs(folders, create_report = TRUE)
head(imported$source_audit)

Read a BioPsyKit-style saliva CSV

Description

Read a BioPsyKit-style saliva CSV

Usage

read_saliva(path, saliva_type = "cortisol")

Arguments

path

CSV containing exactly participant, sample, and one biomarker.

saliva_type

Biomarker column name.

Value

A tibble.

Read a flat CARWatch Study Manager export

Description

Read a flat CARWatch Study Manager export

Usage

read_study_manager_export(path, tz = "Europe/Berlin")

Arguments

path

Path to a Study Manager export.

tz

IANA study timezone.

Value

Complete carwatch_results.

Read canonical CARWatch Study Results

Description

Reads the three-header CSV produced by either carwatch-python or write_study_results(). The returned object preserves opaque identifiers, time zones, and column placement.

Usage

read_study_results(path, tz = "Europe/Berlin", simple = FALSE)

Arguments

path

Path to a three-header Study Results CSV.

tz

IANA study timezone.

simple

Return a display-only subset.

Value

A carwatch_results object.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
as_study_days(results)

Convert wide saliva features to an analysis-friendly long table

Description

Convert wide saliva features to an analysis-friendly long table

Usage

saliva_feature_wide_to_long(data, saliva_type = "cortisol")

Arguments

data

Feature table returned by compute_features().

saliva_type

Biomarker prefix to select.

Value

A tibble with saliva_feature and biomarker value columns.

Convert clock-time sample columns to minutes from the first sample

Description

Convert clock-time sample columns to minutes from the first sample

Usage

sample_times_datetime_to_minute(data, sample_cols = NULL)

Arguments

data

A data frame whose selected columns contain HH:MM or HH:MM:SS values.

sample_cols

Sample-time columns; all columns are used by default.

Value

A tibble of numeric minute offsets.

Compute the slope between two saliva samples

Description

Compute the slope between two saliva samples

Usage

slope(
  data,
  sample_labels = NULL,
  sample_idx = NULL,
  saliva_type = "cortisol",
  sample_times = NULL
)

Arguments

data

Saliva data or canonical results.

sample_labels

Two sample labels.

sample_idx

Two one-based sample indices.

saliva_type

Measurement column name(s).

sample_times

Sampling-time vector or column name.

Value

Group-level slope values.

Summarize descriptive saliva features

Description

Summarize descriptive saliva features

Usage

standard_features(
  data,
  saliva_type = "cortisol",
  group_cols = NULL,
  keep_index = TRUE
)

Arguments

data

Saliva data or canonical results.

saliva_type

Measurement column name(s).

group_cols

Grouping columns.

keep_index

Retained for API compatibility.

Value

Descriptive saliva features.

Summarize sampling compliance

Description

Summarize sampling compliance

Usage

summarize_compliance(data, group_by = "sample_position")

Arguments

data

Canonical results or a sample-event tibble.

group_by

Grouping column(s), or NULL for a cohort total.

Value

A tibble with assessed, compliant, non-compliant, and unassessed counts.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
summarize_compliance(results)

Summarize the reconstructed study protocol

Description

Summarize the reconstructed study protocol

Usage

summarize_protocol(
  raw_logs,
  protocol_manifest = NULL,
  errors = c("raise", "warn", "error")
)

Arguments

raw_logs

Immutable events returned by read_raw_logs().

protocol_manifest

Optional ordered registration configuration.

errors

Unresolved-issue handling.

Value

A registration-level tibble.

Compactly summarize a source audit

Description

Compactly summarize a source audit

Usage

summarize_source_audit(source_audit)

Arguments

source_audit

Source audit returned by read_raw_logs_from_participant_dirs().

Value

A one-column tibble with three values named raw_log_import: the number of selected logs, the total raw events in selected logs, and the number of distinct participants, in that order.

Write an editable conversion report

Description

Write an editable conversion report

Usage

write_conversion_report(report, path)

Arguments

report

A conversion report list or its issue table.

path

Destination CSV path.

Value

path, invisibly.

Write canonical CARWatch Study Results

Description

Write canonical CARWatch Study Results

Usage

write_study_results(data, path)

Arguments

data

Complete carwatch_results object.

path

Destination CSV path.

Value

path, invisibly. The function writes the canonical Study Results CSV and returns its absolute path.

Examples

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
results <- read_study_results(file.path(fixture, "results.csv"))
output <- tempfile(fileext = ".csv")
write_study_results(results, output)
restored <- read_study_results(output)