Package {imedR}

Create a manuscript/synthesis-ready analysis set

Description

This is the preferred entry point for quantitative analysis.

Usage

imed_analysis_set(
  x,
  comparability_class,
  matrix = NULL,
  mass_basis = NULL,
  metric_type = "Abundance",
  include_holds = FALSE
)

Arguments

Value

A 'tibble' of the selected measurement records that passed the comparability check. It has the additional class 'imed_analysis_set' and the attribute 'comparability_class'. The function stops with an informative message if the selected records are not comparable.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
aset <- imed_analysis_set(toy, comparability_class = "CLASS_A")
class(aset)
nrow(aset)

Check scientific comparability of an analysis subset

Description

Check scientific comparability of an analysis subset

Usage

imed_check_comparability(
  x,
  action = c("error", "warn", "return"),
  require_single_unit = TRUE
)

Arguments

Value

A 'tibble' with columns 'comparable' (logical) and 'issue' (character; '"None"' when no problem is found) if 'action = "return"'. For 'action = "error"' or '"warn"' the function returns invisibly a single logical value ('TRUE' if the records are comparable) and stops or warns when they are not.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
imed_check_comparability(toy, action = "return")

Guard analytical size-window compatibility

Description

Uses linked SIZE_METHODS records to identify unresolved or heterogeneous analytical/reporting lower bounds. It never substitutes mesh/filter pore size for an analytical lower size limit.

Usage

imed_check_size_window(
  x,
  sample_id = NULL,
  action = c("error", "warn", "return")
)

Arguments

Value

A 'tibble' with columns 'compatible' (logical) and 'issue' (character; '"None"' when no problem is found) if 'action = "return"'. For 'action = "error"' or '"warn"' the function returns invisibly a single logical value ('TRUE' if the size windows are compatible) and stops or warns when they are not.

Examples

db <- imed_data(tables = "SIZE_METHODS", validate = FALSE)
imed_check_size_window(db, action = "return")

Primary study citations for a subset

Description

Primary study citations for a subset

Usage

imed_cite(x, study_id = NULL)

Arguments

Value

A 'tibble' with one row per study and the citation fields that are available ('Study_ID', 'Authors', 'Year', 'Title', 'Journal', 'DOI').

Examples

db <- imed_data(tables = "STUDIES", validate = FALSE)
head(imed_cite(db))

Grouped comparison summary

Description

Grouped comparison summary

Usage

imed_compare(x, group, value = "Mean")

Arguments

Value

A 'tibble' of descriptive statistics with one row per group, as returned by 'imed_describe()' with a 'group' argument.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
imed_compare(toy, group = "Group")

Load the bundled frozen IMED v1.0 analytical snapshot

Description

The package exposes 19 core scientific/schema tables. Internal extraction, workflow and QC-log sheets are intentionally not part of the analytical API.

Usage

imed_data(tables = NULL, validate = TRUE)

Arguments

Value

An object of class 'c("imed_db", "list")': a named list with one 'tibble' for each requested table. The attribute 'imed_release' holds the data release ('"1.0"') and, when 'validate = TRUE', the attribute 'validation' holds the 'tibble' returned by 'imed_validate()'.

Examples

db <- imed_data(tables = c("STUDIES", "SITES"), validate = FALSE)
class(db)
names(db)

Descriptive statistics for a guarded analysis set

Description

Descriptive statistics for a guarded analysis set

Usage

imed_describe(x, value = "Mean", group = NULL)

Arguments

Value

A 'tibble' with the columns 'n' (number of finite values), 'mean', 'sd', 'median', 'q1', 'q3', 'min' and 'max'. If 'group' is supplied there is one row per group, with the grouping field as the first column.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
imed_describe(toy)
imed_describe(toy, group = "Group")

Join measurement records to their scientific comparability metadata

Description

Join measurement records to their scientific comparability metadata

Usage

imed_enrich_measurements(x)

Arguments

Value

A 'tibble' containing the measurement records joined, by 'Measurement_ID', to their comparability metadata. Columns that occur in both tables carry the suffix '.cmp' in the comparability copy.

