Package 'dqcheckr'

Description

For each column that has allowed_values configured in config$column_rules, returns a dq_result flagging any non-empty values not in the allowed list. Returns an empty list when no allowed_values rules are configured.

Usage

check_allowed_values(df, config)

Arguments

Value

A list of dq_result objects, one per configured column. Status is "FAIL" when unexpected values are found; "PASS" otherwise. Returns an empty list if no allowed_values rules are configured.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_allowed_values(df, cfg)

Description

Returns a single "INFO" dq_result recording the number of columns in the data frame. Never fails or warns.

Usage

check_col_count(df, config)

Arguments

Value

A list containing one dq_result with status "INFO".

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_col_count(df, cfg)

Description

For each column whose resolved type is "character", returns one "INFO" dq_result with the count of distinct non-empty values. Columns inferred as numeric or date are silently skipped.

Usage

check_distinct_counts(df, config, types = NULL)

Arguments

Value

A list of dq_result objects (one per character column), all with status "INFO". Returns an empty list if no character columns are found.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_distinct_counts(df, cfg)

Description

Returns a single dq_result for the whole table. A row is considered a duplicate when every column value is identical to another row.

Usage

check_duplicate_rows(df, config)

Arguments

Value

A list containing one dq_result. Status is "WARN" if any duplicate rows exist; "PASS" otherwise.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_duplicate_rows(df, cfg)

Description

Returns a dq_result per column. A column is considered empty when every value is NA or the empty string "".

Usage

check_empty_column(df, config)

Arguments

Value

A list of dq_result objects, one per column. Status is "FAIL" for entirely empty columns; "PASS" otherwise.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_empty_column(df, cfg)

Description

Verifies that the delivered file's bytes matched the encoding declared in the config. read_dataset scans the whole file for UTF-8 validity before parsing (when the effective encoding is UTF-8) and records the outcome on the returned data frame; this check turns that outcome into a result:

• PASS when the file was valid UTF-8 as declared, or when a declared single-byte encoding (e.g. ISO-8859-1, Windows-1252) made a validity scan meaningless – every byte sequence is valid in those encodings by construction.

• FAIL when the file was not valid UTF-8 as declared. The run still completes: the file is read using a single-byte fallback encoding, and the message reports the detector's best guess at the actual encoding so the config can be corrected.

• WARN when the declared encoding is multi-byte or unknown (e.g. UTF-16LE, Shift-JIS): dqcheckr scans only UTF-8, so such a file is read as declared but its validity is not verified – it is never reported as "valid by construction".

• WARN when the UTF-8 scan itself could not complete (for example out of memory on a very large delivery): validity is unknown, so it is neither a clean PASS nor a definitive FAIL.

A supplier can change their export encoding between deliveries, which is why this runs against every delivery rather than only at configuration time. Returns an empty list when df did not come from read_dataset (no scan outcome to report).

Usage

check_file_encoding(df, config)

Arguments

Value

A list with one dq_result object, or an empty list when no scan outcome is attached to df.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_file_encoding(df, cfg)

Description

Returns one "INFO" dq_result per column recording the type resolved by resolve_col_type ("date", "numeric", "character", or "unknown"). Per-column overrides from config$column_types are respected.

Usage

check_inferred_types(df, config, types = NULL)

Arguments

Value

A list of dq_result objects, one per column, all with status "INFO".

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_inferred_types(df, cfg)

Description

Checks that the column(s) listed in config$key_columns have no duplicate values. When key_columns is a single string, one result is returned for that column. When it is a character vector of length > 1, a single result covering the composite key is returned. Returns an empty list if key_columns is not configured.

Usage

check_key_uniqueness(df, config)

Arguments

Value

A list of dq_result objects. Status is "FAIL" when duplicates or missing key columns are detected; "PASS" otherwise. Returns an empty list if key_columns is not configured.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_key_uniqueness(df, cfg)

Description

Runs up to four sub-checks, each returning a separate dq_result:

1. Empty file – FAIL when the file contains no data rows at all. Emitted unconditionally (independent of min_row_count) so that an empty delivery always fails the run.

