The aim of cartography
is to obtain thematic maps with the visual quality of those build with a classical mapping or GIS software.
Users of the package could belong to one of two categories: cartographers willing to use R or R users willing to create maps. Therefore, its functions have to be intuitive to cartographers and ensure compatibility with common R workflows.
cartography
uses sf
or sp
objects to produce base
graphics. As most of the internals of the package relies on sf
functionalities, the preferred format for spatial objects is sf
.
cartography
’s functions can be classified in the following categories :
Symbology
Each function focuses on a single cartographic representation (e.g. proportional symbols or choropleth representation) and displays it on a georeferenced plot. This solution allows to consider each representation as a layer and to overlay multiple representations on a same map.
Each function has two main arguments that are:
x
, a spatial object (preferably an sf
object),var
, the name of a variable to map.sp
objects are handled through the spdf
argument if the variable is contained within the Spatial*DataFrame
and through spdf
, spdfid
, df
, dfid
if the variable is in a separate data.frame
that needs to be joined to the Spatial*DataFrame
.
Many parameters are available to fine tune the cartographic representations. These parameters are the common ones found in GIS and automatic cartography tools (e.g. classification and color palettes used in choropleth maps, symbols sizes used in proportional symbols maps…).
Transformations
A set of functions is dedicated to the creation or transformation of spatial objects (e.g. borders extraction, grid or links creation). These functions are provided to ease the creation of some more advanced maps that usually need geo-processing.
Map Layout
Along with the cartographic functions, some other functions are dedicated to layout design (e.g. customizable scale bar, north arrow, title, sources or author information…).
Color Palettes
16 original color palettes are shipped within the package. Those palettes can be customized and combined.
Legends
Legends are displayed by default along cartographic layers but more parameters are available through legend*()
functions.
Classification
getBreaks()
give access to most of the classification methods used for data binning.
propSymbolsLayer()
displays symbols with areas proportional to a quantitative variable (stocks). Several symbols are available (circles, squares, bars). The inches
argument is used to customize the symbols sizes.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# plot municipalities (only borders are plotted)
plot(st_geometry(mtq), col = "grey80", border = "grey")
# plot population
propSymbolsLayer(
x = mtq,
var = "POP",
inches = 0.25,
col = "brown4",
legend.pos = "topright",
legend.title.txt = "Total population"
)# layout
layoutLayer(title = "Population Distribution in Martinique",
sources = "Sources: Insee and IGN, 2018",
author = paste0("cartography ", packageVersion("cartography")),
frame = FALSE, north = FALSE, tabtitle = TRUE)
# north arrow
north(pos = "topleft")
In choropleth maps, areas are shaded according to the variation of a quantitative variable. They are used to represent ratios or indices.
choroLayer()
displays choropleth maps . Arguments nclass
, method
and breaks
allow to customize the variable classification. getBreaks()
allow to classify outside of the function itself. Colors palettes are defined with col
and a set of colors can be created with carto.pal()
(see also display.carto.all()
).
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# population density (inhab./km2) using sf::st_area()
$POPDENS <- 1e6 * mtq$POP / st_area(mtq)
mtq# plot municipalities (only the backgroung color is plotted)
plot(st_geometry(mtq), col = NA, border = NA, bg = "#aadaff")
# plot population density
choroLayer(
x = mtq,
var = "POPDENS",
method = "geom",
nclass=5,
col = carto.pal(pal1 = "sand.pal", n1 = 5),
border = "white",
lwd = 0.5,
legend.pos = "topright",
legend.title.txt = "Population Density\n(people per km2)",
add = TRUE
) # layout
layoutLayer(title = "Population Distribution in Martinique",
sources = "Sources: Insee and IGN, 2018",
author = paste0("cartography ", packageVersion("cartography")),
frame = FALSE, north = FALSE, tabtitle = TRUE, theme= "sand.pal")
# north arrow
north(pos = "topleft")
getPencilLayer()
transforms POLYGONS or MULTIPOLYGONS in MULTILINESTRINGS. This function creates a layer that mimicks a pencil hand-drawing.
