Data Display

Default plot

plot(mtq)

Only geometry

plot(st_geometry(mtq))

Centroids Extraction

mtq_c <- st_centroid(mtq)
plot(st_geometry(mtq))
plot(st_geometry(mtq_c), add=TRUE, cex=1.2, col="red", pch=20)

Distance Matrix

mat <- st_distance(x=mtq_c,y=mtq_c)
mat[1:5,1:5]

Units: [m]
          [,1]     [,2]      [,3]      [,4]      [,5]
[1,]     0.000 35297.56  3091.501 12131.617 17136.310
[2,] 35297.557     0.00 38332.602 25518.913 18605.249
[3,]  3091.501 38332.60     0.000 15094.702 20226.198
[4,] 12131.617 25518.91 15094.702     0.000  7177.011
[5,] 17136.310 18605.25 20226.198  7177.011     0.000

Polygons Aggregation

Simple union:

mtq_u <- st_union(mtq)
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u), add=T, lwd=2, border = "red")

Aggregation according to a grouping variable:

library(dplyr)
mtq_u2 <- mtq %>% 
  group_by(STATUT) %>% 
  summarize(P13_POP = sum(P13_POP))
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u2), add=T, lwd=2, border = "red", col=NA)

Buffer Zone

mtq_b <- st_buffer(x = mtq_u, dist = 5000)
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u), add=T, lwd=2)
plot(st_geometry(mtq_b), add=T, lwd=2, border = "red")

Polygon Intersection

# create a polygon
m <- rbind(c(700015,1624212), c(700015,1641586), c(719127,1641586), 
           c(719127,1624212), c(700015,1624212))
p <- st_sf(st_sfc(st_polygon(list(m))), crs = st_crs(mtq))
plot(st_geometry(mtq))
plot(p, border="red", lwd=2, add=T)

st_intersection() extracts the part of mtq that is intersected by the created polygon.

mtq_z <- st_intersection(x = mtq, y = p)
plot(st_geometry(mtq))
plot(st_geometry(mtq_z), col="red", border="green", add=T)

Count Points in Polygons

st_sample() creates a set of points contained in mtq.

pts <- st_sample(x = mtq, size = 50)
plot(st_geometry(mtq))
plot(pts, pch = 20, col = "red", add=TRUE, cex = 1)

st_interects() creates a list of the points within each polygons.

inter <- st_intersects(mtq, pts)
mtq$nbpts <- sapply(X = inter, FUN = length)
plot(st_geometry(mtq))
# display munucipalities that intersect at least 2 point
plot(st_geometry(mtq[mtq$nbpts>2,]), col = "grey", add=TRUE)
plot(pts, pch = 20, col = "red", add=TRUE, cex = 1)

Light Introduction to Graphical Semiology

Data Preparation

library(sf)
library(dplyr)
# Import geo layers
## Communes of Seine Maritime
sm <- st_read(dsn = "data/dep76/seine_maritime.geojson", 
              stringsAsFactors = FALSE, quiet=TRUE)
## French departements
dep <- st_read(dsn = "data/dep76/dep.geojson", 
               stringsAsFactors = FALSE, quiet=TRUE)

# change projection (lambert93)
sm <- st_transform(sm, 2154)
dep <- st_transform(dep, 2154)

# Import dataset  
csp <- read.csv("data/dep76/data_seine_maritime.csv")
# merge geolayer and dataset
sm <- merge(sm, csp, by="INSEE_COM", all.x=TRUE)
head(sm)

Simple feature collection with 6 features and 16 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: 526897.2 ymin: 6922528 xmax: 565312.9 ymax: 6976233
epsg (SRID):    2154
proj4string:    +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
  INSEE_COM               LIBELLE agr art cad int emp ouv  act pagr  part
1     76001 Allouville-Bellefosse   0  35  30  90 130 125  410 0.00  8.54
2     76002              Alvimare   0  13   8  55  55  72  203 0.00  6.40
3     76004           Ambrumesnil   4  16  12  59  55  47  193 2.07  8.29
4     76005 Amfreville-La-Mi-Voie   0  63 151 434 400 250 1298 0.00  4.85
   pcad  pint  pemp  pouv cat                       geometry
1  7.32 21.95 31.71 30.49 emp MULTIPOLYGON (((528381.5 69...
2  3.94 27.09 27.09 35.47 ouv MULTIPOLYGON (((528381.5 69...
3  6.22 30.57 28.50 24.35 int MULTIPOLYGON (((553154.2 69...
4 11.63 33.44 30.82 19.26 int MULTIPOLYGON (((563304.4 69...
 [ getOption("max.print") est atteint -- 2 lignes omises ]

