This is equivalent to the Simpson Index.

ds_hhi(.data, .cols, .name)

hhi(..., .data = dplyr::across(everything()))

ds_simpson(.data, .cols, .name)

simpson(..., .data = dplyr::across(everything()))

Arguments

.data

tibble

.cols

tidy-select Columns to compute the measure with.

.name

name for column with HHI. Leave missing to return a vector.

...

arguments to forward to ds_hhi from hhi

Value

a tibble or numeric vector if .name missing

Examples

data("de_county")
ds_hhi(de_county, starts_with('pop_'))
#> [1] 0.4844772 0.4429565 0.5947231
ds_hhi(de_county, starts_with('pop_'), 'blau')
#> Simple feature collection with 3 features and 21 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -75.78866 ymin: 38.45101 xmax: -75.04894 ymax: 39.83901
#> Geodetic CRS:  NAD83
#> # A tibble: 3 × 22
#>   GEOID NAME        pop pop_white pop_black pop_hisp pop_aian pop_asian pop_nhpi
#>   <chr> <chr>     <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>    <dbl>
#> 1 10001 Kent Co… 162310    105891     37812     9346      916      3266       74
#> 2 10003 New Cas… 538479    331836    124426    46921      984     23132      102
#> 3 10005 Sussex … 197145    149025     24544    16954      924      1910       62
#> # ℹ 13 more variables: pop_other <dbl>, pop_two <dbl>, vap <dbl>,
#> #   vap_white <dbl>, vap_black <dbl>, vap_hisp <dbl>, vap_aian <dbl>,
#> #   vap_asian <dbl>, vap_nhpi <dbl>, vap_other <dbl>, vap_two <dbl>,
#> #   blau <dbl>, geometry <MULTIPOLYGON [°]>