## ✔ 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 viersion 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 data frame: 336 × 3
category id label
<chr> <chr> <chr>
1 Arbeit OGD_konjunkturmonitor_KonMon_1 Konjunkturmonitor
2 Arbeit OGD_aeapp_biber_abschl_ext_BIBER_… BibEr Monitoring 2023 - Bildu…
3 Arbeit OGD_aest_erv_statw_haupt_ERV_STAT… Registerbasierte Erwerbsverlä…
4 Arbeit OGD_veste309_Veste309_1 Verdienststrukturerhebung 201…
5 Arbeit OGD_veste307_Veste307_1 Verdienststrukturerhebung 201…
6 Arbeit OGD_veste305_Veste305_1 Verdienststrukturerhebung 201…
7 Arbeit OGD_veste303_Veste203_1 Verdienststrukturerhebung 201…
8 Arbeit OGD_veste301_Veste301_1 Verdienststrukturerhebung 201…
9 Bevölkerung OGD_gest_kalwo_alter_GEST_KALWOCH… Gestorbene in Österreich (ohn…
10 Bevölkerung OGD_rate_kalwobez_GEST_KALWOCHE_S… Altersstandardisierte Sterber…
# ℹ 326 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_aeapp_biber_abschl_ext_BIBER_ABSCHL_1"
json <- od_json(id)
json#> BibEr Monitoring 2023 - Bildungsabschlüsse nach Jahr,
#> abgeschlossener Ausbildung, Geschlecht und Arbeitsmarktstatus 18
#> Monate nach dem Abschluss
#>
#> Das Projekt Bildungsbezogenes Erwerbskarrierenmonitoring (BibEr)
#> wurde im Auftrag von BMAW und AMS erstellt. Zu diesem Zweck wird die
#> Erwerbskarriere aller in Österreich wohnhaften Personen nach Abgang
#> aus einer formalen Bildungseinrichtung statistisch auswertbar
#> gemacht. Betrachtet werden nur Personen mit einem formalen
#> Bildungsabschluss und einem Hauptwohnsitz in Österreich zum Stichtag
#> 31.10. des Abschlussschuljahres.
#>
#> Measures: Anzahl Personen
#> Fields: Schuljahr des Abschlusses, Abgeschlossene Ausbildung (Ebene +3),
#> Geschlecht, Arbeitsmarktstatus nach 18 Monaten (Ebene +5)
#> Updated: 2024-01-18 09:03:35
#> Tags: Bildung, Arbeitsmarkt, Erwerbskarriere, Registerbasiert, Schule,
#> Lehre, BMS, AHS, BHS, Hochschule
#> Categories: Arbeit, Bildung und ForschungThis output is generated from
OGD_aeapp_biber_abschl_ext_BIBER_ABSCHL_1.json
and shows a summary of the available metatata. Other parts of the
metadata can be extracted with $ using the keys from the
json specification.
json$extras$update_frequency#> [1] "jährlich"Showcase
The population dataset measures the austrian population for 2115 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>