Package 'One4All' reference manual

Title:	Validate, Share, and Download Data
Description:	Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a 'CSV' or 'Excel' file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research.
Authors:	Hannah Sherrod [cre, aut] , Nick Leong [aut] , Hannah Hapich [aut] , Fabian Gomez [aut], Shelly Moore [aut], Win Cowger [aut] , Scott Coffin [ctb], Tony Hale [ctb], Diana Lin [ctb], Gemma Shusterman [ctb], Rebecca Sutton [ctb], Adam Wong [ctb], Haig Minasian [ctb], Holden Ford [ctb], Anja Oca [ctb], Richard Nelson [ctb], Leah Thornton Hampton [ctb], Libby Heeren [ctb], Gabriel Daiess [ctb]
Maintainer:	Hannah Sherrod <[email protected]>
License:	MIT + file LICENSE
Version:	0.5
Built:	2025-03-07 05:20:59 UTC
Source:	https://github.com/moore-institute-4-plastic-pollution-res/one4all

Generate a data frame with certificate information

Description

This function creates a data frame with certificate information including the current time, data and rule hashes, package version, and web hash.

Usage

certificate_df(x, time = Sys.time())
certificate_df(x, time = Sys.time())

Arguments

`x`	A list containing 'data_formatted' and 'rules' elements.
`time`	the time the certificate is generated, can be passed a value or uses current system time.

Value

A data frame with certificate information.

Examples


certificate_df(x = list(data_formatted = data.frame(a = 1:3, b = 4:6),
                        rules = validate::validator(a > 0, b > 0)),
               time = Sys.time())
               

certificate_df(x = list(data_formatted = data.frame(a = 1:3, b = 4:6),
                        rules = validate::validator(a > 0, b > 0)),
               time = Sys.time())

Check if a file exists in a zip file

Description

This function checks if a file with a given name exists in a specified zip file.

Usage

check_exists_in_zip(zip_path, file_name)
check_exists_in_zip(zip_path, file_name)

Arguments

`zip_path`	A character string representing the path of the zip file.
`file_name`	A character string representing the name of the file to check.

Value

A logical value indicating whether the file exists in the zip file (TRUE) or not (FALSE).

Examples

## Not run: 
check_exists_in_zip(zip_path = "/path/to/your.zip", file_name = "file/in/zip.csv")

## End(Not run)
## Not run: 
check_exists_in_zip(zip_path = "/path/to/your.zip", file_name = "file/in/zip.csv")

## End(Not run)

Check for malicious files

Description

This function checks for the presence of files with extensions known to be associated with malicious activities. The function can be used to screen zip files or individual files for these potentially dangerous file types.

Usage

check_for_malicious_files(files)
check_for_malicious_files(files)

Arguments

files

A character vector of file paths. These can be paths to zip files or individual files.

Value

A logical value indicating if any of the files in the input have a malicious file extension. Returns 'TRUE' if any malicious file is found, otherwise 'FALSE'.

Examples

## Not run: 
  check_for_malicious_files("path'(s)'/to/your/files")
  check_for_malicious_files(utils::unzip("path/to/your/file.zip", list = TRUE)$Name)

## End(Not run)

## Not run: 
  check_for_malicious_files("path'(s)'/to/your/files")
  check_for_malicious_files(utils::unzip("path/to/your/file.zip", list = TRUE)$Name)

## End(Not run)

Check and format image URLs

Description

This function checks if the input string contains an image URL (PNG or JPG) and formats it as an HTML img tag with a specified height.

Usage

check_images(x)
check_images(x)

Arguments

`x`	A character string to check for image URLs.

Value

A character string with the HTML img tag if an image URL is found, otherwise the input string.

Examples

check_images("https://example.com/image.png")
check_images("https://example.com/image.jpg")
check_images("https://example.com/text")
check_images("https://example.com/image.png")
check_images("https://example.com/image.jpg")
check_images("https://example.com/text")

Check and format non-image hyperlinks

Description

This function checks if the input string contains a non-image hyperlink and formats it as an HTML anchor tag.

Usage

check_other_hyperlinks(x)
check_other_hyperlinks(x)

Arguments

`x`	A character string to check for non-image hyperlinks.

