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od_table(id) returns an R6-class object containing all relevant data and metadata from https://data.statistik.gv.at/data/

Usage

od_table(id, language = NULL, server = "ext")

Arguments

id

the id of the data-set that should be accessed

language

language to be used for labeling. "en" or "de"

server

the OGD-server to be used. "ext" (the default) for the external server or prod for the production server

Value

The returned objects is of class sc_table and inherits several parsing methods from sc_data. See od_table_class for the full class documentation.

Components

ComponentCorresponding File on Server
$data https://data.statistik.gv.at/data/${id}.csv
$header https://data.statistik.gv.at/data/${id}_HEADER.csv
$field(code) https://data.statistik.gv.at/data/${id}_${code}.csv
$json https://data.statistik.gv.at/ogd/json?dataset=${id}

Examples

x <- od_table("OGD_krebs_ext_KREBS_1")

## metadata
x
#> Cancer statistics by reporting year, province of residence and
#> localisation of cancer
#> 
#> Dataset: OGD_krebs_ext_KREBS_1 (data.statistik.gv.at)
#> Measures: Number of records F-KRE
#> Fields: Tumore ICD/10 3-Steller <95>, Reporting year <37>, Province
#>   of residence <9>, Sex <2>
#> 
#> Request: [2022-12-20 11:34:43]
#> STATcubeR: 0.5.0.1
x$meta
#> $source
#> # STATcubeR metadata: 1 x 7
#>   code                  label                                    lang 
#>   <chr>                 <chr>                                    <chr>
#> 1 OGD_krebs_ext_KREBS_1 Cancer statistics by reporting year, pr… en   
#> # … with 4 more columns: 'label_de', 'label_en', 'requested', 'scr_version'
#> 
#> $measures
#> # STATcubeR metadata: 1 x 7
#>   code  label                     NAs
#>   <chr> <chr>                   <int>
#> 1 F-KRE Number of records F-KRE     0
#> # … with 4 more columns: 'label_de', 'label_en', 'de_desc', 'en_desc'
#> 
#> $fields
#> # STATcubeR metadata: 4 x 9
#>   code               label                   total_code nitems type   
#>   <chr>              <chr>                   <chr>       <int> <chr>  
#> 1 C-TUM_ICD10_3ST-0  Tumore ICD/10 3-Steller NA             95 Catego…
#> 2 C-BERJ-0           Reporting year          NA             37 Time (…
#> 3 C-BUNDESLAND-0     Province of residence   NA              9 Catego…
#> 4 C-KRE_GESCHLECHT-0 Sex                     NA              2 Catego…
#> # … with 4 more columns: 'label_de', 'label_en', 'de_desc', 'en_desc'
#> 
x$field("Sex")
#> # STATcubeR metadata: 2 x 10
#>   code         label  parsed
#>   <chr>        <chr>  <chr> 
#> 1 GESCHLECHT-1 male   male  
#> 2 GESCHLECHT-2 female female
#> # … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order'
x$field(3)
#> # STATcubeR metadata: 9 x 10
#>   code         label           parsed         
#>   <chr>        <chr>           <chr>          
#> 1 BUNDESLAND-1 "Burgenland "   "Burgenland "  
#> 2 BUNDESLAND-2 "Carinthia"     "Carinthia"    
#> 3 BUNDESLAND-3 "Lower Austria" "Lower Austria"
#> 4 BUNDESLAND-4 "Upper Austria" "Upper Austria"
#> 5 BUNDESLAND-5 "Salzburg"      "Salzburg"     
#> 6 BUNDESLAND-6 "Styria"        "Styria"       
#> 7 BUNDESLAND-7 "Tyrol"         "Tyrol"        
#> 8 BUNDESLAND-8 "Vorarlberg"    "Vorarlberg"   
#> 9 BUNDESLAND-9 "Vienna"        "Vienna"       
#> # … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order'

