hubValidations

https://hubverse-org.github.io/hubValidations/
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### Create and inspect a target_validations_collection

Source: https://hubverse-org.github.io/hubValidations/reference/new_target_validations_collection.html

Complete example showing how to create a `target_validations_collection` from two `target_validations` objects, print it, get element names, access a specific file's validations, and access a specific check. The output shows that empty collections print as `Empty <target_validations_collection>`, and that checks with hive-partitioned paths are skipped with an info message.

```r
new_target_validations_collection()
#> Empty <target_validations_collection>

# Create validations for two different files
file_path_1 <- "time-series.csv"
validations_1 <- new_target_validations(
  target_file_name = check_target_file_name(file_path_1),
  target_file_ext_valid = check_target_file_ext_valid(file_path_1)
)

file_path_2 <- "other-data.csv"
validations_2 <- new_target_validations(
  target_file_name = check_target_file_name(file_path_2),
  target_file_ext_valid = check_target_file_ext_valid(file_path_2)
)

# Combine into a collection
collection <- new_target_validations_collection(validations_1, validations_2)

# Print the collection
collection
#> 
#> ── time-series.csv ────
#> 
#> ℹ [target_file_name]: Target file path not hive-partitioned. Check skipped.
#> ✔ [target_file_ext_valid]: Target data file extension is valid.
#> 
#> ── other-data.csv ────
#> 
#> ℹ [target_file_name]: Target file path not hive-partitioned. Check skipped.
#> ✔ [target_file_ext_valid]: Target data file extension is valid.

# Get file paths (element names)
names(collection)
#> [1] "time-series.csv" "other-data.csv" 

# Access validations for a specific file
collection[[file_path_1]]
#> 
#> ── time-series.csv ────
#> 
#> ℹ [target_file_name]: Target file path not hive-partitioned. Check skipped.
#> ✔ [target_file_ext_valid]: Target data file extension is valid.

# Access a specific check within a file's validations
collection[["time-series.csv"]]$target_file_name
#> <message/check_info>
#> Message:
#> Target file path not hive-partitioned. Check skipped.
```

--------------------------------

### Install Air CLI on Windows (PowerShell)

Source: https://hubverse-org.github.io/hubValidations/CONTRIBUTING.html

Installs the Air CLI on Windows using PowerShell.

```powershell
powershell -ExecutionPolicy Bypass -c "irm https://github.com/posit-dev/air/releases/latest/download/air-installer.ps1 | iex"
```

--------------------------------

### Example usage with withr::with_tempdir()

Source: https://hubverse-org.github.io/hubValidations/reference/create_custom_check.html

Example usage of create_custom_check() within a temporary directory, as shown in the documentation examples. The code is incomplete in the source; only the opening line is displayed.

```r
withr::with_tempdir({

```

--------------------------------

### Install Air CLI on macOS/Linux

Source: https://hubverse-org.github.io/hubValidations/CONTRIBUTING.html

Installs the Air CLI on macOS or Linux using the official installer script.

```bash
curl -LsSf https://github.com/posit-dev/air/releases/latest/download/air-installer.sh | sh
```

--------------------------------

### Install Air CLI on macOS (Homebrew)

Source: https://hubverse-org.github.io/hubValidations/CONTRIBUTING.html

Installs the Air CLI on macOS using Homebrew.

```bash
brew install air
```

--------------------------------

### Install arrow from Apache R Universe

Source: https://hubverse-org.github.io/hubValidations/index.html

Alternative installation for the arrow package from the Apache R Universe repository. Use this if you encounter arrow installation problems, as hubValidations depends on arrow.

```r
install.packages("arrow", repos = c("https://apache.r-universe.dev", "https://cran.r-project.org"))
```

--------------------------------

### Example: opt_check_tbl_counts_lt_popn usage

Source: https://hubverse-org.github.io/hubValidations/reference/opt_check_tbl_counts_lt_popn.html

