Introduction to rKolada

The data model

Data in Kolada are stored along three basic dimensions:

• A KPI ID
• A point in time (year)
• A municipality/region/ ID

When downloading data, the user needs to specify search parameters for at least two of these three basic dimensions. (The Kolada API documentation also specifies a fourth basic dimension: gender. However, data for all genders is always automatically downloaded when available.) The parameters can be a single, atomic value or a vector of values.

Also, the Kolada database proves useful groupings of municipalities and KPIs that can be used for further exploration, or to create unweighted averages. Lastly, some KPIs are also available for Organizational units (OUs) within municipalities, e.g. a school, an administrative subdivision or an elderly home.

Downloading data

If the user already has knowledge of the IDs of the KPIs and/or municipalities they want to download, this can be done using the function get_values(). For instance, if you want to download all values for the KPI N00945 (“Tillfälliga föräldrapenningdagar (VAB) som tas ut av män, andel av antal dagar (%)”) for Sweden’s three most populous cities; Stockholm (id "0180"), Gothenburg (Swedish: Göteborg; "1480") and Malmö ("1280"):

#> # A tibble: 75 × 9
#>    kpi    municipality_id  year count gender status value municipality
#>    <chr>  <chr>           <int> <int> <chr>  <chr>  <dbl> <chr>       
#>  1 N00945 0180             1996     1 T      ""      NA   Stockholm   
#>  2 N00945 1280             1996     1 T      ""      NA   Malmö       
#>  3 N00945 1480             1996     1 T      ""      NA   Göteborg    
#>  4 N00945 0180             1997     1 T      ""      NA   Stockholm   
#>  5 N00945 1280             1997     1 T      ""      NA   Malmö       
#>  6 N00945 1480             1997     1 T      ""      NA   Göteborg    
#>  7 N00945 0180             1998     1 T      ""      30.4 Stockholm   
#>  8 N00945 1280             1998     1 T      ""      26.7 Malmö       
#>  9 N00945 1480             1998     1 T      ""      30.5 Göteborg    
#> 10 N00945 0180             1999     1 T      ""      30.6 Stockholm   
#> # ℹ 65 more rows
#> # ℹ 1 more variable: municipality_type <chr>

n00945 <- get_values(
  kpi = "N00945",
  municipality = c("0180", "1480", "1280"),
  period = 1970:2020
)

n00945

In many cases, however, you will not know in advance exactly what KPIs to be looking for, or you might not know the IDs of Sweden’s municipalities.

Downloading metadata: get functions

Kolada has five different kinds of metadata entities Each one of these can be downloaded by using rKolada’s get functions. Each function returns a tibble with all available data for the specified metadata entity:

• KPIs: get_kpi()
• KPI groups: get_kpi_groups()
• Municipalities: get_municipality()
• Municipality groups: get_municipality_groups()
• Organizational Unit: : get_ou()

Each function returns a tibble with all available data for the specified metadata entity.

#> # A tibble: 10 × 13
#>    auspices description has_ou_data id    is_divided_by_gender municipality_type
#>    <chr>    <chr>       <lgl>       <chr>                <int> <chr>            
#>  1 E        Personalko… FALSE       N000…                    0 K                
#>  2 X        Kommunalek… FALSE       N000…                    0 K                
#>  3 <NA>     Externa in… FALSE       N000…                    0 K                
#>  4 <NA>     Inkomstutj… FALSE       N000…                    0 K                
#>  5 <NA>     Kostnadsut… FALSE       N000…                    0 K                
#>  6 X        Reglerings… FALSE       N000…                    0 K                
#>  7 <NA>     Utjämnings… FALSE       N000…                    0 K                
#>  8 X        Införandeb… FALSE       N000…                    0 K                
#>  9 X        Strukturbi… FALSE       N000…                    0 K                
#> 10 <NA>     Externa in… FALSE       N000…                    0 K                
#> # ℹ 7 more variables: operating_area <chr>, ou_publication_date <chr>,
#> #   perspective <chr>, prel_publication_date <chr>, publ_period <chr>,
#> #   publication_date <chr>, title <chr>

# Download all KPI metadata as a tibble (kpi_df)
kpi_df <- get_kpi()

head(kpi_df, n = 10)

All get functions are thin wrappers around the more general function get_metadata(). If you are familiar with the terminology used in the Kolada API for accessing metadata you might want to use this function instead.

Exploring metadata

For each metadata type mentioned in the previous sections, rKolada offers several convenience functions to help exploring and narrowing down metadata tables. (If you are familiar with dplyr semantics, most of these functions are basically wrappers around dplyr/tidyr code.)

Since each get function above returns a table for the selected entity, a metadata table can be one of five different types. All metadata convenience functions are prefixed to reflect which kind of metadata table they operate on: kpi, kpi_grp, municipality, municipality_grp, and ou.