Value

A character string with the HTML anchor tag if a non-image hyperlink is found, otherwise the input string.

Examples

check_other_hyperlinks("https://example.com/page")
check_other_hyperlinks("https://example.com/image.png")
check_other_hyperlinks("https://example.com/image.jpg")
check_other_hyperlinks("https://example.com/page")
check_other_hyperlinks("https://example.com/image.png")
check_other_hyperlinks("https://example.com/image.jpg")

Check if a number passes the Luhn algorithm

Description

This function checks if a given number passes the Luhn algorithm. It is commonly used to validate credit card numbers.

Usage

checkLuhn(number)
checkLuhn(number)

Arguments

number

A character string of the number to check against the Luhn algorithm.

Value

A logical value indicating whether the number passes the Luhn algorithm (TRUE) or not (FALSE).

Examples

checkLuhn("4532015112830366") # TRUE
checkLuhn("4532015112830367") # FALSE
checkLuhn("4532015112830366") # TRUE
checkLuhn("4532015112830367") # FALSE

Create a formatted Excel file based on validation rules

Description

This function creates an Excel file with conditional formatting and data validation based on the given validation rules in a CSV or Excel file. This function is currently compatible with Windows and Linux operating systems. When using a macOS system, the excel file is able to download, but has some bugs with formatting the LOOKUP sheet.

Usage

create_valid_excel(
  file_rules,
  negStyle = createStyle(fontColour = "#9C0006", bgFill = "#FFC7CE"),
  posStyle = createStyle(fontColour = "#006100", bgFill = "#C6EFCE"),
  row_num = 1000
)
create_valid_excel(
  file_rules,
  negStyle = createStyle(fontColour = "#9C0006", bgFill = "#FFC7CE"),
  posStyle = createStyle(fontColour = "#006100", bgFill = "#C6EFCE"),
  row_num = 1000
)

Arguments

`file_rules`	A CSV or Excel file containing validation rules.
`negStyle`	Style to apply for negative conditions (default is red text on a pink background).
`posStyle`	Style to apply for positive conditions (default is green text on a light green background).
`row_num`	Number of rows to create in the output file (default is 1000).

Value

A workbook object containing the formatted Excel file.

Examples


data("test_rules")
create_valid_excel(file_rules = test_rules)

data("test_rules")
create_valid_excel(file_rules = test_rules)

Download all data alternative

Description

This function allows users to download all data rather than one data set at a time.

Usage

download_all(
  file_path = NULL,
  s3_key_id = NULL,
  s3_secret_key = NULL,
  s3_region = NULL,
  s3_bucket = NULL,
  callback = NULL
)
download_all(
  file_path = NULL,
  s3_key_id = NULL,
  s3_secret_key = NULL,
  s3_region = NULL,
  s3_bucket = NULL,
  callback = NULL
)

Arguments

`file_path`	location and name of the zip file to create.
`s3_key_id`	A character string representing the AWS S3 access key ID.
`s3_secret_key`	A character string representing the AWS S3 secret access key.
`s3_region`	A character string representing the AWS S3 region.
`s3_bucket`	A character string representing the AWS S3 bucket name.
`callback`	Prints if the download was a success.

Value

Any return objects from the downloads.

Examples

## Not run: 
    download_all_data <- download_all(file_path = "your/path/file.zip",
                                      s3_key_id = "your_s3_key_id",
                                      s3_secret_key = "your_s3_secret_key",
                                      s3_region = "your_s3_region",
                                      s3_bucket = "your_s3_bucket",
                                      callback = NULL)

## End(Not run)

## Not run: 
    download_all_data <- download_all(file_path = "your/path/file.zip",
                                      s3_key_id = "your_s3_key_id",
                                      s3_secret_key = "your_s3_secret_key",
                                      s3_region = "your_s3_region",
                                      s3_bucket = "your_s3_bucket",
                                      callback = NULL)

## End(Not run)

Invalid example data

Description

This is a list containing three data frames as an example of invalid_example.

