rdeck lets you use R to describe interactive WebGL maps built with Uber’s deck.gl framework and mapbox base maps.
These maps can smoothly render data interactively on a much larger scale than html/svg frameworks like Leaflet. Visually appealing 3D and lighting effects are also possible, thanks to WebGL rendering.
rdeck maps consist of a basemap (requires a mapbox
account) and 0 or more layers. Basemaps are defined via the
rdeck(map_style) parameter, which is any valid mapbox style
identifier, e.g. "mapbox://styles/mapbox/dark-v10". The
basemap can be disabled with rdeck(map_style = NULL).
Layers are added to an rdeck map by calling one of the
add layer methods on an rdeck instance.
e.g. rdeck() %>% add_scatterplot_layer(). The order in
which layers are added determines their z-index; each layer added to the
map will appear on top of all previously added layers.
Every layer can be created with their defaults, however layers
without data are almost useless as they will be empty. All layer
arguments (other than rdeck) must be named.
Some examples follow.
Packages
These packages are required for the following examples. With
exception of RcppSimdJson, these packages will be
frequently used in creating rdeck maps.
Loading example data
In this example, we create a simple scatterplot layer using the manhattan example data from deck.gl. This data can be used directly from the URL, however this is not typical usage of rdeck, so first we’ll load this data into a data frame.
url <- file.path(
"https://raw.githubusercontent.com/visgl/deck.gl-data/master",
"examples/scatterplot/manhattan.json",
fsep = "/"
)
manhattan_data <- fload(url) %>%
as_tibble(.name_repair = ~ c("lon", "lat", "species")) %>%
mutate(
position = sfc_point(lon, lat),
species = as.factor(species),
species_name = if_else(species == 1, "dog", "cat")
)The manhattan data we have loaded. Our scatterplot layer will make
use of the position column to define the centre of each
rendered point, and the species column to change the point
colours and sizes. lon and lat aren’t used in
any of the layer parameters, so they won’t be serialised.
#> # A tibble: 13,987 × 5
#> lon lat species position species_name
#> <dbl> <dbl> <fct> <POINT [°]> <chr>
#> 1 -74.0 40.7 2 (-73.98602 40.73074) cat
#> 2 -74.0 40.7 1 (-73.98429 40.72947) dog
#> 3 -74.0 40.7 1 (-73.98775 40.73202) dog
#> 4 -74.0 40.7 2 (-73.98689 40.73011) cat
#> 5 -74.0 40.7 2 (-73.98516 40.72883) cat
#> 6 -74.0 40.7 1 (-73.98818 40.72979) dog
#> 7 -74.0 40.7 1 (-73.98646 40.73106) dog
#> 8 -74.0 40.7 1 (-73.98386 40.7317) dog
#> 9 -74.0 40.7 1 (-73.98559 40.73042) dog
#> 10 -74.0 40.7 2 (-73.98883 40.73058) cat
#> # ℹ 13,977 more rowsScatterplot map
Here we create a simple scatterplot map with a dark vector basemap
and cividis() colour scale for the scatterplot layer.
The scatterplot points are scaled by the
species categories found in the data (which in this dataset
are dog and cat); this colour scale generates
a categorical legend.
Point density is highlighted with additive blending,
making dense areas appear brighter.
All points have the same radius of 30 metres, with a
minimum of 0.5 pixels (to prevent them from disappearing at
low zooms).
Hovered points will become brighter — by using a similar colour scale
as get_fill_color — and will render a tooltip containing
the species category from the data.
rdeck(
map_style = mapbox_dark(),
# set the bounds of the map to include all of the manhattan data
initial_bounds = st_bbox(manhattan_data$position),
# add a 2 pixel buffer to each point, making it easier to hover
picking_radius = 2
) %>%
add_scatterplot_layer(
name = "manhattan_animals",
data = manhattan_data,
# the coloumn in manhattan_data which contains the location of each point
get_position = position,
# a categorical colour scale, using the species column and a cividis colour palette
get_fill_color = scale_color_category(
col = species,
palette = cividis(2)
),
# the radius of each point (default 1 metre) is scaled by 30
radius_scale = 30,
radius_min_pixels = 0.5,
# highlight dot density
blending_mode = "additive",
# interactivity
pickable = TRUE,
auto_highlight = TRUE,
# per-species highlight colour
highlight_color = scale_color_category(
col = species,
palette = c("#0060e6", "#fff399"),
legend = FALSE
),
tooltip = c(species, species_name)
)
#> Warning in mapbox_access_token(): ! Assertion failed: length(tokens) == 0
#> ! No mapbox access token found, mapbox basemap won't be shown.
#> ℹ Set mapbox token with one of:
#> • option `options(rdeck.mapbox_access_token = <token>)`
#> • environment variable `MAPBOX_ACCESS_TOKEN = <token>`
#> • environment variable `MAPBOX_TOKEN = <token>`
#>
#> ℹ See <https://docs.mapbox.com/help/glossary/access-token>Grouping points
In the following example, we group all points by
species_name so that we can highlight all points of a given
species at once.
manhattan_data_grouped <- manhattan_data %>%
group_by(species_name) %>%
summarise(
position = st_union(position),
count = n(),
.groups = "drop"
)Our grouped data:
#> # A tibble: 2 × 3
#> species_name position count
#> <chr> <MULTIPOINT [°]> <int>
#> 1 cat ((-73.99674 40.76856), (-73.99555 40.76893), (-74.00115 40… 7554
#> 2 dog ((-73.99592 40.7739), (-73.99528 40.77207), (-73.99829 40.… 6433The following map is almost identical to the previous map, we have
replaced the data parameter, the scales to use the
species_name and the tooltip to include the
new column, count.
