Access data from Robonomist Data Server • robonomistClient

The robonomistClient package is a powerful R client designed to simplify access to diverse datasets hosted on the Robonomist Data Server. With seamless integration, the package enables analysts, researchers, and developers to retrieve and analyze up-to-date, multilingual data from numerous national and international sources, all in a tidy format ready for analysis.

Key Features

The robonomistClient package is designed to deliver:

Comprehensive Data Coverage

Access data from national statistical agencies, international organizations, and regional sources — all in one place. This means less time spent searching for data and more time for analysis.

Statistics Finland, OECD, World Bank, European Central Bank, and more.

High Data Fidelity and Trust

Our data is reliable and accurately reflects the information from official statistical agencies. Complete metadata is provided for every dataset to ensure that your analysis is based on credible information.

Full transparency on data sources.
Detailed metadata included.

Flexibility in Data Retrieval

Customize the way data is retrieved to fit your specific needs:

Multilingual support
Choose between variable labels or codes.
Interpret time variables as dates or raw values.

Seamless Integration for Dynamic Workflows

Designed to work smoothly with R, robonomistClient makes building dynamic documents and automatically updating apps easier by providing a stable API:

Reduce disruptions caused by changing official statistics APIs.
Focus on insights rather than wrangling with data inconsistencies.

Datasources

The robonomistClient package provides seamless and efficient access to a wide range of datasources via the Robonomist Data Server. With support for 95 197 up-to-date data tables across 57 distinct datasources and 13 languages, the package is designed to streamline your data analysis workflow.

International Datasources

Global Institutions: OECD, International Monetary Fund (IMF), World Bank, United Nations Economic Commission for Europe Statistical Database (UNECE), United Nations Conference on Trade and Development (UNCTAD), FAO (Food and Agriculture Organization), Bank for International Settlements (BIS).
European Union: Eurostat, European Commission, European Central Bank (ECB).
Nordic Countries: Statistics Sweden, Swedish National Institute of Economic Research, Swedish Agricultural Agency, Statistics Norway, Statistics Denmark, Statistics Iceland, Statistics Estonia, Nordic Statistics Database.

Finnish Datasources

Statistics Finland: StatFin, StatFin archive databases, Municipal figures & financial data, Paavo postal code area statistics, experimental statistics, immigrants and integration database.
Regional Data: Helsingin seudun aluesarjat -tilastotietokanta, Helsingin ympäristötilasto.
National Agencies: Finnish Tax Administration, Finnish Centre for Pensions, Natural Resources Institute Finland (LUKE), Traficom (Finnish Transport and Communications Agency), Customs Finland, THL Sotkanet, Vipunen (Education Statistics Finland), Fingrid, TutkiHallintoa.fi (Valtiokonttori).

Thematic & Specialized Datasources

Energy & Environment: Entso-E Transparency Platform, U.S. Energy Information Administration database, Fingrid.
Financial: Deutsche Bundesbank time series database, FRED (Federal Reserve Economic Data**.
Health & Pandemic Data: THL Epirapo, ECDC.

This list highlights just some of the many datasources available through the robonomistClient package. The package also includes Robonomist’s curated tidy data tables, providing ready-to-use datasets tailored for streamlined analysis and insight generation.

Getting started

To setup a Robonomist Data Server for your organization, please contact team@robonomist.com.

1. Install the Package

First, install the package from GitHub (if you haven’t already):

# Install devtools if necessary
# install.packages("devtools")

# Install the latest version of robonomistClient from GitHub
devtools::install_github("robonomist/robonomistClient")

2. Set Up Your Connection

To start using the package, you need to set the hostname of your Robonomist Data Server and provide an access token. Use the set robonomist_server() function to establish the connection.

library(robonomistClient)

# Set up the connection
set_robonomist_server(hostname = "hostname.com", access_token = "abc")

3. Explore Available Datasources

To see which datasources are available, use the datasources() function. This will give you an overview of all the datasources you can access.

datasources()

