data() is a convenience function that searches the database and returns
search results if multiple matching tables are found, and
data if only one match is found.
data_get() returns data for a given table id without performing searching, url parsing or pattern filters, and is prefered in programmatic use-cases where robustness and speed are important. data_get() will result in error, if no match exists.
data_search() performs a search and returns a list of matching data tables without downloading any data.
Usage
dаta(
pattern = "",
dl_filter = NULL,
labels = getOption("robonomistClient.labels"),
lang = NULL,
na.rm = FALSE,
tidy_time = getOption("robonomistClient.tidy_time"),
...
)
data_get(
id,
dl_filter = NULL,
labels = getOption("robonomistClient.labels"),
lang = NULL,
na.rm = FALSE,
tidy_time = getOption("robonomistClient.tidy_time"),
...
)
data_search(pattern = "")Arguments
- pattern
Character. Search query or table id, possibly followed by a
§-filter.- dl_filter
List or named vector. The download filter is passed to datasource download functions to filter data before download. See Details.
- labels
Logical. If FALSE, then variable values are returned as codes instead of labels.
- lang
Two-letter language code, e.g. "en" or "sv".
- na.rm
Logical. If TRUE, and supported by the datasource, missing values are removed from the returned data frame upon retrieval.
- tidy_time
Logical. If TRUE, the time dimension is parsed into Date class and renamed
time. If NULL, the datasource specific default will be used.- ...
Other arguments passed to datasource download functions.
- id,
Character. Exact table id. If multiple ids are provided as a character vector, they are bound row-wise into a single data frame.
§-filter
The data() function allows for a special §-filter. When the pattern matches a single table and the function returns a data frame, the §-filter can be used to subset rows of data frame. The tibble is filterd by sequence of regular expressions separated by section sign §. The regular expression are applied to data frame's variables sequentially.
If the last variable is a date, it is used as a start date filter.
Download filter
Some datasources (e.g. datasets "ecb" & "tulli") do not allow downloading full data tables, nor is it always preferred due to the large large size of table. For these datasources the user must provide a download filter via the dl_filter argument. When the argument is left as NULL, the data function will return a list of variables and potential values. This list can be used to construct a suitable download filter.
Generally, dl_filter should be named list where names are variable names and values character vectors of selected values (see Examples). Alternatively, some datasources allow for a dot-separated string to define a download filter.
Examples
# Search for datasets matching pattern:
data("consumer indicator")
#> ℹ Connecting to robonomistServer at wss://data.robonomist.app
#> ✔ Connecting to robonomistServer at wss://data.robonomist.app [1.4s]
#>
#> ℹ Connected successfully to robonomistServer 2.10.2
#> ✔ Connected successfully to robonomistServer 2.10.2 [24ms]
#>
#> ⠙ Requesting data
#> ⠹ Requesting data
#> ⠸ Requesting data
#> ⠼ Requesting data
#> ⠴ Requesting data
#> ⠦ Requesting data
#> ℹ Updating dataset tidy
#> ✔ Updating dataset tidy [219ms]
#>
#> ⠦ Requesting data
#> ✔ Requesting data [18.2s]
#>
#> # Robonomist Database search results
#> id title lang
#> <r_id> <chr> <chr>
#> 1 ec/consumer Consumer S… en
#> 2 ec/consumer_nsa Consumer S… en
#> 3 ec/consumer_q Consumer S… en
#> 4 ec/consumer_q_nsa Consumer S… en
#> 5 eurostat/ei_bsco_m Consumers … en
#> 6 ecb/JDF_EXR_HCI_CPI Harmonised… en
#> 7 wb/EP.CPI.1996 Consumer P… en
#> 8 wb/EP.CPI.2002 Consumer P… en
#> 9 wb/EP.CPI.2007 Consumer P… en
