knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
ARAD contains many time series, and the most useful workflow is often to start from a subject rather than from a known indicator ID. aradR provides a small discovery layer for that task.
ARAD metadata endpoints are scoped. Supply exactly one of a set, base, selection, or indicator selector when the endpoint requires it.
library(aradR) catalog <- arad_catalog(set_id = 1058, lang = "en")
arad_catalog() returns one row per indicator and can enrich the basic metadata with hierarchy paths and availability information.
Use arad_find() when you know what you are looking for conceptually but do not know the indicator code:
hits <- arad_find( "inflation", set_id = 1058, lang = "en" )
The search covers indicator IDs and names, hierarchy paths, and—by default—dimension metadata. Results are ranked deterministically.
hits[, c( "indicator_id", "indicator_name", "relevance_score", "matched_in" )]
matched_in explains whether the term matched the indicator ID, indicator name, hierarchy path or dimensions. The numeric relevance score is only an ordering device for the current query; it is not a measure of statistical importance.
When a scope contains several frequencies, filter during discovery:
monthly <- arad_find( "households", base_id = "MBOP", frequency = "M", lang = "en" )
Once you have plausible candidates, inspect them before retrieving observations:
info <- arad_info(hits$indicator_id[1], lang = "en") info$summary info$dimensions info$updates
The summary includes hierarchy and ARAD-reported availability. data_to can include a future forecast or reporting horizon, so do not automatically interpret it as the latest observed historical date.
arad_search() is faster and simpler when searching only basic indicator names and IDs is sufficient:
quick_hits <- arad_search( "inflation", set_id = 1058, lang = "en" )
A practical rule is:
arad_catalog() to browse a scope;arad_find() for human-readable discovery;arad_info() to inspect candidates;arad_search() when you only need a lightweight name/ID filter.After discovery, pass the selected ID directly to arad_get():
x <- arad_get( indicator_ids = hits$indicator_id[1], from = "2020-01-01" )
This keeps discovery and retrieval separate: first decide what the series represents, then download the observations needed for the analysis.
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