2. File size – only when file_path is supplied and max_file_size_mb is configured in rules: FAIL if the file exceeds the size limit.

3. Minimum row count – FAIL if row_count < min_row_count. Skipped (PASS with a note) when min_row_count is 0.

4. Maximum row count – only when max_row_count is configured in rules: FAIL if row_count > max_row_count.

Usage

check_min_row_count(df, config, file_path = NULL)

Arguments

Value

A list of dq_result objects (one to four entries depending on which sub-checks are active).

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_min_row_count(df, cfg, file_path = path)

Description

Returns a dq_result per column flagging columns whose proportion of missing or empty values exceeds max_missing_rate.

Usage

check_missing_rate(df, config)

Arguments

Value

A list of dq_result objects, one per column.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_missing_rate(df, cfg)

Description

For each column whose resolved type is "numeric", computes the proportion of non-empty values that cannot be coerced to numeric. Returns "FAIL" when the rate exceeds max_non_numeric_rate (default 0.01), "WARN" when it exceeds warn_non_numeric_rate (default 0), and "PASS" otherwise. Both thresholds support per-column overrides via config$column_rules.

Usage

check_non_numeric(df, config, types = NULL)

Arguments

Value

A list of dq_result objects, one per numeric column. Returns an empty list if no numeric columns are found.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_non_numeric(df, cfg)

Description

For each column that has min_value or max_value configured in config$column_rules, returns a dq_result flagging any values that fall outside the specified range. Returns an empty list when no bound rules are configured.

Usage

check_numeric_bounds(df, config)

Arguments

Value

A list of dq_result objects, one per configured column. Status is "FAIL" when out-of-range values are found; "PASS" otherwise. Returns an empty list if no bound rules are configured.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_numeric_bounds(df, cfg)

Description

For each column whose resolved type is "numeric", returns one "INFO" dq_result containing min, max, mean, and standard deviation of the parseable values. Columns inferred as non-numeric are silently skipped.

Usage

check_numeric_stats(df, config, types = NULL)

Arguments

Value

A list of dq_result objects (one per numeric column), all with status "INFO". Returns an empty list if no numeric columns are found.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_numeric_stats(df, cfg)

Description

For each column whose resolved type is "numeric", applies up to two outlier detection methods (combined with logical OR):

1. Z-score: values whose absolute Z-score exceeds max_z_score are flagged.

2. IQR fence: values below Q1 - k * IQR or above Q3 + k * IQR (where k = iqr_fence_multiplier) are flagged.

Both thresholds support per-column overrides via config$column_rules. A column is skipped (PASS with a note) when neither threshold is configured or when it has fewer than four parseable values.

Usage

check_outliers(df, config, types = NULL)

Arguments

Value

A list of dq_result objects, one per numeric column. Status is "FAIL" when outliers are detected; "PASS" otherwise. Returns an empty list if no numeric columns are found.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_outliers(df, cfg)

Description

For each column that has a pattern configured in config$column_rules, returns a dq_result reporting how many non-empty values do not match the Perl-compatible regular expression. Returns an empty list when no pattern rules are configured.

Usage

check_pattern(df, config)

Arguments

Value

A list of dq_result objects, one per configured column. Status is "FAIL" when any values violate the pattern; "PASS" otherwise. Returns an empty list if no pattern rules are configured.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_pattern(df, cfg)

Description

Returns a single "INFO" dq_result recording the number of rows in the data frame. Never fails or warns; use check_min_row_count for threshold-based row count checks.

Usage

check_row_count(df, config)

Arguments

Value

A list containing one dq_result with status "INFO".

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_row_count(df, cfg)

Description

Compares the columns present in df against config$expected_columns:

• SC-01: one "FAIL" result per column present in the file but not listed in expected_columns.

• SC-02: one "FAIL" result per column listed in expected_columns but absent from the file.

Returns an empty list if expected_columns is not configured.

Usage

check_schema_contract(df, config)

Arguments

Value

A list of dq_result objects. Each schema violation produces one "FAIL" result; a "PASS" result is emitted for each sub-check when no violations are found. Returns an empty list if expected_columns is not configured.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)
check_schema_contract(df, cfg)

Description

Reads two historical snapshot records (by ID) from the SQLite database and computes table-level, schema, and per-column statistical drift. Optionally renders an HTML drift report.

