Visualize deployment features
Damiano Oldoni
2024-10-14
Source:vignettes/visualize-deployment-features.Rmd
visualize-deployment-features.Rmd
This vignette shows how to use function map_dep()
to
visualize important deployment features on a leaflet map:
- number of identified species
- number of observations
- RAI (Relative Abundance Index)
- effort (duration of a deployment)
Setup
Load packages:
library(camtraptor)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
This load automatically a camera trap data package,
mica
, containing camera trap data about musk rat and coypu.
We will use this variable from now on.
Get taxonomic information
As some features must or could be used in combination with a species name, it’s sometimes useful to have an idea first about which species have been detected and the correspondent vernacular names if present:
get_species(mica)
#> # A tibble: 9 × 5
#> taxonID taxonIDReference scientificName vernacularNames.en vernacularNames.nl
#> <chr> <chr> <chr> <chr> <chr>
#> 1 DGP6 https://www.cata… Anas platyrhy… mallard wilde eend
#> 2 DGPL https://www.cata… Anas strepera gadwall krakeend
#> 3 32FH https://www.cata… Ardea great herons reigers
#> 4 GCHS https://www.cata… Ardea cinerea grey heron blauwe reiger
#> 5 RQPW https://www.cata… Castor fiber Eurasian beaver bever
#> 6 6MB3T https://www.cata… Homo sapiens human mens
#> 7 3Y9VW https://www.cata… Martes foina beech marten steenmarter
#> 8 44QYC https://www.cata… Mustela putor… European polecat bunzing
#> 9 5BSG3 https://www.cata… Vulpes vulpes red fox vos
Create maps
Basic usage
Number of species
You can visualize the number of species detected by each deployment
by using the function map_dep()
with feature
argument set to n_species
:
map_dep(mica,
feature = "n_species")
Number of observations
To visualize the number of observations, set feature
=
n_obs
:
map_dep(mica,
feature = "n_obs")
You can also specify which species you want to calculate the number of observations:
map_dep(mica,
feature = "n_obs",
species = "Anas platyrhynchos")
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
Notice how zero values are also visualized by a specific icon for
ease detection. By default a black multiplication symbol
×
.
You can filter by sex:
map_dep(
mica,
"n_obs",
species = "Anas platyrhynchos",
sex = c("female", "unknown")
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
and life stage:
Number of individuals
To visualize the number of observed individuals, set
feature
= n_individuals
:
map_dep(mica,
feature = "n_individuals")
As for observations, you can specify a species:
map_dep(mica,
feature = "n_individuals",
species = "Anas platyrhynchos")
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
and filter by sex and/or life stage:
RAI
To visualize the Relative Abundance Index (RAI) for a species, set
feature
= rai
and specify a species using its
scientific name:
map_dep(mica,
feature = "rai",
species = "Anas platyrhynchos")
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
Notice that in this package the RAI is normalized over a deployment activity period of 100 days.
As for number of observations and number of individuals, you can filter by sex and/or life stage:
map_dep(
mica,
"rai",
species = "Anas platyrhynchos",
sex = c("female", "unknown")
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
Common names are allowed as values of species
as
well:
map_dep(mica,
feature = "rai",
species = "great herons")
#> Scientific name of great herons: Ardea
#> There are 3 deployments without observations: 29b7d356-4bb4-4ec4-b792-2af5cc32efa8, 577b543a-2cf1-4b23-b6d2-cda7e2eac372 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
Values of species
are also interpreted case
insensitive:
map_dep(mica,
feature = "rai",
species = "CastoR FIBer")
#> There are 3 deployments without observations: 29b7d356-4bb4-4ec4-b792-2af5cc32efa8, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
If species
is not specified or is wrong, an informative
error message listing all valid values is returned:
map_dep(mica,
feature = "rai",
species = "This is not a species name")
#> Error: Invalid value for species parameter: this is not a species name.
#> Valid inputs are: anas platyrhynchos, anas strepera, ardea, ardea cinerea, castor fiber, homo sapiens, martes foina, mustela putorius, vulpes vulpes, mallard, gadwall, great herons, grey heron, eurasian beaver, human, beech marten, european polecat, red fox, wilde eend, krakeend and others...
RAI (individuals)
You can also visualize the RAI based on number of detected
individuals instead of the standard RAI which is based on the number of
observations. Set feature = "rai_individuals"
:
map_dep(mica,
feature = "rai_individuals",
species = "Anas platyrhynchos")
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
Everything described in previous section about visualizing RAI holds true for RAI based on individuals as well.
