第 6 章 Map

mp <- econDV2::Map()

6.1 Geographic geoms

6.1.1 Point, Line and Polygon

ggplot2 is equipped with geoms that can draw all kinds of geographic structure.

fourPoints <- data.frame(
    x=c(2, 3, 5, 3),
    y=c(-1, 0, 6, 6)
)
geometry <- list()
geometry$ggplot <- function(){
  ggplot(
    data=fourPoints,
    mapping=aes(
      x=x, y=y
    )
  )
}

Points:

geometry$points <- geometry$ggplot() + geom_point()
geometry$points

Path:

geometry$path <- geometry$ggplot() + geom_path()
geometry$path

Polygon

geometry$polygon <- geometry$ggplot() + geom_polygon()
geometry$polygon

6.1.2 Multipolygons

  • A school consists of many buildings
bigArea <- data.frame(
  order=1:7,
  x=c(1, 5.5, 5.5, 1,
    7, 8, 9),
  y=c(-2, -2, 7, 7,
    8, 8 , 2),
  building=c(rep("building1", 4), rep("building2", 3)), # same school
  contour=c(rep("outer",4), rep("outer",3)) # two different buildings
)

# draw each building separately
ggplot()+geom_polygon(
  data=bigArea, 
  mapping=aes(x=x, y=y, group=building)
  )

  • geom_polygon can draw a polygon with hole(s) by using group and subgroup aesthetics, with hole(s) as a polygon inside another.

    • A building with a courtyard has two contours (one for the outer contour, the other for the courtyard contour). Both contours are polygons belong to the same group, but different subgroups.
fourPoints$building="building1"
fourPoints$contour="courtyard"
fourPoints$order=8:11
bigArea |> names()
bigAreaWithHoles <-
  dplyr::bind_rows(
    bigArea,
    fourPoints
  ) |> arrange(building, order)

View(bigAreaWithHoles)

ggplot(
  data=bigAreaWithHoles
) + 
  geom_polygon(
    aes(
      x=x, y=y, 
      group=building, 
      subgroup=contour
    )
  )

6.1.3 Fill polygons

  • You can give different polygons different fills.
ggplot(
  data=bigAreaWithHoles
) + 
  geom_polygon(
    aes(
      x=x, y=y, 
      group=building, 
      subgroup=contour,
      fill=building
    )
  )

6.1.4 Application to world map

We apply those geoms to draw some maps.

world = ggplot2::map_data("world")
  • group column is for group aesthetics for polygon.
world0 <- function(color="black", fill="white", size=0.35) {
  ggplot() + geom_polygon(
    data=world,
    aes(
      x=long, y=lat,
      group=group
    ),
  color=color, fill=fill, size=size
  )
}
# Cartesian crs
world0()
# Cartesian with fixed aspect ratio
world0() + coord_fixed()
# Mercator projection: fixed asp, correct direction
world0() + coord_map()
# Mercator projection: fixed asp, correct direction, expand xlim
world0() + coord_map(xlim = c(-180,180))

6.2 Choropleth map

  • A map with different fills to show certain social-economic data variation.

6.2.1 Backgroup fill and borders

Though ggplot2 has geom function to work on Choropleth graph directly, I recommend to lay out a background map first.

Map data

world = ggplot2::map_data("world")

Backgroup map base on Economist style:

world0_background <- function(){
  world0(
    color="white", 
    fill="#c8c5be", 
    size=0.15)
}
world0_background()

6.2.2 Choropleth layer: value layer

Another layer of geom_polygons but with different fills setup based on social-economic data. Instead of using geom_polygon as the background layer does, geom_map is an easier to use geom for Choropleth graph.

  • geom_map is a wrapper of geom_polygon but with a much easier setup for Choropleth map purpose.

  • use geom_map()+expand_limits()

geom_map(
  data=`social-economic data`,
  mapping = aes(
    map_id = ...,
    fill = ...
  ),
  map =`map data`
)+
expand_limits(
  x=`map data`$long, y=`map data`$lat
)

  • map data from ggplot2::map_data (can be filtered if needed)

  • social-economic data: a data frame with a column for map_id and a column for fill.