Usage

compare_snapshots(
  dataset_name,
  snapshot_id_prev = NULL,
  snapshot_id_curr = NULL,
  db_path = NULL,
  config_dir = ".",
  report = TRUE,
  open_report = interactive()
)

Arguments

Value

Invisibly, a named list with elements dataset_name, snap_prev, snap_curr, table_drift, schema_changes, missing_rate_changes, non_numeric_changes, mean_shifts, distinct_changes, and report_path (the full path to the rendered HTML drift report, or NULL when no report was written). Callers should use report_path rather than reconstructing the filename from a pattern.

Note

As with run_dq_check, a relative snapshot_db or report_output_dir from the config resolves against the R process's working directory, not against config_dir.

Examples

tmp     <- tempdir()
db_path <- file.path(tmp, "snap.sqlite")
cfg_yml <- file.path(tmp, "dqcheckr.yml")
ds_yml  <- file.path(tmp, "starwars_csv.yml")
dat     <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
writeLines(c(
  paste0('snapshot_db: "', db_path, '"'),
  paste0('report_output_dir: "', tmp, '"'),
  'default_rules:',
  '  max_missing_rate: 0.60',
  '  min_row_count: 80'
), cfg_yml)
writeLines(c(
  'dataset_name: "starwars_csv"',
  paste0('current_file: "', dat, '"'),
  'format: csv',
  'encoding: "UTF-8"',
  'delimiter: ","'
), ds_yml)
run_dq_check("starwars_csv", config_dir = tmp, open_report = FALSE)
run_dq_check("starwars_csv", config_dir = tmp, open_report = FALSE)
drift <- compare_snapshots("starwars_csv", config_dir = tmp, report = FALSE)
names(drift)

Description

Resolves the current and previous file paths from the configuration. If current_file is set explicitly, it is used directly. Otherwise the two most recently modified files in folder are used.

Usage

detect_files(config)

Arguments

Value

A named list with elements current (character path) and previous (character path or NULL).

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg <- load_config("starwars_csv", config_dir = cfg_dir)
cfg$current_file <- system.file("demonstrations/data/starwars.csv",
                                 package = "dqcheckr")
files <- detect_files(cfg)
files$current

Description

Creates the atomic result unit returned by every check function.

Usage

dq_result(
  check_id,
  check_name,
  column = NA_character_,
  status,
  observed,
  threshold = NA_character_,
  message
)

Arguments

Value

A named list with seven elements: check_id, check_name, column, status, observed, threshold, message.

Examples

dq_result("QC-01", "Missing rate", column = "age",
          status = "PASS", observed = "0% missing",
          message = "No missing values.")

Description

Classifies a character vector as "date", "numeric", "character", or "unknown" by applying rules in priority order.

Usage

infer_col_type(x, threshold = 0.9)

Arguments

Details

Date formats are tried in this fixed precedence order: "%Y-%m-%d", "%d/%m/%Y", "%m/%d/%Y", "%Y%m%d", "%d-%m-%Y". A column is classified as "date" only when every non-empty value both matches that format's exact character shape and parses as a valid calendar date; a single malformed date therefore flips the whole column to "numeric" or "character" (such flips between deliveries are surfaced by check CP-02c). The shape is anchored, so a value with trailing characters ("2024-01-15x") or extra digits (the 9-digit "202401159") is not treated as a date. Two caveats follow from the precedence rules: ambiguous day/month values resolve day-first ("%d/%m/%Y" is tried before "%m/%d/%Y"), and all-8-digit identifier columns whose values happen to be valid "%Y%m%d" dates classify as dates. Pin the type with an entry in the column_types config map when the heuristic gets a column wrong.

Value

A single character string: "date", "numeric", "character", or "unknown".