#> 10 wb/FP.CPI.TOTL.ZG Inflation,… en
#> 11 wb/MO.INDEX.ECON.XQ Sustainabl… en
#> 12 wb/PX.REC.REER Real effec… en
#> 13 wb/SPI.D5.2.4.CPIBY CPI base y… en
#> 14 se/PR/PR0101/PR0101S/SnabbStatPR0101 Consumer P… en
#> 15 konj/hushall/indikatorhus.px Household … en
#> 16 konj/hushall/zhushallhist/hushallpremm/Indikatorhus.px Household … en
#> 17 dk/FORV1 Consumer c… en
#> 18 md/40_Statistica_economica/15_ENE/serii_lunare/ENE010200.px Stocks, in… en
#> 19 si/0811601S.px Consumer s… en
#> 20 si/0811602S.px Consumer s… en
## Limit your search to a specific dataset by providing
## the dataset name and a slash as prefix:
data("ec/ consumer indicator")
#> ⠙ Requesting data
#> ✔ Requesting data [296ms]
#>
#> # Robonomist Database search results
#> id title lang
#> <r_id> <chr> <chr>
#> 1 ec/consumer Consumer Sentiment Indicator en
#> 2 ec/consumer_nsa Consumer Sentiment Indicator, non-seasonally adjusted en
#> 3 ec/consumer_q Consumer Sentiment Indicator, quarterly questions en
#> 4 ec/consumer_q_nsa Consumer Sentiment Indicator, non-seasonally adjusted… en
## Download data by providing exact table id:
data("ec/consumer")
#> ⠙ Requesting data
#> ✔ Requesting data [936ms]
#>
#> # Robonomist id: ec/consumer
#> # Title: Consumer Sentiment Indicator
#> # Vintage: 2025-10-27 16:40:55
#> # A tibble: 205,800 × 4
#> Country Indicator time value
#> * <chr> <chr> <date> <dbl>
#> 1 European Union (current composition) Confidence Indicator (… 1985-01-01 -10.2
#> 2 European Union (current composition) Confidence Indicator (… 1985-02-01 -10.6
#> 3 European Union (current composition) Confidence Indicator (… 1985-03-01 -11.5
#> 4 European Union (current composition) Confidence Indicator (… 1985-04-01 -10.8
#> 5 European Union (current composition) Confidence Indicator (… 1985-05-01 -11.2
#> 6 European Union (current composition) Confidence Indicator (… 1985-06-01 -11.6
#> 7 European Union (current composition) Confidence Indicator (… 1985-07-01 -11.2
#> 8 European Union (current composition) Confidence Indicator (… 1985-08-01 -10.9
#> 9 European Union (current composition) Confidence Indicator (… 1985-09-01 -10.7
#> 10 European Union (current composition) Confidence Indicator (… 1985-10-01 -8
#> # ℹ 205,790 more rows
## With time series based datasets you can retrieve the time series
## using the source's id:
data("ecb/FM.M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA")
#> ⠙ Requesting data
#> ✔ Requesting data [1.8s]
#>
#> # Robonomist id: ecb/FM
#> # Title: Financial market data
#> # Vintage: 2025-11-13 08:00:00
#> # A tibble: 382 × 9
#> Frequency `Reference area` Currency Financial market provid…¹
#> * <chr> <chr> <chr> <chr>
#> 1 Monthly Euro area (changing composition) Euro Refinitiv
#> 2 Monthly Euro area (changing composition) Euro Refinitiv
#> 3 Monthly Euro area (changing composition) Euro Refinitiv
#> 4 Monthly Euro area (changing composition) Euro Refinitiv
#> 5 Monthly Euro area (changing composition) Euro Refinitiv
#> 6 Monthly Euro area (changing composition) Euro Refinitiv
#> 7 Monthly Euro area (changing composition) Euro Refinitiv
#> 8 Monthly Euro area (changing composition) Euro Refinitiv
#> 9 Monthly Euro area (changing composition) Euro Refinitiv
#> 10 Monthly Euro area (changing composition) Euro Refinitiv
#> # ℹ 372 more rows
#> # ℹ abbreviated name: ¹`Financial market provider`
#> # ℹ 5 more variables: `Financial market instrument` <chr>,
#> # `Financial market provider identifier` <chr>,
#> # `Financial market data type` <chr>, time <date>, value <dbl>
## Alternatively, you can copy the full URL from the source's website:
data("https://data.ecb.europa.eu/data/datasets/FM/FM.M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA")
#> ⠙ Requesting data
#> ℹ The URL points to a data table in dataset "ecb".