Format

A list with 3 data frames:

data_frame_1: A data frame with 18 variables: MethodologyID, SamplingDevice, AirFiltration, AirFiltrationType, ClothingPolicy, NonplasticPolicy, SealedEnvironment, SealedEnvironmentType, SieveMeshSizes, FilterType, FilterDiameter, FilterPoreSize, VisIDMethod, VisualSoftware, PickingStrategy, VisMagnification, MatIDMethod, MatIDSoftware
data_frame_2: A data frame with 8 variables: SampleID, OwnerOrganization, AnalysisOrganization, ReportingOrganization, Latitude, Longitude, CollectionDate, SampleVolume
data_frame_3: A data frame with 17 variables: ParticleID, MethodologyID, SampleID, PhotoID, SpectraID, FinalAnalysisDate, Comments, Polymer, Morphology, Color, Length, Width, Height, Mass, SurfaceArea, Volume, Tactile

Examples

data("invalid_example")

data("invalid_example")

Check if an object is of class POSIXct

Description

This function checks if the given object is of class POSIXct. It returns TRUE if the object inherits the POSIXct class, otherwise FALSE.

Usage

is.POSIXct(x)
is.POSIXct(x)

Arguments

`x`	An object to be tested for POSIXct class inheritance.

Value

A logical value indicating if the input object is of class POSIXct.

Examples

x <- as.POSIXct("2021-01-01")
is.POSIXct(x) # TRUE

y <- Sys.Date()
is.POSIXct(y) # FALSE

x <- as.POSIXct("2021-01-01")
is.POSIXct(x) # TRUE

y <- Sys.Date()
is.POSIXct(y) # FALSE

Name datasets

Description

This function extracts the names of the datasets provided in the input files. If specific data names are provided, they are used, otherwise the function tries to extract the names from the files themselves.

Usage

name_data(files_data, data_names = NULL)
name_data(files_data, data_names = NULL)

Arguments

`files_data`	A vector of file paths or list of data frames.
`data_names`	A vector of names to be assigned to datasets.

Value

A vector of dataset names.

Examples

name_data(files_data = c("path/to/data1.csv", "path/to/data2.csv"))
name_data(files_data = c("path/to/data.xlsx"), data_names = c("sheet1", "sheet2"))

name_data(files_data = c("path/to/data1.csv", "path/to/data2.csv"))
name_data(files_data = c("path/to/data.xlsx"), data_names = c("sheet1", "sheet2"))

Query a MongoDB document by an ObjectID

Description

This function queries a mongodb database using its API to retrieve a document by its ObjectID. Use the MongoDB Atlas Data API to create an API key.

Usage

query_document_by_object_id(apiKey, collection, database, dataSource, objectId)
query_document_by_object_id(apiKey, collection, database, dataSource, objectId)

Arguments

`apiKey`	The API key for accessing the MongoDB API.
`collection`	The name of the collection in the MongoDB database.
`database`	The name of the MongoDB database.
`dataSource`	The data source in MongoDB.
`objectId`	The object ID of the document to query.

Value

The queried document.

Examples

## Not run: 
apiKey <- 'your_mongodb_api_key'
collection <- 'your_mongodb_collection'
database <- 'your_database'
dataSource <- 'your_dataSource'
objectId <- 'example_object_id'
query_document_by_object_id(apiKey, collection, database, dataSource, objectId)

## End(Not run)

## Not run: 
apiKey <- 'your_mongodb_api_key'
collection <- 'your_mongodb_collection'
database <- 'your_database'
dataSource <- 'your_dataSource'
objectId <- 'example_object_id'
query_document_by_object_id(apiKey, collection, database, dataSource, objectId)

## End(Not run)

Read and format data from csv or xlsx files

Description

Read and format data from csv or xlsx files

Usage

read_data(files_data, data_names = NULL)
read_data(files_data, data_names = NULL)

Arguments

`files_data`	List of files to be read
`data_names`	Optional vector of names for the data frames

Value

A list of data frames

Examples

read_data(files_data = valid_example, data_names = c("methodology", "particles", "samples"))

read_data(files_data = valid_example, data_names = c("methodology", "particles", "samples"))

Read rules from a file

Description

This function reads rules from a file or a data frame. The file can be in csv or xlsx format. The data should have the column names "name", "description", "dataset", "valid example", "severity", "rule". The function also checks that the rules do not contain sensitive words and that all the rules fields are character type.

