Apply a function to neighbors

Sometimes one may want to create custom lag variables or create some other neighborhood level metric that may not be defined yet. This st_nb_apply() enables you to apply a function to each observation's (xi) neighbors (xij).

Usage

st_nb_apply(x, nb, wt, .f, suffix = "dbl", ...)

Arguments

x

A vector that will be used for neighbor xij values.

nb

A neighbor list object as created by st_neighbors().

wt

A weights list as created by st_weights().

.f

A function definition. There are three default objects that can be used inside of the function definition:

• .xij: neighbor values of x for the ith observation. This is simply the subset of x based on the corresponding nb list values for each element.

• .nb: neighbor positions.

• .wt: neighbor weights value.

If any of these three function arguments are omitted from .f, dots (...) must be supplied.

suffix

The map variant to use. Options are "dbl", "int", "lgl", "chr", "list".

...

arguments to pass to .f

Value

a vector or list of with same length as x.

Details

The below example calculates the spatial lag using st_nb_apply() and st_lag() to illustrate how we can apply functions to neighbors.

Currently questioning the use case. find_xj() is now exported and may negate the need for this function.

Examples

if (requireNamespace("dplyr", quietly = TRUE)) {
library(magrittr)
guerry %>%
  dplyr::transmute(
    nb = st_contiguity(geometry),
    wt = st_weights(nb),
    lag_apply = st_nb_apply(
      crime_pers, nb, wt,
      .f = function(.xij, .wt, ...) sum(.xij *.wt)
    ),
    lag = st_lag(crime_pers, nb, wt)
  )
}
#> Simple feature collection with 85 features and 4 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
#> CRS:           NA
#> # A tibble: 85 × 5
#>    nb        wt        lag_apply    lag                                 geometry
#>  * <nb>      <list>        <dbl>  <dbl>                           <MULTIPOLYGON>
#>  1 <int [4]> <dbl [4]>    23048. 23048. (((801150 2092615, 800669 2093190, 8006…
#>  2 <int [6]> <dbl [6]>    26920. 26920. (((729326 2521619, 729320 2521230, 7292…
#>  3 <int [6]> <dbl [6]>    26195. 26195. (((710830 2137350, 711746 2136617, 7124…
#>  4 <int [4]> <dbl [4]>    14401. 14401. (((882701 1920024, 882408 1920733, 8817…
#>  5 <int [3]> <dbl [3]>    15039. 15039. (((886504 1922890, 885733 1922978, 8854…
#>  6 <int [7]> <dbl [7]>    15749  15749  (((747008 1925789, 746630 1925762, 7457…
#>  7 <int [3]> <dbl [3]>    22112. 22112. (((818893 2514767, 818614 2514515, 8179…
#>  8 <int [3]> <dbl [3]>    13672. 13672. (((509103 1747787, 508820 1747513, 5081…
#>  9 <int [5]> <dbl [5]>    22859. 22859. (((775400 2345600, 775068 2345397, 7735…
#> 10 <int [5]> <dbl [5]>    11475. 11475. (((626230 1810121, 626269 1810496, 6274…
#> # ℹ 75 more rows