#> # Robonomist Server Datasources
#>    dataset          title                                          languages
#>    <r_dataset>      <chr>                                          <iso2>   
#>  1 StatFin          Statistics Finland, StatFin database           fi,sv,en 
#>  2 StatFin_Passiivi Statistics Finland, StatFin archive database   fi,sv,en 
#>  3 Vero             Finnish Tax Administration statistical databa… fi,sv,en 
#>  4 ec               European Commission's Business and Consumer S… en       
#>  5 kunnat           Key statistics of municipalities, Statistics … fi,sv,en 
#>  6 kunnat           Financial data reported by municipalities and… fi,sv,en 
#>  7 paavo            Statistics Finland's Paavo database            fi,sv,en 
#>  8 tulli            Finnish Customs, Uljas Statistical Database    fi,sv,en 
#>  9 luke             Statistics database of Natural Resources Inst… fi,sv,en 
#> 10 etk              Finnish Centre for Pensions' statistical data… fi,sv,en 
#> # ℹ 47 more rows
#> # ℹ 2 more variables: datasource <chr>, available <lgl>

4. Search and Retrieve Data Tables

The data() function allows you to search and retrieve data tables from the datasources. Here’s how to list all available data tables:

data()

#> # Robonomist Database search results
#>    id                                      title                       lang 
#>    <r_id>                                  <chr>                       <chr>
#>  1 StatFin/adopt/statfin_adopt_pxt_11lv.px Adoptiot muuttujina Vuosi,… fi   
#>  2 StatFin/adopt/statfin_adopt_pxt_11lv.px Adoptioner efter År, Födel… sv   
#>  3 StatFin/adopt/statfin_adopt_pxt_11lv.px Adoptions by Year, Country… en   
#>  4 StatFin/adopt/statfin_adopt_pxt_13qh.px Adoptiot muuttujina Vuosi,… fi   
#>  5 StatFin/adopt/statfin_adopt_pxt_13qh.px Adoptioner efter År, Föräl… sv   
#>  6 StatFin/adopt/statfin_adopt_pxt_13qh.px Adoptions by Year, Parents… en   
#>  7 StatFin/aku/statfin_aku_pxt_12dz.px     Aikuiskoulutukseen osallis… fi   
#>  8 StatFin/aku/statfin_aku_pxt_12dz.px     Deltagande i vuxenutbildni… sv   
#>  9 StatFin/aku/statfin_aku_pxt_12dz.px     Participation in adult edu… en   
#> 10 StatFin/aku/statfin_aku_pxt_12ea.px     Aikuiskoulutukseen osallis… fi   
#> # ℹ 170,260 more rows

Search for tables related to employment:

data("Employment")

#> # Robonomist Database search results
#>    id                                        title                     lang 
#>    <r_id>                                    <chr>                     <chr>
#>  1 StatFin/altp/statfin_altp_pxt_12bg.px     Employment and hours wor… en   
#>  2 StatFin/klt/statfin_klt_pxt_13jb.px       Employment of students s… en   
#>  3 StatFin/ntp/statfin_ntp_pxt_11tj.px       Employment and hours wor… en   
#>  4 StatFin/tyokay/statfin_tyokay_pxt_115b.px Population by Area, Main… en   
#>  5 StatFin/tyokay/statfin_tyokay_pxt_115c.px Population by Main type … en   
#>  6 StatFin/tyokay/statfin_tyokay_pxt_115d.px Population by Area, Main… en   
#>  7 StatFin/tyokay/statfin_tyokay_pxt_115e.px Population by Main type … en   
#>  8 StatFin/tyokay/statfin_tyokay_pxt_115f.px Population by Area, Main… en   
#>  9 StatFin/tyokay/statfin_tyokay_pxt_115g.px Population by Main type … en   
#> 10 StatFin/tyokay/statfin_tyokay_pxt_115h.px Employed labour force in… en   
#> # ℹ 3,656 more rows

Search for a specific dataset with language options:

data("StatFin/ntp", lang = "en")

#> # Robonomist Database search results
#>   id                                  title                            lang 
#>   <r_id>                              <chr>                            <chr>
#> 1 StatFin/ntp/statfin_ntp_pxt_11tj.px Employment and hours worked qua… en   
#> 2 StatFin/ntp/statfin_ntp_pxt_132g.px Income and production by indust… en   
#> 3 StatFin/ntp/statfin_ntp_pxt_132h.px Gross domestic product and nati… en

These search capabilities help you quickly locate the data you need without needing to know exact IDs.

