This function is deprecated. Use map_summary() instead.
Usage
map_dep(
x,
feature,
species = NULL,
sex = NULL,
life_stage = NULL,
effort_unit = NULL,
cluster = TRUE,
hover_columns = c("n", "species", "deploymentID", "locationID", "locationName",
"latitude", "longitude", "start", "end"),
palette = "inferno",
zero_values_show = TRUE,
zero_values_icon_url = "https://img.icons8.com/ios-glyphs/30/000000/multiply.png",
zero_values_icon_size = 10,
na_values_show = TRUE,
na_values_icon_url = "https://img.icons8.com/ios-glyphs/30/FA5252/multiply.png",
na_values_icon_size = 10,
relative_scale = TRUE,
max_scale = NULL,
radius_range = c(10, 50)
)Arguments
- x
Camera trap data package object, as returned by
camtrapdp::read_camtrapdp().- feature
Feature to visualize. One of the columns generated by
summarize_deployments()orsummarize_observations()not used for grouping. Possible values present in output ofsummarize_observations()are:n_scientificName: Number of detected species.n_events: Number of events observed.n_observations: Number of observations.sum_count: Number of individuals observed.rai_observations: RAI calculated using the number of observations (n_observations).rai_count: RAI calculated using the number of observed individuals (sum_count).
Possible values present in output of
summarize_deployments()are:effort_duration: Deployment effort duration.
- species
Character with scientific names. Common names are not supported anymore as of camtraptor 1.0.0. Please, check
filter_observations()to know how to filter byscientificName. If"all"(default) all scientific names are automatically selected. IfNULLall observations of all species are taken into account.- sex
Character defining the sex class to filter on, e.g.
"female"orc("male", "unknown"). IfNULL(default) all observations of all sex classes are taken into account. Please, checkfilter_observations()to know how to filter bysex.- life_stage
Character vector defining the life stage class to filter on, e.g.
"adult"orc("subadult", "adult"). IfNULL(default) all observations of all life stage classes are taken into account. Please, checkfilter_observations()to know how to filter bylifeStage.- effort_unit
Time unit to use while visualizing deployment effort duration. Ignored if
featureis not"effort_duration". One of:secondminutehourdaymonthyear
If
NULL(default), the effort is returned in hours.- cluster
Logical value indicating whether using the cluster option while visualizing maps. Default:
TRUE.- hover_columns
Character vector of the columns of
dfto show on mouse hover. UseNULLto disable hovering. Default: all columns indfused for grouping and thefeatureto visualize.- palette
The palette name or the color function that values will be mapped to. Default:
"inferno". Typically one of the following:A character vector of RGB or named colors. Examples:
c("#000000", "#0000FF", "#FFFFFF")),topo.colors(10)).The full name of a RColorBrewer palette, e.g. "BuPu" or "Greens", or viridis palette:
"viridis","magma","inferno"or"plasma".
For more options, see parameter
paletteofleaflet::colorNumeric().- zero_values_show
Logical indicating whether to show deployments with zero values. Default:
TRUE. See details.- zero_values_icon_url
Character with URL to icon for showing deployments with zero values. Use
NULLifzero_values_showisFALSE. Default: a cross (multiply symbol)"https://img.icons8.com/ios-glyphs/30/000000/multiply.png". See details.- zero_values_icon_size
A number to set the size of the icon to show groups (e.g. deployments) with zero values. Use
NULLifzero_values_showisFALSE. Default: a cross (multiply symbol)"https://img.icons8.com/ios-glyphs/30/000000/multiply.png". See details.- na_values_show
Logical indicating whether to show groups (e.g. deployments) with no observations when feature is
"n_scientificName". Default:TRUE. See details.- na_values_icon_url
Character with URL to icon for showing groups (e.g. deployments) with no observations. Used only if
featureis"n_scientificName", ignored otherwise. UseNULLifna_values_showisFALSE. Default: a red cross (multiply symbol)"https://img.icons8.com/ios-glyphs/30/FA5252/multiply.png".- na_values_icon_size
A number to set the size of the icon for showing groups (e.g. deployments) with no observations. Used only if
featureis"n_scientificName", ignored otherwise. UseNULLifna_values_showisFALSE. Default: 10.- relative_scale
Logical indicating whether to use a relative color and radius scale (
TRUE) or an absolute scale (FALSE). If absolute scale is used, specify a validmax_scale.- max_scale
Number indicating the max value used to map color and radius.
- radius_range
Vector of length 2 containing the lower and upper limit of the circle radius. The lower value is used for deployments with zero feature value, e.g. no observations, no identified species, zero RAI or zero effort. The upper value is used for the deployment(s) with the highest feature value (
relative_scale=TRUE) ormax_scale(relative_scale=FALSE). Default:c(10, 50).
Details
Deployments with zero values are shown only if they are present in the
summary, df. If you want to show deployments with zero values not present
in the summary, be sure to have used extend = TRUE in
summarize_observations(). See the examples.
