tuikr provides functions to query Turkish Statistical Institute (TUIK) data from two portals:
- Statistical data: Themes, file downloads, SDMX dataflows, and legacy databases from veriportali.tuik.gov.tr
- Geographic data: Maps and spatial statistics from cip.tuik.gov.tr
Disclaimer: This package is not affiliated with, endorsed by, or connected to Turkish Statistical Institute (TUIK). It is an independent tool for academic research purposes.
Quick Start
Statistical Data
1. List themes
library(tuikr)
library(tibble)
(theme_catalog <- statistical_themes())
#> # A tibble: 19 × 2
#> theme_name theme_id
#> <chr> <chr>
#> 1 Justice and Elections 1
#> 2 Science, Technology and Information Society 2
#> 3 Environment 3
#> 4 Education 4
#> 5 Energy 5
#> 6 Price Statistics 6
#> 7 Income, Consumption and Poverty 7
#> 8 Employment, Unemployment and Wages 8
#> 9 Short-Term Economic Indicators 9
#> 10 Culture and Sports 10
#> 11 Population and Demography 11
#> 12 Health and Social Protection 12
#> 13 Agriculture 13
#> 14 Tourism 14
#> 15 Transport and Communication 15
#> 16 National Accounts 16
#> 17 International Trade 17
#> 18 Structural Business Statistics and Business Demography 18
#> 19 Multi-Domain Statistics 202. Tables for Population & Demography (theme 11)
(population_tables <- statistical_tables("11"))
#> # A tibble: 673 × 6
#> theme_name theme_id table_name node_type dataflow_id table_url
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Population and Demography 11 "Foreign … istab <NA> https://…
#> 2 Population and Demography 11 "Foreign-… istab <NA> https://…
#> 3 Population and Demography 11 "Foreign-… istab <NA> https://…
#> 4 Population and Demography 11 "Median A… istab <NA> https://…
#> 5 Population and Demography 11 "Number a… istab <NA> https://…
#> 6 Population and Demography 11 "Populati… istab <NA> https://…
#> 7 Population and Demography 11 "Populati… istab <NA> https://…
#> 8 Population and Demography 11 "Populati… istab <NA> https://…
#> 9 Population and Demography 11 "Populati… istab <NA> https://…
#> 10 Population and Demography 11 "Populati… istab <NA> https://…
#> # ℹ 663 more rows3. SDMX dataflows only
(population_dataflows <- dplyr::filter(
population_tables,
node_type == "dataflow"
))
#> # A tibble: 86 × 6
#> theme_name theme_id table_name node_type dataflow_id table_url
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Population and Demography 11 Age depen… dataflow TR,DF_ADNK… https://…
#> 2 Population and Demography 11 Age depen… dataflow TR,DF_ADNK… https://…
#> 3 Population and Demography 11 Annual gr… dataflow TR,DF_ADNK… https://…
#> 4 Population and Demography 11 Annual gr… dataflow TR,DF_ADNK… https://…
#> 5 Population and Demography 11 Average s… dataflow TR,DF_ADNK… https://…
#> 6 Population and Demography 11 Foreign p… dataflow TR,DF_ADNK… https://…
#> 7 Population and Demography 11 Foreign p… dataflow TR,DF_ADNK… https://…
#> 8 Population and Demography 11 Foreign-b… dataflow TR,DF_ADNK… https://…
#> 9 Population and Demography 11 Foreign-b… dataflow TR,DF_ADNK… https://…
#> 10 Population and Demography 11 Life Expe… dataflow TR,DF_DNG_… https://…
#> # ℹ 76 more rows4. File downloads expose a direct table_url
population_files <- dplyr::filter(
population_tables,
node_type == "istab"
)
population_files$table_url[1]
#> [1] "https://veriportali.tuik.gov.tr/api/en/data/downloads?t=i&p=y6zmfhOmCjQjF4ZfBaWIpFhBjpeLg3lcu5mxFfrBelfKUj1RnEPdoKevC%2BFZ3GOiiWgNOga6%2BreB5jAY1IkWPxniqzaveWLwU2fGqU8Mrns%3D"5. Download one dataset
(population_observations <- statistical_data(
population_dataflows$dataflow_id[1]
))
#> # A tibble: 54 × 4
#> ADNKS_GOSTERGE ADNKS_GOSTERGE_label obsTime obsValue
#> <chr> <chr> <chr> <dbl>
#> 1 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2007 39.7
#> 2 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2008 39.3
#> 3 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2009 38.8
#> 4 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2010 38.1
#> 5 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2011 37.5
#> 6 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2012 36.9
#> 7 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2013 36.3
#> 8 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2014 35.8
#> 9 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2015 35.4
#> 10 COCUK_BAG_ORAN Child dependency ratio % (0-14 years) 2016 34.9
#> # ℹ 44 more rowsstatistical_data() adds adjacent *_label columns when TUIK exposes human-readable code-list metadata. The default key = "ALL" works for many datasets, but some SDMX dataflows need a narrower key to constrain the remaining dimensions.
