R/data.R

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

#' Human-AI Vibe Coding Interaction Data (Long Format)
#'
#' Coded interaction sequences from 429 human-AI pair programming sessions
#' across 34 projects, in long format.
#'
#' @format Data frames in long format with 9 columns:
#' \describe{
#'   \item{message_id}{Integer. Turn index.}
#'   \item{project}{Character. Project identifier (Project_1 .. Project_34).}
#'   \item{session_id}{Character. Unique session hash.}
#'   \item{timestamp}{Integer. Unix timestamp for ordering.}
#'   \item{session_date}{Character. Date of the session (YYYY-MM-DD).}
#'   \item{code}{Character. Interaction code (17 action labels).}
#'   \item{cluster}{Character. High-level cluster: Action, Communication,
#'     Directive, Evaluative, Metacognitive, or Repair.}
#'   \item{code_order}{Integer. Order of the code within the session.}
#'   \item{order_in_session}{Integer. Absolute turn order within the session.}
#' }
#'
#' \tabular{ll}{
#'   \code{human_long} \tab Human turns only, 10,796 rows \cr
#'   \code{ai_long}    \tab AI turns only, 8,551 rows \cr
#' }
#'
#' @source Saqr, M. (2026). Human-AI vibe coding interaction study.
#'   \url{https://saqr.me/blog/2026/human-ai-interaction-cograph/}
#'
#' @examples
#' net <- build_network(human_long, method = "tna",
#'                      action = "code", actor = "session_id",
#'                      time = "timestamp")
#' net
#'
#' @name long-data
#' @aliases ai_long
NULL

#' @rdname long-data
"human_long"

#' @rdname long-data
"ai_long"


#' Group Regulation in Collaborative Learning (Long Format)
#'
#' Students' regulation strategies during collaborative learning, in long
#' format. Contains 27,533 timestamped action records from multiple students
#' working in groups across two courses.
#'
#' @format A data frame with 27,533 rows and 6 columns:
#' \describe{
#'   \item{Actor}{Integer. Student identifier.}
#'   \item{Achiever}{Character. Achievement level: \code{"High"} or \code{"Low"}.}
#'   \item{Group}{Numeric. Collaboration group identifier.}
#'   \item{Course}{Character. Course identifier (\code{"A"}, \code{"B"}, or \code{"C"}).}
#'   \item{Time}{POSIXct. Timestamp of the action.}
#'   \item{Action}{Character. Regulation action (e.g., cohesion, consensus,
#'     discuss, synthesis).}
#' }
#'
#' @source Synthetically generated from the \code{group_regulation} dataset
#'   in the \pkg{tna} package.
#'
#' @seealso \code{\link{learning_activities}}, \code{\link{srl_strategies}}
#'
#' @examples
#' net <- build_network(group_regulation_long,
#'                      method = "relative",
#'                      actor = "Actor", action = "Action", time = "Time")
#' net
#'
"group_regulation_long"


#' Self-Regulated Learning Strategy Frequencies
#'
#' Simulated frequency counts of 9 self-regulated learning (SRL) strategies
#' for 250 university students. Strategies are grouped into three clusters:
#' metacognitive (Planning, Monitoring, Evaluating), cognitive (Elaboration,
#' Organization, Rehearsal), and resource management (Help_Seeking, Time_Mgmt,
#' Effort_Reg). Within-cluster correlations are moderate (0.3--0.6),
#' cross-cluster correlations are weaker.
#'
#' @format A data frame with 250 rows and 9 columns. Each column is an
#'   integer count of how often the student used that strategy.
#'
#' @examples
#' net <- build_network(srl_strategies, method = "glasso",
#'                      params = list(gamma = 0.5))
#' net
#'
"srl_strategies"


#' Online Learning Activity Indicators
#'
#' Simulated binary time-series data for 200 students across 30 time points.
#' At each time point, one or more learning activities may be active (1) or
#' inactive (0). Activities: Reading, Video, Forum, Quiz, Coding, Review.
#' Includes temporal persistence (activities tend to continue across
#' adjacent time points).
#'
#' @format A data frame with 6,000 rows and 7 columns:
#' \describe{
#'   \item{student}{Integer. Student identifier (1--200).}
#'   \item{Reading}{Integer (0/1). Reading activity indicator.}
#'   \item{Video}{Integer (0/1). Video watching indicator.}
#'   \item{Forum}{Integer (0/1). Discussion forum indicator.}
#'   \item{Quiz}{Integer (0/1). Quiz/assessment indicator.}
#'   \item{Coding}{Integer (0/1). Coding practice indicator.}
#'   \item{Review}{Integer (0/1). Review/revision indicator.}
#' }
#'
#' @examples
#' head(learning_activities)
#'
"learning_activities"


#' ChatGPT Self-Regulated Learning Scale Scores
#'
#' Scale scores on five self-regulated learning (SRL) constructs for 1,000
#' responses generated by ChatGPT to a validated SRL questionnaire. Part of a
#' larger dataset comparing LLM-generated responses to human norms across seven
#' large language models.
#'
#' @format A data frame with 1,000 rows and 5 columns:
#' \describe{
#'   \item{CSU}{Numeric. Comprehension and Study Understanding scale mean.}
#'   \item{IV}{Numeric. Intrinsic Value scale mean.}
#'   \item{SE}{Numeric. Self-Efficacy scale mean.}
#'   \item{SR}{Numeric. Self-Regulation scale mean.}
#'   \item{TA}{Numeric. Task Avoidance scale mean.}
#' }
#'
#' @source Vogelsmeier, L.V.D.E., Oliveira, E., Misiejuk, K.,
#'   Lopez-Pernas, S., & Saqr, M. (2025). Delving into the psychology of
#'   Machines: Exploring the structure of self-regulated learning via
#'   LLM-generated survey responses. \emph{Computers in Human Behavior},
#'   173, 108769. \doi{10.1016/j.chb.2025.108769}
#'
#' @examples
#' net <- build_network(chatgpt_srl, method = "glasso",
#'                      params = list(gamma = 0.5))
#' net
#'
"chatgpt_srl"


#' Student Engagement Trajectories
#'
#' Wide-format state sequences of student engagement over 15 weekly
#' observations. Each row is one student; columns \code{1}..\code{15}
#' hold the engagement state for that week. States: \code{"Active"},
#' \code{"Average"}, \code{"Disengaged"}. Missing weeks are \code{NA}.
#'
#' @format A character matrix with 138 rows and 15 columns. Entries are
#'   one of \code{"Active"}, \code{"Average"}, \code{"Disengaged"}, or
#'   \code{NA}.
#'
#' @examples
#' \donttest{
#' sequence_plot(trajectories, main = "Engagement trajectories")
#' sequence_plot(trajectories, k = 3)
#' sequence_plot(trajectories, type = "distribution")
#' }
#'
"trajectories"