Examples

db <- imed_data(tables = c("MEASUREMENTS", "MEASUREMENT_COMPARABILITY"),
                validate = FALSE)
head(imed_enrich_measurements(db))

Evidence-quality/reporting profile

Description

Evidence-quality/reporting profile

Usage

imed_evidence_profile(x)

Arguments

Value

A one-row 'tibble' with the number of QA/QC records ('n_records') and the numbers that report a procedural blank ('procedural_blank_yes'), a recovery test ('recovery_test_reported') and chemical confirmation ('chemical_confirmation_reported').

Examples

db <- imed_data(tables = "METHODS_QAQC", validate = FALSE)
imed_evidence_profile(db)

Export a publication figure

Description

Export a publication figure

Usage

imed_export_figure(
  plot,
  filename,
  width = 180,
  height = 120,
  units = "mm",
  dpi = 600
)

Arguments

Value

The path 'filename', returned invisibly. The function is called for its side effect of writing the figure to disk.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
p <- imed_plot(toy, type = "histogram")
out <- tempfile(fileext = ".png")
imed_export_figure(p, out, width = 90, height = 60, dpi = 100)
file.exists(out)
unlink(out)

Filter IMED with scientific guard metadata retained

Description

Filter IMED with scientific guard metadata retained

Usage

imed_filter(
  x,
  matrix = NULL,
  comparability_class = NULL,
  mass_basis = NULL,
  metric_type = NULL,
  study_id = NULL,
  include_holds = FALSE
)

Arguments

Value

A 'tibble' of the records that satisfy all supplied filters. If 'x' is an 'imed_db', the result is the measurement-level table with comparability metadata and two added columns: '.imed_hold' (logical) and '.imed_hold_reason' (character).

Examples

db <- imed_data(tables = c("MEASUREMENTS", "MEASUREMENT_COMPARABILITY"),
                validate = FALSE)
nrow(imed_filter(db))
nrow(imed_filter(db, include_holds = TRUE))

Flag measurement-level scientific HOLDs

Description

Flag measurement-level scientific HOLDs

Usage

imed_flag_holds(x)

Arguments

Value

A 'tibble' of measurement records, with comparability metadata when 'x' is an 'imed_db', and two added columns: '.imed_hold' (logical, 'TRUE' for records that must not enter strict synthesis) and '.imed_hold_reason' (character, the reason or 'NA').

Examples

db <- imed_data(tables = c("MEASUREMENTS", "MEASUREMENT_COMPARABILITY"),
                validate = FALSE)
h <- imed_flag_holds(db)
table(h$.imed_hold)

Forest plot for an imedR meta-analysis

Description

Forest plot for an imedR meta-analysis

Usage

imed_forest(fit, ...)

Arguments

Value

The fitted model 'fit', returned invisibly. The function is called for its side effect of drawing a forest plot on the active graphics device.

Examples

if (requireNamespace("metafor", quietly = TRUE)) {
  toy <- data.frame(
    Study_ID = paste0("S", 1:12),
    Comparability_Class = "CLASS_A",
    Reported_Unit = "items/L",
    Metric_Type = "Abundance",
    Strict_Synthesis_Eligibility = "YES",
    Group = rep(c("g1", "g2"), 6),
    Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
    SD = rep(c(0.6, 0.9), 6),
    N = rep(c(10, 12, 8), 4)
  )
  fit <- imed_meta_mean(toy)
  imed_forest(fit)
}

Funnel plot for an imedR meta-analysis

Description

A funnel plot is diagnostic only and should not be interpreted as a direct test of publication bias, especially with small or heterogeneous evidence.

Usage

imed_funnel(fit, ...)

Arguments

Value

The fitted model 'fit', returned invisibly. The function is called for its side effect of drawing a funnel plot on the active graphics device.