All metadata convenience functions have been designed with functional piping in mind, so their first argument is always a metadata tibble. Most of them also return a tibble of the same type

The most important family of metadata convenience functions is the search family. Much like dplyr::filter() they can be used to search for one or several search terms in the entire table or in a subset of named columns:

# Search for KPIs with the term "BRP" in their description or title
kpi_filter <- kpi_df %>% kpi_search("skola", column = c("description", "title"))
kpi_filter
#> # A tibble: 901 × 13
#>    auspices description has_ou_data id    is_divided_by_gender municipality_type
#>    <chr>    <chr>       <lgl>       <chr>                <int> <chr>            
#>  1 <NA>     "Kostnadsu… FALSE       N000…                    0 K                
#>  2 <NA>     "Kostnadsu… FALSE       N000…                    0 K                
#>  3 <NA>     "Kostnadsu… FALSE       N000…                    0 K                
#>  4 T        "Avvikelse… FALSE       N000…                    0 K                
#>  5 T        "Strukturk… FALSE       N001…                    0 K                
#>  6 E        "Antal ans… FALSE       N001…                    0 K                
#>  7 E        "Antal års… FALSE       N001…                    0 K                
#>  8 T        "Kommunind… FALSE       N003…                    0 K                
#>  9 T        "Kommunind… FALSE       N003…                    0 K                
#> 10 T        "Kommunind… FALSE       N003…                    0 K                
#> # ℹ 891 more rows
#> # ℹ 7 more variables: operating_area <chr>, ou_publication_date <chr>,
#> #   perspective <chr>, prel_publication_date <chr>, publ_period <chr>,
#> #   publication_date <chr>, title <chr>

#> # A tibble: 3,312 × 3
#>    id      members       title                                                
#>    <chr>   <list>        <chr>                                                
#>  1 G114418 <df [4 × 2]>  Fyrkommunsnätverket (ovägt medel)                    
#>  2 G114419 <df [4 × 2]>  SMS - Samhällsskydd mellersta Skaraborg (ovägt medel)
#>  3 G114798 <df [4 × 2]>  4M -  Fyra Mälarstäder (ovägt medel)                 
#>  4 G114915 <df [68 × 2]> SmåKom (ovägt medel)                                 
#>  5 G116237 <df [9 × 2]>  Sjuhärad kommunalförbund (ovägt medel)               
#>  6 G116238 <df [15 × 2]> Skaraborgs kommunalförbund (ovägt medel)             
#>  7 G122469 <df [10 × 2]> Region 10 (ovägt medel)                              
#>  8 G122931 <df [5 × 2]>  3KVH (ovägt medel)                                   
#>  9 G128518 <df [11 × 2]> Familjen Helsingborg (ovägt medel)                   
#> 10 G128681 <df [12 × 2]> Stor-Malmö (ovägt medel)                             
#> # ℹ 3,302 more rows

# Search for municipality groups containing the name "Arboga"
munic_g <- get_municipality_groups()

arboga_groups <- munic_g %>% municipality_grp_search("Arboga")
arboga_groups
#> # A tibble: 11 × 3
#>    id      members      title                                             
#>    <chr>   <list>       <chr>                                             
#>  1 G175909 <df [7 × 2]> Liknande kommuner ekonomiskt bistånd, Arboga, 2022
#>  2 G176199 <df [7 × 2]> Liknande kommuner socioekonomi, Arboga, 2022      
#>  3 G176489 <df [7 × 2]> Liknande kommuner äldreomsorg, Arboga, 2022       
#>  4 G35869  <df [7 × 2]> Liknande kommuner grundskola, Arboga, 2022        
#>  5 G36161  <df [7 × 2]> Liknande kommuner gymnasieskola, Arboga, 2022     
#>  6 G36453  <df [7 × 2]> Liknande kommuner IFO, Arboga, 2022               
#>  7 G37329  <df [7 × 2]> Liknande kommuner, övergripande, Arboga, 2022     
#>  8 G39502  <df [7 × 2]> Liknande kommuner LSS, Arboga, 2022               
#>  9 G85463  <df [7 × 2]> Liknande kommuner fritidshem, Arboga, 2022        
#> 10 G85755  <df [7 × 2]> Liknande kommuner förskola, Arboga, 2022          
#> 11 G87629  <df [7 × 2]> Liknande kommuner integration, Arboga, 2022

Another important family of exploration functions is the describe family of functions. These functions take a metadata table and print a human-readable summary of the most important facts about each row in the table (up to a limit, specified by max_n). By default, output is printed directly to the R console. But by specifying format = "md" you can make the describe functions create markdown-ready output which can be added directly to a R markdown file by setting the chunk option results='asis'. The output then looks as follows:

kpi_filter %>% kpi_describe(max_n = 2, format = "md", heading_level = 4)

Extra functions for exploring KPI metadata

KPI metadata is considerably more complex than other types of metadata. To further assist in exploring KPI metadata the function kpi_bind_keywords() can be used to tag data with keywords (these are inferred from the KPI title) to classify KPIs and make them more searchable.