5. Retrieve a Specific Data Table

If you know the ID of a specific data table you want, you can retrieve it by passing the ID to the data() function. For example:

data("StatFin/synt/statfin_synt_pxt_12dx.px")

#> # Robonomist id: StatFin/synt/statfin_synt_pxt_12dx.px
#> # Title:         Väestönmuutokset muuttujina Vuosi ja Tiedot
#> # Last updated:  2024-05-28 08:00:00
#> # Next update:   2025-05-20 08:00:00
#> # A tibble:      3,025 × 3
#>    Vuosi Tiedot                     value
#>  * <chr> <chr>                      <dbl>
#>  1 1749  Elävänä syntyneet          16700
#>  2 1749  Kuolleet                   11600
#>  3 1749  Luonnollinen väestönlisäys  5100
#>  4 1749  Kuntien välinen muutto        NA
#>  5 1749  Maahanmuutto Suomeen          NA
#>  6 1749  Maastamuutto Suomesta         NA
#>  7 1749  Nettomaahanmuutto             NA
#>  8 1749  Solmitut avioliitot         3900
#>  9 1749  Avioerot                      NA
#> 10 1749  Kokonaismuutos                NA
#> # ℹ 3,015 more rows

Retrieve Data for Production Use

The data_get() function is designed for production use cases where robustness is crucial. It allows you to fetch a specific dataset by its ID directly, without any searching. This ensures consistency, especially in automated scripts or applications where you need to reliably retrieve the same dataset over time.

# Fetch a specific dataset using its ID for production use
production_data <- data_get("StatFin/synt/statfin_synt_pxt_12dx.px")

Fetch Data Directly Using a URL

You can also fetch data directly using a link from the datasource’s website using fetch_data_from_url(). This is useful when you have a specific URL from a datasource explorer, such as the OECD’s data explorer:

fetch_data_from_url("https://data-explorer.oecd.org/vis?tm=sna&pg=0&fs[0]=Measure%2C0%7CAquaculture%20production%23AQUA_PD%23&fc=Measure&snb=1&vw=tb&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_FISH_PROD%40DF_FISH_AQUA&df[ag]=OECD.TAD.ARP&df[vs]=1.0&pd=2010%2C&dq=.A.._T.T&ly[rw]=REF_AREA&ly[cl]=TIME_PERIOD&to[TIME_PERIOD]=false")

#> data_get("oecd/DSD_FISH_PROD@DF_FISH_AQUA", dl_filter = ".A.._T.T")

#> # Robonomist id: oecd/DSD_FISH_PROD@DF_FISH_AQUA
#> # Title:         Aquaculture production
#> # Vintage:       2024-07-25 09:39:34
#> # A tibble:      1,431 × 10
#>    REF_AREA  FREQ   MEASURE  SPECIES UNIT_MEASURE time       value UNIT_MULT
#>  * <chr>     <chr>  <chr>    <chr>   <chr>        <date>     <dbl> <chr>    
#>  1 Argentina Annual Aquacul… Total   Tonnes       1995-01-01  1474 0        
#>  2 Argentina Annual Aquacul… Total   Tonnes       1996-01-01  1322 0        
#>  3 Argentina Annual Aquacul… Total   Tonnes       1997-01-01  1314 0        
#>  4 Argentina Annual Aquacul… Total   Tonnes       1998-01-01  1040 0        
#>  5 Argentina Annual Aquacul… Total   Tonnes       1999-01-01  1218 0        
#>  6 Argentina Annual Aquacul… Total   Tonnes       2000-01-01  1784 0        
#>  7 Argentina Annual Aquacul… Total   Tonnes       2001-01-01  1340 0        
#>  8 Argentina Annual Aquacul… Total   Tonnes       2002-01-01  1457 0        
#>  9 Argentina Annual Aquacul… Total   Tonnes       2003-01-01  1647 0        
#> 10 Argentina Annual Aquacul… Total   Tonnes       2004-01-01  1848 0        
#> # ℹ 1,421 more rows
#> # ℹ 2 more variables: DECIMALS <chr>, CONVENTION <chr>

More information

For more detailed information and examples, check the package documentation:

Robonomist Client