Usage

read_rules(file_rules)
read_rules(file_rules)

Arguments

file_rules

The file containing the rules. Can be a CSV or XLSX file, or a data frame.

Value

A data frame containing the rules.

Examples

## Not run: 
read_rules("path/to/rules")

## End(Not run)

## Not run: 
read_rules("path/to/rules")

## End(Not run)

Reformat the rules

Description

This function is responsible for handling the rule reformating, dataset handling and foreign key checks.

Usage

reformat_rules(rules, data_formatted, zip_data = NULL)
reformat_rules(rules, data_formatted, zip_data = NULL)

Arguments

`rules`	A data.frame containing rules to be reformatted.
`data_formatted`	A named list of data.frames with data.
`zip_data`	A file path to a zip folder with additional data to check.

Value

A data.frame with reformatted rules.

Examples

data("test_rules")
data("valid_example")
reformat_rules(rules = test_rules, data_formatted = valid_example)
data("test_rules")
data("valid_example")
reformat_rules(rules = test_rules, data_formatted = valid_example)

Download structured data from remote sources

Description

This function downloads data from remote sources like CKAN, AWS S3, and MongoDB. It retrieves the data based on the hashed_data identifier and assumes the data is stored using the same naming conventions provided in the 'remote_share' function.

Usage

remote_download(
  hashed_data = NULL,
  ckan_url,
  ckan_key,
  ckan_package,
  s3_key_id,
  s3_secret_key,
  s3_region,
  s3_bucket,
  mongo_key,
  mongo_collection
)
remote_download(
  hashed_data = NULL,
  ckan_url,
  ckan_key,
  ckan_package,
  s3_key_id,
  s3_secret_key,
  s3_region,
  s3_bucket,
  mongo_key,
  mongo_collection
)

Arguments

`hashed_data`	A character string representing the hashed identifier of the data to be downloaded.
`ckan_url`	A character string representing the CKAN base URL.
`ckan_key`	A character string representing the CKAN API key.
`ckan_package`	A character string representing the CKAN package identifier.
`s3_key_id`	A character string representing the AWS S3 access key ID.
`s3_secret_key`	A character string representing the AWS S3 secret access key.
`s3_region`	A character string representing the AWS S3 region.
`s3_bucket`	A character string representing the AWS S3 bucket name.
`mongo_key`	A character string representing the mongo key.
`mongo_collection`	A character string representing the mongo collection.

Value

A named list containing the downloaded datasets.

Examples

## Not run: 
  downloaded_data <- remote_download(hashed_data = "example_hash",
                                     ckan_url = "https://example.com",
                                     ckan_key = "your_ckan_key",
                                     ckan_package = "your_ckan_package",
                                     s3_key_id = "your_s3_key_id",
                                     s3_secret_key = "your_s3_secret_key",
                                     s3_region = "your_s3_region",
                                     s3_bucket = "your_s3_bucket",
                                     mongo_key = "mongo_key",
                                     mongo_collection = "mongo_collection")

## End(Not run)

## Not run: 
  downloaded_data <- remote_download(hashed_data = "example_hash",
                                     ckan_url = "https://example.com",
                                     ckan_key = "your_ckan_key",
                                     ckan_package = "your_ckan_package",
                                     s3_key_id = "your_s3_key_id",
                                     s3_secret_key = "your_s3_secret_key",
                                     s3_region = "your_s3_region",
                                     s3_bucket = "your_s3_bucket",
                                     mongo_key = "mongo_key",
                                     mongo_collection = "mongo_collection")

## End(Not run)

Download raw data from remote sources

Description

This function downloads data from remote sources like CKAN and AWS S3. It retrieves the data based on the hashed_data identifier and assumes the data is stored using the same naming conventions provided in the 'remote_share' function.

