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Create overview stats for a mental model

Usage

calculate_descriptive_statistics(mentalmodel)

Arguments

mentalmodel

A mtoolr mental model object

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 ¹​w_in_degree,
#> #   ²​w_out_degree, ³​w_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 ¹​w_out_degree, ²​w_total_degree