Customize your maps
In csmaps: Preformatted Maps of Norway that Don't Need Geolibraries

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(csmaps)
library(ggplot2)
library(data.table)
library(magrittr)

Colored maps

Automatic coloring by `location_code`

pd <- copy(csmaps::nor_county_map_b2024_split_dt)

q <- ggplot()
q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
q <- q + geom_polygon(
  data = pd, 
  mapping = aes(
    x = long, 
    y = lat,
    group = group,
    fill = location_code
  ),  
  color="black",
  linewidth = 0.4
)
q <- q + theme_void()
q <- q + coord_quickmap()
q <- q + labs(title = "")
q

Customized coloring with external information

It is also possible to specify the color by user-defined groups. Here we show an example of assigning different (pseudo) risk level to each county.

pd <- copy(csmaps::nor_county_map_b2024_split_dt)

# assign each location a random category for different colors
location_info <- unique(pd[,c("location_code")])
location_info[,category:=rep(
  c("Good","Normal","Neutral","Bad","Very Bad"),
  each=3)[1:.N]
]
location_info[,category:=factor(
  category,
  levels=c("Good","Normal","Neutral","Bad","Very Bad")
  )
]
print(location_info)

# join the map data.table
pd[
  location_info,
  on="location_code",
  category:=category
]

q <- ggplot()
q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
q <- q + geom_polygon(
  data = pd, 
  mapping = aes(
    x = long, 
    y = lat,
    group = group,
    fill = category
  ),  
  color="black",
  linewidth = 0.4
)
q <- q + coord_quickmap()
q <- q + labs(title="")
q <- q + theme_void()
q

Labeled maps

We can add labels of county index onto the maps. There are several options for adding texts on a graph in ggplot2. We recommend geom_label() to add the labels if no label overlap occurs, otherwise we recommend using ggrepel::geom_label_repel().

q <- ggplot()
q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
q <- q + geom_polygon(
  data = pd, 
  mapping = aes(
    x = long, 
    y = lat,
    group = group,
    fill = category
  ),  
  color="black",
  linewidth = 0.4
)
q <- q + geom_label(
  data = csmaps::nor_county_position_geolabels_b2024_split_dt,
  mapping = aes(
    x = long, 
    y = lat,
    label = location_code
  )
)
# ggrepel::geom_label_repel() for avoiding overlap
q <- q + theme_void()
q <- q + coord_quickmap()
q <- q + labs(title = "")
q

Labels can be easily added to other layouts, such as Oslo wards.

q <- ggplot(mapping = aes(x = long, y = lat))
q <- q + geom_polygon(
  data = csmaps::oslo_ward_map_b2024_default_dt,
  mapping = aes(group = group),
  color = "black",
  fill = "white",
  linewidth = 0.4
)
q <- q + geom_label(
  data = csmaps::oslo_ward_position_geolabels_b2024_default_dt,
  mapping = aes(label = location_code),
  color = "red",
  size = 3,
  label.size = 0.1,
  label.r = grid::unit(0, "lines")
)
q <- q + theme_void()
q <- q + coord_quickmap()
q

Enrich plot with additional data

It is convenient to use csdata package to enrich Norway and Oslo maps with external information, such as location name and population.

Add county name and population to Norway map

# enrich with population and location name
dpop_2024 <- csdata::nor_population_by_age_cats()[
  calyear==2024 &
  granularity_geo %in% "county"
]

# join, create label
labels <- copy(csmaps::nor_county_position_geolabels_b2024_split_dt)
labels[
  dpop_2024, 
  on = "location_code",
  pop_total := pop_jan1_n
]
labels[
  csdata::nor_locations_names(), 
  on = "location_code",
  location_name_short := location_name_short
]
labels[, label := paste0(location_name_short, '\n', pop_total)]
print(head(labels))

# plot
pd <- copy(csmaps::nor_county_map_b2024_split_dt)

q <- ggplot()
q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
q <- q + geom_polygon(
  data = pd, 
  mapping = aes(
    x = long,
    y = lat,
    group = group,
    fill = location_code
  ),
  color="black",
  linewidth = 0.4
)
q <- q + ggrepel::geom_label_repel(
  data = labels[repel==TRUE],
  mapping = aes(x = long, y = lat, label = label),
  size = 3,
  label.size = 0.1,
  label.r = grid::unit(0, "lines"),
  min.segment.length = 0
)
q <- q + geom_label(
  data = labels[repel==FALSE],
  mapping = aes(x = long, y = lat, label = label),
  size = 3,
  label.size = 0.1,
  label.r = grid::unit(0, "lines")
)
q <- q + theme_void()
q <- q + coord_quickmap()
q <- q + labs(title = "")
q

Add location name for ward and population for Oslo map

# enrich with population and location name
dpop_2024 <- csdata::nor_population_by_age_cats()[calyear==2024]

# join, create label
labels <- copy(csmaps::oslo_ward_position_geolabels_b2024_default_dt)
labels[
  dpop_2024, 
  on = "location_code",
  pop_total := pop_jan1_n
]
labels[
  csdata::nor_locations_names(), 
  on = "location_code",
  location_name_short := location_name_short
]
labels[, label := paste0(location_name_short, '\n', pop_total)]
print(head(labels))

q <- ggplot(mapping = aes(x = long, y = lat))
q <- q + geom_polygon(
  data = csmaps::oslo_ward_map_b2024_default_dt,
  mapping = aes(group = group),
  color = "black",
  fill = "white",
  linewidth = 0.4
)
q <- q + geom_label(
  data = labels,
  mapping = aes(label = label),
  color = "red",
  size = 3,
  label.size = 0.1,
  label.r = grid::unit(0, "lines")
)
q <- q + theme_void()
q <- q + coord_quickmap()
q