## ✔ Key could be verified via a test request## ℹ The provided key will be available for this R session## ℹ Add `STATCUBE_KEY_EXT = XXXX` to "~/.Renviron" to set the key
## persistently. Replace `XXXX` with your keyAt the time of writing this article, there are 315 datasets that are
assumed to be compatible with od_table(). This list is not
updated regularly, so to get the most recent list, a call to od_list()
will return the current list.
Interactive overview
Since some of the metadata contained in the OGD JSON files is only available in German, the following overview uses German labels. Click on the individual table cells to get more information.
CLI usage
To get a simplified version of this summary, use the
od_list() function. It uses webscraping techniques to get
dataset ids and German labels based on the contents of https://data.statistik.gv.at/web/catalog.jsp.
all_datasets <- od_list()
all_datasets# A tibble: 384 × 3
category id label
<chr> <chr> <chr>
1 Neueste Daten OGD_kjeunt08mmde_KJE08_MDE_U_M_2 Konjunkturstatistik im Produz…
2 Neueste Daten OGD_kjeunt08mmde_KJE08_MDE_U_M_1 Konjunkturstatistik im Produz…
3 Neueste Daten OGD_kjebet08mmde_KJE08_MDE_B_M_1 Konjunkturstatistik im Produz…
4 Neueste Daten OGD_kjeunt08m_KJE08_U_M_1 Konjunkturstatistik im Produz…
5 Neueste Daten OGD_kjebet08m_KJE08_B_M_1 Konjunkturstatistik im Produz…
6 Neueste Daten OGD_zlf_komm_ZLF_KOM_1 Kommerzielle Zivilluftfahrt
7 Neueste Daten OGD_konjidxdl21_KJIX_DL_21_1 Konjunkturindizes Dienstleist…
8 Neueste Daten OGD_konjidxhan21_KJIX_H_21_1 Konjunkturindizes Handel Basi…
9 Neueste Daten OGD_gvk_ware_2010_GVK_W10_1 Transportaufkommen und Transp…
10 Neueste Daten OGD_gvk_fahrt_2010_GVK_F10_1 Fahrten im Straßengüterverkeh…
# ℹ 374 more rowsOverview via json
If you identify an interesting dataset, consider downloading the metadata json to get more details. The json contains links to further metadata including a link to data.statistik.gv.at.
(id <- all_datasets$id[2])#> [1] "OGD_kjeunt08mmde_KJE08_MDE_U_M_1"
json <- od_json(id)
json#> Konjunkturstatistik im Produzierenden Bereich ab 2008 – monatliche
#> Unternehmensdaten (Grundgesamtheit)
#>
#> Measures: Unternehmen, Beschäftigte - insgesamt, Unselbständig Beschäftigte
#> - insgesamt, Brutto-Verdienste insgesamt in 1.000 EUR, Umsatz in 1.000 EUR
#> - insgesamt, Umsatz in 1.000 EUR - Inland
#> Fields: Berichtsmonat, ÖNACE 2008 (Ebene +4)
#> Updated: 2024-11-29 10:28:31
#> Tags: Konjunkturstatistik, Produzierenden Bereich, monatliche
#> Unternehmensdaten
#> Categories: Wirtschaft und TourismusThis output is generated from
OGD_kjeunt08mmde_KJE08_MDE_U_M_1.json
and shows a summary of the available metadata. Other parts of the
metadata can be extracted with $ using the keys from the
json specification.
json$extras$update_frequency#> [1] "monatlich"Showcase
The population dataset measures the Austrian population for 2117 different regions.
od_table("OGD_bevstandjbab2002_BevStand_2020")$tabulate()# A STATcubeR tibble: 392,144 x 5
`Time section` Sex Commune (aggregation by p…¹ `Age in single years` Number
* <date> <fct> <fct> <fct> <int>
1 2020-01-01 male Eisenstadt <10101> under 1 year old 77
2 2020-01-01 male Eisenstadt <10101> 1 year old 75
3 2020-01-01 male Eisenstadt <10101> 2 years old 70
4 2020-01-01 male Eisenstadt <10101> 3 years old 83
5 2020-01-01 male Eisenstadt <10101> 4 years old 67
6 2020-01-01 male Eisenstadt <10101> 5 years old 56
7 2020-01-01 male Eisenstadt <10101> 6 years old 75
8 2020-01-01 male Eisenstadt <10101> 7 years old 73
9 2020-01-01 male Eisenstadt <10101> 8 years old 74
10 2020-01-01 male Eisenstadt <10101> 9 years old 86
# ℹ 392,134 more rows
# ℹ abbreviated name: ¹`Commune (aggregation by political district)`The hospitalizations dataset is a timeseries from 2009 to 2019 for 115 different medical procedures.
