sc_tabulate() extracts the data in the table and turns it into a tidy
data.frame. It applies labeling of the data and transforms time variables
into a Date format if they satisfy certain 'STATcube' standards.
sc_tabulate(table, ...) is just an alias for table$tabulate(...) and
was added so this rather complicated method can have a separate documentation
page. It is recommended to use the table$tabulate() syntax
the ... argument decides which measures and/or fields should be included
in the output. If no measures are given, all measures are included. The same
is true for fields.
Usage
sc_tabulate(
table,
...,
.list = NULL,
raw = FALSE,
parse_time = TRUE,
recode_zeros = inherits(table, "sc_table"),
language = NULL,
sort = FALSE
)Arguments
- table
An object of class
sc_data- ...
Names of measures and/or fields
- .list
allows to define the arguments for
...as a character vector.- raw
If FALSE (the default), apply labeling to the dataset. Otherwise, return codes.
- parse_time
Should time variables be converted into a
Dateformat? Ignored ifrawis set toTRUE.- recode_zeros
turn zero values into
NAs- language
The language to be used for labeling. By default, the dataset language (
table$language) is used.- sort
If
TRUE, the resulting data will be sorted by all provided field values
Details
Aggregation is done as follows
First, all columns that provide a total code via
table$total_codes()will be used to filter forcolumn == total_codeorcolumn != total_codeThen, the remaining data is aggregated using
rowsum()
The ellipsis (...) supports partial matching of codes and labels.
See Examples
For objects of class sc_table two additional operations are performed.
zeros are recoded to
NAsrounding is done according to the precision of each measure. Rounding happens after the recoding to
NAvalues
Examples
############################ OGD Data #######################################
table <- od_table("OGD_veste309_Veste309_1")
# no arguments -> same output as `table$data`
table$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 …
#> 4 Sum total Total Total "Non-standard employment:…
#> 5 Sum total Total Total "Non-standard employment:…
#> 6 Sum total Total Total "Non-standard employment:…
#> 7 Sum total Total Total "Non-standard employment:…
#> 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>
# provide some fields -> aggregate to keep only these fields
table$tabulate("Sex", "Citizenship")
#> # A STATcubeR tibble: 27 x 7
#> Sex Citizenship `Arithmetic mean` `1st quartile`
#> * <fct> <fct> <dbl> <dbl>
#> 1 Sum total "Total" 266. 181.
#> 2 Sum total "Austrian" 18.5 12.4
#> 3 Sum total "Non-Austrian " 14.4 9.72
#> 4 Sum total "EU-28/EWR/Switzerland … 15.2 10.0
#> 5 Sum total "EU-15/EWR before 2004/… 19.6 11.9
#> 6 Sum total "EU-13 EU Member States… 13.0 9.59
#> 7 Sum total "Former Yugoslavia (wit… 12.8 9.58
#> 8 Sum total "Turkey" 12.2 9.35
#> 9 Sum total "Other" 13.8 9.07
#> 10 Male "Total" 290. 195.
#> # ℹ 17 more rows
#> # ℹ 3 more variables: `2nd quartile (median)` <dbl>,
#> # `3rd quartile` <dbl>, `Number of employees` <dbl>
# provide some measures -> drop all other measures from the output
table$tabulate("Arithmetic mean")
#> # A STATcubeR tibble: 72 x 5
#> 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 …
#> 4 Sum total Total Total "Non-standard employment:…
#> 5 Sum total Total Total "Non-standard employment:…
#> 6 Sum total Total Total "Non-standard employment:…
#> 7 Sum total Total Total "Non-standard employment:…
#> 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 1 more variable: `Arithmetic mean` <dbl>
# mixture of measures and fields -> keep exactly those columns
table$tabulate("Sex", "Arithmetic mean")
#> # A STATcubeR tibble: 3 x 2
#> Sex `Arithmetic mean`
#> * <fct> <dbl>
#> 1 Sum total 385.
#> 2 Male 418.
#> 3 Female 342.
