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Explore raw categories in political and policy documents

Source: R/explore.R
explore.Rd

Wraps the Python catpol.explore() function. Returns every category string extracted from every chunk across every iteration – with duplicates intact.

Usage

explore(
  input_data = NULL,
  api_key = NULL,
  source = NULL,
  doc_type = NULL,
  since = NULL,
  until = NULL,
  n = NULL,
  document_context = "",
  description = "",
  max_categories = 12L,
  categories_per_chunk = 10L,
  divisions = 12L,
  user_model = "gpt-4o",
  creativity = NULL,
  specificity = "broad",
  research_question = NULL,
  filename = NULL,
  model_source = "auto",
  iterations = 8L,
  random_state = NULL,
  focus = NULL,
  chunk_delay = 0
)

Arguments

input_data

A character vector, list, or NULL to fetch from a registered source. Default NULL.

api_key

Character or NULL. API key for the LLM provider.

source

Character or NULL. Registered source name.

doc_type

Character or NULL. Filter source by document type.

since

Character or NULL. Earliest source row date (YYYY-MM-DD).

until

Character or NULL. Latest source row date (YYYY-MM-DD).

n

Integer or NULL. Max number of source rows.

document_context

Character. Context about the document. Default "".

description

Character. Additional context. Default "".

max_categories

Integer. Default 12L.

categories_per_chunk

Integer. Default 10L.

divisions

Integer. Default 12L.

user_model

Character. Default "gpt-4o".

creativity

Numeric or NULL. Default NULL.

specificity

Character. Default "broad".

research_question

Character or NULL.

filename

Character or NULL.

model_source

Character. Default "auto".

iterations

Integer. Default 8L.

random_state

Integer or NULL.

focus

Character or NULL.

chunk_delay

Numeric. Default 0.0.

Value

A character vector of every category string extracted.

Examples

if (FALSE) { # \dontrun{
raw_cats <- explore(
  source     = "federal_executive_orders",
  since      = "2025-01-01",
  n          = 30L,
  api_key    = Sys.getenv("OPENAI_API_KEY"),
  user_model = "gpt-4o-mini",
  iterations = 4L
)
sort(table(raw_cats), decreasing = TRUE)
} # }