# Extract a layer of prefecture and sub-prefectures
pref = sm %>% filter(LIBELLE %in% c("Rouen","Le Havre","Dieppe"))

osmdata allows to extract features from the free and open-source OpenStreetMap database.

library(osmdata)
# Get major roads from osm
bb      <- sm %>% st_transform(4326) %>% st_bbox()
q       <- opq(bbox = bb,timeout = 180)
qm      <- add_osm_feature (q, key = 'highway',value = 'motorway', value_exact = FALSE)
qt      <- add_osm_feature (q, key = 'highway',value = 'trunk', value_exact = FALSE)
qp      <- add_osm_feature (q, key = 'highway',value = 'primary', value_exact = FALSE)

motorway<- osmdata_sf(qm)
trunk   <- osmdata_sf(qt)
primary <- osmdata_sf(qp)

roads    <- c(primary,trunk,motorway)$osm_lines %>% st_transform(st_crs(sm))
roads.geom = st_intersection(st_geometry(roads),sm) 

# Get the shape of the main river "La seine" 
qr <- q %>% 
  add_osm_feature (key = 'waterway') %>% 
  add_osm_feature(key = "name:fr", value = "La Seine")
river <- osmdata_sf(qr)
river.geom <- st_geometry(river$osm_lines %>% filter(name.fr == "La Seine")) %>%
  st_transform(st_crs(sm))

# Export road and river layers to shapefile
st_write(roads.geom, dsn = "data/dep76/road.shp")
st_write(river.geom, dsn = "data/dep76/river.shp")

# the bbox is used to center the map on the Seine Maritime depatement
bb <- st_bbox(sm)
par(mar=c(0.2,0.2,1.4,0.2), bg="azure")
plot(st_geometry(dep), col = "ivory", border="ivory3", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
plot(st_geometry(sm), col="cornsilk2", border = NA, lwd = 0.5, add=T)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)

Proportional symbols

Simple map:

library(cartography)
plot(st_geometry(sm))
propSymbolsLayer(sm, var = "act")
title("Active Population")

Custom map:

# set the figure margins
par(mar=c(0.2,0.2,1.4,0.2))
# the bbox is used to center the map on the Seine Maritime departement
bb <- st_bbox(sm)
# plot the departements centered on Seine Maritime
plot(st_geometry(dep), col = "ivory", border="ivory3",  bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
# plot municipalities
plot(st_geometry(sm), col="cornsilk2", border = NA, add=T)
# plot roads
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# plot rivers
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
# plot symbols proportionnal to the active popylation
propSymbolsLayer(
  x = sm, 
  var = "act",
  inches = 0.6,
  symbols = "square",
  col="darkblue",  
  border = "white", 
  lwd = 0.7, 
  legend.style = "e", 
  legend.pos="topleft",
  legend.title.txt = "Labor Force\n(2014)", 
  legend.values.rnd = 0
)
# plot prefecture and subprefectures labels
labelLayer(
  x = pref, 
  txt = "LIBELLE", 
  halo = TRUE,
  col = "white", 
  bg = "black", 
  cex = c(.8,.8,1)
) 
# plot a north arrow
north(pos = "topright", col = "darkblue")
# plot a full layout (title, sources, authors, scalebar)
layoutLayer(
  title = "Workforce in Seine-Maritime", 
  sources = "Insee, 2018, OpenStreetMap contributors",
  author = "Kim, Tim & Comeetie, 2019", 
  col = "darkblue", 
  coltitle = "white", 
  tabtitle = TRUE, 
  scale = 10
)

Typology Map

# modalities
mod <- c("agr", "art", "cad", "int", "emp", "ouv")
# labels in the legend
modlab <- c("Agriculteurs", "Artisans","Cadres", "Prof. Inter.", "Employés", "Ouvriers")
# colors
cols <- c("#e3b4a2", "#a2d5d6", "#debbd4", "#b5dab6", "#afc2e3", "#e9e2c1")

par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure",
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
typoLayer(
  x = sm, 
  var = "cat",
  col = cols,
  border = "ivory", 
  lwd = 0.5, 
  legend.values.order = mod,
  legend.pos = "n", 
  add=TRUE
)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
labelLayer(pref, txt = "LIBELLE", halo = TRUE, 
           col = "white", bg = "black", cex = c(.8,.8,1)) 
# legend* functions are dedicated to legend display
legendTypo(title.txt = "Dominant Socio-Professional\nCategory", 
           col = cols, 
           categ = modlab, 
           nodata = F)
north(pos = "topright", col = "darkblue")
layoutLayer(title = "Workforce Distribution in Seine-Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "kaki.pal", tabtitle = TRUE,
            scale = 10)

