This vignette demonstrates the use of two datasets: top_stations and weather. After selecting an organism of interest, we linked each occurrence to its nearest weather station. We then counted the matches, identified the top three weather stations most closely associated with the organism’s occurrences, and downloaded the corresponding weather data for further analysis.
This is the glimpse of your top_stations data :
library(dplyr)
library(ecotourism)
data("top_stations")
top_stations |> glimpse()Rows: 12
Columns: 2
$ organism <chr> "orchids", "orchids", "orchids", "gouldian_finch", "gouldian_…
$ ws_id <chr> "946300-99999", "956470-99999", "956410-99999", "941310-99999…and this is weather data related to those top stations:
data("weather")
weather |> glimpse()Rows: 46,390
Columns: 18
$ ws_id <chr> "941200-99999", "941200-99999", "941200-99999", "941200-999…
$ stn_lat <dbl> -12.415, -12.415, -12.415, -12.415, -12.415, -12.415, -12.4…
$ stn_lon <dbl> 130.877, 130.877, 130.877, 130.877, 130.877, 130.877, 130.8…
$ date <date> 2014-01-01, 2014-01-02, 2014-01-03, 2014-01-04, 2014-01-05…
$ year <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014,…
$ month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ day <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
$ weekday <ord> Wednesday, Thursday, Friday, Saturday, Sunday, Monday, Tues…
$ dayofyear <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
$ temp <dbl> 30.06667, 30.50764, 30.49792, 29.54028, 28.89375, 30.40556,…
$ min <dbl> 27.10000, 28.00000, 27.36667, 25.50000, 25.01667, 26.66667,…
$ max <dbl> 33.00000, 32.93333, 33.50000, 33.00000, 33.86667, 35.43333,…
$ dewp <dbl> 25.50694, 24.81736, 24.61736, 22.85694, 23.83889, 24.06333,…
$ rh <dbl> 77.24566, 72.17120, 71.94775, 67.80264, 75.49793, 70.78035,…
$ prcp <dbl> 0.0, 0.0, 0.0, 0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.2, 4.0, 14.0…
$ rainy <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,…
$ wind_speed <dbl> 4.333333, 4.307639, 4.332639, 4.727431, 4.469792, 3.678403,…
$ max_speed <dbl> 6.866667, 5.866667, 7.700000, 7.700000, 7.733333, 5.700000,…Map of Top Weather Stations
library(ggplot2)
library(ggthemes)
top_stations |> left_join(weather_stations) |>
ggplot() +
geom_sf(data = oz_sa2) +
geom_point(aes(x = stn_lon, y = stn_lat, color = organism), shape = 17, size = 3) +
theme_map()