Pipe operator

Description

Re-exports the pipe operator %>% from 'magrittr', allowing you to chain commands together in a readable left-to-right style.

Arguments

Value

The result of evaluating the right-hand side ('rhs') expression, where the left-hand side ('lhs') value is passed as the first argument.

Plot Fix Rate Timelines for Each Individual

Description

Creates a timeline plot showing fix events as horizontal tick marks for each individual. This allows a quick visual assessment of tracking effort, fix density, and coverage gaps.

Usage

ez_fix_rate_plot(
  data,
  date_breaks = NULL,
  date_format = NULL,
  start_date = NULL,
  end_date = NULL
)

Arguments

Value

A 'ggplot' object showing fix timelines per individual.

Examples

df <- data.frame(
  id = rep(c("a", "b"), each = 3),
  timestamp = as.POSIXct(c("2025-01-01", "2025-01-02", "2025-01-03",
                           "2025-01-01", "2025-01-05", "2025-01-06"))
)
ez_fix_rate_plot(df)

Estimate Home Ranges for Tracked Individuals or Population

Description

Computes home range polygons using either Minimum Convex Polygon (MCP) or Kernel Density Estimation (KDE).

Usage

ez_home_range(
  data,
  method = "mcp",
  level = 95,
  crs = NULL,
  population = FALSE,
  start_date = NULL,
  end_date = NULL,
  kde_extent = 1,
  h = "href",
  hlim = NULL
)

Arguments

Value

An 'sf' object of home range polygon(s).

Plot Latitude Over Time

Description

Creates a time series plot of latitude (y-axis) over timestamp (x-axis), with separate lines for each individual animal. Optionally facets the plot by animal and allows customization of x-axis date format and break spacing.

Usage

ez_latitude_plot(
  data,
  color_palette = "turbo",
  facet = FALSE,
  date_format = NULL,
  date_breaks = NULL,
  start_date = NULL,
  end_date = NULL
)

Arguments

Value

A ggplot object.

Plot Tracking Data and Home Ranges Together

Description

A unified function for visualizing tracking data and home range polygons on an interactive map. Accepts tracking points (from 'ez_track') and home ranges (from 'ez_home_range'), and allows layer-specific customization of appearance and filtering by time.

Usage

ez_map(
  tracks = NULL,
  home_ranges = NULL,
  individual = NULL,
  show_points = TRUE,
  show_paths = TRUE,
  point_color = "id",
  point_size = 4,
  point_opacity = 0.8,
  point_stroke = TRUE,
  point_stroke_color = "black",
  path_color = "id",
  path_width = 2,
  path_opacity = 1,
  polygon_color = "id",
  polygon_opacity = 0.4,
  color_palette = "viridis",
  show_labels = TRUE,
  start_date = NULL,
  end_date = NULL
)

Arguments

Value

A 'leaflet' map object.

Examples

# Simulate tracks
tracks <- data.frame(
  id = rep(c("A", "B"), each = 10),
  timestamp = rep(seq.POSIXt(as.POSIXct("2023-01-01"), by = "1 day", length.out = 10), 2),
  x = c(runif(10, -1, 1), runif(10, 0, 2)),
  y = c(runif(10, 51, 52), runif(10, 51.5, 52.5))
)

# Plot map
ez_map(tracks, point_color = "timestamp")

Summarize Animal Tracking Data

Description

Calculate basic summary statistics per tracked individual. This function is useful for quickly understanding data coverage, gaps, and movement distance for each animal. The following summary statistics are returned for each unique 'id': - 'n_fixes': Number of location records - 'first_location': Timestamp of the first recorded location - 'last_location': Timestamp of the last recorded location - 'tracking_duration_days': Duration between first and last fix (in days) - 'fixes_per_day': Average number of fixes per day - 'median_interval_hours': Median interval between fixes (in hours) - 'max_time_gap_days': Longest time gap between consecutive fixes (in days) - 'distance_km': Total distance traveled (in kilometers), calculated using the Haversine formula - 'avg_speed_kmh': Average speed (km/h), computed as distance divided by tracking duration in hours

Usage

ez_summary(data, start_date = NULL, end_date = NULL, report = FALSE)

Arguments

Value

A data frame with summary statistics per 'id', or an HTML table if 'report = TRUE'.

Examples

data(godwit_tracks)
clean <- ez_track(godwit_tracks)
ez_summary(clean)

Create a Clean Tracking Object

Description

Imports and standardizes tracking data into a tidy format with columns: 'id', 'timestamp', 'x', and 'y'. Supports input as data frames, 'sf', 'Spatial*' objects, or file paths to CSV, Excel, Shapefiles, and GeoPackages. Optionally returns a spatial object projected to WGS84 (EPSG:4326), and supports subsampling (e.g., "1 per hour").

Usage

ez_track(
  data,
  format = NULL,
  tz = "UTC",
  crs = 4326,
  as_sf = TRUE,
  id = NULL,
  timestamp = NULL,
  x = NULL,
  y = NULL,
  keep_original_cols = TRUE,
  subsample = "none",
  verbose = TRUE,
  ...
)

Arguments

Value

A data.frame or 'sf' object with columns 'id', 'timestamp', 'x', 'y'.

Example Godwit Tracking Dataset

Description

A dataset of godwit tracking data used as an example for ezTrack functions.

Usage

data(godwit_tracks)

Format

A data frame with columns:

individual.local.identifier

Unique identifier for each tracked animal

timestamp

Datetime of the location fix

location.long

Longitude coordinate (WGS84)

location.lat

Latitude coordinate (WGS84)

Source

Movebank

Examples

data(godwit_tracks)
godwit_tracks <- ez_track(godwit_tracks)
ez_summary(godwit_tracks)
ez_home_range(godwit_tracks)