Examples

if (requireNamespace("metafor", quietly = TRUE)) {
  toy <- data.frame(
    Study_ID = paste0("S", 1:12),
    Comparability_Class = "CLASS_A",
    Reported_Unit = "items/L",
    Metric_Type = "Abundance",
    Strict_Synthesis_Eligibility = "YES",
    Group = rep(c("g1", "g2"), 6),
    Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
    SD = rep(c(0.6, 0.9), 6),
    N = rep(c(10, 12, 8), 4)
  )
  fit <- imed_meta_mean(toy)
  imed_funnel(fit)
}

Leave-one-study-out influence analysis for a guarded mean meta-analysis

Description

Leave-one-study-out influence analysis for a guarded mean meta-analysis

Usage

imed_leave_one_out(x, method = "REML")

Arguments

Value

A 'tibble' with one row per omitted study. The first column, 'omitted', identifies the study that was left out; the remaining columns are the pooled estimate and heterogeneity statistics returned by 'metafor::leave1out()' after that study is removed.

Examples

if (requireNamespace("metafor", quietly = TRUE)) {
  toy <- data.frame(
    Study_ID = paste0("S", 1:12),
    Comparability_Class = "CLASS_A",
    Reported_Unit = "items/L",
    Metric_Type = "Abundance",
    Strict_Synthesis_Eligibility = "YES",
    Group = rep(c("g1", "g2"), 6),
    Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
    SD = rep(c(0.6, 0.9), 6),
    N = rep(c(10, 12, 8), 4)
  )
  imed_leave_one_out(toy)
}

Study-characteristics table for manuscripts

Description

Study-characteristics table for manuscripts

Usage

imed_manuscript_table(x, study_id = NULL)

Arguments

Value

A 'tibble' with one row per study and the descriptive fields that are available ('Study_ID', 'Authors', 'Year', 'States_UTs', 'Matrices_Studied', 'Journal', 'DOI', 'Entry_Status').

Examples

db <- imed_data(tables = "STUDIES", validate = FALSE)
head(imed_manuscript_table(db))

Coordinate-provenance-aware IMED map

Description

Coordinate-provenance-aware IMED map

Usage

imed_map(x, geo_min = c("GEO-4", "GEO-3", "GEO-2"), study_id = NULL)

Arguments

Value

A 'ggplot' object with one point for each site that has numeric coordinates and a retained coordinate class. The function stops if the suggested package 'sf' is not installed.

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  db <- imed_data(tables = c("SITES", "SITE_COORDINATE_CLASS"),
                  validate = FALSE)
  imed_map(db)
}

Evaluate eligibility for quantitative evidence synthesis

Description

Checks class/unit/basis, usable uncertainty, and study-level independence.

Usage

imed_meta_eligibility(x, require_independent_studies = TRUE)

Arguments

Value

A 'tibble' with columns 'eligible' (logical) and 'issue' (character). The value '"None"' in 'issue' means that no problem was found; otherwise each row describes one reason why the records cannot enter a standard meta-analysis.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
imed_meta_eligibility(toy)

Random-effects meta-analysis of compatible raw means

Description

Intended only for one comparable outcome/unit/basis with independent study effects. Requires metafor.

Usage

imed_meta_mean(x, method = "REML")

Arguments

Value

A fitted random-effects model of class 'c("rma.uni", "rma")' from 'metafor::rma.uni()', using the study means as effects and the squared standard error of each mean as its variance. The pooled estimate is on the original measurement scale.

Examples

if (requireNamespace("metafor", quietly = TRUE)) {
  toy <- data.frame(
    Study_ID = paste0("S", 1:12),
    Comparability_Class = "CLASS_A",
    Reported_Unit = "items/L",
    Metric_Type = "Abundance",
    Strict_Synthesis_Eligibility = "YES",
    Group = rep(c("g1", "g2"), 6),
    Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
    SD = rep(c(0.6, 0.9), 6),
    N = rep(c(10, 12, 8), 4)
  )
  fit <- imed_meta_mean(toy)
  fit
}

Random-effects meta-analysis of prevalence

Description

Uses BIOTA_CONTEXT N_Positive/N_Examined and a logit transformed proportion. Multiple rows per Study_ID are blocked unless the user preselects one independent effect per study.

Usage

imed_meta_prevalence(x, study_id = NULL, method = "REML")

Arguments

Value

A fitted random-effects model of class 'c("rma.uni", "rma")' from 'metafor::rma.uni()'. Effects are logit-transformed proportions, so the pooled estimate is on the log-odds scale; use 'plogis()' to back-transform it to a proportion.

