The strength of the geovizr package lies in its ability
to create thematic maps from statistical data. To achieve this, many
functions are available.
First, let’s load the packages and some data.
library(geovizr)
library(sf)
world <- st_read(
system.file("gpkg/world.gpkg", package = "geovizr"),
quiet = TRUE
)
cities <- st_read(
system.file("gpkg/cities.gpkg", package = "geovizr"),
quiet = TRUE
)Proportional symbols
With the viz_prop() function, you can quickly draw a map
with proportionnal symbols. At a minimum, you need two parameters:
data (a spatial data frame) and var (the
variable to map). Note that you should use absolute quantitative data
(stocks) here.
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_path(data = world, fill = "#CCC") |>
viz_prop(data = world, var = "pop", fill = "#38896F") |>
viz_render()With the k attribute (and possibily
fixmax), you can change the size of the circles. With
symbol, it is possible to change the type of symbol—for
example: "circle", "square",
"spike", "halfcircle". Additionally, you can
modify all the usual graphical styles (fill,
stroke, strokeWidth…).
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_path(data = world, fill = "#CCC") |>
viz_prop(
data = world, var = "pop", k = 30,
fill = "#38896F", symbol = "square"
) |>
viz_render()The legend can be configured using the prefixes leg_.
With the prefix leg_circle_..., you can modify the
appearance of circles. With the prefix leg_line_..., you
can modify the appearance of lines. With the prefix
leg_values_..., you can modify the style of the values.
Everything is configurable. To better understand, you can refer to the
documentation of the legend functions and/or to the dedicated
vignette.
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_path(data = world, fill = "#CCC") |>
viz_prop(
data = world, var = "pop", k = 30,
fill = "#38896F", symbol = "circle", leg_type = "separate",
leg_circle_fill = "#38896F", leg_title = "Population",
leg_subtitle = "in million inh.", leg_note = "made with geoviz",
leg_values_round = 0, leg_values_factor = 1 / 1000000
) |>
viz_render()Choropleth
In thematic cartography, according to the rules of graphic semiology,
relative quantitative data (percentages, indices, ratios) must be
represented using visual order variables. The most classic
representation is the choropleth map. The simplest way to create a
chropleth map with the geoviz library is to use
viz_choro(). As previously, you need two parameters:
data (a spatial data frame) and var (the
variable to map).
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_choro(data = world, var = "gdppc") |>
viz_render()To change class boundaries, you have access to several discretization
methods from the JavaScript library statsbreaks. You can
choose them using the method parameter. The available
methods are: “quantile”, “q6”, “equal”, “jenks”, “msd”, “geometric”,
“headtail”, “pretty”, “arithmetic”, “nestedmeans”. In addition, with
nb, you can specify the number classes. For the msd method
only , you can also choose the parameters sd (number of
standard deviations to include) and middle (to define
whether the mean is at the center of a class or at a class
boundary).
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_choro(data = world, var = "gdppc", method = "jenks", nb = 4) |>
viz_render()If you prefer, you can also directly specify the class boundaries
using the breaks parameter.