Usage

remote_raw_download(
  hashed_data = NULL,
  file_path = NULL,
  ckan_url = NULL,
  ckan_key = NULL,
  ckan_package = NULL,
  s3_key_id = NULL,
  s3_secret_key = NULL,
  s3_region = NULL,
  s3_bucket = NULL
)
remote_raw_download(
  hashed_data = NULL,
  file_path = NULL,
  ckan_url = NULL,
  ckan_key = NULL,
  ckan_package = NULL,
  s3_key_id = NULL,
  s3_secret_key = NULL,
  s3_region = NULL,
  s3_bucket = NULL
)

Arguments

`hashed_data`	A character string representing the hashed identifier of the data to be downloaded.
`file_path`	location and name of the zip file to create.
`ckan_url`	A character string representing the CKAN base URL.
`ckan_key`	A character string representing the CKAN API key.
`ckan_package`	A character string representing the CKAN package identifier.
`s3_key_id`	A character string representing the AWS S3 access key ID.
`s3_secret_key`	A character string representing the AWS S3 secret access key.
`s3_region`	A character string representing the AWS S3 region.
`s3_bucket`	A character string representing the AWS S3 bucket name.

Value

Any return objects from the downloads.

Examples

## Not run: 
  downloaded_data <- remote_raw_download(hashed_data = "example_hash",
                                     file_path = "your/path/file.zip",
                                     ckan_url = "https://example.com",
                                     ckan_key = "your_ckan_key",
                                     ckan_package = "your_ckan_package",
                                     s3_key_id = "your_s3_key_id",
                                     s3_secret_key = "your_s3_secret_key",
                                     s3_region = "your_s3_region",
                                     s3_bucket = "your_s3_bucket")

## End(Not run)

## Not run: 
  downloaded_data <- remote_raw_download(hashed_data = "example_hash",
                                     file_path = "your/path/file.zip",
                                     ckan_url = "https://example.com",
                                     ckan_key = "your_ckan_key",
                                     ckan_package = "your_ckan_package",
                                     s3_key_id = "your_s3_key_id",
                                     s3_secret_key = "your_s3_secret_key",
                                     s3_region = "your_s3_region",
                                     s3_bucket = "your_s3_bucket")

## End(Not run)

Share your validated data

Description

This function uploads validated data to specified remote repositories, such as CKAN, Amazon S3, and/or MongoDB.

Usage

remote_share(
  validation,
  data_formatted,
  files,
  verified,
  valid_rules,
  valid_key_share,
  ckan_url,
  ckan_key,
  ckan_package,
  url_to_send,
  rules,
  results,
  s3_key_id,
  s3_secret_key,
  s3_region,
  s3_bucket,
  mongo_key,
  mongo_collection,
  old_cert = NULL
)
remote_share(
  validation,
  data_formatted,
  files,
  verified,
  valid_rules,
  valid_key_share,
  ckan_url,
  ckan_key,
  ckan_package,
  url_to_send,
  rules,
  results,
  s3_key_id,
  s3_secret_key,
  s3_region,
  s3_bucket,
  mongo_key,
  mongo_collection,
  old_cert = NULL
)

Arguments

`validation`	A list containing validation information.
`data_formatted`	A list containing formatted data.
`files`	A vector of file paths to upload.
`verified`	The secret key provided by the portal maintainer.
`valid_rules`	A list of valid rules for the dataset.
`valid_key_share`	A valid key to share data.
`ckan_url`	The URL of the CKAN instance.
`ckan_key`	The API key for the CKAN instance.
`ckan_package`	The CKAN package to which the data will be uploaded.
`url_to_send`	The URL to send the data.
`rules`	A set of rules used for validation.
`results`	A list containing results of the validation.
`s3_key_id`	AWS ACCESS KEY ID
`s3_secret_key`	AWS SECRET ACCESS KEY
`s3_region`	AWS DEFAULT REGION
`s3_bucket`	The name of the Amazon S3 bucket.
`mongo_key`	mongo connection url
`mongo_collection`	collection name
`old_cert`	(Optional) An old certificate to be uploaded alongside the new one to override the previous submission with.

Value

A list containing the status and message of the operation.