Choropleth Map

# Compute the share of "managers" in the active population
sm$pcad <- 100 * sm$cad / sm$act

# Create the map
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure",
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(
  x = sm, 
  var = "pcad", 
  method = "quantile", 
  nclass = 6,
  col = carto.pal("green.pal", 3,"wine.pal",3),
  lwd = 0.4, 
  border = "grey80", 
  legend.values.rnd = 1, 
  legend.horiz = TRUE,
  legend.pos = "topleft", 
  legend.title.txt = "Share of managers (%)", 
  add=TRUE
)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(pref,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
# Add a layout
layoutLayer(title = "Managers Distribution in Seine Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE, 
            frame = TRUE, scale = 10)
north(pos = "topright")

We could create the same map on a cartogram based on the active population stock.

library(cartogram)
sm_c1 <- cartogram_cont(x = sm, weight = "act", prepare = "none")

# The getBreaks() function is used to classify the variable
bks <- getBreaks(v = sm$pcad, method = "quantile", nclass = 6)
# The carto.pal() function give access to various cartographic color palettes
cols <- carto.pal("green.pal", 3,"wine.pal",3)


par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(
  x = sm_c1, 
  var = "pcad", 
  breaks = bks, 
  col = cols, 
  border = "grey80",            
  legend.values.rnd = 1, legend.horiz = TRUE,
  lwd = 0.4, legend.pos = "topleft", 
  legend.title.txt = "Share of managers (%)", add=TRUE
)
# functions are dedicated to legend display
labelLayer(sm_c1 %>% filter(LIBELLE %in% pref$LIBELLE), txt="LIBELLE", 
           halo=TRUE, col="white",
           bg="black", cex=1) 
# Add a layout
layoutLayer(title = "Managers Distribution in Seine Maritime", 
            sources = "Insee, 2018",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE, 
            frame = TRUE, scale = 10)
north(pos = "topright", south = TRUE)

Gridded Map

# Create a grid based on sm (cell area = 4 square km)
grid <- getGridLayer(
  x = sm, 
  cellsize = 4000*4000, 
  type = "regular", 
  var = c('cad', 'act')
)
# Compute the share of managers
grid$pcad <- 100 * grid$cad / grid$act
# Display the map as choropleth layer
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(grid, var = "pcad", breaks=bks, 
           col = cols, border = "grey80", 
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers\n(en %)", add=T)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
labelLayer(pref,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
layoutLayer(title = "Managers Distribution in Seine Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE,
            frame = TRUE, scale = 10)
north(pos = "topright")

It is also possible to create hexagonal grids.

# Create a grid based on sm (cell area = 4 square km)
grid2 <- getGridLayer(x = sm, cellsize = 4000*4000, 
                      type = "hexagonal", var = c('cad', 'act'))
# Compute the share of managers
grid2$pcad <- 100 * grid2$cad / grid2$act
# Display the map as choropleth layer
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(grid2, var = "pcad", breaks=bks, 
           col = cols, border = "grey80", 
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers\n(en %)", add=T)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
labelLayer(pref,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
layoutLayer(title = "Managers Distribution in Seine Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE,
            frame = TRUE, scale = 10)
north(pos = "topright")

Smoothed Map

smoothLayer() uses functions from package SpatialPosition to compute Stewart’s potentials of population.
The computation of potentials could be considered as a spatial interpolation method such as inverse distance weighted interpolation (IDW) or kernel density estimator. These models aim to estimate unknown values of non-observed points from known values given by measured points. Cartographically speaking, they are often used to get a continuous surface from a set of discrete points.

sm$cad100 <- sm$cad * 100
par(mar=c(0.2,0.2,1.4,0.2), bg="azure")
plot(st_geometry(dep), col = "ivory", border="ivory3", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
smoothLayer(
  x = sm, var = "cad100", var2 = "act", 
  typefct = "exponential", span = 4000, beta = 2, 
  breaks = bks, col = cols,
  legend.pos = "topleft", mask = sm,
  legend.values.rnd = 1,
  legend.title.txt = "Share of managers* (%)", 
  border = "grey90", lwd = 0.2, add = TRUE
)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = .8,add=TRUE)
labelLayer(pref,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
layoutLayer(title = "Managers Distribution in Seine Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            postitle = "center",
            frame = TRUE, scale = 10)
north(pos = "topright")
text(x = 488000, y = 6921000,adj = 0,
     font = 3, cex = 0.8,
     labels = "* Potential smoothing\n  exponential function\n  span = 4 km, beta = 2")

