| Title: | Calculate Fetch and Wave Exposure for Lake Sampling Points |
|---|---|
| Description: | Calculates fetch (open water distance) and wave exposure metrics for lake sampling points. Downloads lake boundaries from 'OpenStreetMap', calculates directional fetch using a ray-casting approach, and optionally integrates National Hydrography Dataset ('NHD') data <https://www.usgs.gov/national-hydrography> for hydrological context including outlet and inlet locations. Can estimate lake depth from surface area using empirical relationships, and integrate historical weather data for cumulative wave energy calculations. Includes an optional interactive 'shiny' application for visualization. |
| Authors: | Jeremy Lynch Farrell [aut, cre] |
| Maintainer: | Jeremy Lynch Farrell <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.3 |
| Built: | 2026-05-25 07:06:44 UTC |
| Source: | https://github.com/jeremylfarrell/lakefetch |
The lakefetch package provides tools for calculating fetch (open water distance) and wave exposure metrics for lake sampling points. It downloads lake boundaries from OpenStreetMap, calculates directional fetch using ray-casting, and optionally integrates with NHD for hydrological context.
fetch_calculateMain entry point for fetch calculation
load_sitesLoad and validate site data
get_lake_boundaryGet lake boundary from OSM or file
add_lake_contextAdd NHD hydrological context
fetch_appLaunch interactive Shiny app
plot_fetch_mapMap of sites colored by exposure
plot_fetch_barsBar chart of effective fetch
plot_fetch_roseRose diagram for single site
create_ray_geometriesCreate ray lines for mapping
lakefetch_optionsGet/set package options
lakefetch_reset_optionsReset options to defaults
Maintainer: Jeremy Lynch Farrell [email protected]
Useful links:
Report bugs at https://github.com/jeremylfarrell/lakefetch/issues
Add hydrological context to fetch results using the National Hydrography Dataset (NHD). Includes outlet/inlet locations, watershed area, connectivity classification, and stream order.
add_lake_context(fetch_results, lake_polygons, utm_epsg)add_lake_context(fetch_results, lake_polygons, utm_epsg)
fetch_results |
sf object with fetch calculation results |
lake_polygons |
sf object with lake polygons |
utm_epsg |
EPSG code for UTM projection |
Requires the nhdplusTools package. If not available, returns the input with NA columns added for consistent output format.
Added columns include:
nhd_permanent_id: NHD permanent identifier
nhd_gnis_name: GNIS name from NHD
nhd_areasqkm: Area in square kilometers from NHD
outlet_dist_m: Distance to outlet in meters
outlet_bearing: Compass direction to outlet
inlet_nearest_dist_m: Distance to nearest inlet
inlet_nearest_bearing: Compass direction to nearest inlet
inlet_count: Number of inlets
connectivity_class: Headwater/Drainage/Terminal/Isolated
outlet_stream_order: Strahler stream order at outlet
watershed_area_ha: Watershed area in hectares
lake_watershed_ratio: Lake area / watershed area
sf object with additional columns for NHD context
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) results_with_context <- add_lake_context(results$results, results$lakes, lake$utm_epsg)csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) results_with_context <- add_lake_context(results$results, results$lakes, lake$utm_epsg)
Looks up or estimates depth for each lake in the fetch results and adds depth columns.
add_lake_depth(fetch_results, lakes, user_depths = NULL)add_lake_depth(fetch_results, lakes, user_depths = NULL)
fetch_results |
sf object with fetch results |
lakes |
sf object with lake polygons |
user_depths |
Named vector of user-provided depths (names = lake IDs) |
fetch_results with added depth columns
data(adirondack_sites) sites <- load_sites(adirondack_sites) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) # Add depth estimates results$results <- add_lake_depth(results$results, results$lakes) # Or provide known depths using an actual lake_osm_id from results lake_id <- results$lakes$osm_id[1] depths <- setNames(15.5, lake_id) results$results <- add_lake_depth(results$results, results$lakes, user_depths = depths)data(adirondack_sites) sites <- load_sites(adirondack_sites) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) # Add depth estimates results$results <- add_lake_depth(results$results, results$lakes) # Or provide known depths using an actual lake_osm_id from results lake_id <- results$lakes$osm_id[1] depths <- setNames(15.5, lake_id) results$results <- add_lake_depth(results$results, results$lakes, user_depths = depths)
Adds historical weather metrics and cumulative wave energy to fetch calculation results.