Examples

## Not run: 
shared_data <- remote_share(validation = result_valid,
                            data_formatted = result_valid$data_formatted,
                            files = test_file,
                            verified = "your_verified_key",
                            valid_key_share = "your_valid_key_share",
                            valid_rules = digest::digest(test_rules),
                            ckan_url = "https://example.com",
                            ckan_key = "your_ckan_key",
                            ckan_package = "your_ckan_package",
                            url_to_send = "https://your-url-to-send.com",
                            rules = test_rules,
                            results = valid_example$results,
                            s3_key_id = "your_s3_key_id",
                            s3_secret_key = "your_s3_secret_key",
                            s3_region = "your_s3_region",
                            s3_bucket = "your_s3_bucket",
                            mongo_key = "your_mongo_key",
                            mongo_collection = "your_mongo_collection",
                            old_cert = NULL
)

## End(Not run)
## Not run: 
shared_data <- remote_share(validation = result_valid,
                            data_formatted = result_valid$data_formatted,
                            files = test_file,
                            verified = "your_verified_key",
                            valid_key_share = "your_valid_key_share",
                            valid_rules = digest::digest(test_rules),
                            ckan_url = "https://example.com",
                            ckan_key = "your_ckan_key",
                            ckan_package = "your_ckan_package",
                            url_to_send = "https://your-url-to-send.com",
                            rules = test_rules,
                            results = valid_example$results,
                            s3_key_id = "your_s3_key_id",
                            s3_secret_key = "your_s3_secret_key",
                            s3_region = "your_s3_region",
                            s3_bucket = "your_s3_bucket",
                            mongo_key = "your_mongo_key",
                            mongo_collection = "your_mongo_collection",
                            old_cert = NULL
)

## End(Not run)

Check which rows in the data violated the rules

Description

Get the rows in the data that violate the specified rules.

Usage

rows_for_rules(data_formatted, report, broken_rules, rows)
rows_for_rules(data_formatted, report, broken_rules, rows)

Arguments

`data_formatted`	A formatted data frame.
`report`	A validation report generated by the 'validate' function.
`broken_rules`	A data frame with broken rules information.
`rows`	A vector of row indices specifying which rules from the suite of rules with errors to check for violations.

Value

A data frame with rows in the data that violate the specified rules.

Examples

data("invalid_example")
data("test_rules")
# Generate a validation report
result_invalid <- validate_data(files_data = invalid_example,
                        data_names = c("methodology", "particles", "samples"),
                        file_rules = test_rules)

# Find the broken rules
broken_rules <- rules_broken(results = result_invalid$results[[1]], show_decision = TRUE)

# Get rows for the specified rules
violating_rows <- rows_for_rules(data_formatted = result_invalid$data_formatted[[1]],
                                 report = result_invalid$report[[1]],
                                 broken_rules = broken_rules,
                                 rows = 1)
data("invalid_example")
data("test_rules")
# Generate a validation report
result_invalid <- validate_data(files_data = invalid_example,
                        data_names = c("methodology", "particles", "samples"),
                        file_rules = test_rules)

# Find the broken rules
broken_rules <- rules_broken(results = result_invalid$results[[1]], show_decision = TRUE)

# Get rows for the specified rules
violating_rows <- rows_for_rules(data_formatted = result_invalid$data_formatted[[1]],
                                 report = result_invalid$report[[1]],
                                 broken_rules = broken_rules,
                                 rows = 1)

Check which rules were broken

Description

Filter the results of validation to show only broken rules, optionally including successful decisions.

Usage

rules_broken(results, show_decision)
rules_broken(results, show_decision)

Arguments

`results`	A data frame with validation results.
`show_decision`	A logical value to indicate if successful decisions should be included in the output.

Value

A data frame with the filtered results.

Examples

# Sample validation results data frame
sample_results <- data.frame(
  description = c("Rule 1", "Rule 2", "Rule 3"),
  status = c("error", "success", "error"),
  name = c("rule1", "rule2", "rule3"),
  expression = c("col1 > 0", "col2 <= 5", "col3 != 10"),
  stringsAsFactors = FALSE
)

# Show only broken rules
broken_rules <- rules_broken(sample_results, show_decision = FALSE)
# Sample validation results data frame
sample_results <- data.frame(
  description = c("Rule 1", "Rule 2", "Rule 3"),
  status = c("error", "success", "error"),
  name = c("rule1", "rule2", "rule3"),
  expression = c("col1 > 0", "col2 <= 5", "col3 != 10"),
  stringsAsFactors = FALSE
)

# Show only broken rules
broken_rules <- rules_broken(sample_results, show_decision = FALSE)

Run any of the apps

Description

This wrapper function starts the user interface of your app choice.