Notice also that the argument species is not present anymore. If you want
to visualize a feature only for a specific group of species, please filter
the Camera Trap Data Package using filter_observations() before using this
function. See the examples.
Examples
x <- example_dataset()
# Filter a data package to get only the data of Anas platyrhynchos
x_anas_p <- x %>%
filter_observations(scientificName == "Anas platyrhynchos")
# Show number of species
x %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude")
) %>%
map_summary("n_scientificName")
# Show number of species: show deployments with no
# observations (NA icon and size as defaults)
x_anas_p %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude"),
extend = TRUE
) %>%
map_summary("n_scientificName")
# Show total number of observations (all species)
x %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude")
) %>%
map_summary("n_observations")
# Show number of observations of Anas platyrhynchos - show deployments with
# no observations (zero icon and size as defaults)
x_anas_p %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude"),
extend = TRUE
) %>%
map_summary("n_observations")
# Show number of observations of Anas platyrhynchos without extending the
# summary: deployments with no observations are not shown
x_anas_p %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude")
) %>%
map_summary("n_observations")
# Same result as above if you extend the summary but use
# `zero_values_show` = `FALSE`
x_anas_p %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude"),
extend = TRUE
) %>%
map_summary(
"n_observations",
zero_values_show = FALSE,
zero_values_icon_url = NULL,
zero_values_icon_size = NULL
)
# You can eventually filter the species after summarizing,
# although it could not be the most efficient way for large Camera Trap Data
# Packages
library(dplyr)
x %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude", "scientificName")
) %>%
filter(scientificName == "Anas platyrhynchos") %>%
map_summary("n_scientificName")
# Show number of individuals
x_anas_p %>%
summarize_observations() %>%
map_summary("sum_count")
# Show RAI
x_anas_p %>%
summarize_observations() %>%
map_summary("rai_observations")
# Show RAI (individual counts)
x_anas_p %>%
summarize_observations() %>%
map_summary("rai_count")
# Show effort (hours)
x_anas_p %>%
summarize_deployments() %>%
map_summary("effort_duration")
# Show effort (days)
x %>%
summarize_deployments() %>%
map_summary("effort_duration", effort_unit = "day")
# Show effort per month
x %>%
summarize_deployments(group_time_by = "month") %>%
map_summary("effort_duration", effort_unit = "day")
# Use viridis palette
x_anas_p %>%
summarize_observations() %>%
map_summary("n_observations", palette = "viridis")
# Use a palette defined by color names
x_anas_p %>%
summarize_observations() %>%
map_summary("n_observations", palette = c("black", "blue", "white"))
# Use a palette defined by hex colors
x_anas_p %>%
summarize_observations() %>%
map_summary("n_observations", palette = c("#000000", "#0000FF", "#FFFFFF"))
# Use same icon but a non default color for zero values deployments,
# E.g. red (hex: E74C3C)
x_anas_p %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude"),
extend = TRUE
) %>%
map_summary(
"n_observations",
zero_values_icon_url = "https://img.icons8.com/ios-glyphs/30/E74C3C/multiply.png"
)
# Use another icon via a different URL, e.g. the character Fry from Futurama
# in green (2ECC71)
x_anas_p %>%
summarize_observations(extend = TRUE) %>%
map_summary(
"n_observations",
zero_values_icon_url = "https://img.icons8.com/ios-glyphs/30/2ECC71/futurama-fry.png"
)
# Set size of the icon for zero values deployments
x %>%
filter_observations(scientificName == "Anas platyrhynchos") %>%
summarize_observations(extend = TRUE) %>%
map_summary("n_observations", zero_values_icon_size = 30)
# Use another icon url/size for visualizing groups with no observations
# (NA, only for `n_scientificName` feature)
x %>%
filter_observations(deploymentID != "00a2c20d") %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude")
) %>%
map_summary(
feature = "n_scientificName",
na_values_icon_url = "https://img.icons8.com/ios-glyphs/30/E74C3C/futurama-fry.png",
na_values_icon_size = 60
)
# Disable cluster
x %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude")
) %>%
map_summary("n_observations", cluster = FALSE)
# Show only number of observations and location name while hovering
x %>%
summarize_observations(
group_by = c("deploymentID", "locationName", "latitude", "longitude")
) %>%
map_summary(
"n_observations",
hover_columns = c("locationName", "n_observations")
)
# Use absolute scale for colors and radius
x %>%
summarize_observations(
group_by = c("deploymentID", "latitude", "longitude")
) %>%
map_summary(
"n_scientificName",
relative_scale = FALSE,
max_scale = 4
)
# Change max and min size circles
x %>%
summarize_observations(
group_by = c("deploymentID", "locationName", "latitude", "longitude")
) %>%
map_summary(
"n_observations",
radius_range = c(40, 150)
)