#> ⠙ Requesting data
#> ℹ For direct data retrieval, use:
#> > data_get("ecb/FM.M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA", raw = TRUE)
#> ⠙ Requesting data
#> ✔ Requesting data [902ms]
#>
#> # Robonomist id: ecb/FM
#> # Title: Financial market data
#> # Vintage: 2025-11-13 08:00:00
#> # A tibble: 382 × 9
#> Frequency `Reference area` Currency Financial market provid…¹
#> * <chr> <chr> <chr> <chr>
#> 1 Monthly Euro area (changing composition) Euro Refinitiv
#> 2 Monthly Euro area (changing composition) Euro Refinitiv
#> 3 Monthly Euro area (changing composition) Euro Refinitiv
#> 4 Monthly Euro area (changing composition) Euro Refinitiv
#> 5 Monthly Euro area (changing composition) Euro Refinitiv
#> 6 Monthly Euro area (changing composition) Euro Refinitiv
#> 7 Monthly Euro area (changing composition) Euro Refinitiv
#> 8 Monthly Euro area (changing composition) Euro Refinitiv
#> 9 Monthly Euro area (changing composition) Euro Refinitiv
#> 10 Monthly Euro area (changing composition) Euro Refinitiv
#> # ℹ 372 more rows
#> # ℹ abbreviated name: ¹`Financial market provider`
#> # ℹ 5 more variables: `Financial market instrument` <chr>,
#> # `Financial market provider identifier` <chr>,
#> # `Financial market data type` <chr>, time <date>, value <dbl>
## Most time series datasets also support wildcards. For example,
## in case of ECB, you can leave a part of the series id unspecified:
data("ecb/FM.M.U2.EUR.RT.MM..HSTA")
#> ⠙ Requesting data
#> ✔ Requesting data [1s]
#>
#> # Robonomist id: ecb/FM
#> # Title: Financial market data
#> # Vintage: 2025-11-13 08:00:00
#> # A tibble: 1,528 × 9
#> Frequency `Reference area` Currency Financial market provid…¹
#> * <chr> <chr> <chr> <chr>
#> 1 Monthly Euro area (changing composition) Euro Refinitiv
#> 2 Monthly Euro area (changing composition) Euro Refinitiv
#> 3 Monthly Euro area (changing composition) Euro Refinitiv
#> 4 Monthly Euro area (changing composition) Euro Refinitiv
#> 5 Monthly Euro area (changing composition) Euro Refinitiv
#> 6 Monthly Euro area (changing composition) Euro Refinitiv
#> 7 Monthly Euro area (changing composition) Euro Refinitiv
#> 8 Monthly Euro area (changing composition) Euro Refinitiv
#> 9 Monthly Euro area (changing composition) Euro Refinitiv
#> 10 Monthly Euro area (changing composition) Euro Refinitiv
#> # ℹ 1,518 more rows
#> # ℹ abbreviated name: ¹`Financial market provider`
#> # ℹ 5 more variables: `Financial market instrument` <chr>,
#> # `Financial market provider identifier` <chr>,
#> # `Financial market data type` <chr>, time <date>, value <dbl>
## If the data table too large to download in full, you may need to
## provide a download filter. First get the available variables and values:
data("ecb/AME") |> str()
#> ⠙ Requesting data
#> ✔ Requesting data [334ms]
#>
#> List of 7
#> $ FREQ : tibble [10 × 2] (S3: tbl_df/tbl/data.frame)
#> ..$ id : chr [1:10] "A" "B" "D" "E" ...
#> ..$ label: chr [1:10] "Annual" "Daily - businessweek" "Daily" "Event (not supported)" ...
#> $ AME_REF_AREA : tibble [109 × 2] (S3: tbl_df/tbl/data.frame)
#> ..$ id : chr [1:109] "A10" "A13" "ALB" "AUS" ...
#> ..$ label: chr [1:109] "10 accession countries" "13 candidate countries" "Albania" "Australia" ...