    • map_id column must consist of the levels of map data’s region column.

    • fill column: the social-economic data to show.

  • expand_limits to ensure that no boundary points are right on the border of the plot.

6.2.2.1 WDI example

Social-economic data

se_data <- jsonlite::fromJSON(
  "https://www.dropbox.com/s/78jr6g6xtjz453b/women_in_parliament.json?dl=1"
)
# View(se_data)
ggplot()+geom_map(
    data=se_data,
    mapping=aes(
      map_id=country,
      fill=women_in_parliament
    ),
    map=world
  )+
  expand_limits(x=world$long, y=world$lat)
# geom_map only plot those in the social-economic data
ggplot()+geom_map(
    data=se_data[1:30,],
    mapping=aes(
      map_id=country,
      fill=women_in_parliament
    ),
    map=world
  )+
  expand_limits(x=world$long, y=world$lat)

World background ensure every country is on the map.

world0_background() +
  geom_map(
    data=se_data[1:30, ],
    mapping=aes(
      map_id=country,
      fill=women_in_parliament
    ),
    map=world
  )+
  expand_limits(x=world$long, y=world$lat)

Fixed country name inconsistency between se_data and world data.

se_data |> 
  mp$choropleth$rename_valueData_countryname(
  countryColumnName = "country",
  pattern = c(
    "Russia"="Russian Federation",
    "USA"="United States",
    "Iran"="Iran, Islamic Rep.",
    "Egypt"="Egypt, Arab Rep.",
    "Syria"="Syrian Arab Republic",
    "Yemen"="Yemen, Rep.",
    "UK"="United Kingdom",
    "Democratic Republic of the Congo"="Congo, Dem. Rep.",
    "Republic of Congo"="Congo, Rep.",
    "Ivory Coast"="Cote d'Ivoire",
    "Venezuela"="Venezuela, RB"
    )
  ) -> se_data

world0_background() +
  geom_map(
    data=se_data,
    mapping=aes(
      map_id=country,
      fill=women_in_parliament
    ),
    map=world
  )+
  expand_limits(x=world$long, y=world$lat)
choropleth0 <- function(){
  world0_background() +
  geom_map(
    data=se_data,
    mapping=aes(
      map_id=country,
      fill=women_in_parliament
    ),
    map=world
  )+
  expand_limits(x=world$long, y=world$lat)
}
choropleth0() + 
  theme_void() +
  labs(
    title="2020各國國會女性席次佔比(%)"
  ) +
  theme(
    legend.position = "bottom"
  ) +
  guides(
    fill=guide_colorbar(
        title=NULL,
        barwidth = 4, #input$barwidth
        barheight = 0.35, #input$barheight
      )
    )

# can also
choropleth0() +
  scale_fill_gradient(
    guide = guide_colorbar(...)
  )

6.2.3 Fill colors

6.2.3.1 scale_fill and palette function

scale_fill_continuous(...)

by default is based on:

scale_fill_gradient(...) #

which is based on the palette function palette_continuous generated by scales::seq_gradient_pal

palette_continuous <- scales::seq_gradient_pal(
  low = "#132B43", high = "#56B1F7"
)
  • two pigments “#132B43,” “#56B1F7”
palette_continuous(seq(0, 1, by=0.2)) |> 
  scales::show_col()

aes(fill=z) will rescale z according to

# for sequential
scales::rescale(z, from=range(z), to=c(0,1))
# for divergin
scales::rescale(z, from=mid.point.value_stretching, to=c(-1,1))
scale_fill_discrete(...)

