Sheet structure

This page describes the common structure of actionsheets.

Most actionsheets describe how to create and manipulate a specific data type or object. For example, there are actionsheets about strings, lists, and data.frames.

Sheets are hierarchically structured using sections. The main sections are:

1. Create
   How to define, instantiate or create the data type, possibly from other types.
   Examples: define a date, parse date from string, create set from a list of values
2. Test
   Check or assess something about the object.
   Examples: is it empty? does it contain a given value?
3. Extract
   Get properties, attributes, or other information about the object of a different type than the object (typically scalars). Examples: length of a list, element value at a given index, max value among elements.
4. Derive / Update
   Create a derived or altered object from the given object, preserving the object type, or update the object in-place / by-reference.
   Examples: head of a list, slicing a string, selecting conditional rows from a data frame.
5. Convert
   Export / represent the object as a different data type.
   Examples: datetime as string, dictionary as list of key-value pairs, data frame as matrix.

Additional common categories, used when applicable:

• Constants: Available constants, defaults, or singleton
• Usage: Specific syntax, statements, or recommended patterns.
• Iterate: Operations that iterate or traverse through the object (its elements, keys, rows, nodes).
• Install: Installation instructions.
• Show: Ways to print or visualize the object in a human-readable way.

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Create

How to define, instantiate or create the data type, possibly from other types.
Examples: define a date, parse date from string, create set from a list of values

Test

Check or assess something about the object.
Examples: is it empty? does it contain a given value?

Extract

Get properties, attributes, or other information about the object of a different type than the object (typically scalars). Examples: length of a list, element value at a given index, max value among elements.

Subcategories

• Properties
  Operations which retrieve properties or attributes of the object.
  Examples: length of a list, number of columns of a data frame.
• Find
  Operations which attempt to find a value or index of an object, typically with index output.
  Examples: find max value, find index of min value, find key of most frequent value
• Aggregate
  Operations which aggregate the object in a way that involves a computation, typically with scalar output.
  Examples: sum all elements of a list, number of occurrences per value

Derive / update

Create a derived or altered object from the given object, preserving the object type, or update the object in-place / by-reference.
Examples: head of a list, slicing a string, selecting conditional rows from a data frame.

Subcategories

• Transform
  Operations that apply a transformation to the object or each of its elements, preserving the shape of the object.
  Examples: element-wise operations such as adding a constant to a vector, or computing the cumulative sum over the elements
• Mask
  Generate a boolean mapping. Typically used as a logical mask or logical index for filtering elements in later operations.
• Order
  Operations which change the order of elements, but not their values.
  Examples: reversing the elements of a list, sorting a data frame by column values
• Reshape
  Operations which change the shape of the object, but preserves all elements.
  Examples: transposing a matrix, converting a data frame to narrow format.
• Grow
  Operations which possibly increase the number of elements of the object.
  Examples: appending elements to a list, replicating elements.
  • Add
    Add one or more new elements.
  • Replicate
    Operations which increase the number of elements through replication or derivation.
• Shrink
  Operations which possibly reduce the number of elements of the object.
  Examples: Removing elements from a list, removing duplicates.
  • :Remove
    Remove one or more elements.
  • Aggregate
    Operations which aggregate elements by some grouping logic.
• Combine
  Operations which combines, merge or joins two or more objects. The number of elements may grow or shrink, depending on the operation and input.
  Examples: stacking lists, set union, joining two data frames.

Convert

Export / represent the object as a different data type.
Examples: datetime as string, dictionary as list of key-value pairs, data frame as matrix.

Elements are preserved unless mentioned otherwise. However, some attributes may be lost in the process.