typoLayer()
displays a typology map of a qualitative variable. legend.values.order
is used to set the modalities order in the legend.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# transform municipality multipolygons to (multi)linestrings
getPencilLayer(
mtq_pencil <-x = mtq,
size = 400,
lefthanded = F
)# plot municipalities (only the backgroung color is plotted)
plot(st_geometry(mtq), col = "white", border = NA, bg = "lightblue1")
# plot administrative status
typoLayer(
x = mtq_pencil,
var="STATUS",
col = c("aquamarine4", "yellow3","wheat"),
lwd = .7,
legend.values.order = c("Prefecture",
"Sub-prefecture",
"Simple municipality"),
legend.pos = "topright",
legend.title.txt = "",
add = TRUE
)# plot municipalities
plot(st_geometry(mtq), lwd = 0.5, border = "grey20", add = TRUE, lty = 3)
# labels for a few municipalities
labelLayer(x = mtq[mtq$STATUS != "Simple municipality",], txt = "LIBGEO",
cex = 0.9, halo = TRUE, r = 0.15)
# title, source, author
layoutLayer(title = "Administrative Status",
sources = "Sources: Insee and IGN, 2018",
author = paste0("cartography ", packageVersion("cartography")),
north = FALSE, tabtitle = TRUE, postitle = "right",
col = "white", coltitle = "black")
# north arrow
north(pos = "topleft")
propSymbolsChoroLayer()
creates a map of symbols that are proportional to values of a first variable and colored to reflect the classification of a second variable. A combination of propSymbolsLayer()
and choroLayer()
arguments is used.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# Plot the municipalities
plot(st_geometry(mtq), col="darkseagreen3", border="darkseagreen4",
bg = "lightblue1", lwd = 0.5)
# Plot symbols with choropleth coloration
propSymbolsChoroLayer(
x = mtq,
var = "POP",
border = "grey50",
lwd = 1,
legend.var.pos = "topright",
legend.var.title.txt = "Population",
var2 = "MED",
method = "equal",
nclass = 4,
col = carto.pal(pal1 = "sand.pal", n1 = 4),
legend.var2.values.rnd = -2,
legend.var2.pos = "left",
legend.var2.title.txt = "Median\nIncome\n(in euros)"
) # layout
layoutLayer(title="Population & Wealth in Martinique, 2015",
author = "cartography 2.1.3",
sources = "Sources: Insee and IGN, 2018",
scale = 5, tabtitle = TRUE, frame = FALSE)
# north arrow
north(pos = "topleft")
propSymbolsTypoLayer()
creates a map of symbols that are proportional to values of a first variable and colored to reflect the modalities of a second qualitatice variable. A combination of propSymbolsLayer()
and typoLayer()
arguments is used.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# Plot the municipalities
plot(st_geometry(mtq), col="#f2efe9", border="#b38e43", bg = "#aad3df",
lwd = 0.5)
# Plot symbols with choropleth coloration
propSymbolsTypoLayer(
x = mtq,
var = "POP",
inches = 0.5,
symbols = "square",
border = "white",
lwd = .5,
legend.var.pos = "topright",
legend.var.title.txt = "Population",
var2 = "STATUS",
legend.var2.values.order = c("Prefecture", "Sub-prefecture",
"Simple municipality"),
col = carto.pal(pal1 = "multi.pal", n1 = 3),
legend.var2.pos = c(692000, 1607000),
legend.var2.title.txt = "Administrative\nStatus"
) # layout
layoutLayer(title="Population Distribution in Martinique",
author = "cartography 2.1.3",
sources = "Sources: Insee and IGN, 2018",
scale = 5, tabtitle = TRUE, frame = FALSE)
# north arrow
north(pos = "topleft")
labelLayer()
is dedicated to the display of labels on a map. The overlap = FALSE
argument displays non overlapping labels.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# plot municipalities
plot(st_geometry(mtq), col = "#e4e9de", border = "darkseagreen4",
bg = "lightblue1", lwd = 0.5)
# plot labels
labelLayer(
x = mtq,
txt = "LIBGEO",
col= "black",
cex = 0.7,
font = 4,
halo = TRUE,
bg = "white",
r = 0.1,
overlap = FALSE,
show.lines = FALSE
)# map layout
layoutLayer(
title = "Municipalities of Martinique",
sources = "Sources: Insee and IGN, 2018",
author = paste0("cartography ", packageVersion("cartography")),
frame = FALSE,
north = TRUE,
tabtitle = TRUE,
theme = "taupe.pal"
)
getLinkLayer()
creates a link layer from an sf
object and a link data.frame
(long format).
