ggswissmaps Intro

gibo

2025-06-23

Examples

Using the data

library(ggswissmaps)

data("shp_df")
class(shp_df)
#> [1] "list"
length(shp_df)
#> [1] 8
names(shp_df)
#> [1] "g1b15"      "g1g15_encl" "g1g15_li"   "g1g15"      "g1k15"     
#> [6] "g1l15"      "g1r15"      "g1s15"
# Data description
?shp_df

Some maps

names(maps2)
#> [1] "g1b15"      "g1g15_encl" "g1g15_li"   "g1g15"      "g1k15"     
#> [6] "g1l15"      "g1r15"      "g1s15"

# By name
maps2[["g1k15"]]


# By index
maps2[[5]]

The objects contained in maps2 are ggplot objects. They have been created with ggplot2::ggplot plus a ggplot2::geom_path layer with the data in shp_df. As an example, the previous map is the same as:

library(ggplot2)
ggplot(shp_df[["g1k15"]], aes(x = long, y = lat, group = group)) +
  geom_path() +
  coord_equal() +
  theme_white_f() +
  labs(x = NULL, y = NULL, caption = "Boundaries: BFS GEOSTAT / swisstopo")

Extract a subset of a territory and make a map

The maps2 object, used above, is a list with some maps of swiss territory at various levels (grand regions, cantons, districts, …).

What if one wants to draw a map with a sub-territory? For example, what if I want to have a map with the districts of two cantons? First, I have to select the desired subset from the shp_df data, and then will apply the maps2_ function to it.

# Data frame with the coordinates of all swiss districts
d <- shp_df[["g1b15"]]

# Look at the structure of the data frame
str(d)
#> 'data.frame':    19502 obs. of  21 variables:
#>  $ long   : int  679207 680062 679981 680365 680281 680479 680717 681021 680799 680921 ...
#>  $ lat    : int  245176 244294 244051 243411 241866 241584 240695 240306 239935 239595 ...
#>  $ order  : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ hole   : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
#>  $ piece  : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ group  : Factor w/ 192 levels "0.1","1.1","10.1",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ id     : chr  "0" "0" "0" "0" ...
#>  $ BZNR   : int  101 101 101 101 101 101 101 101 101 101 ...
#>  $ KTNR   : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ GRNR   : int  4 4 4 4 4 4 4 4 4 4 ...
#>  $ AREA_HA: int  11303 11303 11303 11303 11303 11303 11303 11303 11303 11303 ...
#>  $ X_MIN  : int  671862 671862 671862 671862 671862 671862 671862 671862 671862 671862 ...
#>  $ X_MAX  : int  686462 686462 686462 686462 686462 686462 686462 686462 686462 686462 ...
#>  $ Y_MIN  : int  229137 229137 229137 229137 229137 229137 229137 229137 229137 229137 ...
#>  $ Y_MAX  : int  245396 245396 245396 245396 245396 245396 245396 245396 245396 245396 ...
#>  $ X_CNTR : int  678300 678300 678300 678300 678300 678300 678300 678300 678300 678300 ...
#>  $ Y_CNTR : int  235900 235900 235900 235900 235900 235900 235900 235900 235900 235900 ...
#>  $ Z_MIN  : int  380 380 380 380 380 380 380 380 380 380 ...
#>  $ Z_MAX  : int  914 914 914 914 914 914 914 914 914 914 ...
#>  $ Z_AVG  : int  561 561 561 561 561 561 561 561 561 561 ...
#>  $ Z_MED  : int  557 557 557 557 557 557 557 557 557 557 ...

# The cantons are identified by the KTNR column

# Extract from this data the districts of two cantons
library(dplyr)
d <- d %>% dplyr::filter(KTNR %in% c(18, 21))

# And draw the map
maps2_(d)