Analyse temporal patterns in theme prevalence within a single run

Requires the data to contain a std_timestamp column (character, parseable as dates). Detects the appropriate time granularity, computes theme prevalence per period, and builds an emergence timeline showing when each theme first appeared in the dataset.

Usage

analyze_temporal_patterns(data, theme_set, coding_state = NULL)

Arguments

data

Tibble with at least std_timestamp and theme assignment columns (emerged_themes and/or theme_membership_*).

theme_set

ThemeSet object

coding_state

ProgressiveCodingState (or NULL)

Value

A list with elements:

prevalence_over_time

Tibble: period, theme_name, n_entries, pct_of_period, total_in_period

emergence_timeline

Tibble: theme_name, first_appearance_date, first_code_date, n_codes_at_emergence, n_entries (cumulative entry count per theme, used by the temporal_emergence.png top-N filter)

period_type

Character: "daily", "weekly", "monthly", or "quarterly"

has_temporal_data

Logical: TRUE when usable timestamps exist