Example of using `opt_check_tbl_counts_lt_popn` with a test hub. It reads a model output file, filters to a specific target, and runs the check, expecting a success message.

```r
hub_path <- system.file("testhubs/flusight", package = "hubValidations")
file_path <- "hub-ensemble/2023-05-08-hub-ensemble.parquet"
tbl <- hubValidations::read_model_out_file(file_path, hub_path)
# Single target key list
targets <- list("target" = "wk ahead inc flu hosp")
opt_check_tbl_counts_lt_popn(tbl, file_path, hub_path, targets = targets)
#> <message/check_success>
#> Message:
#> Target counts are less than location population size.
```

--------------------------------

### Example: validate_model_metadata with output

Source: https://hubverse-org.github.io/hubValidations/reference/validate_model_metadata.html

Example call to validate_model_metadata on a test hub, showing the validation output including checks for schema, file existence, extension, location, and schema matching.

```r
hub_path <- system.file("testhubs/simple", package = "hubValidations")
validate_model_metadata(hub_path,
  file_path = "hub-baseline.yml"
)
#>
#> ── hub-baseline.yml ────
#>
#> ✔ [metadata_schema_exists]: Model metadata schema file exists at path
#>   hub-config/model-metadata-schema.json.
#> ✔ [metadata_file_exists]: File exists at path
#>   model-metadata/team1-goodmodel.yaml.
#> ✔ [metadata_file_ext]: Metadata file extension is "yml" or "yaml".
#> ✔ [metadata_file_location]: Metadata file directory name matches
#>   "model-metadata".
#> ✔ [metadata_matches_schema]: Metadata file contents are consistent with schema
#>   specifications.
#> ✔ [metadata_file_name]: Metadata file name matches the `model_id` specified
#>   within the metadata file.
validate_model_metadata(hub_path,
  file_path = "team1-goodmodel.yaml"
)
#> 
#> ── team1-goodmodel.yaml ────
#> 
#> ✔ [metadata_schema_exists]: Model metadata schema file exists at path
#>   hub-config/model-metadata-schema.json.
#> ✔ [metadata_file_exists]: File exists at path
#>   model-metadata/team1-goodmodel.yaml.
#> ✔ [metadata_file_ext]: Metadata file extension is "yml" or "yaml".
#> ✔ [metadata_file_location]: Metadata file directory name matches
#>   "model-metadata".
#> ⓧ [metadata_matches_schema]: Metadata file contents must be consistent with
#>   schema specifications.  - must have required property 'model_details' . -
#>   must NOT have additional properties; saw unexpected property
#>   'models_details'. - must NOT have additional properties; saw unexpected
#>   property 'ensemble_of_hub_models"'. - /include_ensemble must be boolean .
```

--------------------------------

### Example usage of get_config_derived_task_ids

Source: https://hubverse-org.github.io/hubValidations/reference/get_config_derived_task_ids.html

Example usage of get_config_derived_task_ids with a hub path and optional round_id, showing the returned character vector.

```r
hub_path <- system.file("testhubs/v4/flusight", package = "hubUtils")
config_tasks <- read_config(hub_path)
get_config_derived_task_ids(config_tasks)
#> [1] "target_date"
get_config_derived_task_ids(config_tasks, round_id = "2023-05-08")
#> [1] "target_date"
```

--------------------------------

### validate_model_file() example with missing file

Source: https://hubverse-org.github.io/hubValidations/reference/validate_model_file.html

Example validating a model output file that does not exist. The output shows a check_error for file_exists and the function returns early.

```r
validate_model_file(hub_path,
  file_path = "team1-goodmodel/2022-10-15-team1-goodmodel.csv"
)
#> 
#> ── team1-goodmodel/2022-10-15-team1-goodmodel.csv ────
#> 
#> ⓧ [file_exists]: File does not exist at path
#>   model-output/team1-goodmodel/2022-10-15-team1-goodmodel.csv.
```

--------------------------------

### Example: get_tbl_compound_taskid_set with sample hub

Source: https://hubverse-org.github.io/hubValidations/reference/get_tbl_compound_taskid_set.html

