Create agents from an in-memory data frame of respondents

Like [create_agents_from_survey()] but starting from a data frame already in memory (for example `LLMR::anes_2024_personas`). Demographic columns are rendered as background; the remaining columns are rendered as survey responses, keyed by their question wording when the frame carries a `dictionary` attribute (see [LLMR::llm_persona_split()]), else by their column names. Values are taken as-is (decode and clean them first if they are still coded).

Usage

create_agents_from_data(
  data,
  n_participants,
  config,
  demographic_cols = NULL,
  rows = NULL,
  weights = NULL,
  .runner = NULL
)

Arguments

data

A data frame, one respondent per row.

n_participants

Integer number of participants (excludes the moderator).

config

An explicit `LLMR::llm_config` for all agents.

demographic_cols

Character vector of columns to render as demographics. Defaults to the `data`'s `"demographic_fields"` attribute when present, else a small set of common demographic column names found in `data`.

rows, weights

See [create_agents_from_survey()].

.runner

Optional function used instead of live model calls and stored on every agent.

Value

A named list of `FGAgent` objects (participants + moderator), keyed by agent ID.

Details

A frame of class `silicon_panel`, or a frame with both `persona` and `persona_id` columns, is treated as a pre-rendered persona panel. Its `persona` text becomes each participant's direct persona description; `persona` and `persona_id` are not rendered as survey answers. This bridge uses the frame's structure and does not require LLMRpanel.

Examples

if (FALSE) { # \dontrun{
data(anes_2024_personas, package = "LLMR")
cfg <- LLMR::llm_config("openai", "gpt-4o-mini")
agents <- create_agents_from_data(
  anes_2024_personas, n_participants = 6, config = cfg
)
} # }