| Type: | Package |
| Title: | Collection of Data on Wildlife Sightings, Tourism Counts, and Weather from Australia |
| Version: | 0.1.0 |
| Description: | This is a collection of data files for exploring sightings of wild things, relative to weather and tourism patterns in Australia. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| LazyDataCompression: | xz |
| Suggests: | dplyr, ggplot2, ggthemes, ggbeeswarm, sf, knitr, lubridate, quarto, testthat (≥ 3.0.0), tidyr |
| VignetteBuilder: | quarto |
| SystemRequirements: | Quarto |
| Depends: | R (≥ 3.5) |
| URL: | https://github.com/vahdatjavad/ecotourism |
| BugReports: | https://github.com/vahdatjavad/ecotourism/issues |
| RoxygenNote: | 7.3.2 |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2025-09-11 06:01:31 UTC; jvah0003 |
| Author: | Dianne Cook  [aut],
Lyn Cook [aut],
Javad Vahdat Atashgah
[aut, cre] |
| Maintainer: | Javad Vahdat Atashgah <vahdatjavad@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-09-16 06:00:11 UTC |
Glowworms Occurrence Data (2014–2024)
Description
This dataset contains cleaned and enriched occurrence records for glowworms (*Arachnocampa tasmaniensis*) in Australia from 2014 to 2024. It includes spatial, temporal, taxonomic, and weather station metadata.
Usage
glowworms
Format
A tibble with 124 rows and 14 variables:
- obs_lat
Latitude of the observation (decimal degrees)
- obs_lon
Longitude of the observation (decimal degrees)
- date
Observation date (YYYY-MM-DD)
- time
Observation time (HH:MM:SS, character)
- year
Observation year
- month
Month of the observation
- day
Day of the month
- hour
Hour of the day (0–23)
- weekday
Day of the week (ordered factor)
- dayofyear
Day of the year (1–366)
- sci_name
Scientific name of the observed species
- record_type
Type of observation (e.g., HUMAN_OBSERVATION)
- obs_state
Australian state where the observation occurred
- ws_id
ID of the nearest weather station (e.g., "949610-99999")
Details
Data was sourced via the 'galah' package from the Atlas of Living Australia, filtered and cleaned, then enriched by linking each record to the nearest weather station using geospatial methods.
Source
Atlas of Living Australia via galah
Examples
data(glowworms)
head(glowworms)
Gouldian Finch Occurrence Data (2014–2024)
Description
This dataset contains cleaned and processed occurrence records for the Gouldian Finch (*Chloebia gouldiae*) in Australia between 2014 and 2024. It includes spatial coordinates, temporal details, species information, and the ID of the nearest weather station for each observation.
Usage
gouldian_finch
Format
A tibble with 3,921 rows and 14 variables:
- obs_lat
Latitude of the observation (decimal degrees)
- obs_lon
Longitude of the observation (decimal degrees)
- date
Date of the observation (YYYY-MM-DD)
- time
Time of the observation (HH:MM:SS)
- year
Year of the observation
- month
Month (1–12)
- day
Day of the month
- hour
Hour extracted from the time (0–23)
- weekday
Day of the week (as ordered factor)
- dayofyear
Day of the year (1–366)
- sci_name
Scientific name of the species
- record_type
Type of observation (e.g., HUMAN_OBSERVATION)
- obs_state
Australian state where the observation was recorded
- ws_id
Nearest weather station ID (e.g., "948280-99999")
Details
The data was retrieved from the Atlas of Living Australia using the galah package, then standardized, cleaned, and matched to the three closest weather stations using geospatial tools.
Source
Atlas of Living Australia via galah
See Also
Examples
data(gouldian_finch)
head(gouldian_finch)
Manta Ray Occurrence Data (2014–2024)
Description
This dataset contains occurrence records for the reef manta ray (*Mobula alfredi*) observed in Australian waters from 2014 to 2024. The data includes spatial and temporal metadata, species identifiers, and linked weather station IDs.