Usage

run_app(
  path = "system",
  log = TRUE,
  ref = "main",
  test_mode = FALSE,
  app = "validator",
  ...
)
run_app(
  path = "system",
  log = TRUE,
  ref = "main",
  test_mode = FALSE,
  app = "validator",
  ...
)

Arguments

`path`	to store the downloaded app files; defaults to `"system"` pointing to `system.file(package = "One4All")`.
`log`	logical; enables/disables logging to `tempdir()`
`ref`	git reference; could be a commit, tag, or branch name. Defaults to "main". Only change this in case of errors.
`test_mode`	logical; for internal testing only.
`app`	your app choice
`...`	arguments passed to `runApp()`.

Details

After running this function the Validator, Microplastic Image Explorer, or Data Visualization GUI should open in a separate window or in your computer browser.

Value

This function normally does not return any value, see runGitHub().

Author(s)

Hannah Sherrod, Nick Leong, Hannah Hapich, Fabian Gomez, Win Cowger

Examples

## Not run: 
run_app(app = "validator")

## End(Not run)

## Not run: 
run_app(app = "validator")

## End(Not run)

Test for profanity in a string

Description

This function checks if the input string contains any profane words.

Usage

test_profanity(x)
test_profanity(x)

Arguments

`x`	A character string to check for profanity.

Value

A logical value indicating whether the input string contains no profane words.

Examples

test_profanity("This is a clean sentence.")
test_profanity("This sentence contains a badword.")
test_profanity("This is a clean sentence.")
test_profanity("This sentence contains a badword.")

Rules data

Description

A dataset containing rules and their descriptions, datasets, valid examples, severity, and rules.

Format

A data frame with 6 columns:

name: Name of the rule (e.g., "MethodologyID_valid")
description: Description of the rule (e.g., "URL address is valid and can be found on the internet.")
dataset: Dataset associated with the rule (e.g., "methodology")
valid_example: A valid example of the rule (e.g., "https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/documents/microplastics/mcrplsts_plcy_drft.pdf")
severity: Severity of the rule (e.g., "error")
rule: The actual rule (e.g., "check_uploadable(MethodologyID) == TRUE")

Examples

data("test_rules")

data("test_rules")

Valid example data

Description

This is a list containing three data frames as an example of valid_example.

Format

A list with 3 data frames:

data_frame_1: A data frame with 15 variables: MethodID, MatIDMethod, Equipment, Magnification, MethodComments, Protocols, Deployment, SamplingDevice, SmallestParticle, TopParticle, FilterType, FilterDiameter, FilterPoreSize, ImageFile, ImageType
data_frame_2: A data frame with 131 variables: SampleID, SampleSize, Project, Affiliation, Citation, OwnerContributor, AnalysisContributor, ReportingContributor, SiteName, Location, Compartment, SampleComments, SamplingDepth, SamplingVolume, SamplingWeight, BlankContamination, Latitude, Longitude, Matrix, CollectionStartDateTime, CollectionEndDateTime, SpatialFile, Concentration, ConcentrationUnits, StandardizedConcentration, StandardizedConcentrationUnits, Color_Transparent, Color_Blue, Color_Red, Color_Brown, Color_Green, Color_Orange, Color_White, Color_Yellow, Color_Pink, Color_Black, Color_Other, Material_PEST, Material_PE, Material_PP, Material_PA, Material_PE_PS, Material_PS, Material_CA, Material_PVC, Material_ER, Material_PAM, Material_PET, Material_PlasticAdditive, Material_PBT, Material_PU, Material_PET_PEST, Material_PAN, Material_Silicone, Material_Acrylic, Material_Vinyl, Material_Vinyon, Material_Other, Material_PA_ER, Material_PTT, Material_PE_PP, Material_PPS, Material_Rayon, Material_PAA, Material_PMPS, Material_PI, Material_Olefin, Material_Styrene_Butadiene, Material_PBA, Material_PMMA, Material_Cellophane, Material_SAN, Material_PC, Material_PDMS, Material_PLA, Material_PTFE, Material_SBR, Material_PET_Olefin, Material_PES, Material_ABS, Material_LDPE, Material_PEVA, Material_AR, Material_PVA, Material_PPE, Morphology_Fragment, Morphology_Fiber, Morphology_Nurdle, Morphology_Film, Morphology_Foam, Morphology_Sphere, Morphology_Line, Morphology_Bead, Morphology_Sheet, Morphology_Film_Fragment, Morphology_Rubbery_Fragment, Size_3000um, Size_2_5mm, Size_1_5mm, Size_1_2mm, Size_0.5_1mm, Size_less_than_0.5mm, Size_500um, Size_300_500um, Size_125_300um, Size_100_500um, Size_greater_than_100um, Size_50_150um, Size_50_100um, Size_50um, Size_45_125um, Size_greater_than_25um, Size_20um_5mm, Size_20_100um, Size_20_50um, Size_10_50um, Size_10_45um, Size_10_20um, Size_greater_than_10um, Size_8_316um, Size_5_100um, Size_5_10um, Size_4_10um, Size_1.5_5um, Size_less_than_1.5um, Size_1_100um, Size_1_50um, Size_1_10um, Size_1_5um, Size_110_124nm, Size_0_20um
data_frame_3: A data frame with 19 variables: ParticleID, Amount, Color, Polymer, Shape, PhotoID, ParticleComments, PlasticType, Length, Width, Height, Units, Mass, SurfaceArea, SizeDimension, Volume, Tactile, ArrivalDate, AnalysisDate

