Create overview stats for a mental model
Value
A tibble with the following stats:
- weighted betwenness - weighted indegree - weighted outdegree - weighted total degree
If an aggregated mental model is supplied, statistics are returned for all aggregated groups.
If a mental model object with individual user data is supplied, statistics are returned per user.
Examples
# This returns statistics for each concept by user
calculate_descriptive_statistics(example_models)
#> # A tibble: 901 × 6
#> concept w_betweenness w_in_d…¹ w_out…² w_tot…³ user
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 Energy transition 0 3 0 3 d20f…
#> 2 Climate compensation 0 0 0 0 d20f…
#> 3 Wind farms 0 3 0 3 d20f…
#> 4 Hydropower 0 0 0 0 d20f…
#> 5 Nuclear power 0 0 0 0 d20f…
#> 6 Carbon capture and storage 0 0 1 1 d20f…
#> 7 Regulations 0 0 1 1 d20f…
#> 8 Energy saving 0 0 0 0 d20f…
#> 9 Walking and cycling 0 0 0 0 d20f…
#> 10 Energy efficient home appliances 0 0 1 1 d20f…
#> # … with 891 more rows, and abbreviated variable names ¹w_in_degree,
#> # ²w_out_degree, ³w_total_degree
# If an aggregated object is supplied, statistics are supplied for the in the aggregate
aggregated_model <- aggregate_mentalmodel(example_models)
calculate_descriptive_statistics(aggregated_model)
#> # A tibble: 17 × 5
#> concept w_betweenness w_in_degree w_out_de…¹ w_tot…²
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Energy transition 0 16 0 16
#> 2 Climate compensation 16.5 3 10 13
#> 3 Wind farms 0 5 6 11
#> 4 Hydropower 0 5 3 8
#> 5 Nuclear power 1 4 2 6
#> 6 Carbon capture and storage 1 4 2 6
#> 7 Regulations 2.5 1 16 17
#> 8 Energy saving 15 11 4 15
#> 9 Walking and cycling 2.5 6 2 8
#> 10 Energy efficient home appliances 3 9 3 12
#> 11 Energy efficient houses 3.5 10 3 13
#> 12 Subsidies 0 2 14 16
#> 13 Public transportation 4.33 8 2 10
#> 14 Electrics cars 3.33 7 3 10
#> 15 Environmental education 0 3 10 13
#> 16 Solar panels 3 5 7 12
#> 17 Science 14.5 4 16 20
#> # … with abbreviated variable names ¹w_out_degree, ²w_total_degree