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aradR is an independent R client for the public Czech
National Bank ARAD API. It is designed around a simple workflow:
aradR is a personal open-source project authored and
maintained by Simona Malovana. It is not official Czech National Bank
software.
For the released package on CRAN:
For the current development version:
ARAD API access requires a key associated with an ARAD user account.
Generate the key in ARAD and store it outside source code. A convenient
place is ~/.Renviron:
ARAD_API_KEY=your_key_here
Restart R after editing .Renviron. Never commit the key
to a repository, notebook, vignette, screenshot, or example output.
aradR 0.2.0 targets the public ARAD API only.
Organization-internal endpoints and integrated authentication are
intentionally outside this package.
ARAD metadata endpoints are scoped. Start from a known set, base,
selection, or indicator ID. Within that scope, aradR
removes the need to know individual series IDs in advance.
library(aradR)
hits <- arad_find(
"inflation",
set_id = 1058,
lang = "en"
)
hits[, c("indicator_id", "indicator_name", "relevance_score", "matched_in")]arad_find() searches indicator names and IDs, hierarchy
paths and, by default, dimension metadata. Results are ranked
deterministically, and matched_in explains which metadata
fields produced the match.
For browsing rather than searching, build a catalogue:
For a lightweight name/ID search, use arad_search():
See the Finding data article for a fuller discovery workflow.
Before downloading observations, inspect the selected series:
The summary combines basic metadata, hierarchy path and ARAD-reported
availability. data_from and data_to are
availability boundaries; data_to can extend into a forecast
or reporting horizon and should not automatically be interpreted as the
latest observed historical date.
For a reproducible analytical window, specify dates explicitly:
For the full ARAD-reported history, omit the date boundaries:
aradR resolves missing boundaries through
/updates and divides long histories into bounded requests.
The returned data are long-format tibbles with
indicator_id, snapshot_id,
period, and value. Genuine missing source
values remain NA.
arad_wide() keeps one row per period. If the same
indicator is present in several snapshot contexts, snapshot IDs are
added to the series names automatically so observations are not
conflated.
List available snapshots with:
Then request a specific snapshot or an ARAD snapshot selector through
snapshot_ids:
Caching is off by default. Enable it explicitly when repeated calls should reuse raw responses:
x <- arad_get("SMV5M603", cache = "session")
x <- arad_get(
"SMV5M603",
cache = "disk",
cache_max_age = 24 * 60 * 60
)Clear package caches with:
Disk caching uses R’s user cache directory. API keys are not stored in cached response files.
Every arad_get() result carries retrieval
diagnostics:
The diagnostics report the retrieval strategy, resolved range, number
of data requests and chunk size used. arad_wide() preserves
this attribute.
For analytical work, record the indicator IDs, requested date range, snapshot choice if relevant, package version and retrieval diagnostics. When presenting ARAD data, cite the underlying data source as Czech National Bank ARAD; package citation and data-source citation are separate.
For details on chunking, parsing, missing values and error handling, continue with the Reliability and reproducibility article.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.