## data
x$data
#> # A STATcubeR tibble: 46,479 x 5
#>    `C-TUM_ICD10_3ST-0` `C-BERJ-0` `C-BUNDESLAND-0` C-KRE_GES…¹ `F-KRE`
#>  * <fct>               <fct>      <fct>            <fct>         <int>
#>  1 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-1     GESCHLECHT…       2
#>  2 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-2     GESCHLECHT…       8
#>  3 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-2     GESCHLECHT…       2
#>  4 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-3     GESCHLECHT…       6
#>  5 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-3     GESCHLECHT…       2
#>  6 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-4     GESCHLECHT…      12
#>  7 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-4     GESCHLECHT…       2
#>  8 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-5     GESCHLECHT…       4
#>  9 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-6     GESCHLECHT…       5
#> 10 TUM_ICD10_3ST-C00   BERJ-1983  BUNDESLAND-6     GESCHLECHT…       2
#> # … with 46,469 more rows, and abbreviated variable name
#> #   ¹​`C-KRE_GESCHLECHT-0`
x$tabulate()
#> # A STATcubeR tibble: 46,479 x 5
#>    `Tumore ICD/10 3-Steller`          Reportin…¹ Provi…² Sex   Numbe…³
#>  * <fct>                              <date>     <fct>   <fct>   <int>
#>  1 <C00> Bösartige Neubildung der Li… 1983-01-01 "Burge… male        2
#>  2 <C00> Bösartige Neubildung der Li… 1983-01-01 "Carin… male        8
#>  3 <C00> Bösartige Neubildung der Li… 1983-01-01 "Carin… fema…       2
#>  4 <C00> Bösartige Neubildung der Li… 1983-01-01 "Lower… male        6
#>  5 <C00> Bösartige Neubildung der Li… 1983-01-01 "Lower… fema…       2
#>  6 <C00> Bösartige Neubildung der Li… 1983-01-01 "Upper… male       12
#>  7 <C00> Bösartige Neubildung der Li… 1983-01-01 "Upper… fema…       2
#>  8 <C00> Bösartige Neubildung der Li… 1983-01-01 "Salzb… male        4
#>  9 <C00> Bösartige Neubildung der Li… 1983-01-01 "Styri… male        5
#> 10 <C00> Bösartige Neubildung der Li… 1983-01-01 "Styri… fema…       2
#> # … with 46,469 more rows, and abbreviated variable names
#> #   ¹​`Reporting year`, ²​`Province of residence`,
#> #   ³​`Number of records F-KRE`

## tabulation: see `?sc_tabulate` for more examples
x$tabulate("Reporting year", "Sex")
#> # A STATcubeR tibble: 74 x 3
#>    `Reporting year` Sex    `Number of records F-KRE`
#>  * <date>           <fct>                      <int>
#>  1 1983-01-01       male                       14492
#>  2 1983-01-01       female                     17476
#>  3 1984-01-01       male                       14794
#>  4 1984-01-01       female                     17449
#>  5 1985-01-01       male                       14552
#>  6 1985-01-01       female                     17445
#>  7 1986-01-01       male                       14626
#>  8 1986-01-01       female                     17236
#>  9 1987-01-01       male                       14850
#> 10 1987-01-01       female                     17838
#> # … with 64 more rows

## switch language
x$language <- "de"
x
#> Krebsstatistik
#> 
#> Dataset: OGD_krebs_ext_KREBS_1 (data.statistik.gv.at)
#> Measures: Anzahl der Datensätze F-KRE
#> Fields: Tumore ICD/10 3-Steller <95>, Berichtsjahr <37>, Bundesland
#>   <9>, Geschlecht <2>
#> 
#> Request: [2022-12-20 11:34:43]
#> STATcubeR: 0.5.0.1
x$tabulate()
#> # A STATcubeR tibble: 46,479 x 5
#>    `Tumore ICD/10 3-Steller`        Berichts…¹ Bunde…² Gesch…³ Anzah…⁴
#>  * <fct>                            <date>     <fct>   <fct>     <int>
#>  1 <C00> Bösartige Neubildung der … 1983-01-01 Burgen… männli…       2
#>  2 <C00> Bösartige Neubildung der … 1983-01-01 Kärnten männli…       8
#>  3 <C00> Bösartige Neubildung der … 1983-01-01 Kärnten weibli…       2
#>  4 <C00> Bösartige Neubildung der … 1983-01-01 Nieder… männli…       6
#>  5 <C00> Bösartige Neubildung der … 1983-01-01 Nieder… weibli…       2
#>  6 <C00> Bösartige Neubildung der … 1983-01-01 Oberös… männli…      12
#>  7 <C00> Bösartige Neubildung der … 1983-01-01 Oberös… weibli…       2
#>  8 <C00> Bösartige Neubildung der … 1983-01-01 Salzbu… männli…       4
#>  9 <C00> Bösartige Neubildung der … 1983-01-01 Steier… männli…       5
#> 10 <C00> Bösartige Neubildung der … 1983-01-01 Steier… weibli…       2
#> # … with 46,469 more rows, and abbreviated variable names
#> #   ¹​Berichtsjahr, ²​Bundesland, ³​Geschlecht,
#> #   ⁴​`Anzahl der Datensätze F-KRE`

