ena.accumulate.data: Accumulate data from a data frame into a set of adjacency...

In rENA: Epistemic Network Analysis

Accumulate data from a data frame into a set of adjacency (co-occurrence) vectors

Description

This function initializes an ENAdata object, processing conversations from coded data to generate adjacency (co-occurrence) vectors

Usage

ena.accumulate.data(
  units = NULL,
  conversation = NULL,
  codes = NULL,
  metadata = NULL,
  model = c("EndPoint", "AccumulatedTrajectory", "SeparateTrajectory"),
  weight.by = "binary",
  window = c("MovingStanzaWindow", "Conversation"),
  window.size.back = 1,
  window.size.forward = 0,
  mask = NULL,
  include.meta = T,
  as.list = T,
  ...
)

Arguments

units	A data frame where the columns are the properties by which units will be identified
conversation	A data frame where the columns are the properties by which conversations will be identified
codes	A data frame where the columns are the codes used to create adjacency (co-occurrence) vectors
metadata	(optional) A data frame with additional columns of metadata to be associated with each unit in the data
model	A character, choices: EndPoint (or E), AccumulatedTrajectory (or A), or SeparateTrajectory (or S); default: EndPoint. Determines the ENA model to be constructed
weight.by	(optional) A function to apply to values after accumulation
window	A character, choices are Conversation (or C), MovingStanzaWindow (MSW, MS); default MovingStanzaWindow. Determines how stanzas are constructed, which defines how co-occurrences are modeled
window.size.back	A positive integer, Inf, or character (INF or Infinite), default: 1. Determines, for each line in the data frame, the number of previous lines in a conversation to include in the stanza window, which defines how co-occurrences are modeled
window.size.forward	(optional) A positive integer, Inf, or character (INF or Infinite), default: 0. Determines, for each line in the data frame, the number of subsequent lines in a conversation to include in the stanza window, which defines how co-occurrences are modeled
mask	(optional) A binary matrix of size ncol(codes) x ncol(codes). 0s in the mask matrix row i column j indicates that co-occurrence will not be modeled between code i and code j
include.meta	Locigal indicating if unit metadata should be attached to the resulting ENAdata object, default is TRUE
as.list	R6 objects will be deprecated, but if this is TRUE, the original R6 object will be returned, otherwise a list with class 'ena.set'
...	additional parameters addressed in inner function

Details

ENAData objects are created using this function. This accumulation receives separate data frames for units, codes, conversation, and optionally, metadata. It iterates through the data to create an adjacency (co-occurrence) vector corresponding to each unit - or in a trajectory model multiple adjacency (co-occurrence) vectors for each unit.

In the default MovingStanzaWindow model, co-occurrences between codes are calculated for each line k in the data between line k and the window.size.back-1 previous lines and window.size.forward-1 subsequent lines in the same conversation as line k.

In the Conversation model, co-occurrences between codes are calculated across all lines in each conversation. Adjacency (co-occurrence) vectors are constructed for each unit u by summing the co-occurrences for the lines that correspond to u.

Options for how the data is accumulated are endpoint, which produces one adjacency (co-occurrence) vector for each until summing the co-occurrences for all lines, and two trajectory models: AccumulatedTrajectory and SeparateTrajectory. Trajectory models produce an adjacency (co-occurrence) model for each conversation for each unit. In a SeparateTrajectory model, each conversation is modeled as a separate network. In an AccumulatedTrajectory model, the adjacency (co-occurrence) vector for the current conversation includes the co-occurrences from all previous conversations in the data.

Value

ENAdata object with data [adjacency (co-occurrence) vectors] accumulated from the provided data frames.

See Also

ENAdata, ena.make.set

rENA documentation built on March 26, 2022, 1:10 a.m.