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• R already has an implicit concept of scalars || vs &&
• we use constants in regression

imagine implementing a scalar

e.g.

scalar(x, ptype = character() (with inspo from vctrs)

rlang::inject() lets you inject parameters using !!! partially. Rather than having a list of arguments that you need to pass in using do.call

use case bringing this up:

  item_res <- rlang::inject(
    add_item(
      x,
      title,
      user = user,
      host = host,
      token = token,
      !!!adtl_args
    )
  )

Please leave your suggestions or other topics below

Focus on simple feature standard then how they are implemented in R

• sf
• wk
• geos

https://mastodon.social/@jrosell/110344533644932822

example for diffusing code:

dplyr_but_later <- rlang::expr(library(dplyr)) 
rlang::eval_tidy(dplyr_but_later)

taking named arguments via dots, and collecting them into a list

make_new_list <- function(...) {
  my_list <- rlang::list2(...)
  my_list
}

splicing named list into argument
!!!

splice_dfs <- function(...) {
  args <- rlang::list2(...)
  
  dplyr::bind_rows(!!!args)
}

# equivalent
splice_dfs <- function(...) {
  args <- rlang::list2(...)
  
  dplyr::bind_rows(rlang::splice(args))
}

Most analyses that use aerial geometries utilize row standardized weights because there is not good theoretical reason to apply diifferent weights.

If, however, we have a good reason to use polygon centroids or, probably more approrpiately, using point data, we can use kernel weights. Kernel weights are used to introduce a decay function ito our weighting.

Say we have a point with 8 neighbors which are not at a uniform distance away. How much weight should we give each location in our subsequent calculations? Should we really be allocating equal weight? Or, should we recognize that things that are closer likely have more impact than those that are further away

A good use case of say gaussian kernel weights is if we have a hexagonal grid of incidents that are fairly small. First order neighbors may be limiting and we want to include 2nd or third order neighbors, but those should have less weight.

DONT pass the whole ass data frame
pass just the columns you need

https://twitter.com/JosiahParry/status/1664709143431729174

alt + tab configuration
why it's friggin sweet.

https://josiahparry.com/posts/2023-01-19-raw-strings-in-r.html