#> $ AME_TRANSFORMATION: tibble [3 × 2] (S3: tbl_df/tbl/data.frame)
#> ..$ id : chr [1:3] "1" "3" "9"
#> ..$ label: chr [1:3] "Original data and moving arithmetic mean" "Index numbers and moving arithmetic mean" "Annual changes (and moving arithmetic mean for time periods)"
#> $ AME_AGG_METHOD : tibble [5 × 2] (S3: tbl_df/tbl/data.frame)
#> ..$ id : chr [1:5] "0" "1" "2" "3" ...
#> ..$ label: chr [1:5] "Standard aggregation" "Weighted mean by GDP, weights in current euro" "Weighted mean by GDP, weights in current PPS" "Weighted mean by private consumption in euro" ...
#> $ AME_UNIT : tibble [11 × 2] (S3: tbl_df/tbl/data.frame)
#> ..$ id : chr [1:11] "0" "212" "30" "310" ...
#> ..$ label: chr [1:11] "National currency" "PPS [1960-1993 GDP EU-12, 1994- GDP EU-15]" "US Dollar" "Percentage of GDP at market prices" ...
#> $ AME_REFERENCE : tibble [28 × 2] (S3: tbl_df/tbl/data.frame)
#> ..$ id : chr [1:28] "0" "215" "30" "315" ...
#> ..$ label: chr [1:28] "No reference" "EU-15 = 100 including former West Germany" "US = 100" "EU-15 = 100 including unified Germany" ...
#> $ AME_ITEM : tibble [910 × 2] (S3: tbl_df/tbl/data.frame)
#> ..$ id : chr [1:910] "AAGE" "AAGT" "ADGGFI" "ADGGFU" ...
#> ..$ label: chr [1:910] "Average share of imports and exports of goods in world trade excluding intra EU trade - Foreign trade statistic"| __truncated__ "Average share of imports and exports of goods in world trade including intra EU trade - Foreign trade statistics" "Snow ball effect on general government consolidated gross debt Maastricht and former definition (linked series)" "Impact of the nominal increase of GDP on general government consolidated gross debt, Maastricht and former defi"| __truncated__ ...
#> - attr(*, "robonomist_id")= r_id [1:1] ecb/AME
#> - attr(*, "robonomist_vintage")= POSIXct[1:1], format: "2025-11-13 08:00:00"
#> - attr(*, "robonomist_title")= chr "AMECO"
#> - attr(*, "robonomist_language")= chr "en"
#> - attr(*, "robonomist_source")= chr NA
#> - attr(*, "robonomist_frequency")= chr NA
#> - attr(*, "class")= chr [1:2] "robonomist_data" "list"
## Then provide a suitable filter to download data:
data("ecb/AME", dl_filter = list(ame_ref_area = "FIN"))
#> ⠙ Requesting data
#> ⠹ Requesting data
#> ✔ Requesting data [1s]
#>
#> # Robonomist id: ecb/AME
#> # Title: AMECO
#> # Vintage: 2025-11-13 08:00:00
#> # A tibble: 243 × 9
#> Frequency `Ameco reference area` `Ameco transformation`
#> * <chr> <chr> <chr>
#> 1 Annual Finland Original data and moving arithmetic mean
#> 2 Annual Finland Original data and moving arithmetic mean
#> 3 Annual Finland Original data and moving arithmetic mean
#> 4 Annual Finland Original data and moving arithmetic mean
#> 5 Annual Finland Original data and moving arithmetic mean
#> 6 Annual Finland Original data and moving arithmetic mean
#> 7 Annual Finland Original data and moving arithmetic mean
#> 8 Annual Finland Original data and moving arithmetic mean
#> 9 Annual Finland Original data and moving arithmetic mean
#> 10 Annual Finland Original data and moving arithmetic mean
#> # ℹ 233 more rows
#> # ℹ 6 more variables: `Ameco aggregation method` <chr>, `Ameco unit` <chr>,
#> # `Ameco reference` <chr>, `Ameco item` <chr>, time <date>, value <dbl>
## Another example with Finish Customs dataset:
data("tulli/uljas_cpa2008",
dl_filter = list(