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_choro(
data = world, var = "gdppc",
breaks = c(200, 1000, 5000, 10000, 50000, 200000)
) |>
viz_render()Regarding colors, you have access to all palettes available in the JavaScript library dispocal. See here. For example, you can use:
- Sequential
"BluGrn", "BluYl", "BrwnYl",
"BurgYl", "Burg", "DarkMint",
"Emrld", "Magenta", "Mint",
"OrYel", "Peach", "PinkYl",
"PurpOr", "Purp", "RedOr",
"SunsetDark", "Sunset",
"TealGrn", "Teal", "agGrnYl",
"agSunset", "Algae", "Amp",
"Deep", "Dense", "Gray",
"Haline", "Ice", "Matter",
"Oxy", "Phase", "Solar",
"Speed", "Tempo", "Thermal",
"Turbid", "Blues", "BuGn",
"BuPu", "GnBu", "Greens",
"Greys", "OrRd", "Oranges",
"PuBuGn", "PuBu", "PuRd",
"Purples", "RdPu", "Reds",
"YlGnBu", "YlGn", "YlOrBr",
"YlOrRd", "Blues10", "Blues7",
"Inferno", "Magma", "Plasma",
"Viridis", "Cube1", "CubeYF",
"LinearL", "Acton", "Bamako",
"Batlow", "Bilbao", "Buda",
"Davos", "Devon", "GrayC",
"Hawaii", "Imola", "LaJolla",
"LaPaz", "Nuuk", "Oleron",
"Oslo", "Tokyo", "Turku",
"Carrots", "BlueFluorite",
"AridElevation", "Florida"
- Diverging
"ArmyRose", "Earth", "Fall",
"Geyser", "TealRose", "Temps",
"Tropic", "Balance", "Curl",
"Delta", "BrBG", "PRGn",
"PiYG", "PuOr", "RdBu",
"RdGy", "RdYlBu", "RdYlGn",
"Spectral", "BlueDarkOrange12",
"BlueDarkOrange18", "BlueDarkRed12",
"BlueDarkRed18", "BlueGray",
"BlueGreen", "BlueGrey",
"BlueOrange10", "BlueOrange12",
"BlueOrange8", "BlueOrangeRed",
"BrownBlue10", "BrownBlue12",
"GreenMagenta", "RedYellowBlue",
"Berlin", "Broc", "Cork",
"Lisbon", "Roma", "Tofino",
"Vik"
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_choro(data = world, var = "gdppc", colors = "BlueOrangeRed") |>
viz_render()You can also choose any colors you want by specifying a vector.
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_choro(
data = world, var = "gdppc",
colors = c(
"#ffffcc", "#c7e9b4", "#7fcdbb",
"#41b6c4", "#2c7fb8", "#253494"
)
) |>
viz_render()Typology
To create a typology map, you can use the viz_typo()
function. As before, you need at least two parameters: data
(a spatial data frame) and var (a qualitative
variable).
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_path(data = world, fill = "#CCC") |>
viz_typo(data = world, var = "region") |>
viz_render()Of course, you can change the palette with the colors
parameter. For example, you can use for example:
"Antique", "Bold", "Pastel",
"Prism", "Safe", "Vivid",
"Accent", "Dark2", "Paired",
"Pastel1", "Pastel2", "Set1",
"Set2", "Set3", "BlueRed",
"GreenOrange", "PurpleGray",
"GrandBudapest3", "IsleOfDogs2",
"Moonrise5"
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_typo(data = world, var = "region", colors = "Pastel") |>
viz_render()There are other palettes in dicopal, but some are only available for a fixed number of classes. Fortunately, you can also directly choose your own colors.
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_typo(
data = world, var = "region",
colors = c(
"#e41a1c", "#377eb8", "#4daf4a",
"#984ea3", "#ff7f00", "#ffff33"
)
) |>
viz_render()To properly determine the order of the types in the legend and which
color corresponds to which category, you can also use the
order argument.
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_typo(
data = world, var = "region",
order = c(
"Antarctica", "Oceania", "Africa",
"America", "Asia", "Europe"
),
colors = c(
"#e41a1c", "#377eb8", "#4daf4a",
"#984ea3", "#ff7f00", "#ffff33"
)
) |>
viz_render()Obviously, all these functions also work on point objects.
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_typo(
data = st_centroid(world), var = "region", r = 10,
order = c(
"Antarctica", "Oceania", "Africa",
"America", "Asia", "Europe"
),
colors = c(
"#e41a1c", "#377eb8", "#4daf4a",
"#984ea3", "#ff7f00", "#ffff33"
)
) |>
viz_render()## Warning: st_centroid assumes attributes are constant over geometriespictograms
Another way to represent qualitative data is to use pictograms. For
this, you can use the viz_picto() function with the
data and var parameters.
viz_create(projection = "EqualEarth", zoomable = T) |>
viz_path(data = world, fill = "#CCC") |>
viz_picto(data = world, var = "region") |>
viz_render()