## other interesting tables
od_table("OGD_veste309_Veste309_1")
#> Structure of Earnings Survey (SES) 2018 Gross hourly earnings
#> in EUR by citizenship, region (NUTS 2) and form of employment
#> 
#> Dataset: OGD_veste309_Veste309_1 (data.statistik.gv.at)
#> Measures: Arithmetic mean, 1st quartile, 2nd quartile (median), 3rd
#>   quartile, Number of employees
#> Fields: Sex <3>, Citizenship <9>, Region (NUTS2) <10>, Form of
#>   employment <7>
#> 
#> Request: [2022-12-20 11:34:44]
#> STATcubeR: 0.5.0.1
od_table("OGD_konjunkturmonitor_KonMon_1")
#> Economic Trend Monitor
#> 
#> Dataset: OGD_konjunkturmonitor_KonMon_1 (data.statistik.gv.at)
#> Measures: Production Index Industry (wd; 2015=100), Technical total
#>   production Industry (in 1.000 €), Turnover Index Industry
#>   (2015=100), Turnover Industry (in 1.000 €), Index of new orders
#>   Industry (2015=100), Index of persons employed Industry (2015=100),
#>   Persons employed Industry, Productivity Index Industry per employee
#>   (2015=100), Productivity Index Industry per hours worked
#>   (2015=100), Industrial Output Price Index (2021=100), … (76 more)
#> Fields: reporting period <236>, value indication <3>
#> 
#> Request: [2022-12-20 11:34:44]
#> STATcubeR: 0.5.0.1
od_table("OGD_krankenbewegungen_ex_LEISTUNGEN_1")
#> Medical procedures during inpatient stays since 1989 by
#> patient characteristics (number of medical procedures)
#> 
#> Dataset: OGD_krankenbewegungen_ex_LEISTUNGEN_1 (data.statistik.gv.at)
#> Measures: Medical procedures
#> Fields: Year of discharge <12>, Sex <2>, Age (four classes) <4>,
#>   NUTS-2 region (place of residence) <12>, Medical procedures -
#>   subchapters <115>
#> 
#> Request: [2022-12-20 11:34:44]
#> STATcubeR: 0.5.0.1
od_table("OGD_f1741_HH_Proj_1")
#> Household forecast
#> 
#> Dataset: OGD_f1741_HH_Proj_1 (data.statistik.gv.at)
#> Measures: Private households at the end of the year, Annual average
#>   of private households
#> Fields: Time <70>, Province (NUTS 2-Einheit) <9> <9>, Type of
#>   household <2> <2>, Age of household representative in 10-year
#>   groups <7> <15>, Sex of household reference person <2> <2>
#> 
#> Request: [2022-12-20 11:34:44]
#> STATcubeR: 0.5.0.1
od_table("OGD_veste303_Veste203_1")
#> Structure of Earnings Survey (SES) 2018 Gross hourly earnings
#> in EUR by characteristics of the enterprise
#> 
#> Dataset: OGD_veste303_Veste203_1 (data.statistik.gv.at)
#> Measures: Arithmetic mean, 1st quartile, 2nd quartile (median), 3rd
#>   quartile, Number of employees
#> Fields: ÖNACE 2008 (NACE Rev.2) <94>, Sex <3>, Regions (Nuts1) <4>,
#>   Size of the enterprise <6>
#> 
#> Request: [2022-12-20 11:34:44]
#> STATcubeR: 0.5.0.1