Examples

infer_col_type(c("2024-01-01", "2024-06-15"))   # "date"
infer_col_type(c("1.5", "2.0", "3.1"))          # "numeric"
infer_col_type(c("high", "low", "medium"))       # "character"
infer_col_type(c(NA, "", NA))                    # "unknown"
infer_col_type(c(rep("1", 17), "a", "b", "c"), threshold = 0.80)  # "numeric"

Description

Returns a data frame of snapshot records for the given dataset (or all datasets if dataset_name is NULL), ordered by dataset name and snapshot ID.

Usage

list_snapshots(dataset_name = NULL, db_path = NULL)

Arguments

Value

A data frame with columns id, dataset_name, file_name, run_timestamp, row_count, overall_status. Returns an empty data frame if the database does not exist or contains no matching records.

Examples

list_snapshots(db_path = tempfile(fileext = ".sqlite"))

Description

Reads the global dqcheckr.yml and the dataset-specific YAML, merging rule_overrides from the dataset config on top of default_rules from the global config. Top-level keys snapshot_db and report_output_dir are inherited from the global config when absent from the dataset config.

Usage

load_config(dataset_name, config_dir)

Arguments

Value

A named list representing the merged configuration.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg <- load_config("starwars_csv", config_dir = cfg_dir)
cfg$format

Description

Returns the single worst status in precedence order: "FAIL" > "WARN" > "PASS" > "INFO".

Usage

overall_status(results)

Arguments

Value

A single character string: "FAIL", "WARN", "PASS", or "INFO".

Examples

r1 <- dq_result("QC-01", "test", status = "PASS", observed = "ok", message = "ok")
r2 <- dq_result("QC-02", "test", status = "WARN", observed = "ok", message = "ok")
overall_status(list(r1, r2))  # "WARN"

Description

Reads a CSV or fixed-width file, coercing all columns to character and trimming whitespace. Encoding and delimiter are taken from config. A declared encoding of ASCII (or a formal alias such as US-ASCII) is read as UTF-8: ASCII is a strict subset of UTF-8, so this is lossless, and it protects against deliveries whose non-ASCII bytes appear beyond any sample a sniffer looked at. When the effective encoding is UTF-8 the whole file is validity-scanned before parsing; a delivery that is not valid UTF-8 is read using a single-byte fallback encoding instead, and the mismatch is surfaced by check_file_encoding (QC-16) as a FAIL result rather than crashing the run.

Usage

read_dataset(path, config)

Arguments

Value

A data frame with all columns as character vectors.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg  <- load_config("starwars_csv", config_dir = cfg_dir)
path <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df   <- read_dataset(path, cfg)

Description

Retrieves the n most recent run records for a given dataset from the snapshot database, ordered newest-first.

Usage

read_recent_snapshots(db_path, dataset_name, n = 10)

Arguments

Value

A data frame with one row per run and columns including id, dataset_name, run_timestamp, file_name, row_count, col_count, overall_status, check_pass_count, check_warn_count, check_fail_count, check_info_count, new_cols_vs_previous, missing_cols_vs_previous, new_cols_vs_schema, missing_cols_vs_schema, comparison_mode, render_status, type_changed_cols_vs_previous, and report_file (the rendered report's filename, NA for snapshots written before dqcheckr 0.2.3). render_status is one of "pending" (0.2.5+: the row was written but its report has not finished rendering yet – report_file is NA in this window), "success" (report written; report_file names it), or "failed" (render skipped or errored; report_file is NA). Consumers linking to a report should treat a "pending" row as not-yet-available rather than reconstructing a filename for a report that does not exist. Returns an empty data frame with the same columns if the database does not exist or contains no records for the dataset. If the database exists but cannot be read (corrupt file, permissions, an unresolved lock), it emits a warning naming the cause and returns the same empty data frame, so a read failure is visible rather than masquerading as an empty history.

Examples

history <- read_recent_snapshots(tempfile(fileext = ".sqlite"), "starwars_csv")

Description

Returns the type for col from the column_types map in config if one is set, otherwise falls back to infer_col_type. Use this in custom check scripts instead of calling infer_col_type() directly so that type overrides are respected.

Usage

resolve_col_type(col, x, config)

Arguments

Value

A single character string: "date", "numeric", "character", or "unknown".