rdeck(
map_style = mapbox_dark(),
# set the bounds of the map to include all of the manhattan data
initial_bounds = st_bbox(manhattan_data_grouped$position),
# add a 2 pixel buffer to each point, making it easier to hover
picking_radius = 2
) %>%
add_scatterplot_layer(
name = "manhattan_animals",
data = manhattan_data_grouped,
# the coloumn in manhattan_data which contains the location of each point
get_position = position,
# a categorical colour scale, using the species column and a cividis colour palette
get_fill_color = scale_color_category(
col = species_name,
palette = cividis(2)
),
# the radius of each point (default 1 metre) is scaled by 30
radius_scale = 30,
radius_min_pixels = 0.5,
# highlight dot density
blending_mode = "additive",
# interactivity
pickable = TRUE,
auto_highlight = TRUE,
# per-species highlight colour
highlight_color = scale_color_category(
col = species_name,
palette = c("#0060e6", "#fff399"),
legend = FALSE
),
tooltip = everything()
)
#> Warning in mapbox_access_token(): ! Assertion failed: length(tokens) == 0
#> ! No mapbox access token found, mapbox basemap won't be shown.
#> ℹ Set mapbox token with one of:
#> • option `options(rdeck.mapbox_access_token = <token>)`
#> • environment variable `MAPBOX_ACCESS_TOKEN = <token>`
#> • environment variable `MAPBOX_TOKEN = <token>`
#>
#> ℹ See <https://docs.mapbox.com/help/glossary/access-token>Scaling additional parameters
We are able to scale many parameters on each layer. In the following
example, we additionally scale the radius by the count of
species_name.
rdeck(
map_style = mapbox_dark(),
# set the bounds of the map to include all of the manhattan data
initial_bounds = st_bbox(manhattan_data_grouped$position),
# add a 2 pixel buffer to each point, making it easier to hover
picking_radius = 2
) %>%
add_scatterplot_layer(
name = "manhattan_animals",
data = manhattan_data_grouped,
# the coloumn in manhattan_data which contains the location of each point
get_position = position,
# a categorical colour scale, using the species column and a cividis colour palette
get_fill_color = scale_color_category(
col = species_name,
palette = cividis(2)
),
# we only have 2 groups, so this scale is equivalent to a categorical scale with the
# same parameters
get_radius = scale_linear(
col = count,
range = sqrt(1:2)
),
# the radius of each point is scaled by 30
radius_scale = 30,
radius_min_pixels = 0.5,
# highlight dot density
blending_mode = "additive",
# interactivity
pickable = TRUE,
auto_highlight = TRUE,
# per-species highlight colour
highlight_color = scale_color_category(
col = species_name,
palette = c("#0060e6", "#fff399"),
legend = FALSE
),
tooltip = everything()
)
#> Warning in mapbox_access_token(): ! Assertion failed: length(tokens) == 0
#> ! No mapbox access token found, mapbox basemap won't be shown.
#> ℹ Set mapbox token with one of:
#> • option `options(rdeck.mapbox_access_token = <token>)`
#> • environment variable `MAPBOX_ACCESS_TOKEN = <token>`
#> • environment variable `MAPBOX_TOKEN = <token>`
#>
#> ℹ See <https://docs.mapbox.com/help/glossary/access-token>High-level layers
This example code is repetitive, copy-pasta that will inevitably result in maintenance problems, as well as increased developer effort.
Generally when creating maps, we will encapsulate the creation of one
or more layers in a function. Similar to creating functions to
encapsulate tidy-methods, the use of curly-curly is needed here for any
parameters that accept an accessor parameter.
The only rule in creating a layer function is that the function takes an rdeck map as a parameter (typically first parameter) and that map must be returned; this makes the function chainable.
In the following example, we parameterise the data, fill palette, highlight palette, and get radius (which will require curl-curly) for our original scatterplot layer.
add_manhattan_layer <- function(rdeck, manhattan_data,
fill_palette, highlight_palette, get_radius) {
rdeck %>%
add_scatterplot_layer(
name = "manhattan_animals",
data = manhattan_data,
get_position = position,
get_fill_color = scale_color_category(
col = species_name,
palette = fill_palette
),
# we need curly-curly for get_radius
get_radius = {{ get_radius }},
radius_scale = 30,
radius_min_pixels = 0.5,
blending_mode = "additive",
pickable = TRUE,
auto_highlight = TRUE,
highlight_color = scale_color_category(
col = species_name,
palette = highlight_palette,
legend = FALSE
),
tooltip = everything()
)
}Usage of our new function is like adding any other layer, just now that it is opinionated and isn’t limited to adding a single layer.
rdeck(
map_style = mapbox_dark(),
# set the bounds of the map to include all of the manhattan data
initial_bounds = st_bbox(manhattan_data$position),
# add a 2 pixel buffer to each point, making it easier to hover
picking_radius = 2
) %>%
add_manhattan_layer(
manhattan_data = manhattan_data_grouped,
fill_palette = viridis(2, alpha = 0.7),
highlight_palette = viridis(2),
get_radius = scale_category(
col = species_name,
# swap the levels order, dogs are now bigger
levels = c("cat", "dog"),
range = c(1, sqrt(3))
)
)
#> Warning in mapbox_access_token(): ! Assertion failed: length(tokens) == 0
#> ! No mapbox access token found, mapbox basemap won't be shown.
#> ℹ Set mapbox token with one of:
#> • option `options(rdeck.mapbox_access_token = <token>)`
#> • environment variable `MAPBOX_ACCESS_TOKEN = <token>`
#> • environment variable `MAPBOX_TOKEN = <token>`
#>
#> ℹ See <https://docs.mapbox.com/help/glossary/access-token>