Send requests against the /table endpoint of the STATcube REST API. The
requests can use three formats with corresponding functions
sc_table()uses a json file downloaded via the STATcube GUIsc_table_custom()uses the ids of a database, measures and fieldssc_table_saved()uses a table uri of a saved table.
Those three functions all return an object of class "sc_table".
Usage
sc_table(json, language = NULL, add_totals = TRUE, key = NULL, json_file = NA)
sc_examples_list()
sc_example(filename)
sc_table_saved_list(key = NULL, server = "ext")
sc_table_saved(table_uri, language = NULL, key = NULL, server = "ext")Arguments
- json
Path to a json file, which was downloaded via the STATcube GUI ("Open Data API Request"). Alternatively, a json string which passes
jsonlite::validate().- language
The language to be used for labeling.
"en"(the default) will use english."de"uses german. The third option"both"will import both languages by sending two requests to the/tableendpoint.- add_totals
Should totals be added for each classification field in the json request?
- key
(
string) An API key. To display your key, callsc_browse_preferences().- json_file
Depricated. Use
jsoninstead- filename
The name of an example json file.
- server
A STATcube API server. Defaults to the external Server via
"ext". Other options are"red"for the editing server and"prod"for the production server. External users should always use the default option"ext".- table_uri
Identifier of a saved table as returned by
sc_table_saved_list()
Value
An object of class sc_table which contains the return
value of the httr::POST() request in obj$response. The object also
provides member functions to parse this response object. See
sc_table_class for the class documentation.
Examples
my_table <- sc_table(json = sc_example("population_timeseries.json"))
# print
my_table
#> Population at the beginning of the quarter since 2002
#>
#> Database: debevstand (STATcube)
#> Measures: Number of persons
#> Fields: Quarter <82>, Age in single years <96> <7>, Sex <2> <3>,
#> Commune <2383> (Province-District) <10>
#>
#> Request: [2022-08-29 09:52:04]
#> STATcubeR: 0.5.0.1
# get matadata for the table
my_table$meta
#> $source
#> # STATcubeR metadata: 1 x 6
#> code label lang
#> <chr> <chr> <chr>
#> 1 debevstand Population at the beginning of the quarter since 2… en
#> # … with 3 more columns: 'label_de', 'label_en', 'scr_version'
#>
#> $measures
#> # STATcubeR metadata: 1 x 8
#> code label NAs
#> <chr> <chr> <int>
#> 1 F-ISIS-1 Number of persons 0
#> # … with 5 more columns: 'fun', 'precision', 'annotations', 'label_de', 'label_en'
#>
#> $fields
#> # STATcubeR metadata: 4 x 7
#> code label total_c…¹ nitems type
#> <chr> <chr> <chr> <int> <chr>
#> 1 C-A10-0 Quarter SC_TOTAL 82 Time…
#> 2 C-BESC51-0 Age in single years <96> SC_TOTAL 7 Cate…
#> 3 C-BESC11-0 Sex <2> SC_TOTAL 3 Cate…
#> 4 C-C41-2 Commune <2383> (Province-District) SC_TOTAL 10 Cate…
#> # … with abbreviated variable name ¹total_code
#> # … with 2 more columns: 'label_de', 'label_en'
#>
# get a data.frame
as.data.frame(my_table)
#> # A STATcubeR tibble: 17,220 x 5
#> Quarter `Age in single years <96>` `Sex <2>` Commune <…¹ Numbe…²
#> <date> <fct> <fct> <fct> <dbl>
#> 1 2002-01-01 Up to 14 years old male Burgenland… 21287
#> 2 2002-01-01 Up to 14 years old male Carinthia … 47230
#> 3 2002-01-01 Up to 14 years old male Vienna <AT… 117920
#> 4 2002-01-01 Up to 14 years old male Vorarlberg… 34798
#> 5 2002-01-01 Up to 14 years old male Tyrol <AT3… 62794
#> 6 2002-01-01 Up to 14 years old male Styria <AT… 97538
#> 7 2002-01-01 Up to 14 years old male Salzburg <… 46955
#> 8 2002-01-01 Up to 14 years old male Upper Aust… 127316
#> 9 2002-01-01 Up to 14 years old male Lower Aust… 133928
#> 10 2002-01-01 Up to 14 years old male Total 689766
#> # … with 17,210 more rows, and abbreviated variable names
#> # ¹`Commune <2383> (Province-District)`, ²`Number of persons`
# get metadata for field 2
my_table$field(2)
#> # STATcubeR metadata: 7 x 7
#> code label parsed
#> <chr> <chr> <chr>
#> 1 BESN07-1 Up to 14 years old Up to 14 years old
#> 2 BESN07-2 15 to 29 years old 15 to 29 years old
#> 3 BESN07-3 30 to 44 years old 30 to 44 years old
#> 4 BESN07-4 45 to 59 years old 45 to 59 years old
#> 5 BESN07-5 60 to 74 years old 60 to 74 years old
#> 6 BESN07-6 75 plus years old 75 plus years old
#> 7 SC_TOTAL Total Total
#> # … with 4 more columns: 'label_de', 'label_en', 'visible', 'order'
# get the ids and labels of all saved tables
(saved_tables <- sc_table_saved_list())
#> # A data frame: 5 × 2
#> label id
#> <chr> <chr>
#> 1 Bier auf Wein str:table:4520fba3-dffd-4f2a-9cd1-d46d710b6da4
#> 2 hierarchical str:table:1529fb5c-8f71-425c-bc95-a017f6534c8f
#> 3 neu str:table:4d55ca7c-e999-49e4-90f4-396163195da5
#> 4 lebenserwartung str:table:b0f4779b-546e-4a6c-88a1-43038cdeaf7b
#> 5 test123 str:table:9ba81454-32f5-4ab7-b904-c446940946f5
table_uri <- saved_tables$id[1]
# get a table based on one of these ids
my_response <- sc_table_saved(table_uri)
as.data.frame(my_response)
#> # A STATcubeR tibble: 156 x 4
#> Year `Wine colour` `Production in hectolitres` Area in …¹
#> <date> <fct> <dbl> <dbl>
#> 1 2021-01-01 Wine (in total) 2460275. 42835.
#> 2 2021-01-01 White wine 1730468. 29428.
#> 3 2021-01-01 Red wine and rosé 729806. 13407.
#> 4 1970-01-01 Wine (in total) 3096130 41821
#> 5 1970-01-01 White wine 2723071 36694
#> 6 1970-01-01 Red wine and rosé 373059 5127
#> 7 1971-01-01 Wine (in total) 1812790 42714
#> 8 1971-01-01 White wine 1563710 37157
#> 9 1971-01-01 Red wine and rosé 249080 5557
#> 10 1972-01-01 Wine (in total) 2595615 42714
#> # … with 146 more rows, and abbreviated variable name
#> # ¹`Area in hectares`