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg <- load_config("starwars_csv", config_dir = cfg_dir)
resolve_col_type("name", c("Luke", "Leia", "Han"), cfg)   # "character"

Description

Runs CP-01 to CP-08 comparing a current delivery against the previous one.

Usage

run_comparison_checks(
  df_current,
  df_previous,
  config,
  types_current = NULL,
  types_previous = NULL
)

Arguments

Value

A list of dq_result objects. The list carries attributes new_cols, dropped_cols, and type_changed_cols (character vectors) for use by the snapshot writer.

Examples

cfg_dir   <- system.file("demonstrations/config", package = "dqcheckr")
cfg       <- load_config("starwars_csv", config_dir = cfg_dir)
curr_path <- system.file("demonstrations/data2/starwars_v2.csv", package = "dqcheckr")
prev_path <- system.file("demonstrations/data2/starwars_v1.csv", package = "dqcheckr")
curr      <- read_dataset(curr_path, cfg)
prev      <- read_dataset(prev_path, cfg)
results   <- run_comparison_checks(curr, prev, cfg)

Description

Sources the R file specified by config$custom_checks_file, which must define a function custom_checks(df) returning a list of dq_result objects. Returns an empty list if custom_checks_file is not set in the config.

Usage

run_custom_checks(df, config)

Arguments

Details

The file is sourced into an isolated environment whose parent is baseenv(), so only base R functions are available by default. dq_result is explicitly injected and can be called without qualification. All other dqcheckr exports (e.g. resolve_col_type, infer_col_type) must be qualified: dqcheckr::resolve_col_type(). Any error – missing file, undefined function, runtime failure, or a malformed result element (each element must have the seven dq_result fields and a valid status) – stops the run with a clear message.

Value

A list of dq_result objects (may be empty).

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg     <- load_config("starwars_csv", config_dir = cfg_dir)
path    <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df      <- read_dataset(path, cfg)
results <- run_custom_checks(df, cfg)

Description

Orchestrates the complete dqcheckr pipeline: loads configuration, detects files, runs QC and comparison checks, writes a snapshot to SQLite, and renders an HTML report.

Usage

run_dq_check(dataset_name, config_dir = ".", open_report = TRUE)

Arguments

Value

Invisibly, a named list with:

status

Overall status string: "PASS", "WARN", "FAIL", or "INFO".

report_path

Absolute path to the rendered HTML report, or NULL if rendering was skipped.

snapshot_id

Integer row ID of the snapshot written to SQLite, or NULL if the write failed.

Note

Relative snapshot_db and report_output_dir config values resolve against the R process's working directory, not against config_dir. Run from the deployment root (the directory containing config/, data/, reports/) or use absolute paths in the config; otherwise a fresh snapshot database is silently created wherever the process happens to be running.

Examples

tmp <- gsub("\\\\", "/", tempdir())
dat <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
writeLines(c(
  paste0('snapshot_db: "',       tmp, '/snap.sqlite"'),
  paste0('report_output_dir: "', tmp, '"'),
  'default_rules:',
  '  max_missing_rate: 0.60',
  '  min_row_count: 80'
), file.path(tmp, "dqcheckr.yml"))
writeLines(c(
  'dataset_name: "starwars_csv"',
  paste0('current_file: "', dat, '"'),
  'format: csv',
  'encoding: "UTF-8"',
  'delimiter: ","'
), file.path(tmp, "starwars_csv.yml"))
result <- run_dq_check("starwars_csv", config_dir = tmp, open_report = FALSE)
result$status

Description

Runs the full QC check suite (QC-01 to QC-16, SC-01, SC-02) against a single data frame snapshot.

Usage

run_qc_checks(df, config, file_path = NULL, types = NULL)

Arguments

Value

A list of dq_result objects.

Examples

cfg_dir <- system.file("demonstrations/config", package = "dqcheckr")
cfg     <- load_config("starwars_csv", config_dir = cfg_dir)
path    <- system.file("demonstrations/data/starwars.csv", package = "dqcheckr")
df      <- read_dataset(path, cfg)
results <- run_qc_checks(df, cfg)