add_weather_context( fetch_results, datetime_col = "datetime", windows_hours = c(24, 72, 168), depth_m = NULL )add_weather_context( fetch_results, datetime_col = "datetime", windows_hours = c(24, 72, 168), depth_m = NULL )
fetch_results |
sf object with fetch results (must have datetime column) |
datetime_col |
Name of the datetime column |
windows_hours |
Vector of time windows in hours (default c(24, 72, 168)) |
depth_m |
Water depth for orbital velocity calculation |
The input data must have a datetime column in POSIXct format or a format that can be parsed (ISO 8601, or common date-time formats).
sf object with additional weather columns
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) # Add datetime to results results$results$datetime <- as.POSIXct("2024-07-15 14:00:00") # Add weather context results_with_weather <- add_weather_context( results$results, datetime_col = "datetime" )csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) # Add datetime to results results$results$datetime <- as.POSIXct("2024-07-15 14:00:00") # Add weather context results_with_weather <- add_weather_context( results$results, datetime_col = "datetime" )
A dataset containing example lake sampling sites from the Adirondack region of New York State. These synthetic but realistic coordinates demonstrate typical multi-lake sampling scenarios.
adirondack_sitesadirondack_sites
A data frame with 12 rows and 5 variables:
Unique site identifier
Name of the lake
Latitude in decimal degrees (WGS84)
Longitude in decimal degrees (WGS84)
Date and time of sampling (POSIXct)
The dataset includes sites from four Adirondack lakes:
Blue Mountain Lake (3 sites)
Raquette Lake (4 sites)
Long Lake (2 sites)
Tupper Lake (3 sites)
Synthetic data for demonstration purposes
# Load the dataset data(adirondack_sites) # View structure str(adirondack_sites) # Use with lakefetch (requires internet connection) sites <- load_sites(adirondack_sites) lake_data <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake_data)# Load the dataset data(adirondack_sites) # View structure str(adirondack_sites) # Use with lakefetch (requires internet connection) sites <- load_sites(adirondack_sites) lake_data <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake_data)
Perform spatial join to assign each site to its containing lake polygon.
assign_sites_to_lakes(sites_sf, water_polygons, tolerance_m = NULL)assign_sites_to_lakes(sites_sf, water_polygons, tolerance_m = NULL)
sites_sf |
sf object with site points |
water_polygons |
sf object with lake polygons |
tolerance_m |
Buffer distance for matching sites near lake edges |
sf object with sites and added columns for lake_osm_id, lake_name, lake_area_km2
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake_data <- get_lake_boundary(sites) # Assign sites to their containing lakes sites_assigned <- assign_sites_to_lakes( lake_data$sites, lake_data$all_lakes, tolerance_m = 50 ) # Check assignments table(sites_assigned$lake_name)csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake_data <- get_lake_boundary(sites) # Assign sites to their containing lakes sites_assigned <- assign_sites_to_lakes( lake_data$sites, lake_data$all_lakes, tolerance_m = 50 ) # Check assignments table(sites_assigned$lake_name)
Create line geometries representing fetch rays from each site. Useful for detailed visualization of the ray-casting results.
