Clarion: Clarion R6-class definition

Description

Use this to create a clarion object. This object is used by all top-level wilson modules.

Constructor

Clarion$new(header = NULL, metadata, data, validate = TRUE)

Constructor Arguments

Variable	Return	`header`
A named list. Defaults to NULL.	`metadata`	Clarion metadata in form of a data.table.
`data`	Data.table according to metadata.	`validate`

Public fields

header: List of global information regarding the whole experiment.
metadata: Data.table with additional information for each column.
data: Data.table containing experiment result data.

Methods

Public methods

`Clarion$get_id()`

Returns name of unique identifier column. Assumes first feature to be unique if not specified.

Usage

Clarion$get_id()

Returns

Name of the id column.

`Clarion$get_name()`

Returns name of name column. If not specified return unique Id.

Usage

Clarion$get_name()

Returns

Name of the name column.

`Clarion$get_delimiter()`

Return delimiter used within multi-value fields (no delimiter = NULL).

Usage

Clarion$get_delimiter()

`Clarion$is_delimited()`

Logical whether the given column name is delimited.

Usage

Clarion$is_delimited(x)

Arguments

x: Name of the column.

Returns

boolean

`Clarion$get_factors()`

Get factors to all columns.

Usage

Clarion$get_factors()

Details

Named factors (e.g. factor1="name") will be cropped to their name.

Returns

Returns a data.table columns: key and factor(s) if any.

`Clarion$get_level()`

Get level(s) to given column name(s).

Usage

Clarion$get_level(column)

Arguments

column: One or more column name(s).

Returns

Provide a vector of levels to the given columnnames in column. Returns NA for missing columns and character(0) if column = NULL.

`Clarion$get_label()`

Get label(s) to given column name(s).

Usage

Clarion$get_label(column = NULL, sub_label = TRUE, sep = " ")

Arguments

column: One or more column name(s).
sub_label: Whether the sub_label should be included.
sep: Separator between label and sub_label.

Details

If a column does not have a label the key is returned.

Returns

Provides a vector of labels (+ sub_label) to the given columnnames in column. Returns NA for missing columns and all labels if column = NULL.

`Clarion$validate()`

Check the object for inconsistencies.

Usage

Clarion$validate(solve = TRUE)

Arguments

solve: For solve = TRUE try to resolve some warnings.

`Clarion$new()`

Initialize a new clarion object.

Usage

Clarion$new(header = NULL, metadata, data, validate = TRUE)

Arguments

header: A named list. Defaults to NULL.
metadata: Clarion metadata in form of a data.table.
data: Data.table according to metadata.
validate: Logical value to validate on initialization. Defaults to TRUE.

Returns

Clarion object.

`Clarion$write()`

Save the object as a clarion file.

Usage

Clarion$write(file)

Arguments

file: Filename for the file to be written.

`Clarion$clone()`

The objects of this class are cloneable with this method.

Usage

Clarion$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

Run this code

# generate clarion contents
header <- list(format = "Clarion", version = "1.0", experiment_id = "123456")

metadata <- data.table::as.data.table(list(
  key = c("id", "name", "sample_a", "sample_b"),
  factor1 = c("", "", "sample_a", "sample_b"),
  level = c("feature", "feature", "sample", "sample"),
  type = c("unique_id", "name", "score", "score"),
  label = c("Identifier", "Name", "Sample A", "Sample B")
))

data <- data.table::data.table(
  id = c("id_1", "id_2", "id_3"),
  name = c("AAA", "BBB", "CCC"),
  sample_a = c(10000, 300, 20),
  sample_b = c(50, 40000, 12002)
)

# initializing a new object
object <- Clarion$new(header = header, metadata = metadata, data = data, validate = TRUE)

# create a deep copy
object_copy <- object$clone(deep = TRUE)

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