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
Deprecated. 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$response. The object also
provides member functions to parse this response object. See
sc_table_class for the class documentation.
Examples
my_table <- sc_table(json = sc_example("population_timeseries.json"))
# print
my_table
#> Population at the beginning of the quarter since 2002
#>
#> Database: debevstand (STATcube)
#> Measures: Number of persons
#> Fields: Quarter <90>, Age in single years <96> <7>, Sex <2> <3>,
#> Commune <2383> (Province-District) <10>
#>
#> Request: [2024-11-29 09:52:48]
#> STATcubeR: 1.0.0
# get matadata for the table
my_table$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'
#>
#> $fields
#> # STATcubeR metadata: 4 x 7
#> code label total_code nitems type
#> <chr> <chr> <chr> <int> <chr>
#> 1 C-A10-0 Quarter SC_TOTAL 90 Time…
#> 2 C-BESC51-0 Age in single years <96> SC_TOTAL 7 Cate…
#> 3 C-BESC11-0 Sex <2> SC_TOTAL 3 Cate…
#> 4 C-C41-2 Commune <2383> (Province-Distric… SC_TOTAL 10 Cate…
#> # … with 2 more columns: 'label_de', 'label_en'
#>
# get a data.frame
as.data.frame(my_table)
#> # A STATcubeR tibble: 18,900 x 5
#> Quarter Age in single years <…¹ `Sex <2>` Commune <2383> (Prov…²
#> <date> <fct> <fct> <fct>
#> 1 2002-01-01 Up to 14 years old male Burgenland <AT11>
#> 2 2002-01-01 Up to 14 years old male Carinthia <AT21>
#> 3 2002-01-01 Up to 14 years old male Vienna <AT13>
#> 4 2002-01-01 Up to 14 years old male Vorarlberg <AT34>
#> 5 2002-01-01 Up to 14 years old male Tyrol <AT33>
#> 6 2002-01-01 Up to 14 years old male Styria <AT22>
#> 7 2002-01-01 Up to 14 years old male Salzburg <AT32>
#> 8 2002-01-01 Up to 14 years old male Upper Austria <AT31>
#> 9 2002-01-01 Up to 14 years old male Lower Austria <AT12>
#> 10 2002-01-01 Up to 14 years old male Total
#> # ℹ 18,890 more rows
#> # ℹ abbreviated names: ¹`Age in single years <96>`,
#> # ²`Commune <2383> (Province-District)`
#> # ℹ 1 more variable: `Number of persons` <dbl>
# get metadata for field 2
my_table$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: 2 × 2
#> label id
#> <chr> <chr>
#> 1 meineErsteTabelle str:table:16f39429-8a1b-4593-a129-d5c646368f0f
#> 2 meineZweiteTabelle str:table:e4e1b473-32c4-42b4-a67d-18169af557cc
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: 9 x 4
#> `Fruit variety` `Commune [partly ABO]` `Number of holdings`
#> <fct> <fct> <dbl>
#> 1 Apples Burgenland <AT11> 111
#> 2 Apples Carinthia <AT21> 31
#> 3 Apples Lower Austria <AT12> 439
#> 4 Apples Upper Austria <AT31> 108
#> 5 Apples Salzburg <AT32> 2
#> 6 Apples Styria <AT22> 1116
#> 7 Apples Tyrol <AT33> 88
#> 8 Apples Vorarlberg <AT34> 31
#> 9 Apples Vienna <AT13> 6
#> # ℹ 1 more variable: `Area in ha` <dbl>