Resources

• A cheat sheet displays a quick overview of cartography’s main features:
  

• A vignette introduces the package and provides examples of how to create some thematic maps:
  

• A website shows the package documentation and vignette:
  

ggplot2

Proportional symbols map

library(ggplot2)

labgg = pref %>% st_centroid() 
labgg$x = st_coordinates(labgg)[,1]
labgg$y = st_coordinates(labgg)[,2]

ggplot() +
  geom_sf(data = dep, colour = "ivory3",fill = "ivory") +
  geom_sf(data = river.geom, colour = "azure",size=2) +
  geom_sf(data = roads.geom, colour = "#666666",size=0.5) +
  geom_sf(data = sm %>%  st_centroid(),
          aes(size= act), colour="#E84923CC", show.legend = 'point') +
  geom_text(data=labgg,aes(x=x,y=y,label=LIBELLE))+
  scale_size(name = "Active population",
             breaks = c(100,1000,10000),
             range = c(0,20)) +
  coord_sf(crs = 2154, datum = NA,
           xlim = st_bbox(sm)[c(1,3)],
           ylim = st_bbox(sm)[c(2,4)]) +
  theme_minimal() +
  theme(panel.background = element_rect(fill = "azure",color=NA)) +
  labs(title = "Active population",
       caption = "Insee, 2018\nKim, Tim & Comeetie, 2019",x="",y="")

Choropleth map

ggplot() +
  geom_sf(data = dep, colour = "ivory3",fill = "ivory") +
  geom_sf(data = sm, aes(fill = pcad), colour = "grey80") +
  geom_sf(data = river.geom, colour = "azure",size=2) +
  geom_sf(data = roads.geom, colour = "#666666",size=0.5) +
  geom_text(data=labgg,aes(x=x,y=y,label=LIBELLE))+
  scale_fill_gradientn(name = "Share of managers (%)", 
                       values=bks/max(bks),
                       colours = carto.pal("green.pal", 3,"wine.pal",3)) +
  coord_sf(crs = 2154, datum = NA,
           xlim = st_bbox(sm)[c(1,3)],
           ylim = st_bbox(sm)[c(2,4)]) +
  theme_minimal() +
  theme(panel.background = element_rect(fill = "azure",color=NA)) +
  labs(title = "Managers",
       caption = "Insee, 2018\nKim, Tim & Comeetie, 2019",x="",y="")

tmap

Proportional symbols map

library(tmap)
tm_shape(dep, bbox = st_bbox(sm)) + 
  tm_polygons(col = "ivory", border.col = "ivory3")+
  tm_shape(roads.geom) + 
  tm_lines(col="#666666",lwd = 1.2) +
  tm_shape(river.geom) +
  tm_lines(col="azure",lwd = 3) + 
  tm_shape(sm)  +
  tm_polygons(col="cornsilk2", border.alpha = 0) +
  tm_shape(river.geom) +
  tm_lines(col="azure",lwd = 3) + 
  tm_shape(sm)  +
  tm_symbols(size = "act", col = "darkblue",
             scale = 4,
             title.size = "Active Population") +
  tm_compass(type = "arrow", position = c("right", "top")) +
  tm_scale_bar(position = c("right", "bottom"), breaks = c(0,5,10)) + 
  tm_credits("Insee, 2018
Kim, Tim & Comeetie, 2019", position = c("left", "bottom")) +
  tm_layout(title = "Workforce in Seine-Maritime")

Choropleth map

library(tmap)
tm_shape(dep, bbox = st_bbox(sm)) + 
  tm_polygons(col = "ivory", border.col = "ivory3")+
  tm_shape(roads.geom) + 
  tm_lines(col="#666666",lwd = 1.2) +
  tm_shape(river.geom) +
  tm_lines(col="azure",lwd = 3) + 
  tm_shape(sm)  +
  tm_polygons(col="cornsilk2", border.alpha = 0) +
  tm_shape(river.geom) +
  tm_lines(col="azure",lwd = 3) + 
  tm_shape(sm)  +
  tm_polygons("pcad", 
              breaks = bks,
              palette=cols, title = "Share of Managers") +
  tm_compass(type = "arrow", position = c("right", "top")) +
  tm_scale_bar(position = c("right", "bottom"), breaks = c(0,5,10)) + 
  tm_credits("Insee, 2018
Kim, Tim & Comeetie, 2019", position = c("left", "bottom")) +
  tm_layout(title = "Managers in Seine-Maritime")

Proportional symbols layer

These interactive maps, as appealing as they seem to be, are not really suitable for presenting geostatistical information.

For proportional symbols both leaflet and mapview lack a proper way to build legends. Nonetheless, they can be really useful for exploratory data analysis.