Example usage of get_tbl_compound_taskid_set with a sample hub. It reads a model output file as character, loads the tasks config, and shows the default compact output (only modeling task 2) and the non-compact output (including NULL for task 1).

```r
hub_path <- system.file("testhubs/samples", package = "hubValidations")
file_path <- "flu-base/2022-10-22-flu-base.csv"
round_id <- "2022-10-22"
tbl <- read_model_out_file(
  file_path = file_path,
  hub_path = hub_path,
  coerce_types = "chr"
)
config_tasks <- read_config(hub_path, "tasks")
get_tbl_compound_taskid_set(tbl, config_tasks, round_id)
#> $`2`
#> [1] "reference_date" "location"      

get_tbl_compound_taskid_set(tbl, config_tasks, round_id,
  compact = FALSE
)
#> $`1`
#> NULL
#> 
#> $`2`
#> [1] "reference_date" "location"      

```

--------------------------------

### expand_model_out_grid with sample output type and compound task ID structure

Source: https://hubverse-org.github.io/hubValidations/reference/expand_model_out_grid.html

These examples demonstrate how to use expand_model_out_grid() with a config file that has a sample output type and a compound task ID structure. The first example uses the default compound task ID set, the second overrides it with a coarser set for the first modeling task, and the third sets both compound task ID sets to NULL. All examples use round_id = "2022-12-26" and include_sample_ids = TRUE, and each returns a tibble with 42 rows.

```r
config_tasks <- read_config_file(
  system.file("config", "tasks-comp-tid.json", package = "hubValidations")
)
expand_model_out_grid(config_tasks,
  round_id = "2022-12-26",
  include_sample_ids = TRUE
)
#> # A tibble: 42 × 6
#>    forecast_date target              horizon location output_type output_type_id
#>    <date>        <chr>                 <int> <chr>    <chr>       <chr>         
#>  1 2022-12-26    wk ahead inc flu h…       2 US       mean        NA            
#>  2 2022-12-26    wk ahead inc flu h…       1 US       mean        NA            
#>  3 2022-12-26    wk ahead inc flu h…       2 01       mean        NA            
#>  4 2022-12-26    wk ahead inc flu h…       1 01       mean        NA            
#>  5 2022-12-26    wk ahead inc flu h…       2 02       mean        NA            
#>  6 2022-12-26    wk ahead inc flu h…       1 02       mean        NA            
#>  7 2022-12-26    wk ahead inc flu h…       2 US       sample      1             
#>  8 2022-12-26    wk ahead inc flu h…       2 01       sample      1             
#>  9 2022-12-26    wk ahead inc flu h…       2 02       sample      1             
#> 10 2022-12-26    wk ahead inc flu h…       1 US       sample      2             
#> # ℹ 32 more rows
```

```r
# Override config compound task ID set
# Create coarser compound task ID set for the first modeling task which contains
# samples
expand_model_out_grid(config_tasks,
  round_id = "2022-12-26",
  include_sample_ids = TRUE,
  compound_taskid_set = list(
    c("forecast_date", "target"),
    NULL
  )
)
#> # A tibble: 42 × 6
#>    forecast_date target              horizon location output_type output_type_id
#>    <date>        <chr>                 <int> <chr>    <chr>       <chr>         
#>  1 2022-12-26    wk ahead inc flu h…       2 US       mean        NA            
#>  2 2022-12-26    wk ahead inc flu h…       1 US       mean        NA            
#>  3 2022-12-26    wk ahead inc flu h…       2 01       mean        NA            
#>  4 2022-12-26    wk ahead inc flu h…       1 01       mean        NA            
#>  5 2022-12-26    wk ahead inc flu h…       2 02       mean        NA            
#>  6 2022-12-26    wk ahead inc flu h…       1 02       mean        NA            
#>  7 2022-12-26    wk ahead inc flu h…       2 US       sample      1             
#>  8 2022-12-26    wk ahead inc flu h…       1 US       sample      1             
#>  9 2022-12-26    wk ahead inc flu h…       2 01       sample      1             
#> 10 2022-12-26    wk ahead inc flu h…       1 01       sample      1             
#> # ℹ 32 more rows
```

```r
expand_model_out_grid(config_tasks,
  round_id = "2022-12-26",
  include_sample_ids = TRUE,
  compound_taskid_set = list(
    NULL,
    NULL
  )
)
#> # A tibble: 42 × 6
#>    forecast_date target              horizon location output_type output_type_id
#>    <date>        <chr>                 <int> <chr>    <chr>       <chr>         
#>  1 2022-12-26    wk ahead inc flu h…       2 US       mean        NA            
#>  2 2022-12-26    wk ahead inc flu h…       1 US       mean        NA            
#>  3 2022-12-26    wk ahead inc flu h…       2 01       mean        NA            
#>  4 2022-12-26    wk ahead inc flu h…       1 01       mean        NA            
#>  5 2022-12-26    wk ahead inc flu h…       2 02       mean        NA            
#>  6 2022-12-26    wk ahead inc flu h…       1 02       mean        NA            
#>  7 2022-12-26    wk ahead inc flu h…       2 US       sample      1             
#>  8 2022-12-26    wk ahead inc flu h…       1 US       sample      2             
#>  9 2022-12-26    wk ahead inc flu h…       2 01       sample      3             
#> 10 2022-12-26    wk ahead inc flu h…       1 01       sample      4             
#> # ℹ 32 more rows
```