Effort
Visualize duration of the deployments, also called effort, as number of active hours:
map_dep(mica,
feature = "effort",
effort_unit = "hour")
The same using days as time unit
map_dep(mica,
feature = "effort",
effort_unit = "day")
or months
map_dep(mica,
feature = "effort",
effort_unit = "month")
Clustering and hovering
You can specify which information you want to show while hovering
with the mouse over the deployment. You can choose among all columns
from deployments (see allowed fields in camera trap
data package standard documentation) and n
(number of
species, number of observations or RAI).
Here below the lat/lon, the camera height and the tags are shown while hovering:
map_dep(mica,
hover_columns = c("latitude",
"longitude",
"cameraHeight",
"tags"),
feature = "n_obs"
)
Deactivating both cluster mode and hovering is also possible:
map_dep(mica,
feature = "n_species",
cluster = FALSE,
hover_columns = NULL)
Visualize deployments without detected animals
It can happen that some deployments didn’t detected any recognizable
animal (scientificName
= NA
) or didn’t observe
anything at all (deployments with no observations). While visualizing
the number of species, these two situations are shown by default as red
and black multiplication symbols ×
respectively. A message
about deployment with zero observations is also returned to the R
console:
# create data package with one deployment with 0 obs and one delpoyment with
# observations of unknown species
unknown_species_vs_no_obs <- mica
unknown_species_vs_no_obs$data$observations <-
unknown_species_vs_no_obs$data$observations %>%
# a deployment has detected only unknown species
filter(is.na(.data$scientificName) |
.data$scientificName != "Homo sapiens") %>%
# a deployment has no observations
filter(deploymentID != "62c200a9-0e03-4495-bcd8-032944f6f5a1")
# create new map
map_dep(unknown_species_vs_no_obs,
feature = "n_species")
#> There are 1 deployments without observations: 62c200a9-0e03-4495-bcd8-032944f6f5a1
Use a color palette
The default color palette is a viridis
color palette called "inferno"
. You can specify another
viridis color palette, e.g. "viridis"
or
"magma"
, or a RColorBrewer
palette, e.g. "BuPu"
or "Oranges"
. Below we
use the viridis
color palette:
map_dep(
mica,
"n_obs",
palette = "viridis"
)
We can use a palette from RColorBrewer
, e.g. the
"BuPu"
palette:
map_dep(
mica,
"n_obs",
palette = "BuPu"
)
Another easy way to specify a palette is to create it by passing a
vector of colors as names or hex colors,
e.g. c("black", "blue", "#A3675F")
:
Use a specific icon and color for zero values
You can pass to zero_value_icon_url
argument the URL to
an icon and to zero_values_icon_size
the size in pixels of
such icon. There are several icon libraries you can choose from. A
library with many free icons is icons8. Here, an example with the icon of
Fry from Futurama animation series and icon size 50:
map_dep(
mica,
"n_obs",
life_stage = "subadult",
zero_values_icon_url = "https://img.icons8.com/color/48/000000/futurama-fry.png",
zero_values_icon_size = 50
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
Typically the colour is part of the URL. Here below two examples where we change the color of the default icon to green (2ECC71):
map_dep(
mica,
"n_obs",
life_stage = "subadult",
zero_values_icon_url =
"https://img.icons8.com/ios-glyphs/30/2ECC71/multiply.png"
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
or the INBO fuchsia (#C04384):
map_dep(
mica,
"n_obs",
life_stage = "subadult",
zero_values_icon_url =
"https://img.icons8.com/ios-glyphs/30/C04384/multiply.png"
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7
Modifying the default value ("black"
) can be useful as
the color of deployments with zero values can be sometimes too similar
to one of the colors used in the palette.
Modify circle size
You can also modify the upper and lower limit of the circle sizes by
specifying radius_range
(default:
c(10,50
):
Use absolute scale
By default the upper limit of color palette and radius are defined
based on the actual feature values. However, sometimes can be useful to
set up an absolute upper limit. This can be done by setting argument
relative_scale
to FALSE
and specifying the
upper limit in max_scale
.
Upper limit lower than number of observations:
map_dep(mica,
feature = "n_obs",
relative_scale = FALSE,
max_scale = 2)
Upper limit higher than number of observations:
map_dep(mica,
feature = "n_obs",
relative_scale = FALSE,
max_scale = 50)
Use filter predicates
You maybe would like to visualize deployment information for a subset of deployments. To do this, you can use filter predicates. E.g. visualize number of observations for the deployments with longitude equal or higher than 5.6:
map_dep(mica,
pred_gt("longitude", 5.6),
feature = "n_obs")
#> df %>% dplyr::filter((longitude > 5.6))
More about filter predicates in filter predicates article.