## define total codes
table$total_codes(
`C-A11-0` = "A11-1",
`C-STAATS-0` = "STAATS-9",
`C-VEBDL-0` = "VEBDL-10",
`C-BESCHV-0` = "BESCHV-1"
)
## alternatively, use partial matching to define totals
table$total_codes(
Sex = "Sum total",
Citizenship = "Total",
Region = "Total",
`Form of employment` = "Total"
)
# filter for totals in `Region (NUTS2)` and `Form of employment`. Drop totals
# in `Sex` and `Citizenship`.
table$tabulate("Sex", "Citizenship")
#> # A STATcubeR tibble: 16 x 7
#> Sex Citizenship `Arithmetic mean` `1st quartile`
#> * <fct> <fct> <dbl> <dbl>
#> 1 Male "Austrian" 20.4 13.8
#> 2 Male "Non-Austrian " 15.3 10.6
#> 3 Male "EU-28/EWR/Switzerland (wi… 16.0 10.9
#> 4 Male "EU-15/EWR before 2004/Swi… 21.2 12.6
#> 5 Male "EU-13 EU Member States af… 13.6 10.3
#> 6 Male "Former Yugoslavia (withou… 14.1 11.2
#> 7 Male "Turkey" 13.3 10.4
#> 8 Male "Other" 14.0 9.07
#> 9 Female "Austrian" 15.9 11.1
#> 10 Female "Non-Austrian " 13.0 9.19
#> 11 Female "EU-28/EWR/Switzerland (wi… 13.7 9.38
#> 12 Female "EU-15/EWR before 2004/Swi… 17.1 11.0
#> 13 Female "EU-13 EU Member States af… 12.1 9.06
#> 14 Female "Former Yugoslavia (withou… 10.9 9
#> 15 Female "Turkey" 10.3 8.68
#> 16 Female "Other" 13.3 9.07
#> # ℹ 3 more variables: `2nd quartile (median)` <dbl>,
#> # `3rd quartile` <dbl>, `Number of employees` <dbl>
## switch language
table$language <- "de"
## `...` matches for codes and labels
table$tabulate("C-A11-0", "Staats", "2. Quartil (Median)")
#> # A STATcubeR tibble: 16 x 3
#> Geschlecht Staatsangehörigkeit `2. Quartil (Median)`
#> * <fct> <fct> <dbl>
#> 1 männlich "Österreich" 17.4
#> 2 männlich "Nicht-Österreich" 13.2
#> 3 männlich "EU-28/EWR/Schweiz (ohne Österrei… 13.5
#> 4 männlich "EU-15/EWR vor 2004/Schweiz " 16.7
#> 5 männlich "EU-13 Beitrittsstaaten ab 2004" 12.7
#> 6 männlich "ehemaliges Jugoslawien (außerhal… 13.6
#> 7 männlich "Türkei" 12.8
#> 8 männlich "Andere" 11.2
#> 9 weiblich "Österreich" 14.0
#> 10 weiblich "Nicht-Österreich" 10.6
#> 11 weiblich "EU-28/EWR/Schweiz (ohne Österrei… 11.3
#> 12 weiblich "EU-15/EWR vor 2004/Schweiz " 14.7
#> 13 weiblich "EU-13 Beitrittsstaaten ab 2004" 10.3
#> 14 weiblich "ehemaliges Jugoslawien (außerhal… 9.78
#> 15 weiblich "Türkei" 9.51
#> 16 weiblich "Andere" 10.6
## Keep totals in the output by removing total codes
table$tabulate("C-A11-0") # -> 2 rows: "male" "female"
#> # A STATcubeR tibble: 2 x 6
#> Geschlecht Arithmetisches Mitte…¹ `1. Quartil` `2. Quartil (Median)`
#> * <fct> <dbl> <dbl> <dbl>
#> 1 männlich 19.2 12.8 16.3
#> 2 weiblich 15.3 10.4 13.4
#> # ℹ abbreviated name: ¹`Arithmetisches Mittel`
#> # ℹ 2 more variables: `3. Quartil` <dbl>,
#> # `Zahl d unselbst Beschäftigten` <dbl>
table$total_codes(`C-A11-0` = NA)
table$tabulate("C-A11-0") # -> 3 rows: "total", "male", "female"