by default is based on:

scale_fill_hue(...) #

which is based on the palette function palette_discrete generated by scales::hue_pal

palette_discrete <- 
  scales::hue_pal(h = c(0, 360) + 15, c = 100, l = 65, h.start = 0, direction = 1)
  • pigments: a hue (色像環)
palette_discrete(3) |> scales::show_col()
palette_discrete(6) |> scales::show_col()
DiagrammeR::mermaid("
graph LR
  A[continuous palette function]-->B[continuous scales_fill]
  C[discrete palette function]-->D[discrete scales_fill]
")

6.2.3.2 colorspace

Construct your palette function

pal <- colorspace::choose_palette(gui="shiny")

Register

colorspace::sequential_hcl(n = 7, h = 135, c = c(45, NA, NA), l = c(35, 95), power = 1.3, register = "my_pal")
colorspace::scale_fill_continuous_sequential(palette="my_pal")

6.2.3.3 application

  • Color is to signal one direction strength.

  • Gradient: Good for spot the highest and the lowest. Not easy to compare neighbors

Adjust fill limits-values mapping:

# reverse fill high-low value
choropleth0() +
  scale_fill_gradient(
    high = "#132B43",
    low = "#56B1F7",
    na.value="#919191"
  )
choropleth0() +
  colorspace::scale_fill_continuous_sequential(palette="my_pal", na.value="#919191")
# use binned fill
choropleth0() +
  scale_fill_binned(
    high = "#003870",
    low = "#d3e3f3",
    na.value="#919191",
    guide=guide_colorbar(
      reverse = FALSE,
      label.vjust = 0.5
    )
  )
choropleth0() +
  colorspace::scale_fill_binned_sequential(
    palette="my_pal", na.value="#919191"
  )

6.2.3.4 Cut your color

  • Continuous color change is easy to spot those outliers, but not easy to compare non-outliers across borders.

  • Discretize your continuous various and use scale_fill_manual to apply your sequential palette.

Discretize continuous variable using cut:

se_data2 <- se_data
# cut into 4 ordered levels
se_data2$women_in_parliament |>
  cut(c(0, 10, 20, 40, 50, 70), ordered_result = T) -> .fct
.fct |> class()
# rename levels for legend labels
levels(.fct) <- c("0-10%","10-20%","20-40%","40-50%", "50-70%")
.fct -> se_data2$women_in_parliament
choropleth1 <- function(){
  world0_background() +
  geom_map(
    data=se_data2,
    mapping=aes(
      map_id=country,
      fill=women_in_parliament
    ),
    map=world
  )
}
choropleth1() +
  scale_fill_brewer(type="seq", na.value="#919191")
choropleth1() +
  colorspace::scale_fill_discrete_sequential(
    palette="my_pal",
    na.value="#919191"
  )

6.2.3.5 Diverging color

se_data3 <- se_data
se_data3$women_in_parliament |>
  scales::rescale(
    from=c(0, 100), 
    to=c(-1, 1)
  ) -> se_data3$women_in_parliament
choropleth3 <- function(){
  world0_background() +
  geom_map(
    data=se_data3,
    mapping=aes(
      map_id=country,
      fill=women_in_parliament
    ),
    map=world
  )
}

colorspace package:

## Register custom color palette
colorspace::diverging_hcl(n = 7, h = c(255, 12), c = c(50, 80), l = c(20, 97), power = c(1, 1.3),  register = "man_woman")

choropleth3() +
colorspace::scale_fill_binned_diverging(
  labels = function(x) (x+1)*50,
  palette="man_woman"
)
choropleth0() +
colorspace::scale_fill_binned_diverging(
  mid=50,
  palette="man_woman"
)

6.3 Map tile

6.3.1 ggmap

Two steps:

  1. get_xxxmap to obtain raster map image.

  2. ggmap to produce a gg(plot) object as a background layer.

