spdgt.sight provides R wrappers for SpeedGoat’s count data and sightability analysis APIs. It handles authentication, request building, and response parsing so you can work with aerial survey data as tibbles.
Installation
Install from the SpeedGoat Nexus repository:
install.packages(
"spdgt.sight",
repos = c(SPDGT = "https://nexus.spdgt.com/repository/public-group/")
)Quick start
library(spdgt.sight)
# Authenticate (opens browser)
spdgt.auth::auth_login()
# Discover available surveys
sight_surveys(species = "Mule Deer")
# Pull observation + design data for a sightability analysis
dat <- sight_data(
species = "Mule Deer",
survey_type = "Sightability",
analysis_unit = "North Converse 755",
bio_year = 2024
)
# Fit a sightability model
sight_fit(
species = "Mule Deer",
spatial_focus = "DAU",
bio_year = 2024,
analysis_unit = "North Converse 755",
survey_type = "Sightability",
model = "Mule Deer"
)
# Optimize sample allocation for next year's survey
sight_optimize(
method = "fixed",
value = 100,
species = "Mule Deer",
survey_type = "Sightability",
analysis_unit = "North Converse 755",
bio_year = 2024
)Every function has a name-based variant (e.g., sight_data()) and an ID-based variant (e.g., sight_data_id()). Use names for interactive work and IDs for programmatic pipelines. See vignette("getting-started") for details.
Lookup helpers
Re-exported from spdgt.core for convenience:
lkup_species() # all species
lkup_species_id("Elk") # name -> ID
lkup_dau_opts("Mule Deer") # named vector for Shiny selectInputVignettes
-
vignette("getting-started")– dual-function pattern, lookups, hierarchical relationships -
vignette("reading-survey-data")– pulling entries, designs, and survey summaries -
vignette("model-fitting")– fitting sightability models and interpreting results -
vignette("survey-design")– optimization and GRTS sampling