brain2 with sf manipulation

Example simple feature to data frame…

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.1     ✔ tibble    3.1.8
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(sf)

## Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE

#> Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1

fips_geometries <- readRDS(url("https://wilkelab.org/SDS375/datasets/US_counties.rds")) %>%
  rename(FIPS = GEOID)

US_census <- read_csv("https://wilkelab.org/SDS375/datasets/US_census.csv",
                      col_types = cols(FIPS = "c")
          )

# works
fips_geometries %>%
  left_join(US_census, by = "FIPS") %>%
  ggplot() +
  geom_sf(aes(fill = home_ownership)) + 
  scale_fill_viridis_c()

# from Claus Wilke on ggplot2

fips_geometries %>%
  left_join(US_census, by = "FIPS") %>%
  ggplot() +
  geom_sf(aes(fill = mean_work_travel), linewidth = .1) + 
  scale_fill_viridis_c(option = "magma") ->
classic_plot_sf_layer

classic_plot_sf_layer

layer_data(classic_plot_sf_layer) %>% 
  select(geometry, xmin, xmax, ymin, ymax) %>% 
  bind_cols(tibble(FIPS = fips_geometries$FIPS)) %>% 
  rename(fips = FIPS) ->
reference

compute_county <- function(data, scales){
 
  data %>% 
    # inner_join(fips_ggplot2_reference, multiple = "all") 
    inner_join(reference) %>% 
    mutate(group = -1)

}

Step 2: pass to ggproto

StatCounty <- ggplot2::ggproto(`_class` = "StatCounty",
                                  `_inherit` = ggplot2::Stat,
                                  # required_aes = c("fips"),
                                  # setup_data = my_setup_data,
                                  compute_panel = compute_county,
                                  default_aes = aes(geometry = after_stat(geometry))
                                  )

Step 3: write geom_* function

geom_sf_county <- function(
  mapping = NULL,
  data = NULL,
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE, ...) {
  ggplot2::layer(
    stat = StatCounty,  # proto object from step 2
    geom = ggplot2::GeomSf,  # inherit other behavior
    data = data,
    mapping = mapping,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(na.rm = na.rm, ...)
  )
}

step 4: test geom_* function, celebrate!

us_census <- read_csv("https://wilkelab.org/SDS375/datasets/US_census.csv",
                      col_types = cols(FIPS = "c")
          )  

us_census %>% 
  # slice(1340) %>% 
  ggplot() + 
  aes(fips = FIPS) +
  geom_sf_county(linewidth = .02, 
                      color = "darkgrey") +
  aes(fill = mean_work_travel) + 
  coord_sf() +
  scale_fill_viridis_c(option = "magma")

## Joining with `by = join_by(fips)`