6.3.2 get_googlemap

taipei_google <- 
  ggmap::get_googlemap(
    center = c(long=121.4336018, lat=25.0341671),
  zoom = 10,
    scale=2
  )
taipei0 <- function(){
  ggmap(taipei_google)
}

6.3.3 get_stamenmap

taipei_stamen <- ggmap::get_stamenmap(
  bbox = c(left = 121.0316, bottom = 24.7384, right = 122.1096, top = 25.3114),
  zoom=9,
  maptype = "toner-lite"
)
taipei1 <- function(){
  ggmap(taipei_stamen)
}

You can copy map info from Google map link https://www.google.com/maps/@24.9475275,121.3795888,15z then:

# mp <- econDV2::Map()
mp$extract$googleMapLocation()
# then paste to get_googlemap

Copy map info from OSM map bbox

then:

mp$extract$osmBBox()
# then paste to get_stamenmap

6.4 Simple feature

install.packages("sf")

Another type of geographic data structure.

  • a new class. Each value in a simple feature vector represents a form of geometry (called feature) and its geographic notation (i.e. latitude and longitude); and could carry its coordinate reference system.

  • Each value is a parsing result from sf::st_xxx functions where xxx can be point (台北大學的一個地標，如YouBike站), multipoint (台北大學所有地標), linestring(台北大學的一條路徑), polygon(台北大學的一棟建築), multilinestring(台北大學的所有路徑), multipolygon(台北大學的所有建築), or geometrycollection(整個台北大學)

6.4.1 Define simple features

A simple feature is composed of numeric vector, matrix, or list. No data frame is used.

geoValues <- list()
geoValues$simple_feature$point <-
  sf::st_point(
    c(24.9433123, 121.3699526)
  )
geoValues$simple_feature$multipoint <-
  sf::st_multipoint(
    rbind( # will form a matrix
      c(24.9443019, 121.3714944), # 1st point
      c(24.9440709, 121.3728518) # 2nd point
    )
  )
geoValues$simple_feature$linestring <-
  sf::st_linestring(
    rbind(
      c(24.9423755, 121.3679438), # 1st trace point
      c(24.9429941, 121.3679432), # 2nd trace point
      c(24.9432087, 121.3686713) # 3rd trace point
    )
  )
geoValues$simple_feature$polygon <-
  sf::st_polygon(
    list(
      # 1st closed trace
      rbind(
        c(24.9441895, 121.3695181),
        c(24.9442244, 121.3692544),
        c(24.9437158, 121.3694094),
        c(24.9438647, 121.3696271),
        c(24.9441895, 121.3695181) # close the polygon
      )
    )
  )
# polygon: a list consists of matrices.
class(geoValues$simple_feature$point)
class(geoValues$simple_feature$linestring)
class(geoValues$simple_feature$polygon)
# all have sfg class

Simple feature value can be graphed directly via geom_sf()

geoValues$simple_feature$polygon |> ggplot()+geom_sf()

6.4.2 Form simple feature column

A collection column of multiple simple features.

sf::st_sfc(geoValues$simple_feature) -> geoValues$simple_feature_column

class(geoValues$simple_feature_column) # "sfc_GEOMETRY" "sfc" 
# has class sfc
geoValues$simple_feature_column |> 
  ggplot()+geom_sf()

6.4.3 Form simple feature data frame

  • A simple feature data frame is basically a data frame with a column named geometry which is a list of each obervation’s simple feature value.
# initiate a data frame
df_sf <- data.frame(
  name=c("landmark 1", "must-see landmarks", "path 1", "building 1")
)

# add simple feature column
df_sf |> sf::st_set_geometry(geoValues$simple_feature_column) ->
  geoValues$simple_feature_df

class(df_sf) # only "data frame"
df_sf |>
  dplyr::mutate(
    geometry=geoValues$simple_feature_column
  ) -> df_sf2
class(df_sf2) # still a "data frame"
# Must go through sf::st_set_geometry to get sf class
class(geoValues$simple_feature_df) # "sf" "data.frame"

geoValues$simple_feature_df |> ggplot()+geom_sf()

6.5 Import Simple Feature Data

6.5.1 From SHP file

台灣的sf data太細，以平常輸出並不需要這麼細，可以進行簡化。使用：

sf_data |> mp$sf$simplify() -> sf_simplified_data

6.5.2 From OSM

  • Define bounding box

  • Find feature key and value

  • Make open pass query to get sf data.

bbox = c(left = 121.0316, bottom = 24.7384, right = 122.1096, top = 25.3114) |>
  osmdata::opq() |>
  osmdata::add_osm_feature(key="admin_level", value="4") |>
  osmdata::osmdata_sf() -> taipei_osm
  • If you have multiple features to request, chain add_osm_features one after the other.