--------------------------------

### validate_model_file() example with successful validation

Source: https://hubverse-org.github.io/hubValidations/reference/validate_model_file.html

Example validating a model output file that exists and passes all checks. The output shows check results for file_exists, file_name, file_location, round_id_valid, file_format, file_n, and metadata_exists.

```r
hub_path <- system.file("testhubs/simple", package = "hubValidations")
validate_model_file(hub_path,
  file_path = "team1-goodmodel/2022-10-08-team1-goodmodel.csv"
)
#> 
#> ── team1-goodmodel/2022-10-08-team1-goodmodel.csv ────
#> 
#> ✔ [file_exists]: File exists at path
#>   model-output/team1-goodmodel/2022-10-08-team1-goodmodel.csv.
#> ✔ [file_name]: File name "2022-10-08-team1-goodmodel.csv" is valid.
#> ✔ [file_location]: File directory name matches `model_id` metadata in file
#>   name.
#> ✔ [round_id_valid]: `round_id` is valid.
#> ✔ [file_format]: File is accepted hub format.
#> ✔ [file_n]: Number of accepted model output files per round met.
#> ✔ [metadata_exists]: Metadata file exists at path
#>   model-metadata/team1-goodmodel.yaml.
```

--------------------------------

### Example: Create and access a hub_validations_collection

Source: https://hubverse-org.github.io/hubValidations/reference/new_hub_validations_collection.html

Example from the documentation. It creates an empty collection, then builds two hub_validations objects for different files, combines them into a collection, prints it, extracts element names, and accesses validations by file path and by check. The output shows a success and a failure check, and demonstrates that collection elements are named by their 'where' attribute.

```r
new_hub_validations_collection()
#> Empty <hub_validations_collection>

hub_path <- system.file("testhubs/simple", package = "hubValidations")

# Create validations for two different files
file_path_1 <- "team1-goodmodel/2022-10-08-team1-goodmodel.csv"
validations_1 <- new_hub_validations(
  file_exists = check_file_exists(file_path_1, hub_path),
  file_name = check_file_name(file_path_1)
)

file_path_2 <- "team1-goodmodel/2022-10-15-team1-goodmodel.csv"
validations_2 <- new_hub_validations(
  file_exists = check_file_exists(file_path_2, hub_path),
  file_name = check_file_name(file_path_2)
)

# Combine into a collection
collection <- new_hub_validations_collection(validations_1, validations_2)

# Print the collection
collection
#> 
#> ── team1-goodmodel/2022-10-08-team1-goodmodel.csv ────
#> 
#> ✔ [file_exists]: File exists at path
#>   model-output/team1-goodmodel/2022-10-08-team1-goodmodel.csv.
#> ✔ [file_name]: File name "2022-10-08-team1-goodmodel.csv" is valid.
#> 
#> ── team1-goodmodel/2022-10-15-team1-goodmodel.csv ────
#> 
#> ⓧ [file_exists]: File does not exist at path
#>   model-output/team1-goodmodel/2022-10-15-team1-goodmodel.csv.
#> ✔ [file_name]: File name "2022-10-15-team1-goodmodel.csv" is valid.

# Get file paths (element names)
names(collection)
#> [1] "team1-goodmodel/2022-10-08-team1-goodmodel.csv"
#> [2] "team1-goodmodel/2022-10-15-team1-goodmodel.csv"

# Access validations for a specific file
collection[[file_path_1]]
#> 
#> ── team1-goodmodel/2022-10-08-team1-goodmodel.csv ────
#> 
#> ✔ [file_exists]: File exists at path
#>   model-output/team1-goodmodel/2022-10-08-team1-goodmodel.csv.
#> ✔ [file_name]: File name "2022-10-08-team1-goodmodel.csv" is valid.

# Access validations for a specific file and check
collection$`team1-goodmodel/2022-10-08-team1-goodmodel.csv`$file_exists
#> <message/check_success>
#> Message:
#> File exists at path
#> model-output/team1-goodmodel/2022-10-08-team1-goodmodel.csv.
```