od_table("OGD_krankenbewegungen_ex_LEISTUNGEN_1")$tabulate()# A STATcubeR tibble: 91,898 x 6
`Year of discharge` Sex `Age (four classes)` NUTS-2 region (place of resi…¹
* <date> <fct> <fct> <fct>
1 2009-01-01 male Up to 14 years old Non-Austria
2 2009-01-01 male Up to 14 years old Non-Austria
3 2009-01-01 male Up to 14 years old Non-Austria
4 2009-01-01 male Up to 14 years old Non-Austria
5 2009-01-01 male Up to 14 years old Non-Austria
6 2009-01-01 male Up to 14 years old Non-Austria
7 2009-01-01 male Up to 14 years old Non-Austria
8 2009-01-01 male Up to 14 years old Non-Austria
9 2009-01-01 male Up to 14 years old Non-Austria
10 2009-01-01 male Up to 14 years old Non-Austria
# ℹ 91,888 more rows
# ℹ abbreviated name: ¹`NUTS-2 region (place of residence)`
# ℹ 2 more variables: `Medical procedures - subchapters` <fct>,
# `Medical procedures` <int>The structure of earnings dataset showcases average earnings by four different classifications. See the tabulation article for some usage examples with this dataset.
od_table("OGD_veste309_Veste309_1")$tabulate()# A STATcubeR tibble: 72 x 9
Sex Citizenship `Region (NUTS2)` `Form of employment`
* <fct> <fct> <fct> <fct>
1 Sum total Total Total "Total"
2 Sum total Total Total "Standard employment "
3 Sum total Total Total "Non-standard employment (total)"
4 Sum total Total Total "Non-standard employment: part-time…
5 Sum total Total Total "Non-standard employment: fixed-ter…
6 Sum total Total Total "Non-standard employment: marginal …
7 Sum total Total Total "Non-standard employment: temporary…
8 Sum total Total AT11 Burgenland "Total"
9 Sum total Total AT12 Lower Austria "Total"
10 Sum total Total AT13 Vienna "Total"
# ℹ 62 more rows
# ℹ 5 more variables: `Arithmetic mean` <dbl>, `1st quartile` <dbl>,
# `2nd quartile (median)` <dbl>, `3rd quartile` <dbl>,
# `Number of employees` <dbl>The household forecast contains predictions about the number of private households by 4 household characteristics from 2011 to 2080.
od_table(dat_name)$tabulate()# A STATcubeR tibble: 630 x 4
Time `Province (NUTS 2-Einheit) <9>` Private households at the end of…¹
* <date> <fct> <int>
1 2011-01-01 Burgenland <AT11> 117588
2 2011-01-01 Carinthia <AT21> 241461
3 2011-01-01 Lower Austria <AT12> 682380
4 2011-01-01 Upper Austria <AT31> 593029
5 2011-01-01 Salzburg <AT32> 224629
6 2011-01-01 Styria <AT22> 515258
7 2011-01-01 Tyrol <AT33> 299024
8 2011-01-01 Vorarlberg <AT34> 152948
9 2011-01-01 Vienna <AT13> 843181
10 2012-01-01 Burgenland <AT11> 118776
# ℹ 620 more rows
# ℹ abbreviated name: ¹`Private households at the end of the year`
# ℹ 1 more variable: `Annual average of private households` <int>The GRP dataset contains GRP for all NUTS-3 regions between 2000 and 2019.
od_table("OGD_vgrrgr104_RGR104_1")$tabulate()# A STATcubeR tibble: 1,114 x 6
Time `NUTS-3` Gross regional product; current …¹
* <date> <fct> <dbl>
1 2000-01-01 Mittelburgenland <AT111> 597
2 2000-01-01 Nordburgenland <AT112> 2641
3 2000-01-01 Südburgenland <AT113> 1559
4 2000-01-01 Mostviertel-Eisenwurzen <AT121> 4778
5 2000-01-01 Niederösterreich-Süd <AT122> 4714
6 2000-01-01 Sankt Pölten <AT123> 3647
7 2000-01-01 Waldviertel <AT124> 3947
8 2000-01-01 Weinviertel <AT125> 1722
9 2000-01-01 Wiener Umland-Nordteil <AT126> 4841
10 2000-01-01 Wiener Umland-Südteil <AT127> 9886
# ℹ 1,104 more rows
# ℹ abbreviated name: ¹`Gross regional product; current prices in million Euro`
# ℹ 3 more variables: `Gross regional product per inhabitant` <dbl>,
# `Gross regional product per person employed` <dbl>,
# `Change in % to previous year prices` <dbl>