6. Legacy database URLs
(population_databases <- statistical_databases("11"))
#> # A tibble: 20 × 4
#> theme_name theme_id db_name db_url
#> <chr> <chr> <chr> <chr>
#> 1 Population and Demography 11 Address Based Population Registrat… http:…
#> 2 Population and Demography 11 Family Structure http:…
#> 3 Population and Demography 11 Child Statistics-Culture and Sports http:…
#> 4 Population and Demography 11 Child Statistics-Demographic Chara… http:…
#> 5 Population and Demography 11 Child Statistics-Education http:…
#> 6 Population and Demography 11 Child Statistics-Health http:…
#> 7 Population and Demography 11 Child Statistics-Housing Character… http:…
#> 8 Population and Demography 11 Child Statistics-ICT Usage http:…
#> 9 Population and Demography 11 Child Statistics-Labour Force http:…
#> 10 Population and Demography 11 Child Statistics-Poverty http:…
#> 11 Population and Demography 11 Child Statistics-Security and Just… http:…
#> 12 Population and Demography 11 General Population Census http:…
#> 13 Population and Demography 11 Survey on Building and Dwelling Ch… http:…
#> 14 Population and Demography 11 Internal migration http:…
#> 15 Population and Demography 11 Life Tables http:…
#> 16 Population and Demography 11 Marriage Statistics http:…
#> 17 Population and Demography 11 Divorce Statistics http:…
#> 18 Population and Demography 11 Birth Statistics http:…
#> 19 Population and Demography 11 Suicide Statistics http:…
#> 20 Population and Demography 11 Death Statistics http:…7. All portal resources
(population_resources <- statistical_resources("11"))
#> # A tibble: 718 × 6
#> theme_name theme_id resource_name resource_type dataflow_id resource_url
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Population and… 11 "The Results… press <NA> https://ver…
#> 2 Population and… 11 "Urban-Rural… press <NA> https://ver…
#> 3 Population and… 11 "Foreign pop… istab <NA> https://ver…
#> 4 Population and… 11 "Foreign-bor… istab <NA> https://ver…
#> 5 Population and… 11 "Foreign-bor… istab <NA> https://ver…
#> 6 Population and… 11 "Median Age … istab <NA> https://ver…
#> 7 Population and… 11 "Number and … istab <NA> https://ver…
#> 8 Population and… 11 "Population … istab <NA> https://ver…
#> 9 Population and… 11 "Population … istab <NA> https://ver…
#> 10 Population and… 11 "Population … istab <NA> https://ver…
#> # ℹ 708 more rows8. Press releases and reports keep their portal URLs
population_publications <- dplyr::filter(
population_resources,
resource_type %in% c("press", "report")
)
population_publications |>
dplyr::select(resource_type, resource_name, resource_url) |>
dplyr::slice(3)
#> # A tibble: 1 × 3
#> resource_type resource_name resource_url
#> <chr> <chr> <chr>