gradLinkTypoLayer()
displays graduated and colored links.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# path to the csv file embedded in cartography
system.file("csv/mob.csv", package="cartography")
path_to_csv <-# import to a data.frame
read.csv(path_to_csv)
mob <-# select workplaces with administrative status = Prefecture or Sub-prefecture
mob[mob$sj != "Simple municipality",]
mob <-# create an sf object of links
getLinkLayer(
mtq_mob <-x = mtq,
xid = "INSEE_COM",
df = mob,
dfid = c("i","j")
)# set figure background color
par(bg="grey25")
# plot municipalities
plot(st_geometry(mtq), col = "grey13", border = "grey25",
bg = "grey25", lwd = 0.5)
# plot graduated links
gradLinkTypoLayer(
x = mtq_mob,
xid = c("i", "j"),
df = mob,
dfid = c("i","j"),
var = "fij",
breaks = c( 100, 500, 1200, 2500, 4679.0),
lwd = c(1,4,8,16),
legend.var.pos = "left",
legend.var.title.txt = "Nb. of\nCommuters",
var2 = "sj",
col = c("grey85", "red4"),
legend.var2.title.txt = "Workplace",
legend.var2.pos = "topright"
) # map layout
layoutLayer(title = "Commuting to Prefectures in Martinique",
sources = "Sources: Insee and IGN, 2018",
author = paste0("cartography ", packageVersion("cartography")),
frame = FALSE, col = "grey25", coltitle = "white",
tabtitle = TRUE)
Isopleth maps are based on the assumption that the phenomenon to be represented has a continuous distribution. These maps use a spatial interaction modeling approach which aims to compute indicators based on stock values weighted by distance. It allows a spatial representation of the phenomenon independent from the initial heterogeneity of the territorial division.
smoothLayer()
heavily depends on the SpatialPosition
package. The function uses a marked point layer and a set of parameters (a spatial interaction function and its parameters) and displays an isopleth map layer.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# plot municipalities (only the backgroung color is plotted)
plot(st_geometry(mtq), col = NA, border = NA, bg = "lightblue1")
# plot isopleth map
smoothLayer(
x = mtq,
var = 'POP',
typefct = "exponential",
span = 4000,
beta = 2,
nclass = 12,
col = carto.pal(pal1 = 'brown.pal', n1 = 12),
border = "grey",
lwd = 0.1,
mask = mtq,
legend.values.rnd = -3,
legend.title.txt = "Population\nPotential",
legend.pos = "topright",
add=TRUE
)## Functions related to Stewart's potential are deprecated.
## Please use the `potential` package instead.
## https://riatelab.github.io/potential/
# annotation on the map
text(x = 692582, y = 1611478, cex = 0.8, adj = 0, font = 3, labels =
"Distance function:\n- type = exponential\n- beta = 2\n- span = 4 km")
# layout
layoutLayer(title = "Population Distribution in Martinique",
sources = "Sources: Insee and IGN, 2018",
author = paste0("cartography ", packageVersion("cartography")),
frame = FALSE, north = FALSE, tabtitle = TRUE, theme = "brown.pal")
# north arrow
north(pos = "topleft")
The grid-cell method is an option to overcome the arbitrariness and irregularity of an administrative division. It highlights the main trends in the data spatial distribution, splitting the territory in regular blocks. Statistical values are distributed over a regular grid. Cell values are classified and then displayed in areas of color. The principle adopted here is to set each cell’s value with a proportion of the initial geometrical units it overlay (share of intersected area).
getGridLayer()
builds a regular grid (squares or hexagons) based on a spatial object and computes data that match the grid layer. choroLayer()
is then used to display the grid on a choropleth map.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# Create a grid layer, cell size area match the median municipality area
getGridLayer(
mygrid <-x = mtq,
cellsize = median(as.numeric(st_area(mtq))),
var = "POP",
type = "hexagonal"
)# Compute population density in people per km2
$POPDENS <- 1e6 * mygrid$POP / mygrid$gridarea
mygrid# plot municipalities (only the backgroung color is plotted)
plot(st_geometry(mtq), col = NA, border = NA, bg = "#deffff")
# Plot the population density
choroLayer(x = mygrid, var = "POPDENS", method = "geom", nclass=5,
col = carto.pal(pal1 = "turquoise.pal", n1 = 5), border = "grey80",
lwd = 0.5, legend.pos = "bottomleftextra", add = TRUE,
legend.title.txt = "Population Density\n(people per km2)")
layoutLayer(title = "Population Distribution in Martinique",
sources = "Sources: Insee and IGN, 2018",
author = paste0("cartography ", packageVersion("cartography")),
frame = FALSE, north = FALSE, tabtitle = TRUE,
theme = "turquoise.pal")
# north arrow
north(pos = "topleft")
Discontinuities maps are based on the variation of a phenomena between contiguous units. This kind of representation focuses spatial breaks. The discontinuity intensity is expressed by the borders’ thickness.