Examples

if (requireNamespace("metafor", quietly = TRUE)) {
  biota <- data.frame(
    Sample_ID = paste0("SA", 1:6),
    N_Positive = c(5, 12, 3, 20, 8, 15),
    N_Examined = c(30, 40, 25, 60, 35, 50)
  )
  fit <- imed_meta_prevalence(biota)
  plogis(coef(fit))
}

Meta-regression with minimum-information guard

Description

Requires at least 10 independent effects by default and at least 5 effects per model coefficient. These are guardrails, not guarantees of adequate power.

Usage

imed_meta_regression(x, moderators, min_k = 10, method = "REML")

Arguments

Value

A fitted random-effects meta-regression model of class 'c("rma.uni", "rma")' from 'metafor::rma.uni()', with the study identifiers as labels. The coefficients describe the change in the mean outcome associated with each moderator.

Examples

if (requireNamespace("metafor", quietly = TRUE)) {
  toy <- data.frame(
    Study_ID = paste0("S", 1:12),
    Comparability_Class = "CLASS_A",
    Reported_Unit = "items/L",
    Metric_Type = "Abundance",
    Strict_Synthesis_Eligibility = "YES",
    Group = rep(c("g1", "g2"), 6),
    Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
    SD = rep(c(0.6, 0.9), 6),
    N = rep(c(10, 12, 8), 4)
  )
  imed_meta_regression(toy, moderators = "Group")
}

Missingness profile for a table

Description

Missingness profile for a table

Usage

imed_missingness(x, table = "MEASUREMENTS")

Arguments

Value

A 'tibble' with one row per field and the columns 'field', 'n' (number of rows), 'missing' (number of missing or not-reported values) and 'missing_pct' (the percentage missing).

Examples

db <- imed_data(tables = "MEASUREMENTS", validate = FALSE)
head(imed_missingness(db, "MEASUREMENTS"))

Publication-oriented abundance/distribution figure

Description

Publication-oriented abundance/distribution figure

Usage

imed_plot(
  x,
  xvar = NULL,
  value = "Mean",
  type = c("box", "violin", "scatter", "histogram"),
  guard = TRUE
)

Arguments

Value

A 'ggplot' object that can be printed, modified with further 'ggplot2' layers or saved with 'imed_export_figure()'.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
imed_plot(toy, xvar = "Group", type = "box")
imed_plot(toy, type = "histogram")

Plot polymer or particle-characteristic composition

Description

Plot polymer or particle-characteristic composition

Usage

imed_plot_composition(
  x,
  what = c("polymers", "particles"),
  study_id = NULL,
  characteristic_type = NULL
)

Arguments

Value

A 'ggplot' object showing the reported values by polymer or particle category as horizontal bars.

Examples

db <- imed_data(tables = "POLYMERS", validate = FALSE)
imed_plot_composition(db, what = "polymers")

Plot method/QAQC reporting profile

Description

Plot method/QAQC reporting profile

Usage

imed_plot_methods(x)

Arguments

Value

A 'ggplot' object showing, for each QA/QC item, the number of records with each reported status (including '"Missing/NR"').

Examples

db <- imed_data(tables = "METHODS_QAQC", validate = FALSE)
imed_plot_methods(db)

Provenance records for studies or target records

Description

Provenance records for studies or target records

Usage

imed_provenance(x, study_id = NULL, record_id = NULL)

Arguments

Value

A 'tibble' with the provenance records (the PROVENANCE table), restricted to the requested studies or target records when these are supplied.

Examples

db <- imed_data(tables = "PROVENANCE", validate = FALSE)
head(imed_provenance(db))

Tidy a statistical result object for manuscript reporting

Description

Tidy a statistical result object for manuscript reporting

Usage

imed_results_table(x)

Arguments

Value

A 'tibble'. For an 'imed_stats' object, the data-frame components of the result are stacked and labelled in a 'section' column. For a 'metafor' 'rma' object, a single row with the pooled 'estimate', its standard error 'se', the confidence limits 'ci_lb' and 'ci_ub', the test statistic 'z', 'p_value', the heterogeneity measures 'tau2', 'I2' and 'H2', and the number of effects 'k'. Other objects cause an error.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
res <- imed_stats(toy, group = "Group", test = "welch")
imed_results_table(res)

Sensitivity summary by evidence restriction

Description

Compares all eligible records with a stricter subset when evidence fields are available. This is descriptive sensitivity analysis, not causal proof.

