personal art map

art-map.R

# source
# https://ggplot2tutor.com/streetmaps/streetmaps/
# http://estebanmoro.org/post/2020-10-19-personal-art-map-with-r/

# create the streetmap
pacman::p_load(osmdata, ggplot2)
# bbx <- getbb("Boston, MA")
bbx <- getbb("Canberra, Australia")
min_lon <- bbx[1,1]
max_lon <- bbx[1,2]
min_lat <- bbx[2,1]
max_lat <- bbx[2,2]

# sometimes a more specifc area
# min_lon <- x; max_lon <- y
# min_lat <- z; max_lat <- m
# bbx <- rbind(x=c(min_lon, max_lon), y=c(min_lat, max_lat))
# colnames(box) <- c("min", "max")

# OSM data has many layers/features, see something related to "highway"
available_tags("highway")

# pick out some
highways <- bbx %>%
  opq() %>%
  add_osm_feature(key = "highway",
                  value = c("motorway", "trunk",
                            "primary","secondary",
                            "tertiary","motorway_link",
                            "trunk_link","primary_link",
                            "secondary_link",
                            "tertiary_link")) %>%
  osmdata_sf()

# look at the lines:
require(sf) # hey what is sf?
ggplot() +
  geom_sf(data = highways$osm_lines,
          aes(color=highway),
          size = .4,
          alpha = .65) +
  theme_void()

streets <- bbx %>%
  opq()%>%
  add_osm_feature(key = "highway",
                  value = c("residential", "living_street",
                            "service","unclassified",
                            "pedestrian", "footway",
                            "track","path")) %>%
  osmdata_sf()

ggplot() +
  geom_sf(data = streets$osm_lines,
          aes(color=highway),
          size = .4,
          alpha = .65)+
  theme_void()

# combine the two above, add different width for different road types, crop streets out of box
# color_roads <- rgb(0.42,0.449,0.488)
color_roads <- "#a8dadc"
color_background <- "#1d3557"
p <- ggplot() +
  geom_sf(data = streets$osm_lines,
          col = color_roads,
          size = .2,
          alpha = .35) +
  geom_sf(data = highways$osm_lines,
          col = color_roads,
          size = .5,
          alpha = .7)+
  coord_sf(xlim = c(min_lon,max_lon),
           ylim = c(min_lat,max_lat),
         expand = FALSE)+
  theme(legend.position = F) + theme_void() +
  theme(panel.background=
          element_rect(fill = color_background))

ggsave(plot = p, "map-canberra.png")

# this looks really really nice.

# adding the geography, like the land, rivers, coastlines
# use polygons instead of raster images

# {tigris} extracts boundaries in ESRI's shapefile format, provided by the US Census

require(tigris)
counties_MA <- counties(state="MA",cb=T,class="sf",)
counties_MA <- st_crop(counties_MA,
                       xmin=min_lon,xmax=max_lon,
                       ymin=min_lat,ymax=max_lat)
ggplot() +
  geom_sf(data=counties_MA,fill="gray",lwd=0)+
  coord_sf(xlim = c(min(bbx[1,]), max(bbx[1,])),
         ylim = c(min(bbx[2,]), max(bbx[2,])),
         expand = FALSE)+
  theme(legend.position = F) + theme_void()

get_water <- function(county_GEOID){
  area_water("MA", county_GEOID, class = "sf")
}
water <- do.call(rbind,
                 lapply(counties_MA$COUNTYFP,get_water))
water <- st_crop(water,
                 xmin=min_lon,xmax=max_lon,
                 ymin=min_lat,ymax=max_lat)

ggplot() +
  geom_sf(data=counties_MA)+
  geom_sf(data=water,
          inherit.aes = F,
          col="red")+
  coord_sf(xlim = c(min(bbx[1,]), max(bbx[1,])),
         ylim = c(min(bbx[2,]), max(bbx[2,])),
         expand = FALSE)+
  theme(legend.position = F) + theme_void()

st_erase <- function(x, y) {
  st_difference(x, st_union(y))
}
counties_MA <- st_erase(counties_MA,water)

ggplot() +
  geom_sf(data=counties_MA,
          lwd=0)+
  coord_sf(xlim = c(min(bbx[1,]), max(bbx[1,])),
         ylim = c(min(bbx[2,]), max(bbx[2,])),
         expand = FALSE)+
  theme(legend.position = F) + theme_void()

ggplot() +
  geom_sf(data=counties_MA,
          inherit.aes= FALSE,
          lwd=0.0,fill=rgb(0.203,0.234,0.277))+
  coord_sf(xlim = c(min(bbx[1,]), max(bbx[1,])),
         ylim = c(min(bbx[2,]), max(bbx[2,])),
         expand = FALSE)+
  theme(legend.position = F) + theme_void()+
  theme(panel.background=
          element_rect(fill = rgb(0.92,0.679,0.105)))+
  ggtitle("Dark + Yellow theme")

ggplot() +
  geom_sf(data=counties_MA,
          inherit.aes= FALSE,
          lwd=0.0,fill="white")+
  coord_sf(xlim = c(min(bbx[1,]), max(bbx[1,])),
         ylim = c(min(bbx[2,]), max(bbx[2,])),
         expand = FALSE)+
  theme(legend.position = F) + theme_void()+
  theme(panel.background=
          element_rect(fill = rgb(0.9,0.9,0.9)))+
  ggtitle("Black + White theme")

ggplot() +
  geom_sf(data=counties_MA,
          inherit.aes= FALSE,
          lwd=0.0,fill=rgb(0.95,0.95,0.95))+
  coord_sf(xlim = c(min(bbx[1,]), max(bbx[1,])),
         ylim = c(min(bbx[2,]), max(bbx[2,])),
         expand = FALSE)+
  theme(legend.position = F) + theme_void()+
  theme(panel.background=element_rect(fill = "black"))+
  ggtitle("White + Black theme")

ggplot() +
  geom_sf(data=counties_MA,
          inherit.aes= FALSE,
          lwd=0.0,fill=rgb(0.203,0.234,0.277))+
  geom_sf(data = streets$osm_lines,
          inherit.aes = FALSE,
          color=color_roads,
          size = .4,
          alpha = .65) +
  geom_sf(data = highways$osm_lines,
          inherit.aes = FALSE,
          color=color_roads,
          size = .6,
          alpha = .65) +
  coord_sf(xlim = c(min(bbx[1,]), max(bbx[1,])),
           ylim = c(min(bbx[2,]), max(bbx[2,])),
           expand = FALSE) +
  theme(legend.position = F) + theme_void()+
  theme(panel.background=
          element_rect(fill = rgb(0.92,0.679,0.105)))

hey. thanks for sharing this. can you share the last version of the script. i cannot recreate the latest image. thanks in advance.

hey. thanks for sharing this. can you share the last version of the script. i cannot recreate the latest image. thanks in advance.

@hodanli

Hey, everything above the call of {tigris} still works fine. The reason why it raises errors (probably warnings actually) is that {tigris} extracts boundaries in ESRI's shapefile format, which is originally provided by the US Census Bureau. In other words, the shapefile wouldn't actually fit in to draw the shapes of any places outside the States.

As for the img above, I made some modifications based on the ggplot output with Figma.