#> 1 press World Population Day https://veriportali.tuik.gov.tr/en/press/5…Geographic Data
1. List available geographic variables
(geo_variable_catalog <- geo_data())
#> # A tibble: 80 × 4
#> var_name var_num var_levels var_period
#> <chr> <chr> <list> <chr>
#> 1 Rate of population served by municipal waste s… CVRBA-… <int [2]> yillik
#> 2 Ratio of Population Served by Wastewater Treat… CVRAS-… <int [2]> yillik
#> 3 Daily Per Capita Wastewater Amount (L/Capita-D… CVRAS-… <int [2]> yillik
#> 4 Ratio of Population Provided with Sewerage Ser… CVRAS-… <int [2]> yillik
#> 5 Proportion of Population with Potable Water Ne… CVRBS-… <int [2]> yillik
#> 6 Ratio of Population Served by Drinking Water T… CVRBS-… <int [2]> yillik
#> 7 Total electricity consumption per capita (kWh) ENR-GK… <int [1]> yillik
#> 8 Average Socioeconomic Level Scores ses123 <int [2]> yillik
#> 9 Mean Years of Schooling (year) ULE-GK… <int [2]> yillik
#> 10 Number of Illiterate ULE-GK… <int [3]> yillik
#> # ℹ 70 more rows2. List geographic variables in Turkish
(geo_variable_catalog_tr <- geo_data(lang = "tr"))
#> # A tibble: 80 × 4
#> var_name var_num var_levels var_period
#> <chr> <chr> <list> <chr>
#> 1 Atık hizmeti verilen belediye nüfusunun toplam… CVRBA-… <int [2]> yillik
#> 2 Atıksu Arıtma Hizmeti Verilen Nüfus Oranı (%) CVRAS-… <int [2]> yillik
#> 3 Kişi Başı Günlük Atıksu Miktarı (L/Kişi-Gün) CVRAS-… <int [2]> yillik
#> 4 Kanalizasyon Hizmeti Verilen Nüfus Oranı (%) CVRAS-… <int [2]> yillik
#> 5 İçme Suyu Şebekesi Bulunan Nüfus Oranı (%) CVRBS-… <int [2]> yillik
#> 6 İçme Suyu Arıtma Hizmeti Verilen Nüfus Oranı (… CVRBS-… <int [2]> yillik
#> 7 Kişi Başına Elektrik Tüketimi (kWh) ENR-GK… <int [1]> yillik
#> 8 Ortalama Sosyoekonomik Seviye Skorları ses123 <int [2]> yillik
#> 9 Ortalama Eğitim Süresi (yıl) ULE-GK… <int [2]> yillik
#> 10 Okuma Yazma Bilmeyen Sayısı ULE-GK… <int [3]> yillik
#> # ℹ 70 more rows3. Download data for a specific variable
(population_values <- geo_data(
var_num = "ADNKS-GK137473-O29001",
var_level = 3
))
#> # A tibble: 405 × 3
#> code date population_of_sre_1_sre_2_provinces_and_districts
#> <chr> <chr> <chr>
#> 1 39 2025 379595
#> 2 39 2024 379031
#> 3 39 2023 377156
#> 4 39 2022 369347
#> 5 39 2021 366363
#> 6 8 2025 167531
#> 7 8 2024 169280
#> 8 8 2023 172356
#> 9 8 2022 169403
#> 10 8 2021 169543
#> # ℹ 395 more rows4. Get map boundaries at different levels
nuts2_map <- geo_map(level = 2) # 26 regions
nuts3_map <- geo_map(level = 3) # 81 provinces
lau1_map <- geo_map(level = 4) # 973 districts
settlements <- geo_map(level = 9) # settlement pointsgeo_map() returns sf objects in WGS 84 (EPSG:4326), ready for dplyr::left_join() on the code column when you want to combine boundaries with values returned by geo_data().