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Built in plotting function to check user-corrected territory class assignment.

check_user_terr.Rd

Function to plot the user-corrected territory classes from: beavertools::user_classify()

Usage

check_user_terr(
  terr_poly,
  fill_col = c("#7EAAC7", "#F87223", "#61E265"),
  label = TRUE,
  basemap = FALSE,
  basemap_type = "cartolight",
  axes_units = TRUE,
  scalebar = TRUE,
  scalebar_loc = "tl",
  north_arrow = TRUE,
  north_arrow_loc = "br",
  north_arrow_size = 0.75,
  wgs = TRUE,
  guide = TRUE,
  plot_extent
)

Arguments

terr_poly

a territory polygon created using beavertools::estimate_territories()

fill_col

character vector of R colours or HEX codes.

label

label activity areas with polygon ID. important when checking the predicted classification

basemap

Boolean, include an OSM basemap. (optional)

basemap_type

Character vector for osm map type. for options see rosm::osm.types()

axes_units

Boolean to include coordinate values on axis.

scalebar

Boolean to include a scalebar.

scalebar_loc

character vector for the scalebar location one of:'tl', 'bl', 'tr', 'br' Meaning "top left" etc.

north_arrow

Boolean to include a north arrow

north_arrow_loc

character vector for the arrow location one of:'tl', 'bl', 'tr', 'br' Meaning "top left" etc.

north_arrow_size

numeric vector for the arrow

wgs

Boolean to transform coordinate reference system (CRS) to WGS84 (EPSG:4326)

guide

Boolean to include a legend

plot_extent

'bbox', 'sf' or 'sp' object defining the desired plot extent.

Value

ggplot object of the territory check map.

Examples

# Here we filter the filter the built in 2019-2020 ROBT feeding sign data `RivOtter_FeedSigns`
# Then pipe this 'sf' object to forage_density.

ROBT_201920 <- RivOtter_FeedSigns %>%
dplyr::filter(SurveySeason == "2019 - 2020")%>%
  forage_density(., 'FeedCat')
#> No value supplied for "kd_extent" argument: default extent will be used
#> 
#> calculating weighted kde

# Now we load the ROBT `RivOtter_OtherSigns` dataset and filter to the same
# year as the forage density raster.

CS_201920 <- RivOtter_OtherSigns %>%
dplyr::filter(SurveySeason == "2019 - 2020")

# run territory classification
otter_poly <- estimate_territories(ROBT_201920, confirm_signs = CS_201920)

# create the map for checking automated territory classification
otter_poly_uc <- user_classify(otter_poly, territory = c(10, 28))

# generate the user territory check plot.
check_user_terr(otter_poly_uc, basemap=FALSE)
#> Warning: st_point_on_surface may not give correct results for longitude/latitude data