Examples

data("valid_example")

data("valid_example")

Validate data based on specified rules

Description

Validate data based on specified rules

Usage

validate_data(
  files_data,
  data_names = NULL,
  file_rules = NULL,
  zip_data = NULL
)
validate_data(
  files_data,
  data_names = NULL,
  file_rules = NULL,
  zip_data = NULL
)

Arguments

`files_data`	A list of file paths for the datasets to be validated.
`data_names`	(Optional) A character vector of names for the datasets. If not provided, names will be extracted from the file paths.
`file_rules`	A file path for the rules file, either in .csv or .xlsx format.
`zip_data`	A file path to a zip folder for validating unstructured data.

Value

A list containing the following elements: - data_formatted: A list of data frames with the validated data. - data_names: A character vector of dataset names. - report: A list of validation report objects for each dataset. - results: A list of validation result data frames for each dataset. - rules: A list of validator objects for each dataset. - status: A character string indicating the overall validation status ("success" or "error"). - issues: A logical vector indicating if there are any issues in the validation results. - message: A data.table containing information about any issues encountered.

Examples

# Validate data with specified rules
data("valid_example")
data("invalid_example")
data("test_rules")

result_valid <- validate_data(files_data = valid_example,
                        data_names = c("methodology", "particles", "samples"),
                        file_rules = test_rules)
                       
result_invalid <- validate_data(files_data = invalid_example,
                        data_names = c("methodology", "particles", "samples"),
                        file_rules = test_rules)

# Validate data with specified rules
data("valid_example")
data("invalid_example")
data("test_rules")

result_valid <- validate_data(files_data = valid_example,
                        data_names = c("methodology", "particles", "samples"),
                        file_rules = test_rules)
                       
result_invalid <- validate_data(files_data = invalid_example,
                        data_names = c("methodology", "particles", "samples"),
                        file_rules = test_rules)

Package 'One4All'

Help Index

Generate a data frame with certificate information

Description

Usage

Arguments

Value

Examples

Check if a file exists in a zip file

Description

Usage

Arguments

Value

Examples

Check for malicious files

Description

Usage

Arguments

Value

Examples

Check and format image URLs

Description

Usage

Arguments

Value

Examples

Check and format non-image hyperlinks

Description

Usage

Arguments

Value

Examples

Check if a number passes the Luhn algorithm

Description

Usage

Arguments

Value

Examples

Create a formatted Excel file based on validation rules

Description

Usage

Arguments

Value

Examples

Download all data alternative

Description

Usage

Arguments

Value

Examples

Invalid example data

Description

Format

Examples

Check if an object is of class POSIXct

Description

Usage

Arguments

Value

Examples

Name datasets

Description

Usage

Arguments

Value

Examples

Query a MongoDB document by an ObjectID

Description

Usage

Arguments

Value

Examples

Read and format data from csv or xlsx files

Description

Usage

Arguments

Value

Examples

Read rules from a file

Description