OR

bbox = c(left = 121.0316, bottom = 24.7384, right = 122.1096, top = 25.3114)
features = c("admin_level"="4")
mp$osm$request_data(
  bbox, features = features
) -> taipei_osm
  • features is a named character vector, with keys be element names and values be element values.
taipei_osm$osm_multipolygons |> 
  dplyr::filter(name != "臺灣省") -> sf_northTaipei 

sf_northTaipei |>
  ggplot()+geom_sf()

sf_northTaipei |>
  ggplot()+geom_sf(aes(fill=name))

6.5.3 sf overlay ggmap

Taipei MRT

bbox = c(left = 121.0316, bottom = 24.7384, right = 122.1096, top = 25.3114)
features = c("railway"= "subway")
mp$osm$request_data(
  bbox, features
) -> taipei_mrt
taipei_mrt$osm_lines |> 
  ggplot()+geom_sf()
# Error, since ggmap has some aes setting at the ggplot() stage which will be inherited by all the following geoms
taipei1()+
  geom_sf(
    data=taipei_mrt$osm_lines,
    color="dodgerblue"
  )
taipei1()+
  geom_sf(
    data=taipei_mrt$osm_lines,
    color="dodgerblue",
    inherit.aes = FALSE
  )

sf overlay google map from ggmap might have problem. If it does, try the following tip:

6.5.4 data frame overlay ggmap

taipei_mrt$osm_lines |> sf::st_coordinates() -> coordinates
coordinates |> class()

taipei1() +
  geom_path(
    data=coordinates |> as.data.frame(),
    mapping=aes(
      x=X, y=Y, group=L1
    ),
    inherit.aes = FALSE
  )

6.6 Taiwan drug problem

sf_taiwan_simplified <- econDV2::sf_taiwan_simplified

sf_taiwan_simplified$台灣本島$縣市 |> 
  ggplot()+geom_sf()
sf_taiwan_simplified$台灣本島$鄉鎮區 |>
  ggplot()+geom_sf()

Cast the geometry column to have uniform feature if your want to use plotly::ggplotly later:

# Originally polygons mixed with multipolygons
sf_taiwan_simplified$台灣本島$鄉鎮區 |> sf::st_geometry()

# re-cast to uniform multipolygon
sf_taiwan_simplified$台灣本島$鄉鎮區 |>
  sf::st_cast("MULTIPOLYGON") -> 
  sf_taiwan_simplified$台灣本島$鄉鎮區
mp <- econDV2::Map()
  • mp$sf$make_background_map 底圖製作

6.6.1 縣市底圖

econDV2::Object(background)
sf_taiwan_simplified$台灣本島$縣市 |>
  mp$sf$make_background_map()

background$台灣本島$縣市 <- function(){
  sf_taiwan_simplified$台灣本島$縣市 |>
  mp$sf$make_background_map(
      color="white",
      size=0.14
  )
}

background$台灣本島$縣市()

If you don’t know how much to crop and want to explore different possibilities, you can:

sf_taiwan_simplified$縣市 |> 
  mp$sf$gg_crop()

6.6.2 各鄉/鎮/區底圖

background$台灣本島$鄉鎮區 <- function(){
  sf_taiwan_simplified$台灣本島$鄉鎮區 |>
    sf::st_cast("MULTIPOLYGON") |>
    mp$sf$make_background_map(
      color="white",
      size=0.14
    )
}

background$台灣本島$鄉鎮()

