Compute Dissimilarity Index
ds_dissim(.data, .cols, .name, .comp = FALSE)
dissim(..., .data = dplyr::across(everything()))tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
name for column with dissimilarity index. Leave missing to return a vector.
Default is FALSE. FALSE returns the sum, TRUE returns the components.
arguments to forward to ds_dissim from dissim
a tibble or numeric vector if .name missing
data('de_county')
ds_dissim(de_county, c(pop_white, starts_with('pop_')))
#> [1] 0.09934675 0.09934675 0.09934675
ds_dissim(de_county, c(pop_white, starts_with('pop_')), .comp = TRUE)
#> [1] 0.0004178507 0.0492555242 0.0496733749
ds_dissim(de_county, starts_with('pop_'), 'dissim')
#> 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>,
#> # dissim <dbl>, geometry <MULTIPOLYGON [°]>