Both librairies are quite similar with some advantage for mapview (e.g the possibility to render to canvas for bigger datasets) and some other for leaflet (e.g using a customized projection different from web-mercator).

Two other packages mapdeck and leafgl deserve attention since they enable mapdrawing in webgl and therefore can be used with large datasets. They are not introduced here but keep them in mind if needed.

library(mapview)
# convert polygon to point and compute the circle radius for porportional area
actifs = sm %>% st_centroid() %>% mutate(radius = sqrt(act)) %>% arrange(desc(act))
# build a map by providing a map tile provider and some options for the circles.
# with mapview the circle size will stay constant whatever the zoom level is.
mapview(actifs, map.types = "Stamen.TonerLite", cex="radius", legend=FALSE,
        col.regions="#217844", lwd=0, alpha=0.4)

With leaflet data must be provided in long/lat (st_transform(4326)) and will be converted by default to the webmercator coordinate reference system (CRS).

You may choose between circles with radius specified in meters (addCircle) or in pixels (addCircleMarkers). If specified in pixel, the radius will stay the same whatever the zoom level is. If specified in meters, it will evolve with zoom level.

library(leaflet)
leaflet(actifs %>% st_transform(4326)) %>% addProviderTiles(providers$Stamen.TonerLite) %>%
  addCircles(radius = ~radius * 30, popup = ~paste0(LIBELLE," : ",act,"actifs"),
             fillColor = "#217844", stroke = FALSE, fillOpacity = 0.4)

Gridded Map

mapview and leaflet enable the use and styling of sf polygons. We may for example reuse the grid built previously to show the share of managers in Seine-Maritime.

Default choropleth can be obtained with just three arguments with mapview.

mapview(grid,map.types = "Stamen.TonerLite",zcol="pcad")

leaflet enables fine tuning of the color scale and legend.

# define a color scale function from colors and breaks
color.scale <- colorBin(cols, domain = range(bks), bins=bks)

leaflet(grid  %>% st_transform(4326)) %>% addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(fillColor = ~color.scale(pcad), stroke = FALSE, fillOpacity = 0.7) %>% 
  addLegend(colors = cols, labels = round(bks[1:(length(bks)-1)]*10)/10,
            title = "Share of Manager (%)")

Projection

leaflet enables to use other projections than the web-mercator projection for webmaps. You may deal with tiles built with another projection system such as Lambert 93 by defining a custom leaflet crs (see gis.stackexchange resolution-from-wmts-getcapacilities-scaledenomin and mathematics-behind-converting-scale-to-resolution to deal with WMTS tiles).

We use this approach here to deal with Hypsometric tints tiles (tiles colors which encode elevation) and the contour lines of chamois and ibex population area provided by the french national office of hunting and wild animals.

epsg2154 <- leafletCRS(crsClass = "L.Proj.CRS", code = "EPSG:2154",
                       proj4def = "+proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3
                       +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0
                       +units=m +no_defs",
                       resolution = 4838095.23807/cumprod(c(1,rep(2,15)))*0.00028,
                       origin= c(-357823.2365,7230727.3772)
)


tile_mnt <- "http://tiles.craig.fr/mnt/service?service=WMTS&request=GetTile&version=1.0.0&
layer=relief&style=default&tilematrixset=lambert93&format=image%2Fpng&tilematrix={z}&
tilerow={y}&tilecol={x}"

tile_attrib <- "Map data &copy;
<a href='https://www.craig.fr/contenu/1377-flux-tuile-wmswmtstms'> craig / IGN </a>
and <a href='http://www.oncfs.gouv.fr/Cartographie-ru4/Le-portail-
cartographique-de-donnees-ar291'>ONCF</a>"

bouq = read_sf("./data/BOQ_2017_massif_uni/BOQ_2017_massif_uni_L93.shp")
cha = read_sf("./data/CHA_2017_massif_uni/CHA_2017_massif_uni_L93.shp")

leaflet(options = leafletOptions(worldCopyJump = F, crs = epsg2154)) %>%
  addTiles(urlTemplate = tile_mnt,attribution = tile_attrib) %>% 
  addPolygons(data = bouq %>% st_transform(4326), fillColor = '#50162D',color = '#772A7F',
              fillOpacity = 0.05,opacity = 1, weight = 2.5, dashArray = "3") %>% 
  addPolygons(data = cha %>% st_transform(4326), fillColor = '#88AA00',color = '#CCFF00',
              fillOpacity = 0.05,opacity = 1, weight=2.5, dashArray = "3") %>% 
  setView(2.692174, 45.067230, 4)