create_ray_geometries(fetch_data)create_ray_geometries(fetch_data)
fetch_data |
Results from |
An sf object with ray line geometries
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) rays <- create_ray_geometries(results) # Plot rays for a specific site site_name <- results$results$Site[1] site_rays <- rays[rays$Site == site_name, ] ggplot2::ggplot() + ggplot2::geom_sf(data = site_rays, ggplot2::aes(color = Distance))csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) rays <- create_ray_geometries(results) # Plot rays for a specific site site_name <- results$results$Site[1] site_rays <- rays[rays$Site == site_name, ] ggplot2::ggplot() + ggplot2::geom_sf(data = site_rays, ggplot2::aes(color = Distance))
A simple circular lake polygon for demonstration and testing purposes. This synthetic lake has known geometry (1 km radius) which makes it useful for validating fetch calculations.
example_lakeexample_lake
An sf object with 1 row and 3 variables:
Identifier (synthetic)
Lake name
Surface area in square kilometers (~3.14 km²)
POLYGON geometry in UTM Zone 18N (EPSG:32618)
The lake is centered at UTM coordinates (500000, 4800000) with a radius of 1000 meters. For a site at the center, fetch should equal 1000 m in all directions.
Synthetic data for demonstration and validation
# Load the dataset data(example_lake) # View structure print(example_lake) # Plot the lake library(ggplot2) ggplot(example_lake) + geom_sf()# Load the dataset data(example_lake) # View structure print(example_lake) # Plot the lake library(ggplot2) ggplot(example_lake) + geom_sf()
Launch a Shiny app for interactive exploration of fetch calculation results. Click on site markers to view fetch rays and detailed information. Click anywhere on the map to analyze a new point.
fetch_app(fetch_data, title = NULL)fetch_app(fetch_data, title = NULL)
fetch_data |
Results from |
title |
Optional app title |
Requires the shiny, leaflet, and base64enc packages (suggested dependencies).
The app displays:
Interactive map with satellite imagery
Site markers colored by exposure category
Click markers to see fetch rays
Popup with rose diagram and metrics
Click anywhere on the map to analyze a new point
Launches a Shiny app (does not return)
if (interactive()) { sites <- load_sites("my_sites.csv") lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) fetch_app(results) }if (interactive()) { sites <- load_sites("my_sites.csv") lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) fetch_app(results) }
Launch a standalone Shiny app where users can upload a CSV file with GPS coordinates, and the app will automatically download lake boundaries, calculate fetch, and display interactive results.
fetch_app_upload(title = "Lake Fetch Calculator")fetch_app_upload(title = "Lake Fetch Calculator")
title |
Optional app title (default: "Lake Fetch Calculator") |
Requires the shiny, leaflet, and base64enc packages (suggested dependencies).
The app workflow:
Upload a CSV file with latitude/longitude columns
App downloads lake boundaries from OpenStreetMap
Calculates fetch for all uploaded points
Displays interactive map with results
Click anywhere on a lake to analyze additional points
Download results as CSV or GeoPackage
CSV file requirements:
Must have columns starting with "lat" and "lon" (case-insensitive)
Optional "Site" column for point names
Additional columns are preserved in output
Launches a Shiny app (does not return)
if (interactive()) { # Launch the upload app fetch_app_upload() }if (interactive()) { # Launch the upload app fetch_app_upload() }
Main entry point for fetch calculation. Takes sites and lake boundaries, calculates directional fetch using ray-casting, and returns results with exposure metrics.
fetch_calculate( sites, lake, depth_m = NULL, fetch_method = NULL, add_context = TRUE, find_max_fetch = FALSE )fetch_calculate( sites, lake, depth_m = NULL, fetch_method = NULL, add_context = TRUE, find_max_fetch = FALSE )
sites |
Data frame or sf object with site locations |
lake |
Lake boundary data from |
depth_m |
Water depth in meters for orbital velocity calculation. Can be a single value (applied to all sites), a vector (one per site), or NULL to use depth from sites data or default from options. |
fetch_method |
Method for calculating effective fetch. Options:
If NULL, uses the value from |
add_context |
Logical; add NHD context if available (default TRUE) |
find_max_fetch |
Logical; if TRUE, finds the location in each lake with
the maximum possible fetch using a longest-internal-chord algorithm. The
result is returned as a |
For each site, the function:
Assigns the site to its containing lake polygon
Buffers the site inward from shore (GPS accuracy adjustment)
Casts rays in all directions at specified angle resolution
Measures distance to shore in each direction
Calculates summary metrics (mean, max, effective fetch)
Calculates orbital velocity using depth
Derives exposure category (Sheltered/Moderate/Exposed)
Exposure thresholds can be configured via lakefetch_options.
