Look up and request dataset — bps

Search and get datasets that BPS distributes in JSONs. The API calls them dynamic tables.

bps_dataset() requests the dataset table. This table contains dataset IDs, which are needed to request datasets
bps_get_dataset() requests a dataset
bps_get_datasets() requests multiple datasets

Usage

bps_dataset(
  subject_id = NULL,
  vertical_var_group_id = NULL,
  domain_id = "0000",
  page = NULL,
  lang = c("ind", "eng")
)

bps_get_dataset(
  dataset_id,
  vertical_var_id = NULL,
  derived_var_id = NULL,
  year_id = NULL,
  period_id = NULL,
  domain_id = "0000",
  lang = c("ind", "eng"),
  keep = "none"
)

bps_get_datasets(
  dataset_id,
  domain_id = "0000",
  lang = c("ind", "eng"),
  keep = "none"
)

Arguments

subject_id: The subject ID. Use bps_subject() to see the list of subject IDs.
vertical_var_group_id: The vertical variable group ID. Use bps_vertical_var() to see the list of vertical variable group IDs.
domain_id: The domain ID of a BPS bureau. Defaults to "0000", which is the domain ID of the central bureau. Use bps_domain() to see the list of domain IDs.
page: The page number of the result. Defaults to page one. Set to Inf to request all pages.
lang: The result's language. Must be either "ind" for Indonesian or "eng" for English. Defaults to Indonesian.
dataset_id: The dataset ID. To request multiple datasets, supply a vector of the IDs to bps_get_datasets(). Use bps_dataset() to see the list of dataset IDs.
vertical_var_id: The vertical variable ID. Use bps_vertical_var() to see the list of vertical variable IDs.
derived_var_id: The derived variable ID. Use bps_derived_var() to see the list of derived variable IDs.
year_id: The year ID. Use bps_year() to see the list of year IDs.
period_id: The period ID. Use bps_period() to see the list of period IDs.
keep: ID columns to keep. Defaults to "none", which drops all of them. Set to "all" to keep all ID columns. Otherwise, must be "vertical_var_id", "derived_var_id", "year_id" or "period_id". To keep multiple ID columns, supply a vector of them.

Value

bps_dataset() returns a tibble containing dataset IDs and titles.
bps_get_dataset() returns a tibble with the bpsr_tbl subclass, which has a metadata attribute. Use bps_metadata() to read it.
bps_get_datasets() returns a list with the bpsr_multiple_tbl class, which contains tibbles with the bpsr_tbl subclass.

Request filter

Filter a request by multiple values of vertical_var_id, derived_var_id, year_id or period_id by supplying a vector of the IDs, to bps_get_dataset(). For example, vertical_var_id = c("1100", "1200").

For year_id and period_id, filter by a range of values by concatenating the start and the end of the range using a colon, e.g. year_id = "118:121" for observations from 2018 to 2021.

Examples

# Get the dataset on consumer price index of food category
bps_get_dataset("1905", lang = "eng")
#> # A tibble: 16,852 × 5
#>    vertical_var  derived_var                   year period      var
#>    <chr>         <chr>                        <int> <chr>     <dbl>
#>  1 KOTA MEULABOH Foods, Beverages and Tobacco  2020 January    112.
#>  2 KOTA MEULABOH Foods, Beverages and Tobacco  2020 February   111.
#>  3 KOTA MEULABOH Foods, Beverages and Tobacco  2020 March      112.
#>  4 KOTA MEULABOH Foods, Beverages and Tobacco  2020 April      111.
#>  5 KOTA MEULABOH Foods, Beverages and Tobacco  2020 May        113.
#>  6 KOTA MEULABOH Foods, Beverages and Tobacco  2020 June       112.
#>  7 KOTA MEULABOH Foods, Beverages and Tobacco  2020 July       111.
#>  8 KOTA MEULABOH Foods, Beverages and Tobacco  2020 August     113.
#>  9 KOTA MEULABOH Foods, Beverages and Tobacco  2020 September  113.
#> 10 KOTA MEULABOH Foods, Beverages and Tobacco  2020 October    114.
#> # ℹ 16,842 more rows
#> # ℹ Read the metadata with `bps_metadata()`

# Get some Human Development Index datasets
bps_get_datasets(c("414", "415", "416", "417"), lang = "eng")
#> <bpsr_multiple_datasets>
#> List of 4
#>  $ 414: bpsr_tbl [7,125 × 5] (S3: bpsr_tbl/tbl_df/tbl/data.frame)
#>   ..$ vertical_var: chr [1:7125] "ACEH" ...
#>   ..$ derived_var : chr [1:7125] NA ...
#>   ..$ year        : int [1:7125] 2010 2011 ...
#>   ..$ period      : chr [1:7125] "Annual" ...
#>   ..$ var         : num [1:7125] 69.1 ...
#>  $ 415: bpsr_tbl [7,067 × 5] (S3: bpsr_tbl/tbl_df/tbl/data.frame)
#>   ..$ vertical_var: chr [1:7067] "ACEH" ...
#>   ..$ derived_var : chr [1:7067] NA ...
#>   ..$ year        : int [1:7067] 2010 2011 ...
#>   ..$ period      : chr [1:7067] "Annual" ...
#>   ..$ var         : num [1:7067] 8.28 8.32 ...
#>  $ 416: bpsr_tbl [7,067 × 5] (S3: bpsr_tbl/tbl_df/tbl/data.frame)
#>   ..$ vertical_var: chr [1:7067] "ACEH" ...
#>   ..$ derived_var : chr [1:7067] NA ...
#>   ..$ year        : int [1:7067] 2010 2011 ...
#>   ..$ period      : chr [1:7067] "Annual" ...
#>   ..$ var         : int [1:7067] 7934 8044 ...
#>  $ 417: bpsr_tbl [7,077 × 5] (S3: bpsr_tbl/tbl_df/tbl/data.frame)
#>   ..$ vertical_var: chr [1:7077] "ACEH" ...
#>   ..$ derived_var : chr [1:7077] NA ...
#>   ..$ year        : int [1:7077] 2010 2011 ...
#>   ..$ period      : chr [1:7077] "Annual" ...
#>   ..$ var         : num [1:7077] 12.9 ...