getBorders()
is used to build a spatial object of borders between units. Each resulting borders contains the ids of its two neighboring units. It is possible to complement these borders with getOuterBorders()
to compute borders between non-contiguous units (e.g. maritime borders). discLayer()
computes and displays discontinuities, lines widths reflect the ratio or the absolute difference between values of an indicator in two neighboring units.
library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
system.file("gpkg/mtq.gpkg", package="cartography")
path_to_gpkg <-# import to an sf object
st_read(dsn = path_to_gpkg, quiet = TRUE)
mtq <-# Compute the population density (inhab./km2) using sf::st_area()
$POPDENS <- as.numeric(1e6 * mtq$POP / st_area(mtq))
mtq# Get a SpatialLinesDataFrame of countries borders
getBorders(mtq)
mtq.contig <-# plot municipalities (only the backgroung color is plotted)
plot(st_geometry(mtq), col = NA, border = NA, bg = "lightblue1",
xlim = c(690574, 745940))
# Plot the population density with custom breaks
choroLayer(x = mtq, var = "MED",
breaks = c(min(mtq$MED), seq(13000, 21000, 2000), max(mtq$MED)),
col = carto.pal("green.pal", 6),border = "white", lwd = 0.5,
legend.pos = "topright", legend.title.txt = "Median Income\n(euros)",
add = TRUE)
# Plot discontinuities
discLayer(
x = mtq.contig,
df = mtq,
var = "MED",
type = "rel",
method = "geom",
nclass = 3,
threshold = 0.4,
sizemin = 0.7,
sizemax = 6,
col = "red4",
legend.values.rnd = 1,
legend.title.txt = "Relative\nDiscontinuities",
legend.pos = "right",
add = TRUE
)# Layout
layoutLayer(title = "Wealth Disparities in Martinique, 2015",
author = paste0("cartography ", packageVersion("cartography")),
sources = "Sources: Insee and IGN, 2018",
frame = FALSE, scale = 5, tabtitle = TRUE,theme = "grey.pal")
# north arrow
north(pos = "topleft")
sp
ObjectsSpatialPointsDataFrame
and SpatialPolygonsDataFrame
(from sp
) are handled through the spdf
argument if the variable is contained within the Spatial*DataFrame
and through spdf
, spdfid
, df
, dfid
if the variable is in a separate data.frame
that needs to be joined to the Spatial*DataFrame
.
library(sp)
library(cartography)
data("nuts2006")
# Plot a layer with the extent of the EU28 countries with only a background color
plot(nuts0.spdf, border = NA, col = NA, bg = "#A6CAE0")
# Plot non european space
plot(world.spdf, col = "#E3DEBF", border = NA, add = TRUE)
# Plot Nuts2 regions
plot(nuts0.spdf, col = "grey60",border = "white", lwd = 0.4, add = TRUE)
# plot the countries population
propSymbolsLayer(
spdf = nuts0.spdf,
df = nuts0.df,
spdfid = "id",
dfid = "id",
var = "pop2008",
legend.pos = "topright",
col = "red4",
border = "white",
legend.title.txt = "Population"
)# layout
layoutLayer(title = "Population in Europe, 2008",
sources = "Data: Eurostat, 2008",
author = paste0("cartography ", packageVersion("cartography")),
scale = 500, frame = TRUE, col = "#688994")
# north arrow
north("topleft")
Several datasets are embedded in the package:
st_read()
function of the sf
package.
sp
objects and data.frame
s on European regions (NUTS) can be loaded in the environment via data(nuts2006)
. Each layer of this dataset is directly described in the documentation (e.g. ?nuts0.spdf
).