A list with elements:
results |
sf object with fetch results for each site |
lakes |
sf object with lake polygons used |
angles |
Vector of angles used for fetch calculation |
max_fetch |
(only if |
Shore Protection Manual (1984). U.S. Army Corps of Engineers, Coastal Engineering Research Center. 4th Edition.
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) # With explicit depth results <- fetch_calculate(sites, lake, depth_m = 5) # Using cosine-weighted effective fetch (SPM method) results <- fetch_calculate(sites, lake, fetch_method = "cosine") # Access results results$results # sf with all fetch data results$lakes # lake polygons # Find the location with maximum fetch in each lake results <- fetch_calculate(sites, lake, find_max_fetch = TRUE) results$max_fetch # sf with max fetch location per lakecsv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) # With explicit depth results <- fetch_calculate(sites, lake, depth_m = 5) # Using cosine-weighted effective fetch (SPM method) results <- fetch_calculate(sites, lake, fetch_method = "cosine") # Access results results$results # sf with all fetch data results$lakes # lake polygons # Find the location with maximum fetch in each lake results <- fetch_calculate(sites, lake, find_max_fetch = TRUE) results$max_fetch # sf with max fetch location per lake
Get lake boundary polygon(s) either from OpenStreetMap or from a local file.
get_lake_boundary(sites, file = NULL)get_lake_boundary(sites, file = NULL)
sites |
A data.frame with latitude and longitude columns, or an sf object. |
file |
Optional file path to a shapefile or geopackage with lake boundaries. |
If file is provided, the lake boundary is loaded from the file.
Otherwise, the function downloads lake boundaries from OpenStreetMap
based on the bounding box of the provided sites.
A list with elements:
all_lakes |
sf object with lake polygons in UTM projection |
sites |
sf object with site points in UTM projection |
utm_epsg |
EPSG code for the UTM projection used |
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake_data <- get_lake_boundary(sites)csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake_data <- get_lake_boundary(sites)
Retrieves or estimates lake depth for wave calculations. Uses user-provided depth if available, otherwise estimates from lake surface area using empirical relationships.
get_lake_depth( lake_polygon, site_coords = NULL, user_depth = NULL, method = "auto" )get_lake_depth( lake_polygon, site_coords = NULL, user_depth = NULL, method = "auto" )
lake_polygon |
sf polygon of the lake |
site_coords |
Coordinates of the sample site (optional, for future bathymetry grid support) |
user_depth |
User-provided depth in meters (highest priority) |
method |
Method for depth estimation: "auto" or "empirical" |
Depth estimation methods:
User-provided: Direct input, highest confidence
Empirical: Estimated from lake surface area using published relationships
The empirical method uses the relationship from Cael et al. (2017): mean_depth ~ 10.3 * area_km2^0.25
A list with elements:
depth_mean |
Estimated mean depth in meters |
depth_max |
Estimated maximum depth in meters (if available) |
source |
Source of the estimate ("user" or "empirical") |
confidence |
Confidence level ("high", "medium", "low") |
Messager, M.L., Lehner, B., Grill, G., Nedeva, I., Schmitt, O. (2016): Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7: 13603.
Cael, B.B., Heathcote, A.J., Seekell, D.A. (2017): The volume and mean depth of Earth's lakes. Geophysical Research Letters, 44: 209-218.
data(example_lake) # With user-provided depth depth <- get_lake_depth(example_lake, user_depth = 8.5) # Estimate from lake area depth <- get_lake_depth(example_lake)data(example_lake) # With user-provided depth depth <- get_lake_depth(example_lake, user_depth = 8.5) # Estimate from lake area depth <- get_lake_depth(example_lake)
Get or set options that control the behavior of lakefetch functions.
lakefetch_options(...)lakefetch_options(...)
