The class sc_data defines a
common interface for open data datasets and responses from the
/table endpoint of the STATcube REST API. It defines
methods that are applicable to both datasources like aquiring metadata,
labeling the data and aggregating results.
Constructing sc_data objects
The sc_data class itself is not exported in STATcubeR. Therefore, objects of the class should be created with one of the following functions
-
od_table()obtains data from the OGD portal. See the OGD article -
sc_table()creates a request against the table endpoint of the STATcube REST API. See the JSON requests article-
sc_table_saved()andsc_table_custom()also use the/tableendpoint. However, the request is specified via ids rather than a json file.
-
To illustrate, we will use one of the OGD datasets to showcase the
functionalities of this class. Notice however, that objects created with
sc_table() can be used interchangibly.
x <- od_table("OGD_krebs_ext_KREBS_1")Data
The data from the table can be extracted using the active binding
$data. Notice how
OGD_krebs_ext_KREBS_1
only includes codes and possibly some totals. The data is always
provided in a long format with one column for each field and one column
for each measure.
x$data# A STATcubeR tibble: 46,479 x 5
`C-TUM_ICD10_3ST-0` `C-BERJ-0` `C-BUNDESLAND-0` `C-KRE_GESCHLECHT-0` `F-KRE`
* <fct> <fct> <fct> <fct> <int>
1 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-1 GESCHLECHT-1 2
2 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-2 GESCHLECHT-1 8
3 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-2 GESCHLECHT-2 2
4 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-3 GESCHLECHT-1 6
5 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-3 GESCHLECHT-2 2
# … with 46,474 more rowsIt will be explained how labeled data can be obtained in the Tabulation section.
Metadata
Metadata for a sc_data object
includes labels and other information that is relevant to correctly
parse the raw data. The active binding $meta contains at
least the entries $source, $measures and
$fields.
Source
The source field contains information about the data source. The most
important entries are code and label.
x$meta$source# STATcubeR metadata: 1 x 7
code label lang
<chr> <chr> <chr>
1 OGD_krebs_ext_KREBS_1 Cancer statistics by reporting year, province of … en
# … with 4 more columns: 'label_de', 'label_en', 'requested', 'scr_version'On the bottom, we see that additional information about the source is
available, namely label_en, label_de, etc.
These additional metadata entries might not be available for
sc_table objects.
Measures
This part of the metadata is a data.frame with one row
for each measure. It contains codes and labels as well as the number of
NAs found in $data for that particular column.
x$meta$measures# STATcubeR metadata: 1 x 7
code label NAs
<chr> <chr> <int>
1 F-KRE Number of records F-KRE 0
# … with 4 more columns: 'label_de', 'label_en', 'de_desc', 'en_desc'Fields
The fields entry summarizes all classification fields i.e. categorical variables. It includes the codes and labels as well as the total code registered for the particular field.
x$meta$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 Category
2 C-BERJ-0 Reporting year NA 37 Time (year)
3 C-BUNDESLAND-0 Province of residence NA 9 Category
4 C-KRE_GESCHLECHT-0 Sex NA 2 Category
# … with 4 more columns: 'label_de', 'label_en', 'de_desc', 'en_desc'Field information
To get more info about specific fields, use the $field()
method. This will return all classification elements as a
data.frame.
x$field("Tumore")# STATcubeR metadata: 95 x 10
code label
<chr> <chr>
1 TUM_ICD10_3ST-C00 <C00> Bösartige Neubildung der Lippe
2 TUM_ICD10_3ST-C01 <C01> Bösartige Neubildung des Zungengrundes
3 TUM_ICD10_3ST-C02 <C02> Bösartige Neubildung sonstiger und nicht näher bezeic…
4 TUM_ICD10_3ST-C03 <C03> Bösartige Neubildung des Zahnfleisches
5 TUM_ICD10_3ST-C04 <C04> Bösartige Neubildung des Mundbodens
# … with 90 more rows, and 1 more variable: parsed <chr>
# … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order'
x$field("Reporting year")# STATcubeR metadata: 37 x 10
code label parsed
<chr> <chr> <date>
1 BERJ-1983 1983 1983-01-01
2 BERJ-1984 1984 1984-01-01
3 BERJ-1985 1985 1985-01-01
4 BERJ-1986 1986 1986-01-01
5 BERJ-1987 1987 1987-01-01
# … with 32 more rows
# … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order'
x$field("Province")# 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'
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'Tabulation
The method $tabulate() can be used to turn
sc_table objects into tidy data.frames. See the
tabulation article
for more defails.
x$tabulate()# A STATcubeR tibble: 46,479 x 5
`Tumore ICD/10 3-Steller` Reportin…¹ Province of r…² Sex Numbe…³
* <fct> <date> <fct> <fct> <int>
1 <C00> Bösartige Neubildung der Lippe 1983-01-01 "Burgenland " male 2
2 <C00> Bösartige Neubildung der Lippe 1983-01-01 "Carinthia" male 8
3 <C00> Bösartige Neubildung der Lippe 1983-01-01 "Carinthia" female 2
4 <C00> Bösartige Neubildung der Lippe 1983-01-01 "Lower Austria" male 6
5 <C00> Bösartige Neubildung der Lippe 1983-01-01 "Lower Austria" female 2
# … with 46,474 more rows, and abbreviated variable names ¹`Reporting year`,
# ²`Province of residence`, ³`Number of records F-KRE`