16 Read and use

library(frictionless)
library(GeoLocatoR)
library(tidyverse)
library(ggplot2)
library(leaflet)

16.1 Read Geolocator Data Package

A cool feature of Zenodo is that you can load the data directly without downloading the files!

pkg <- read_gldp("https://zenodo.org/records/14641765/files/datapackage.json")

16.2 Analyse data with GeoPressureTemplate

If this data package has not yet been analysed with GeoPressureR, you can create a GeoPressureTemplate folder from the data package using the following code. Note that this code will open a new RStudio session.

project_dir <- create_geopressuretemplate(path = tempfile(), pkg = pkg)

✔ Cloning repo from <https://github.com/Rafnuss/GeoPressureTemplate/> into
  '/private/var/folders/cm/dh8lmmxn3b5bcb2_gbz16ycm0000gn/T/RtmplwGeJd/file655c61b38b7d/'.

✔ Setting active project to
  "/private/var/folders/cm/dh8lmmxn3b5bcb2_gbz16ycm0000gn/T/RtmplwGeJd/file655c61b38b7d".

✔ Adding "CC BY 4.0" to 'License'.

✔ Writing 'LICENSE.md'.

⠙ 1/5 ETA: 22s |

⠹ 2/5 ETA: 13s |

⠸ 3/5 ETA:  8s |

⠼ 4/5 ETA:  4s |

Note

At this stage, you’ll need to go through all the steps to make yourself at home.

You’re now ready to analyse your data according to the GeoPressureTemplate instructions!

16.3 Use analysed data

If the datapackage you want to use has already been analysed, you can retrieve all the resources of the datapackage:

# For instance, display the edges
edges(pkg) %>%
  head() %>%
  knitr::kable(digits = 1)

tag_id	stap_s	stap_t	lat_s	lon_s	lat_t	lon_t	s	t	j	start	end	n	distance	bearing	gs_u	gs_v	ws_u	ws_v
16LN	1	2	-22.7	28.8	-20.1	26.6	8503	21109	1	2017-04-04 19:42:30	2017-04-05 03:52:30	1	363.6	-38.1	-26.9	31.4	-28.0	-3.5
16LN	2	3	-20.1	26.6	-18.9	26.4	21109	34936	1	2017-04-05 17:37:30	2017-04-05 21:12:30	1	140.8	-10.8	-6.0	31.7	-13.8	-1.0
16LN	3	4	-18.9	26.4	-18.1	27.1	34936	49373	1	2017-04-06 01:02:30	2017-04-06 03:42:30	1	114.7	43.8	23.0	24.0	-2.3	-0.1
16LN	4	5	-18.1	27.1	-17.1	26.6	49373	63049	1	2017-04-06 23:52:30	2017-04-07 03:07:30	1	122.7	-25.7	-12.9	26.9	-16.9	-9.8
16LN	5	6	-17.1	26.6	-17.1	27.4	63049	77489	1	2017-04-07 18:17:30	2017-04-07 21:12:30	1	79.8	90.1	18.2	0.0	-9.5	-3.5
16LN	6	7	-17.1	27.4	-15.6	25.6	77489	90403	1	2017-04-08 17:37:30	2017-04-08 22:17:30	1	250.0	-48.6	-36.0	31.7	-23.2	3.4

Here is an example showing all the tracks

Code

bird_data <- paths(pkg) %>%
  filter(type == "most_likely")

# Generate a color palette for unique tag IDs
tag_ids <- unique(bird_data$tag_id)
color_palette <- colorFactor(palette = "Set1", domain = tag_ids)

leaflet_map <- leaflet(height = 600) %>% addTiles()

# Add polylines and markers for each tag_id
for (tag in tag_ids) {
  bird_subset <- bird_data %>% filter(tag_id == tag)
  leaflet_map <- leaflet_map %>%
    addPolylines(
      lng = ~lon,
      lat = ~lat,
      data = bird_subset,
      color = color_palette(tag),
      weight = 2,
      popup = ~ paste0("Tag ID: ", tag_id, "<br>Step: ", stap_id)
    ) %>%
    addCircleMarkers(
      lng = ~lon,
      lat = ~lat,
      data = bird_subset,
      color = color_palette(tag),
      radius = 4,
      fillOpacity = 0.8,
      popup = ~ paste0("Tag ID: ", tag_id, "<br>Step: ", stap_id)
    )
}

# Add a legend
leaflet_map <- leaflet_map %>%
  addLegend(
    position = "topright",
    pal = color_palette,
    values = tag_ids,
    title = "Bird Trajectories",
    opacity = 1
  )

# Display the map
leaflet_map