... |
Named arguments to set options. If empty, returns all current options. |
Available options:
GPS accuracy buffer in meters (default: 10)
Direction resolution in degrees (default: 5)
Maximum fetch distance in meters (default: 50000)
Shore detection validation buffer (default: 10)
Default wind speed in m/s (default: 10)
Default water depth in meters (default: 10)
Buffer for matching sites to lakes (default: 100)
Effective fetch calculation method: "top3" (mean of 3 highest directional fetches, default), "max" (maximum directional fetch), or "cosine" (SPM/CERC cosine-weighted average across 9 radials at 6-degree intervals; see Shore Protection Manual, 1984)
Fetch threshold below which sites are classified as "Sheltered" (default: 2500 m). This is a practical default; no universal standard exists in the literature. Adjust based on your study system.
Fetch threshold above which sites are classified as "Exposed" (default: 5000 m). Sites between thresholds are "Moderate". See Mason et al. (2018) for Great Lakes exposure mapping methodology.
Proportion of lake maximum fetch below which sites are classified as "Sheltered" in the relative exposure system (default: 0.25). Sites are classified relative to the lake's longest internal chord (maximum possible fetch).
Proportion of lake maximum fetch above which sites are classified as "Exposed" in the relative exposure system (default: 0.50). Sites between thresholds are "Moderate".
Use parallel processing for multi-lake (default: TRUE)
Use NHD integration if available (default: TRUE)
If no arguments, returns a list of all current options. If arguments provided, sets those options and returns invisible NULL.
Shore Protection Manual (1984). U.S. Army Corps of Engineers, Coastal Engineering Research Center. 4th Edition.
Mason, L. A., Riseng, C. M., Laber, A. L., & Rutherford, E. S. (2018). Effective fetch and relative exposure index maps for the Laurentian Great Lakes. Scientific Data, 5, 180295.
# Get all options lakefetch_options() # Get specific option lakefetch_options()$buffer_distance_m # Set options lakefetch_options(buffer_distance_m = 20, angle_resolution_deg = 10)# Get all options lakefetch_options() # Get specific option lakefetch_options()$buffer_distance_m # Set options lakefetch_options(buffer_distance_m = 20, angle_resolution_deg = 10)
Reset all lakefetch package options to their default values.
lakefetch_reset_options()lakefetch_reset_options()
Invisible NULL
lakefetch_reset_options()lakefetch_reset_options()
Load and validate site data for fetch calculation. Automatically detects coordinate columns (latitude/longitude) and cleans the data.
load_sites(x, lat_col = NULL, lon_col = NULL, site_col = NULL, lake_col = NULL)load_sites(x, lat_col = NULL, lon_col = NULL, site_col = NULL, lake_col = NULL)
x |
Either a file path to a CSV file or a data.frame with site data. |
lat_col |
Optional character string specifying the name of the latitude column. If NULL (default), auto-detects columns starting with "lat". |
lon_col |
Optional character string specifying the name of the longitude column. If NULL (default), auto-detects columns starting with "lon". |
site_col |
Optional character string specifying the name of the site identifier column. If NULL (default), auto-detects a column named "site". |
lake_col |
Optional character string specifying the name of the lake name column. If NULL (default), auto-detects common lake name patterns. |
The function:
Detects latitude/longitude columns (names starting with "lat"/"lon")
Cleans coordinate values (removes non-numeric characters)
Creates Site column if not present
Removes rows with invalid or missing coordinates
Detects location name from data columns or filename
Column names can be specified explicitly using the lat_col,
lon_col, site_col, and lake_col arguments. This is
useful when your data uses non-standard column names that the auto-detection
cannot find.