--------------------------------

### Configure custom check function in validation.yml

Source: https://hubverse-org.github.io/hubValidations/articles/deploying-custom-functions

Use the pkg configuration property in validation.yml to specify the package namespace for custom functions. The example sources the cstm_check_tbl_example function from the installed validations package.

```yaml
default:
    validate_model_data:
      custom_check:
        fn: "cstm_check_tbl_example"
        pkg: "validations"
```

--------------------------------

### Format entire project with Air

Source: https://hubverse-org.github.io/hubValidations/CONTRIBUTING.html

Formats the entire project using the Air CLI.

```bash
air format .
```

--------------------------------

### Expand model out grid with required_vals_only and force_output_types

Source: https://hubverse-org.github.io/hubValidations/reference/expand_model_out_grid.html

Both examples use the same hub_path and config_tasks setup. The first call with required_vals_only = TRUE returns only required output types and output_type_ids, producing a tibble with 23 rows. The second call adds force_output_types = TRUE to also include the 'mean' output type with NA output_type_id, resulting in 24 rows.

```r
hub_path <- system.file("testhubs", "v4", "simple", package = "hubUtils")
config_tasks <- read_config(hub_path)
# Return required output types and output_types_ids only
expand_model_out_grid(
  config_tasks = config_tasks,
  round_id = "2022-10-22",
  required_vals_only = TRUE
)
#> # A tibble: 23 × 6
#>    origin_date target          horizon age_group output_type output_type_id
#>    <date>      <chr>             <int> <chr>     <chr>                <dbl>
#>  1 2022-10-22  wk inc flu hosp       1 65+       quantile             0.01 
#>  2 2022-10-22  wk inc flu hosp       1 65+       quantile             0.025
#>  3 2022-10-22  wk inc flu hosp       1 65+       quantile             0.05 
#>  4 2022-10-22  wk inc flu hosp       1 65+       quantile             0.1  
#>  5 2022-10-22  wk inc flu hosp       1 65+       quantile             0.15 
#>  6 2022-10-22  wk inc flu hosp       1 65+       quantile             0.2  
#>  7 2022-10-22  wk inc flu hosp       1 65+       quantile             0.25 
#>  8 2022-10-22  wk inc flu hosp       1 65+       quantile             0.3  
#>  9 2022-10-22  wk inc flu hosp       1 65+       quantile             0.35 
#> 10 2022-10-22  wk inc flu hosp       1 65+       quantile             0.4  
#> # ℹ 13 more rows
```

```r
# Force all output types to be required
expand_model_out_grid(
  config_tasks = config_tasks,
  round_id = "2022-10-22",
  required_vals_only = TRUE,
  force_output_types = TRUE
)
#> # A tibble: 24 × 6
#>    origin_date target          horizon age_group output_type output_type_id
#>    <date>      <chr>             <int> <chr>     <chr>                <dbl>
#>  1 2022-10-22  wk inc flu hosp       1 65+       mean                NA    
#>  2 2022-10-22  wk inc flu hosp       1 65+       quantile             0.01 
#>  3 2022-10-22  wk inc flu hosp       1 65+       quantile             0.025
#>  4 2022-10-22  wk inc flu hosp       1 65+       quantile             0.05 
#>  5 2022-10-22  wk inc flu hosp       1 65+       quantile             0.1  
#>  6 2022-10-22  wk inc flu hosp       1 65+       quantile             0.15 
#>  7 2022-10-22  wk inc flu hosp       1 65+       quantile             0.2  
#>  8 2022-10-22  wk inc flu hosp       1 65+       quantile             0.25 
#>  9 2022-10-22  wk inc flu hosp       1 65+       quantile             0.3  
#> 10 2022-10-22  wk inc flu hosp       1 65+       quantile             0.35 
#> # ℹ 14 more rows
```