6.6.3 stamenmap底圖

bbox <- c(left=119.99690,
bottom=21.89988,
right=122.00000,
top=25.29999)
econDV2::Object(taiwan_stamen)
# toner-lite
taiwan_stamen$tonerlite <- 
  ggmap::get_stamenmap(bbox, maptype="toner-lite", zoom=9) 

background$台灣本島$tonerlite <- 
  function(){
    ggmap::ggmap(taiwan_stamen$tonerlite)
  }

background$`台灣本島`$tonerlite()

# terrain
taiwan_stamen$terrain <- 
  ggmap::get_stamenmap(bbox, maptype="terrain", zoom=9)
background$台灣本島$terrain <- function(){
  ggmap::ggmap(taiwan_stamen$terrain,
    darken=c("0.3", "white"))
}

background$台灣本島$terrain()

6.6.4 毒品破獲

  1. 創立帶有geometry column的sf毒品資料

  2. 繪製面量圖

econDV2::Object(drug)
drug$data <- jsonlite::fromJSON("https://www.dropbox.com/s/dry3nvahkmcmqoi/drug.json?dl=1")
drug$data <- drug$data |>
  dplyr::filter(發生西元年=="2018")
# 計算不同發生地點的案件破獲次數 
library(dplyr)
drug$data |>
  group_by(發生地點) |>
  summarise(
    案件次數=n(),
    案件次數2=cut(案件次數, c(0, 10, 20, 30))
  ) -> drug$data_frequency

6.6.4.1 create sf econ-social data

left_data <-
  sf_taiwan_simplified$`台灣本島`$鄉鎮區

library(sf) # You need this for dplyr join functions to work with sf class data frame

right_data <- 
  drug$data_frequency |>
  mutate(
    發生地點=
    stringr::str_replace(發生地點, '台', "臺"))

## left_join
drug$join$left <- {
  left_data |> 
    dplyr::left_join(
      right_data, by=c("map_id"="發生地點")
    )
}
## right_join
drug$join$right <- {
  left_data |> 
    dplyr::right_join(
      right_data, by=c("map_id"="發生地點")
    )
}## inner_join
drug$join$inner <- {
  left_data |> 
    dplyr::inner_join(
      right_data, by=c("map_id"="發生地點")
    )
}
drug$join$left |> ggplot()+geom_sf()
# right covers some small island data whose geometry is empty
drug$join$right |> ggplot() + geom_sf()
# inner like right but no small island data
drug$join$inner |> ggplot() + geom_sf()

drug$sf <- drug$join$inner

6.6.4.2 overlay sf background

drug$map$choropleth0 <- function(){
  background$`台灣本島`$鄉鎮區() +
    geom_sf(
      data=drug$sf,
      mapping=aes(
        fill=案件次數
      ),
      color="white",
      size=0.15
    ) 
}

drug$map$choropleth0()
# mp$choose_palette()
drug$register_palette <- function(){
  colorspace::sequential_hcl(n = 3, h = c(-83, 20), c = c(65, NA, 18), l = c(32, 90), power = c(0.5, 1), rev = TRUE, register = "drug")
}

drug$register_palette()
drug$map$choropleth0() +
  colorspace::scale_fill_continuous_sequential(
    palette="drug", 
    na.value="#dbd7a8"
  )
drug$map$choropleth1 <- function(){
  background$`台灣本島`$鄉鎮區() +
    geom_sf(
      data=drug$sf,
      mapping=aes(
        fill=案件次數2,
        label=map_id
      ),
      color="white",
      size=0.15
    ) 
}
drug$map$choropleth1() +
  colorspace::scale_fill_discrete_sequential(
    palette="drug", 
    na.value="#dbd7a8"
  ) + 
  theme_void() -> drug$map$choropleth_final

drug$map$choropleth_final
 
plotly::ggplotly(
  drug$map$choropleth_final
)

If you want to make the entire Taiwan island’s choropleth map down to “縣市” or “鄉鎮區,” you can also use:

  • econDV2::geom_sf_taiwan which only need social-econ data with map_id. It will create sf social-econ data in the background for you.