Usage
manta_rays
Format
A tibble with 1,088 rows and 14 variables:
- obs_lat
Latitude of the observation (decimal degrees)
- obs_lon
Longitude of the observation (decimal degrees)
- date
Date of the observation (YYYY-MM-DD)
- time
Time of the observation (HH:MM:SS)
- year
Year of the observation
- month
Month (1–12)
- day
Day of the month
- hour
Hour extracted from the time (0–23)
- weekday
Day of the week (as ordered factor)
- dayofyear
Day of the year (1–366)
- sci_name
Scientific name — all observations are Mobula alfredi
- record_type
Type of observation (e.g., MACHINE_OBSERVATION)
- obs_state
Australian state where the observation occurred (may be missing)
- ws_id
Nearest weather station ID (e.g., "947800-99999")
Details
Records were accessed using the galah package and filtered specifically for *Mobula alfredi*. Data has been cleaned and enriched with spatial proximity to weather stations for climate-related analysis.
Source
Atlas of Living Australia via galah
See Also
Examples
data(manta_rays)
head(manta_rays)
Orchid Occurrence Data (2014–2024)
Description
This dataset contains over 300,000 occurrence records of orchid species (*Orchidaceae*) in Australia from 2014 to 2024. The data includes spatial, temporal, and taxonomic details, as well as associated weather station metadata for ecological analysis.
Usage
orchids
Format
A tibble with 302,123 rows and 14 variables:
- obs_lat
Latitude of the observation (decimal degrees)
- obs_lon
Longitude of the observation (decimal degrees)
- date
Date of the observation (YYYY-MM-DD)
- time
Time of the observation (HH:MM:SS)
- year
Year of the observation
- month
Month (1–12)
- day
Day of the month
- hour
Hour extracted from the time (0–23)
- weekday
Day of the week (as ordered factor)
- dayofyear
Day of the year (1–366)
- sci_name
Scientific name of the observed orchid species
- record_type
Type of observation (e.g., HUMAN_OBSERVATION, PRESERVED_SPECIMEN)
- obs_state
Australian state where the observation occurred (may be missing)
- ws_id
Nearest weather station ID linked to the observation
Details
The data was collected using the galah package from the Atlas of Living Australia, cleaned, and linked to nearby weather stations for ecological and climatic studies. The records span multiple orchid genera and include a range of observation types.
Source
Atlas of Living Australia via galah
See Also
glowworms, gouldian_finch, weather
Examples
data(orchids)
head(orchids)
oz_lga
Description
LGA polygons for Australia
Usage
oz_lga
Format
A spatial polygon object
Examples
head(oz_lga)
Top Weather Stations for Each Organism
Description
A lookup table identifying the top 3 most frequently linked weather stations for each focal organism in the ecotourism package. These stations were selected based on the number of linked observations across a 10-year period (2014–2024).
Usage
top_stations
Format
A tibble with 12 rows and 2 variables:
- organism
Name of the organism (e.g., "glowworms", "orchids")
- ws_id
Weather station ID (e.g., "948720-99999")
Details
This table was created by counting the frequency of 'ws_id' assignments within each organism dataset and selecting the top 3 stations per organism. These top stations are used for downloading daily weather data via the GSODR package.
See Also
Examples
data(top_stations)
head(top_stations)
Quarterly Tourism Trips by Region and Purpose
Description
A dataset containing quarterly estimates of overnight tourism trips in Australia, broken down by trip purpose and tourism region.
Usage
tourism_quarterly
Format
A data frame with 'r nrow(tourism_quarterly)' rows and 4 variables:
* **year**: The year of the tourism data
* **quarter**: Quarter number like 1, 2, 3, 4
* **purpose**: Purpose of visit category:
- '"Holiday"'
- '"Business"'
* **trips**: Number of overnight trips (in thousands).
* **region_id**: Unique integer identifier linking to the
tourism_region dataset.
* **ws_id**: Identifier of the nearest Bureau of Meteorology weather station
to the tourism region.