# Add keywords to a KPI table
kpis_with_keywords <- kpi_filter %>% kpi_bind_keywords(n = 4)

# count keywords
kpis_with_keywords %>%
  tidyr::pivot_longer(dplyr::starts_with("keyword"), values_to = "keyword") %>%
  dplyr::count(keyword, sort = TRUE)
#> # A tibble: 417 × 2
#>    keyword            n
#>    <chr>          <int>
#>  1 elever           176
#>  2 grundskola       146
#>  3 gymnasieelever   112
#>  4 pedagogisk       112
#>  5 åk               112
#>  6 gymnasieskola    100
#>  7 förskola          91
#>  8 personal          87
#>  9 år                73
#> 10 kommunal          67
#> # ℹ 407 more rows

Some KPIs can be very similar-looking and it can sometimes be hard to discern which of the KPIs to use. To make sifting through data easier, kpi_minimize() can be used to remove all redundant columns from a KPI table. (In this case, “redundant” means “containing no information that helps in differentiating KPIs from one another”, i.e. columns containing only one single value for all observations in the table):

# Top 10 rows of the table
kpi_filter %>% dplyr::slice(1:10)
#> # A tibble: 10 × 13
#>    auspices description has_ou_data id    is_divided_by_gender municipality_type
#>    <chr>    <chr>       <lgl>       <chr>                <int> <chr>            
#>  1 <NA>     "Kostnadsu… FALSE       N000…                    0 K                
#>  2 <NA>     "Kostnadsu… FALSE       N000…                    0 K                
#>  3 <NA>     "Kostnadsu… FALSE       N000…                    0 K                
#>  4 T        "Avvikelse… FALSE       N000…                    0 K                
#>  5 T        "Strukturk… FALSE       N001…                    0 K                
#>  6 E        "Antal ans… FALSE       N001…                    0 K                
#>  7 E        "Antal års… FALSE       N001…                    0 K                
#>  8 T        "Kommunind… FALSE       N003…                    0 K                
#>  9 T        "Kommunind… FALSE       N003…                    0 K                
#> 10 T        "Kommunind… FALSE       N003…                    0 K                
#> # ℹ 7 more variables: operating_area <chr>, ou_publication_date <chr>,
#> #   perspective <chr>, prel_publication_date <chr>, publ_period <chr>,
#> #   publication_date <chr>, title <chr>

# Top 10 rows of the table, with non-distinct data removed
kpi_filter %>% dplyr::slice(1:10) %>% kpi_minimize()
#> # A tibble: 10 × 8
#>    id     title     description operating_area perspective prel_publication_date
#>    <chr>  <chr>     <chr>       <chr>          <chr>       <chr>                
#>  1 N00022 Kostnads… "Kostnadsu… Skatter och u… Resurser    2023-09-28           
#>  2 N00023 Kostnads… "Kostnadsu… Skatter och u… Resurser    2023-09-28           
#>  3 N00026 Kostnads… "Kostnadsu… Skatter och u… Resurser    2023-09-28           
#>  4 N00097 Nettokos… "Avvikelse… Kommunen, öve… Resurser    2024-05-11           
#>  5 N00100 Struktur… "Strukturk… Kommunen, öve… Resurser    2023-09-28           
#>  6 N00108 Månadsav… "Antal ans… Barn och utbi… Resurser    <NA>                 
#>  7 N00114 Årsarbet… "Antal års… Barn och utbi… Resurser    <NA>                 
#>  8 N00300 Medborga… "Kommunind… Kommunen, öve… Kvalitet o… <NA>                 
#>  9 N00301 Trygghet… "Kommunind… Kommunen, öve… Kvalitet o… <NA>                 
#> 10 N00303 Tillgäng… "Kommunind… Kommunen, öve… Kvalitet o… <NA>                 
#> # ℹ 2 more variables: publ_period <chr>, publication_date <chr>

Note that kpi_minimize() operates on the current table. This means that results may vary depending on the data you’re operating on.

Metadata groups

Kolada provides pre-defined groups of KPIs and municipalities/regions. Exploring an using thse groups can facilitate meaningful comparisons between different entities or help paint a broader picture of developments in a certain field or area.

To get, search or describe group metadata, use the same techniques as described above for regular metadata (relevant prefixes are kpi_grp_ and municipality_grp_).

A crucial difference between group metadata and other metadata tables, however, is that group metadata comes in the form of a nested table. Typically you might want to unnest the groups in a group metadata table once yo hae found the relevant group(s) for your query. To do this, use the unnest functions to create a table containing unnested entities, e.g. running kpi_grp_unnest(kpi_grp_df) using a KPI group metadata table as argument creates a kpi_df that can be further processed using the kpi_ functios described in previous sections of this vignette.