Package: tuiksdmx 0.1.1

Muhammed Fatih Tuzen

tuiksdmx: Access the Turkish Statistical Institute SDMX REST API

Provides an authenticated, metadata-driven R client for the Turkish Statistical Institute (TURKSTAT) SDMX REST API. Supports dataflow discovery, data structure definitions, concepts, codelists, metadata-based filter validation, SDMX query construction, statistical data retrieval, and observation-based availability exploration. Authentication supports an environment variable or an explicitly supplied API key argument.

Authors:Muhammed Fatih Tuzen [aut, cre]

tuiksdmx_0.1.1.tar.gz
tuiksdmx_0.1.1.zip(r-4.7-any)tuiksdmx_0.1.1.zip(r-4.6-any)
tuiksdmx_0.1.1.tar.gz(r-4.7-any)tuiksdmx_0.1.1.tar.gz(r-4.6-any)
tuiksdmx_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tuiksdmx/json (API)

# Install 'tuiksdmx' in R:
install.packages('tuiksdmx', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mfatihtuzen/tuiksdmx/issues

Pkgdown/docs site:https://mfatihtuzen.github.io

On CRAN:

Conda:

3.48 score 23 exports 34 dependencies

Last updated from:b281a7d06c. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK120
source / vignettesOK203
linux-releaseOK115
windows-develOK188
windows-releaseOK195
wasm-releaseOK100

Exports:tuik_attributestuik_available_codestuik_build_keytuik_conceptstuik_dataflowstuik_decode_datatuik_dimension_codestuik_dimensionstuik_exploretuik_filter_dimensionstuik_filter_templatetuik_flow_profiletuik_get_codelisttuik_get_concept_schemetuik_get_datatuik_get_dataflowtuik_get_dsdtuik_key_dimensionstuik_measurestuik_missing_filterstuik_search_conceptstuik_search_dataflowstuik_validate_filters

Dependencies:askpassbitbit64clicliprcpp11crayoncurldigestdplyrgenericsgluehmshttr2lifecyclemagrittropensslpillarpkgconfigprettyunitsprogresspurrrR6readrrlangsystibbletidyselecttzdbutf8vctrsvroomwithrxml2

Authentication and secure use
Why authentication is required | Choosing how to supply an API key | Option 1 — Persistent environment variable (recommended) | Option 2 — Supply the key explicitly | Option 3 — Set the environment variable for this R session | What the package does with the key | Credentials in reproducible analyses | Troubleshooting
Rendered fromauthentication.Rmdusingknitr::rmarkdown

Last update: 2026-11-13
Started: 2026-11-13

Defined codes versus observed availability
The question: which codes actually appear? | 1. Start with the defined domain | 2. Ask which codes occur in a bounded period | 3. Add partial filters | 4. A complete key can be more selective | 5. Correct interpretation
Rendered fromobserved-availability.Rmdusingknitr::rmarkdown

Last update: 2026-11-13
Started: 2026-11-13

Filtering, SDMX keys and performance
Purpose | 1. The easiest request: a bounded period | 2. Partial filters: download all, then select locally | 3. Complete filters: use a server-side SDMX key | 4. Guardrails: allow_all versus allow_unbounded | 5. Decoding labels without discarding SDMX codes | 6. Why the same request can have different durations
Rendered fromfiltering-and-performance.Rmdusingknitr::rmarkdown

Last update: 2026-11-13
Started: 2026-11-13

Getting started with tuiksdmx
Overview | 1. Install and authenticate | 2. Discover a Dataflow | 3. Explore the dataset before downloading data | 4. Inspect available defined codes | 5. Beginner route: download a short period | A small, empirically tested labour-market example | 6. Intermediate route: select only the dimensions you know | Why allow_all = TRUE? | 7. Advanced route: a complete SDMX key | 8. Defined codes versus observed codes | 9. Common questions | Next steps
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-11-13
Started: 2026-11-13

Labels and decoding SDMX data
Why keep codes and add labels? | 1. Start with raw codes | 2. Add Turkish, English or both labels at retrieval time | 3. Decode data you have already downloaded | 4. Choose dimensions from metadata, not guesswork | 5. Labels do not prove availability | 6. Common pitfalls | Next steps
Rendered fromlabels-and-decoding.Rmdusingknitr::rmarkdown

Last update: 2026-11-13
Started: 2026-11-13

Understanding Dataflows, DSDs and metadata
Purpose | Authentication | 1. Find and inspect a Dataflow | 2. Read the structure without downloading observations | 3. Distinguish concepts and Codelists | 4. Examine candidate filters | 5. Reproducibility and metadata changes
Rendered frommetadata-and-dsd.Rmdusingknitr::rmarkdown

Last update: 2026-11-13
Started: 2026-11-13