Usage

imed_sensitivity(x, value = "Mean")

Arguments

Value

A 'tibble' with columns 'stratum', 'n' (number of finite values), 'mean' and 'median'. The first row summarises all compatible records; a second row for strict-synthesis-eligible records is added when the field 'Strict_Synthesis_Eligibility' is present.

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
imed_sensitivity(toy)

Conservative inferential statistics

Description

Tests are explicit, never auto-selected from p-values or normality alone.

Usage

imed_stats(
  x,
  value = "Mean",
  group = NULL,
  test = c("none", "welch", "wilcoxon", "kruskal")
)

Arguments

Value

An object of class 'c("imed_stats", "list")' with the elements 'descriptive' (a 'tibble' from 'imed_describe()'), 'normality' (a 'tibble' with the Shapiro-Wilk test, present when 3 to 5000 finite values are available) and 'test' (a 'tibble' with 'method', 'statistic' and 'p_value', present when 'test' is not '"none"').

Examples

toy <- data.frame(
  Study_ID = paste0("S", 1:12),
  Comparability_Class = "CLASS_A",
  Reported_Unit = "items/L",
  Metric_Type = "Abundance",
  Strict_Synthesis_Eligibility = "YES",
  Group = rep(c("g1", "g2"), 6),
  Mean = c(2.1, 3.4, 1.8, 4.0, 2.9, 3.1, 2.4, 3.8, 1.9, 3.3, 2.7, 4.2),
  SD = rep(c(0.6, 0.9), 6),
  N = rep(c(10, 12, 8), 4)
)
imed_stats(toy, group = "Group", test = "welch")

List loaded IMED tables

Description

List loaded IMED tables

Usage

imed_tables(x = imed_data(validate = FALSE))

Arguments

Value

A 'tibble' with one row per table and the columns 'table' (table name), 'rows' (number of rows) and 'columns' (number of columns).

Examples

db <- imed_data(tables = c("STUDIES", "SITES"), validate = FALSE)
imed_tables(db)

IMED v1.0 bundled analytical tables

Description

The package snapshot contains the core scientific tables from the frozen IMED v1.0 release. Use 'imed_data()' rather than accessing CSV files directly. The relational backbone is STUDIES -> SITES -> SAMPLES -> MEASUREMENTS. Supporting tables preserve size-method hierarchy, particle characteristics, polymers, biota, processing, QA/QC, derived metrics, risk assessments, source discrepancies, provenance, comparability and coordinate classes.

Validate IMED core scientific and relational integrity

Description

Validate IMED core scientific and relational integrity

Usage

imed_validate(x, strict = FALSE)

Arguments

Value

A 'tibble' with one row per check and the columns 'check', 'severity' ('"ERROR"' or '"HOLD"'), 'passed' (logical), 'n_issues' (integer) and 'detail'. With 'strict = TRUE' the function stops if any ERROR-level check fails.

Examples

db <- imed_data(validate = FALSE)
v <- imed_validate(db)
table(v$severity, v$passed)

Validation report for manuscript/release audit

Description

Returns database integrity, HOLD burden, missingness and comparability counts.

Usage

imed_validation_report(x)

Arguments

Value

A named list with five elements: 'integrity' (the 'tibble' returned by 'imed_validate()'), 'cardinalities' (table, row and column counts from 'imed_tables()'), 'hold_summary' (a one-row 'tibble' with the number of measurements, the number flagged as HOLD and the number eligible), 'comparability' (counts of records by 'Comparability_Class' and 'Strict_Synthesis_Eligibility') and 'measurement_missingness' (the 'tibble' returned by 'imed_missingness()' for the measurements table).

Examples

db <- imed_data(validate = FALSE)
rep <- imed_validation_report(db)
names(rep)
rep$hold_summary

Package and targeted data-release version

Description

Package and targeted data-release version

Usage

imed_version()

Value

A named character vector with two elements: 'package' (the version of imedR) and 'imed_release' (the version of the bundled IMED data release).

Examples

imed_version()