--------------------------------

### Install development version from GitHub

Source: https://hubverse-org.github.io/hubValidations/index.html

Installs the development version of hubValidations from GitHub. Use this to test unreleased features.

```r
remotes::install_github("hubverse-org/hubValidations")
```

--------------------------------

### Install hubValidations from R-universe

Source: https://hubverse-org.github.io/hubValidations/index.html

Installs the latest release of hubValidations from the R-universe repository. Use this when you want the current stable version.

```r
install.packages("hubValidations", repos = c("https://hubverse-org.r-universe.dev", "https://cloud.r-project.org"))
```

--------------------------------

### Expand model output grid with default settings

Source: https://hubverse-org.github.io/hubValidations/reference/expand_model_out_grid.html

Connects to the example hub 'flusight' and expands the model output grid for round '2023-01-02' using the default settings, returning a tibble with 3,132 rows.

```r
hub_con <- hubData::connect_hub(
  system.file("testhubs/flusight", package = "hubUtils")
)
config_tasks <- attr(hub_con, "config_tasks")
expand_model_out_grid(config_tasks, round_id = "2023-01-02")
#> # A tibble: 3,132 × 6
#>    forecast_date target              horizon location output_type output_type_id
#>    <date>        <chr>                 <int> <chr>    <chr>       <chr>         
#>  1 2023-01-02    wk flu hosp rate c…       2 US       pmf         large_decrease
#>  2 2023-01-02    wk flu hosp rate c…       1 US       pmf         large_decrease
#>  3 2023-01-02    wk flu hosp rate c…       2 01       pmf         large_decrease
#>  4 2023-01-02    wk flu hosp rate c…       1 01       pmf         large_decrease
#>  5 2023-01-02    wk flu hosp rate c…       2 02       pmf         large_decrease
#>  6 2023-01-02    wk flu hosp rate c…       1 02       pmf         large_decrease
#>  7 2023-01-02    wk flu hosp rate c…       2 04       pmf         large_decrease
#>  8 2023-01-02    wk flu hosp rate c…       1 04       pmf         large_decrease
#>  9 2023-01-02    wk flu hosp rate c…       2 05       pmf         large_decrease
#> 10 2023-01-02    wk flu hosp rate c…       1 05       pmf         large_decrease
#> # ℹ 3,122 more rows
```

--------------------------------

### Install local package in GitHub Action

Source: https://hubverse-org.github.io/hubValidations/articles/writing-custom-fns.html

Add this step to the validate-submission.yaml workflow to install the local 'validations' package and its dependencies. This ensures the package is available during validation.

```yaml
      - uses: r-lib/actions/setup-r-dependencies@v2
        with:
          packages: |
            any::hubValidations
            any::sessioninfo
            local::./src/validations
```

--------------------------------

### Add additional dependency to setup-r-dependencies step

Source: https://hubverse-org.github.io/hubValidations/articles/deploying-custom-functions

Edit the setup-r-dependencies step in the validate-submission.yaml workflow to add additional packages to the packages field list. This ensures the dependency is available during validation on GitHub but not locally for hub administrators or submitting teams.

```yaml
      - uses: r-lib/actions/setup-r-dependencies@v2
        with:
          packages: |
            any::hubValidations
            any::sessioninfo
            any::additionalPackage
```

--------------------------------

### Create custom check with extra_args = TRUE (create_custom_check)

Source: https://hubverse-org.github.io/hubValidations/articles/writing-custom-fns.html

Creates a custom check function template with an extra example argument by setting extra_args = TRUE. The output shows the file created and instructions to edit the template.

```R
create_custom_check("cstm_check_tbl_args",
  hub_path = hub_path,
  extra_args = TRUE
)
#> ✔ Custom validation check template function file "cstm_check_tbl_args.R" created.
#> → Edit the function template to add your custom check logic.
#> ℹ See the Writing custom check functions article for more information.
#> (<https://hubverse-org.github.io/hubValidations/articles/writing-custom-fns.html>)
```