  • econDV2::geom_sf_taiwan default at cast2multipolygon=T to ensure plotly::ggplotly work.

ggplot()+  
  econDV2::geom_sf_taiwan(
    data=drug$data_frequency,
    mapping=aes(
      fill=案件次數
    ),
    map_id = "發生地點",
    type="鄉鎮區"
  )

last_plot() +
  colorspace::scale_fill_continuous_sequential(
    palette="drug", 
    na.value="#dbd7a8"
  )

plotly::ggplotly(last_plot())
  • background map design change is also possible.
ggplot()+
  econDV2::geom_sf_taiwan(
    data=drug$data_frequency,
    mapping=aes(
      fill=案件次數2
    ),
    color="white",
    size=0.15,
    map_id = "發生地點",
    type="鄉鎮區",
    background.fill = "#ebffd9",
    background.color = "#d9d9d9",
    background.size = 0.1
  )

last_plot() +
  colorspace::scale_fill_discrete_sequential(
    palette="drug", 
    na.value="#dbd7a8"
  ) + 
  theme_void()

plotly::ggplotly(last_plot())

6.6.4.3 overlay stamenmap background

drug$map$overstamen_tonerlite <- 
  background$`台灣本島`$tonerlite() +
    geom_sf(
      data=drug$sf,
      mapping=aes(
        fill=案件次數2,
        label=map_id
      ),
      color="white",
      size=0.15,
      alpha=0.3,
      inherit.aes = F
    ) +
  colorspace::scale_fill_discrete_sequential(
    palette="drug", 
    na.value=ggplot2::alpha("#dbd7a8", 0.1)
  )

drug$map$overstamen_tonerlite
plotly::ggplotly(drug$map$overstamen_tonerlite)
  • The overlay is slightly off due to some bug in ggmap. To fix it, you can use econDV2::ggmap2() + econDV2::geom_sf_overggmap()
drug$map$overstamen_tonerlite_fixed <- 
  econDV2::ggmap2(taiwan_stamen$tonerlite)+
    econDV2::geom_sf_overggmap(
        data=drug$sf,
        mapping=aes(
          fill=案件次數2,
          label=map_id
        ),
        color="white",
        size=0.15,
        alpha=0.7,
        inherit.aes = F
      ) +
    colorspace::scale_fill_discrete_sequential(
      palette="drug", 
      na.value=ggplot2::alpha("#dbd7a8", 0.1)
    )+theme_void()

drug$map$overstamen_tonerlite_fixed

plotly::ggplotly(drug$map$overstamen_tonerlite_fixed)
  • There is still some unfit due to the fact that sf geometry is simplified.

Terrain

drug$map$overstamen_terrain <- 
  background$`台灣本島`$terrain() +
    geom_sf(
      data=drug$sf,
      mapping=aes(
        fill=案件次數2,
        label=map_id
      ),
      color="white",
      size=0.15,
      alpha=0.7,
      inherit.aes = F
    ) +
  colorspace::scale_fill_discrete_sequential(
    palette="drug", 
    na.value=ggplot2::alpha("#dbd7a8", 0.1)
  )

drug$map$overstamen_terrain
drug$map$overstamen_terrain_fixed <- 
  econDV2::ggmap2(taiwan_stamen$terrain) +
    econDV2::geom_sf_overggmap(
      data=drug$sf,
      mapping=aes(
        fill=案件次數2,
        label=map_id
      ),
      color="white",
      size=0.15,
      alpha=0.5,
      inherit.aes = F
    ) +
  colorspace::scale_fill_discrete_sequential(
    palette="drug", 
    na.value=ggplot2::alpha("#dbd7a8", 0.1)
  )+theme_void()

drug$map$overstamen_terrain_fixed
 
plotly::ggplotly(drug$map$overstamen_terrain_fixed)