This function retrieves and combines survey entry observations with design
information and serves as the beginning of the estimation pipeline. The next
step would be to format the data, which is the output of sight_prep_data()
and sight_prep_data_id().
Usage
sight_read_data(
species,
survey_type,
analysis_unit,
bio_year,
filter_species = TRUE,
filter_survey_type = TRUE
)
sight_read_data_id(species_id, survey_type_id, analysis_unit_id, bio_year)Arguments
- species
The name of the species to filter by.
- survey_type
A vector of survey type names to filter by.
- analysis_unit
A vector of analysis unit names to filter by.
- bio_year
the biological year the survey was conducted in, for example the winter of 2022-2023 would be 2022
- filter_species
logical, if FALSE species is only used for lookup context and not passed as a filter (default TRUE)
- filter_survey_type
logical, if FALSE survey_type is only used for lookup context and not passed as a filter (default TRUE)
- species_id
The ID of the species to filter by.
- survey_type_id
The ID of the survey type to filter by.
- analysis_unit_id
The ID of the analysis unit to filter by.
Value
A tibble combining entry observations with design information. Key columns:
- entry_id
Integer. Unique identifier for the observation.
- survey_id, design_id
Integer. Survey and design identifiers.
- species_id, species
Species identifier and name.
- survey_type_id
Integer. Survey type identifier.
- bio_year
Integer. Biological year.
- analysis_unit_id, analysis_unit
Analysis unit identifier and name.
- management_unit_id, management_unit
Management unit (GMU) identifier and name.
- subunit_id, subunit
Subunit identifier and name.
- stratum_id, stratum
Stratum identifier and name.
- is_selected, is_surveyed
Logical. Whether subunit was selected/surveyed.
- total, males, females, youngs, unclass
Integer. Count columns.
- activity
Character. Animal activity (e.g., "Moving", "Bedded"). May be NA for zero-count observations.
- vegetation_type
Character. Vegetation classification. May be NA.
- snow_percentage, screen_percentage
Integer. Covariate percentages (0-100).
- metadata
List column. Nested tibble with additional observation attributes.
- latitude, longitude
Numeric. Observation coordinates. NA for zero counts.
See also
lkup_species_id(), lkup_survey_type_id(), lkup_dau_id() for
converting names to IDs. For data formatted appropriately to fit a model
see sight_prep_data() and sight_prep_data_id() which include additional
processing.
Examples
if (FALSE) { # \dontrun{
sight_read_data(
species = "Mule Deer",
survey_type = "Sightability",
analysis_unit = "North Converse 755",
bio_year = 2024
)
sight_read_data_id(
species_id = lkup_species_id("Mule Deer"),
survey_type_id = lkup_survey_type_id(
"Sightability",
species_name = "Mule Deer"
),
analysis_unit_id = lkup_dau_id(
"North Converse 755",
species_name = "Mule Deer"
),
bio_year = 2024
)
} # }