A data.frame with columns Site, latitude, longitude, and any additional columns from the input. Includes attributes "location_name" and "location_column" if a location was detected.
# Load from data frame df <- data.frame( Site = c("A", "B", "C"), latitude = c(43.42, 43.43, 43.41), longitude = c(-73.69, -73.68, -73.70) ) sites <- load_sites(df) # Load with custom column names df2 <- data.frame( sample_id = c("A", "B"), y_coord = c(43.42, 43.43), x_coord = c(-73.69, -73.68), reservoir = c("Lake One", "Lake One") ) sites <- load_sites(df2, lat_col = "y_coord", lon_col = "x_coord", site_col = "sample_id", lake_col = "reservoir")# Load from data frame df <- data.frame( Site = c("A", "B", "C"), latitude = c(43.42, 43.43, 43.41), longitude = c(-73.69, -73.68, -73.70) ) sites <- load_sites(df) # Load with custom column names df2 <- data.frame( sample_id = c("A", "B"), y_coord = c(43.42, 43.43), x_coord = c(-73.69, -73.68), reservoir = c("Lake One", "Lake One") ) sites <- load_sites(df2, lat_col = "y_coord", lon_col = "x_coord", site_col = "sample_id", lake_col = "reservoir")
Create a bar chart showing effective fetch by site.
plot_fetch_bars(fetch_data, title = "Effective Fetch by Site")plot_fetch_bars(fetch_data, title = "Effective Fetch by Site")
fetch_data |
Results from |
title |
Optional plot title |
A ggplot2 object
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) plot_fetch_bars(results)csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) plot_fetch_bars(results)
Create a map showing site locations colored by exposure category.
plot_fetch_map(fetch_data, title = "Fetch Analysis - Site Locations")plot_fetch_map(fetch_data, title = "Fetch Analysis - Site Locations")
fetch_data |
Results from |
title |
Optional plot title |
A ggplot2 object
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) plot_fetch_map(results)csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) plot_fetch_map(results)
Create a rose diagram showing directional fetch for a single site.
plot_fetch_rose(fetch_data, site, title = NULL)plot_fetch_rose(fetch_data, site, title = NULL)
fetch_data |
Results from |
site |
Site name to plot |
title |
Optional plot title (defaults to site name) |
Invisible NULL (creates base R plot)
csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) plot_fetch_rose(results, results$results$Site[1])csv_path <- system.file("extdata", "sample_sites.csv", package = "lakefetch") sites <- load_sites(csv_path) lake <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake) plot_fetch_rose(results, results$results$Site[1])
Remove or replace invalid filename characters.
sanitize_filename(name)sanitize_filename(name)
name |
Character string to sanitize |
A sanitized string safe for use as a filename
sanitize_filename("Lake O'Brien (2024)")sanitize_filename("Lake O'Brien (2024)")
A dataset containing example sampling sites from well-known Wisconsin lakes. These coordinates are useful for testing with real lake boundaries from OpenStreetMap.
wisconsin_lakeswisconsin_lakes
A data frame with 8 rows and 4 variables:
Unique site identifier
Name of the lake
Latitude in decimal degrees (WGS84)
Longitude in decimal degrees (WGS84)
The dataset includes sites from three Wisconsin lakes:
Lake Mendota (3 sites) - Madison's largest lake, well-studied
Lake Monona (2 sites) - Connected to Mendota via Yahara River
Geneva Lake (3 sites) - Popular recreational lake in SE Wisconsin
Synthetic data based on real lake locations
# Load the dataset data(wisconsin_lakes) # View the data head(wisconsin_lakes) # Use with lakefetch (requires internet connection) sites <- load_sites(wisconsin_lakes) lake_data <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake_data)# Load the dataset data(wisconsin_lakes) # View the data head(wisconsin_lakes) # Use with lakefetch (requires internet connection) sites <- load_sites(wisconsin_lakes) lake_data <- get_lake_boundary(sites) results <- fetch_calculate(sites, lake_data)