Details
Tourism regions are formed through the aggregation of Statistical
Local Areas (SLAs) or similar ABS-defined geographies, as determined
by state and territory tourism authorities. This dataset is designed
for analysis of seasonal tourism patterns and can be joined to
tourism_region for spatial analysis.
References
Tourism Research Australia: https://www.tra.gov.au
Examples
data(tourism_quarterly)
head(tourism_quarterly)
Tourism Regions and Nearest Weather Stations
Description
A dataset containing the locations of Australian tourism regions, their geographic coordinates, and the nearest Bureau of Meteorology weather station. Each region is assigned a unique identifier for linking to other tourism datasets.
Usage
tourism_region
Format
A data frame with 'r nrow(tourism_region)' rows and 5 variables: * **region**: Name of the tourism region. Tourism regions are defined by Tourism Research Australia and generally formed through the aggregation of Statistical Local Areas (SLAs) or other ABS-defined geographies. * **lon**: Longitude of the tourism region representative point (WGS84). * **lat**: Latitude of the tourism region representative point (WGS84). * **region_id**: Unique integer identifier for the tourism region. Useful for joining with other tourism-related datasets. * **ws_id**: Identifier of the nearest Bureau of Meteorology weather station to the tourism region.
Details
Coordinates for each tourism region are intended to represent a central location within the region (e.g., polygon centroid). The nearest weather station is determined using great-circle distance calculations based on the Bureau of Meteorology's official station list.
References
Tourism Research Australia: https://www.tra.gov.au Australian Bureau of Meteorology: http://www.bom.gov.au
Examples
data(tourism_region)
head(tourism_region)
Daily Weather Data for Top Stations (2014–2024)
Description
This dataset contains daily weather observations for the top weather stations associated with focal species in the ecotourism package. Data spans from 2014 to 2024 and includes temperature, humidity, precipitation, and wind measures.
Usage
weather
Format
A tibble with 35,527 rows and 18 variables:
- ws_id
Weather station ID (e.g., "948720-99999")
- stn_lat
Latitude of the weather station
- stn_lon
Longitude of the weather station
- date
Observation date (YYYY-MM-DD)
- year
Year of observation
- month
Month of observation (1–12)
- day
Day of the month
- weekday
Day of the week (as ordered factor)
- dayofyear
Day of the year (1–366)
- temp
Average temperature (°C)
- min
Minimum temperature (°C)
- max
Maximum temperature (°C)
- dewp
Dew point temperature (°C)
- rh
Relative humidity (%)
- prcp
Precipitation (mm)
- rainy
Binary flag indicating whether PRCP > 5 mm (1 = rainy day)
- wind_speed
Average wind speed (m/s)
- max_speed
Maximum sustained wind speed (m/s)
Details
The weather data was retrieved from the Global Surface Summary of the Day (GSOD) dataset via the GSODR package for the top 3 weather stations per organism in the ecotourism project. This data supports climate-biodiversity interaction analyses.
Source
GSOD via GSODR
See Also
top_stations, glowworms, gouldian_finch, weather_stations
Examples
data(weather)
head(weather)
Australian Weather Station Metadata
Description
This dataset contains metadata for 732 weather stations across Australia, including coordinates, station names, and geocoded location details.
Usage
weather_stations
Format
A tibble with 732 rows and 7 variables:
- ws_id
Weather station ID (e.g., "941000-99999")
- stname
Station name (e.g., "KALUMBURU")
- stn_lat
Latitude of the station (decimal degrees)
- stn_lon
Longitude of the station (decimal degrees)
- address
Full geocoded address (from reverse geocoding)
- stn_city
Parsed city or locality name
- stn_state
Australian state or territory
Details
This data was derived from the GSOD inventory using the GSODR package, filtered for Australian stations, and geocoded using OpenStreetMap APIs. It is used to match ecological observations with relevant local weather conditions.
Source
GSOD inventory via GSODR; geocoded with OpenStreetMap
See Also
weather, top_stations, gouldian_finch
Examples
data(weather_stations)
head(weather_stations)