--------------------------------

### Example of validate_submission_time with output

Source: https://hubverse-org.github.io/hubValidations/reference/validate_submission_time.html

Example usage of validate_submission_time with a test hub and file path. The output shows a failed check because the current time is outside the accepted submission window.

```r
hub_path <- system.file("testhubs/simple", package = "hubValidations")
file_path <- "team1-goodmodel/2022-10-08-team1-goodmodel.csv"
validate_submission_time(hub_path, file_path)
#> 
#> ── team1-goodmodel/2022-10-08-team1-goodmodel.csv ────
#> 
#> ✖ [submission_time]: Submission time must be within accepted submission window
#>   for round.  Current time "2026-07-15 15:03:59 UTC" is outside window
#>   2022-10-02 EDT--2022-10-09 23:59:59 EDT.
```

--------------------------------

### Create and convert hub_validations objects

Source: https://hubverse-org.github.io/hubValidations/reference/new_hub_validations.html

Create a new empty hub_validations object, then create one with two checks (file_exists and file_name) and convert a list of checks to a hub_validations object. The output shows the validation summary for the file path.

```r
new_hub_validations()
#> Empty <hub_validations>

hub_path <- system.file("testhubs/simple", package = "hubValidations")
file_path <- "team1-goodmodel/2022-10-08-team1-goodmodel.csv"
new_hub_validations(
  file_exists = check_file_exists(file_path, hub_path),
  file_name = check_file_name(file_path)
)
#> 
#> ── team1-goodmodel/2022-10-08-team1-goodmodel.csv ────
#> 
#> ✔ [file_exists]: File exists at path
#>   model-output/team1-goodmodel/2022-10-08-team1-goodmodel.csv.
#> ✔ [file_name]: File name "2022-10-08-team1-goodmodel.csv" is valid.
x <- list(
  file_exists = check_file_exists(file_path, hub_path),
  file_name = check_file_name(file_path)
)
as_hub_validations(x)
#> 
#> ── team1-goodmodel/2022-10-08-team1-goodmodel.csv ────
#> 
#> ✔ [file_exists]: File exists at path
#>   model-output/team1-goodmodel/2022-10-08-team1-goodmodel.csv.
#> ✔ [file_name]: File name "2022-10-08-team1-goodmodel.csv" is valid.
```

--------------------------------

### Example check function with skip logic

Source: https://hubverse-org.github.io/hubValidations/articles/writing-custom-fns.html

Example of a check function that skips when no cdf or quantile output types are present. It uses capture_check_info() to return a message and capture_check_cnd() for the main check.

```R
check_tbl_value_col_ascending
function (tbl, file_path, hub_path, round_id, derived_task_ids = get_hub_derived_task_ids(hub_path)) 
{
    check_output_types <- intersect(c("cdf", "quantile"), unique(tbl[["output_type"]]))
    if (length(check_output_types) == 0L) {
        return(capture_check_info(file_path, "No quantile or cdf output types to check for non-descending values.\n        Check skipped."))
    }
    config_tasks <- hubUtils::read_config(hub_path, "tasks")
    if (!is.null(derived_task_ids)) {
        tbl[derived_task_ids] <- NA_character_
    }
    error_tbl <- purrr::list_rbind(purrr::map(check_output_types, 
        function(.x) {
            check_values_ascending_by_output_type(.x, tbl, config_tasks, 
                round_id, derived_task_ids)
        }))
    check <- nrow(error_tbl) == 0L
    if (check) {
        details <- NULL
        error_tbl <- NULL
    }
    else {
        details <- cli::format_inline("See {.var error_tbl} attribute for details.")
    }
    capture_check_cnd(check = check, file_path = file_path, msg_subject = "Quantile or cdf {.var value} values", 
        msg_verbs = c("increase", "do not all increase"), msg_attribute = "when ordered by {.var output_type_id}.", 
        details = details, error_tbl = error_tbl)
}
<bytecode: 0x56219e74d588>
<environment: namespace:hubValidations>
```
Tokens:
Raw