"Tavaraluokitus CPA2008_2" = "*A-X",
"Aika" = c("201505", "201506"),
"Maa" = "=ALL",
"Suunta" = "=FIRST 1",
"Indikaattorit" = "=FIRST 1"
)
)
#> ⠙ Requesting data
#> ✔ Requesting data [278ms]
#>
#> # Robonomist id: tulli/uljas_cpa2008
#> # Title: CPA2008, CC BY 4.0
#> # Vintage: 2023-12-20 16:36:42
#> # A tibble: 508 × 6
#> `Tavaraluokitus CPA2008_2` Maa Suunta Indikaattorit time value
#> * <chr> <chr> <chr> <chr> <date> <dbl>
#> 1 *A-X (2008--.) KAIKKI RYHMÄT AA (200… Tuont… Tilastoarvo … 2015-05-01 4.23e9
#> 2 *A-X (2008--.) KAIKKI RYHMÄT AD (200… Tuont… Tilastoarvo … 2015-05-01 2.18e5
#> 3 *A-X (2008--.) KAIKKI RYHMÄT AE (200… Tuont… Tilastoarvo … 2015-05-01 7.22e5
#> 4 *A-X (2008--.) KAIKKI RYHMÄT AF (200… Tuont… Tilastoarvo … 2015-05-01 7.99e4
#> 5 *A-X (2008--.) KAIKKI RYHMÄT AG (200… Tuont… Tilastoarvo … 2015-05-01 NA
#> 6 *A-X (2008--.) KAIKKI RYHMÄT AI (200… Tuont… Tilastoarvo … 2015-05-01 NA
#> 7 *A-X (2008--.) KAIKKI RYHMÄT AL (200… Tuont… Tilastoarvo … 2015-05-01 2.25e4
#> 8 *A-X (2008--.) KAIKKI RYHMÄT AM (200… Tuont… Tilastoarvo … 2015-05-01 2.52e2
#> 9 *A-X (2008--.) KAIKKI RYHMÄT AN (200… Tuont… Tilastoarvo … 2015-05-01 NA
#> 10 *A-X (2008--.) KAIKKI RYHMÄT AO (200… Tuont… Tilastoarvo … 2015-05-01 NA
#> # ℹ 498 more rows
## Using §-filter to filter data after download:
data("ec/consumer§Fin§Confidence")
#> ⠙ Requesting data
#> ✔ Requesting data [489ms]
#>
#> # Robonomist id: ec/consumer
#> # Title: Consumer Sentiment Indicator
#> # Vintage: 2025-10-27 16:40:55
#> # A tibble: 490 × 4
#> Country Indicator time value
#> <chr> <chr> <date> <dbl>
#> 1 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-01-01 NA
#> 2 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-02-01 NA
#> 3 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-03-01 NA
#> 4 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-04-01 NA
#> 5 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-05-01 NA
#> 6 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-06-01 NA
#> 7 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-07-01 NA
#> 8 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-08-01 NA
#> 9 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-09-01 NA
#> 10 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 1985-10-01 NA
#> # ℹ 480 more rows
## Using §-filter with start date:
data("ec/consumer§Fin§Confidence§2020-01-01")
#> ⠙ Requesting data
#> ✔ Requesting data [380ms]
#>
#> # Robonomist id: ec/consumer
#> # Title: Consumer Sentiment Indicator
#> # Vintage: 2025-10-27 16:40:55
#> # A tibble: 70 × 4
#> Country Indicator time value
#> <chr> <chr> <date> <dbl>
#> 1 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-01-01 -4.4
#> 2 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-02-01 -4.9
#> 3 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-03-01 -7
#> 4 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-04-01 -13.9
#> 5 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-05-01 -8.8
#> 6 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-06-01 -3.8
#> 7 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-07-01 -1.9
#> 8 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-08-01 -5.1
#> 9 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-09-01 -6
#> 10 Finland Confidence Indicator (Q1 + Q2 + Q4 + Q9) / 4 2020-10-01 -6.8
#> # ℹ 60 more rows