A quick start guide to rKolada

1. Get metadata

Kolada contains five different types of metadata entities:

1. kpi: Key Performance Indicators — the measures themselves (e.g. “Share with post-secondary education”)
2. municipality: Municipalities and regions, identified by numeric codes (e.g. Stockholm = "0180")
3. ou: Operating Units — sub-units within municipalities (e.g. a specific school or elderly home)
4. kpi_groups: Thematic groupings of KPIs
5. municipality_groups: Thematic groupings of municipalities

To obtain data using rKolada it is usually a good idea to start by exploring metadata. rKolada comes with convenience functions for each of the five above mentioned entities. These functions are all names get_[entity]() and can be called as follows. The cache parameter allows you to temporarily store results on disk to avoid repeated calls to the API in case you need to re-run your code:

kpis <- get_kpi(cache = FALSE)
munic <- get_municipality(cache = FALSE)

If you have already familiarised yourself with the Kolada API (e.g. by reading the official docs on GitHub) you can access the full metadata API using get_metadata().

2. Search metadata

Metadata tables are stored as regular tibbles so you can start inspecting them by simply viewing them in RStudio. For example, the KPI metadata we downloaded looks like this:

dplyr::glimpse(kpis)
#> Rows: 6,099
#> Columns: 12
#> $ id                    <chr> "N00003", "N00005", "N00009", "N00011", "N00012"…
#> $ title                 <chr> "Personalkostnader, kr/inv", "Utjämningssystemet…
#> $ description           <chr> "Personalkostnader kommunen totalt, dividerat me…
#> $ is_divided_by_gender  <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
#> $ municipality_type     <chr> "K", "K", "K", "K", "K", "K", "K", "K", "K", "K"…
#> $ auspice               <chr> "E", "X", NA, NA, NA, "X", NA, "X", "X", NA, NA,…
#> $ operating_area        <chr> "Kommunen, övergripande", "Kommunen, övergripand…
#> $ perspective           <chr> "Resurser", "Resurser", "Resurser", "Resurser", …
#> $ prel_publication_date <chr> "2026-04-01", "2026-04-01", NA, "2025-09-28", "2…
#> $ publication_date      <chr> "2026-08-28", "2026-08-28", "2027-02-24", "2026-…
#> $ publ_period           <chr> "2025", "2025", "2026", "2026", "2026", "2026", …
#> $ has_ou_data           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…

But rKolada also comes with a set of convenience functions to simplify the task of exploring KPI metadata. kpi_search() filters down a list of KPIs using a search term, and kpi_minimize() can be used to clean the KPI metadata table from columns that don’t contain any information that distinguish KPIs from each other:

# Get a list KPIs matching a search for "bruttoregionprodukt" (Gross regional product)
kpi_res <- kpis |>
  kpi_search("bruttoregionprodukt") |>
  # "K" = data available at municipality level (not just region)
  kpi_search("K", column = "municipality_type") |>
  kpi_minimize(remove_monotonous_data = TRUE)

dplyr::glimpse(kpi_res)
#> Rows: 2
#> Columns: 3
#> $ id          <chr> "N03702", "N03703"
#> $ title       <chr> "Bruttoregionprodukt, kommun (BRP), 1000 kr/invånare", "Br…
#> $ description <chr> "BRP dividerat med antal invånare i det geografiska område…

Let’s say we are interested in retrieving data for three Swedish municipalities. We thus want to create a table containing metadata about these municipalities:

munic_res <- munic |>
  # type "K" = municipality (kommun), vs "L" for region (län)
  municipality_search("K", column = "type") |>
  # Only keep Stockholm, Gothenburg and Malmö
  municipality_search(c("Stockholm", "Göteborg", "Malmö"))

dplyr::glimpse(munic_res)
#> Rows: 3
#> Columns: 3
#> $ id    <chr> "0180", "1280", "1480"
#> $ title <chr> "Stockholm", "Malmö", "Göteborg"
#> $ type  <chr> "K", "K", "K"

3. Describe KPIs

In addition to the information provided about every KPI in the title and description columns of a KPI table, kpi_bind_keywords() can be used to create a rough summary of every KPI creating a number of keyword columns. The function kpi_describe() can be used to print a huamn readable table containing a summary of a table of KPIs. For instance, by setting the knitr chunk option results='asis', the following code renders a Markdown section that is automatically inluded as a part of the HTML that renders this web page:

kpi_res |>
  kpi_bind_keywords(n = 4) |>
  kpi_describe(max_n = 1, format = "md", heading_level = 4, sub_heading_level = 5)

4. Get data

Once we have settled on what KPIs we are interested in the next step is to download actual data from Kolada. Use get_values() to do this. To download data from the Kolada API you need to provide at least two of the following parameters:

1. kpi: One or a vector of several KPI IDs
2. municipality: One or a vector of several municipality IDs or municipality group IDs
3. period: The years for which data should be downloaded.

The ID tags for KPIs and municipalities can be extracted using the convenience functions kpi_extract_ids() and municipality_extract_ids():

kld_data <- get_values(
  kpi = kpi_extract_ids(kpi_res),
  municipality = municipality_extract_ids(munic_res),
  period = 1990:2019,
  simplify = TRUE
)

simplify = TRUE does two things: replaces numeric IDs with readable names (e.g. "0180" becomes "Stockholm") and drops columns that are only used internally.

5. Inspect and visualise results

Finally, time to inspect our data:

# Visualise results
library("ggplot2")

ggplot(kld_data, aes(x = year, y = value)) +
  # One line per municipality, coloured by name
  geom_line(aes(color = municipality)) +
  # Separate panel per KPI (stacked vertically)
  facet_grid(kpi ~ .) +
  # Thousand separators for readability
  scale_y_continuous(labels = scales::comma) +
  labs(
    title = "Gross Regional Product",
    subtitle = "Yearly development in Sweden's three\nmost populous municipalities",
    x = "Year",
    y = "",
    caption = values_legend(kld_data, kpis)  # Auto-generated KPI legend
  )

Note the use of the helper function values_legend() to produce a legend containing the names and keys of all KPIs included in the graph.

Next steps

• Deeper walkthrough — vignette("introduction-to-rkolada") covers the full data model, metadata groups, and more complex workflows.
• ggplot2 reference — https://ggplot2-book.org/ for more on visualisation.

Related packages

If you work with data from PX-Web APIs (Statistics Sweden, Statistics Norway, Statistics Finland, etc.), see pixieweb — a sibling package that follows the same design principles as rKolada.