我不是整个想法的大fan(尽管我从技术角度感兴趣),也是为了改变总体的地貌,我必须承认——而且我也不是<代码>{ggplot2}的电力用户。 因此,或许可以将以下做法作为一种蓝图,但也许需要你根据你们的需要进行一些细微调整,特别是在模式的密度和间隔方面(见您,柏林、汉堡、布雷曼)。

然而,正如评论所示,我使用<代码>geom_sf_pattern(>,从{ggpattern},采用多科全线的脱光线模式。

为了开始,我们首先需要每个国家的肤色:

library(geodata)
#> terra 1.7.71
library(sf)
#> Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE
library(RColorBrewer)
library(ggplot2)
library(ggpattern)

# get administrative levels for Germany
ger <- geodata::gadm("Germany", path = tempfile()) |> 
  sf::st_as_sf()

# from how many different colors do you want to choose?
n <- 5
colors_base <- brewer.pal(n, "Spectral")

# pick 1 to 4 colors per state
set.seed(42)
colors <- sample(colors_base, size = sample.int(n-1, size = 1)) |> replicate(n = 16, expr = _)
names(colors) <- ger[["NAME_1"]]

colors
#> $`Baden-Württemberg`
#> [1] "#2B83BA"
#> 
#> $Bayern
#> [1] "#D7191C"
#> 
#> $Berlin
#> [1] "#ABDDA4" "#FDAE61"
#> 
#> $Brandenburg
#> [1] "#D7191C" "#ABDDA4"
#> 
#> $Bremen
#> [1] "#ABDDA4" "#D7191C" "#2B83BA"
#> 
#> $Hamburg
#> [1] "#ABDDA4" "#FDAE61"
#> 
#> $Hessen
#> [1] "#FFFFBF" "#D7191C"
#> 
#> $`Mecklenburg-Vorpommern`
#> [1] "#FFFFBF"
#> 
#> $Niedersachsen
#> [1] "#2B83BA" "#FFFFBF" "#D7191C" "#ABDDA4"
#> 
#> $`Nordrhein-Westfalen`
#> [1] "#FDAE61" "#ABDDA4" "#FFFFBF" "#2B83BA"
#> 
#> $`Rheinland-Pfalz`
#> [1] "#FDAE61"
#> 
#> $Saarland
#> [1] "#FFFFBF" "#2B83BA" "#FDAE61" "#ABDDA4"
#> 
#> $Sachsen
#> [1] "#ABDDA4" "#2B83BA" "#FDAE61" "#FFFFBF"
#> 
#> $`Sachsen-Anhalt`
#> [1] "#ABDDA4"
#> 
#> $`Schleswig-Holstein`
#> [1] "#ABDDA4"
#> 
#> $Thüringen
#> [1] "#FDAE61" "#FFFFBF"

multicolor_stripe_pattern <- function(params, boundary_df, aspect_ratio, 
                                      legend = FALSE) {
  args <- as.list(params)
  args <- args[grep("^pattern_", names(args))]
  
  args$pattern_colour <- strsplit(args$pattern_colour, ",")[[1]]
  args$pattern_fill <- strsplit(args$pattern_fill, ",")[[1]]
  
  args$pattern <- "stripe"
  args$x <- boundary_df$x
  args$y <- boundary_df$y
  args$id <- boundary_df$id
  args$prefix <- ""
  
  do.call(gridpattern::patternGrob, args)
}

options(ggpattern_geometry_funcs = list(multicolor_stripe = multicolor_stripe_pattern))

最后和相关的部分可能远远没有说服力,但这是我非常冷静地处理这个问题: 简单地在各州进行渗透,选择相关的肤色和期望的称谓。 显然需要一些调整,因为我对此并不感到完全满意,但如果你认为这只是概念的证明,整个想法似乎在发挥作用(我认为,这仍然是一种可怕的视化方式)。

# initialize data without aesthetics
gg <- ggplot() +
  geom_sf(data = ger, aes())


# iterate over your states and fill the polygons according to specifications
for (i in 1:dim(ger)[1]) {
  
  # just fill the polygon, if there is only one color
  if (length(colors[[i]]) == 1) {
    
    gg <- gg + 
      geom_sf(data = ger[i, ], fill = colors[[i]])
    
    # use stripe pattern, if there are only two colors
  } else if (length(colors[[i]]) == 2) {
    
    gg <- gg + 
      geom_sf_pattern(data = ger[i, ], 
                      mapping = aes(fill = colors[[i]][1]),
                      pattern = "stripe",
                      pattern_fill = colors[[i]][2],
                      pattern_density = 0.5,
                      pattern_spacing = 0.02)
    
    # use multicolor stripe pattern, if there are more than two colors
  } else if (length(colors[[i]]) > 2) {
    
    n_cols <- length(colors[[i]])
    
    gg <- gg + 
      geom_sf_pattern(data = ger[i, ], 
                      mapping = aes(fill = colors[[i]][1]),
                      pattern = "multicolor_stripe",
                      pattern_fill = colors[[i]][2:n_cols] |> paste0(collapse = ","),
                      pattern_density = 0.5,
                      pattern_spacing = 0.02)
  }
}

gg