#> # A STATcubeR tibble: 3 x 6
#> Geschlecht Arithmetisches Mitte…¹ `1. Quartil` `2. Quartil (Median)`
#> * <fct> <dbl> <dbl> <dbl>
#> 1 insgesamt 17.6 11.6 15.1
#> 2 männlich 19.2 12.8 16.3
#> 3 weiblich 15.3 10.4 13.4
#> # ℹ abbreviated name: ¹`Arithmetisches Mittel`
#> # ℹ 2 more variables: `3. Quartil` <dbl>,
#> # `Zahl d unselbst Beschäftigten` <dbl>
## table$tabulate(...) is an alias for sc_tabulate(table, ...)
sc_tabulate(table, "C-A11-0")
#> # A STATcubeR tibble: 3 x 6
#> Geschlecht Arithmetisches Mitte…¹ `1. Quartil` `2. Quartil (Median)`
#> * <fct> <dbl> <dbl> <dbl>
#> 1 insgesamt 17.6 11.6 15.1
#> 2 männlich 19.2 12.8 16.3
#> 3 weiblich 15.3 10.4 13.4
#> # ℹ abbreviated name: ¹`Arithmetisches Mittel`
#> # ℹ 2 more variables: `3. Quartil` <dbl>,
#> # `Zahl d unselbst Beschäftigten` <dbl>
######################## 'STATcube' REST API ################################
table_tourism <- sc_table(sc_example("accomodation.json"), "de")
table_tourism$tabulate()
#> # A STATcubeR tibble: 2,691 x 5
#> `Saison/Tourismusmonat` Herkunftsland Beherbergungsbetrieb
#> * <date> <fct> <fct>
#> 1 1999-11-01 Österreich Hotels gesamt
#> 2 1999-11-01 Österreich Ferienwohnungen insgesamt
#> 3 1999-11-01 Österreich Sonstige
#> 4 1999-11-01 Deutschland Hotels gesamt
#> 5 1999-11-01 Deutschland Ferienwohnungen insgesamt
#> 6 1999-11-01 Deutschland Sonstige
#> 7 1999-11-01 andere Länder Hotels gesamt
#> 8 1999-11-01 andere Länder Ferienwohnungen insgesamt
#> 9 1999-11-01 andere Länder Sonstige
#> 10 1999-12-01 Österreich Hotels gesamt
#> # ℹ 2,681 more rows
#> # ℹ 2 more variables: Übernachtungen <dbl>, Ankünfte <dbl>
table_tourism$tabulate("Saison/Tourismusmonat")
#> # A STATcubeR tibble: 299 x 3
#> `Saison/Tourismusmonat` Übernachtungen Ankünfte
#> * <date> <dbl> <dbl>
#> 1 1999-11-01 2646717 961334
#> 2 1999-12-01 7373034 1771530
#> 3 2000-01-01 12437812 2462258
#> 4 2000-02-01 13480000 2532404
#> 5 2000-03-01 11340174 2273436
#> 6 2000-04-01 6138146 1666777
#> 7 2000-05-01 5150347 1673232
#> 8 2000-06-01 8908931 2438518
#> 9 2000-07-01 14618147 3118576
#> 10 2000-08-01 16498662 3248084
#> # ℹ 289 more rows
table_tourism$tabulate("Saison/Tourismusmonat", "Ankünfte")
#> # A STATcubeR tibble: 299 x 2
#> `Saison/Tourismusmonat` Ankünfte
#> * <date> <dbl>
#> 1 1999-11-01 961334
#> 2 1999-12-01 1771530
#> 3 2000-01-01 2462258
#> 4 2000-02-01 2532404
#> 5 2000-03-01 2273436
#> 6 2000-04-01 1666777
#> 7 2000-05-01 1673232
#> 8 2000-06-01 2438518
#> 9 2000-07-01 3118576
#> 10 2000-08-01 3248084
#> # ℹ 289 more rows
table_tourism$tabulate("Ankünfte")
#> # A STATcubeR tibble: 2,691 x 4
#> `Saison/Tourismusmonat` Herkunftsland Beherbergungsbetrieb Ankünfte
#> * <date> <fct> <fct> <dbl>
#> 1 1999-11-01 Österreich Hotels gesamt 371215
#> 2 1999-11-01 Österreich Ferienwohnungen ins… 5365
#> 3 1999-11-01 Österreich Sonstige 67272
#> 4 1999-11-01 Deutschland Hotels gesamt 223856
#> 5 1999-11-01 Deutschland Ferienwohnungen ins… 13095
#> 6 1999-11-01 Deutschland Sonstige 24262
#> 7 1999-11-01 andere Länder Hotels gesamt 235343
#> 8 1999-11-01 andere Länder Ferienwohnungen ins… 6493
#> 9 1999-11-01 andere Länder Sonstige 14433
#> 10 1999-12-01 Österreich Hotels gesamt 346040
#> # ℹ 2,681 more rows