Nothing
## ---- include = FALSE---------------------------------------------------------
BUILD_VIGNETTES <- isTRUE(as.logical(Sys.getenv("BUILD_VIGNETTES")))
knitr::opts_chunk$set(warning = FALSE,
message = FALSE,
collapse = TRUE,
eval = BUILD_VIGNETTES,
comment = "#>",
out.width = "\\textwidth",
fig.height = 4,
fig.width = 7,
fig.align = "center")
## ----libraries----------------------------------------------------------------
# library(colorist)
# library(ggplot2)
# library(raster)
# library(rnaturalearth)
# library(sf)
# library(tigris)
## ----fiespa-------------------------------------------------------------------
# # calculate metrics, choose a palette, make a map
# m_fiespa <- metrics_distill(fiespa_occ)
# p_fiespa <- palette_timecycle(fiespa_occ)
# map_fiespa <- map_single(m_fiespa, p_fiespa)
## ----ne-downloads, results = "hide"-------------------------------------------
# # download, transform, and crop spatial data
# countries <- ne_download(category = "cultural", type = "countries",
# returnclass = "sf", scale = 50) %>%
# st_transform(crs = st_crs(fiespa_occ)) %>%
# st_crop(st_bbox(fiespa_occ))
#
# rivers <- ne_download(category = "physical", type = "rivers_lake_centerlines",
# returnclass = "sf", scale = 50) %>%
# st_transform(crs = st_crs(fiespa_occ)) %>%
# st_crop(st_bbox(fiespa_occ))
#
# lakes <- ne_download(category = "physical", type = "lakes", returnclass = "sf",
# scale = 50) %>%
# st_transform(crs = st_crs(fiespa_occ)) %>%
# st_crop(st_bbox(fiespa_occ))
#
# # create polygon describing ocean
# ocean <- st_as_sfc(st_bbox(fiespa_occ)) %>%
# st_difference(st_union(countries))
## ----fiespa-ne----------------------------------------------------------------
# # add supplementary spatial data to map
# map_fiespa_ne <- map_fiespa +
# geom_sf(data = ocean, fill = "gray95", color = "black", size = 0.25) +
# geom_sf(data = rivers, size = 0.25, color = "gray35") +
# geom_sf(data = lakes, fill = "gray95", size = 0.25, color = "gray35") +
# geom_sf(data = st_as_sfc(st_bbox(fiespa_occ)), fill = NA, color = "black", size = 0.25)
#
# # show the map
# print(map_fiespa_ne)
## ----fisher-multiples---------------------------------------------------------
# # calculate metrics, choose a palette, make a series of maps
# m_fisher <- metrics_pull(fisher_ud)
# p_fisher <- palette_timeline(9, start_hue = -40)
# map_fisher <- map_multiples(m_fisher, p_fisher, lambda_i = -5,
# labels = paste("April", 7:15))
## ----tigris-download, results="hide"------------------------------------------
# # download, transform, and crop spatial data
# streams <- linear_water("NY", "Rensselaer") %>%
# st_transform(crs = st_crs(fisher_ud)) %>%
# st_crop(st_bbox(fisher_ud))
#
# ponds <- area_water("NY", "Rensselaer") %>%
# st_transform(crs = st_crs(fisher_ud)) %>%
# st_crop(st_bbox(fisher_ud))
#
# roads <- roads("NY", "Rensselaer") %>%
# st_transform(crs = st_crs(fisher_ud)) %>%
# st_crop(st_bbox(fisher_ud))
## ----fisher-multiples-tigris, fig.height=6------------------------------------
# # add supplementary spatial data to the series of maps
# map_fisher +
# geom_sf(data = streams, linetype = 6, color = "lightblue4", size = 0.25) +
# geom_sf(data = ponds, linetype = 6, color = "lightblue4", fill = "lightblue",
# size = 0.25) +
# geom_sf(data = roads, size = 0.25, color = alpha("black", 0.5)) +
# geom_sf(data = st_as_sfc(st_bbox(fisher_ud)), fill = NA, color = "gray",
# size = 0.25)
## ----fiespa-legend------------------------------------------------------------
# # create a legend using default settings
# legend_timecycle(p_fiespa)
## ----fiespa-legend-custom-----------------------------------------------------
# # change labels on legend
# l_fiespa <- legend_timecycle(p_fiespa,
# origin_label = "Jan 1",
# # specificity labels
# label_s = c("Low\nseasonality",
# "Moderate\nseasonality",
# "High\nseasonality"),
# # intensity label
# label_i = "Peak occurrence",
# # layers label
# label_l = "Month of peak occurrence")
#
# # show legend
# print(l_fiespa)
## ----fiespa-map, fig.height = 7, fig.width = 7--------------------------------
# # position legend below map
# map_fiespa_ne +
# coord_sf(ylim = c(-3153281, 1405830)) +
# annotation_custom(ggplotGrob(l_fiespa),
# xmin = -1482551, xmax = 1850606,
# ymin = -3153281, ymax = -1453281)
## ----fisher-single------------------------------------------------------------
# # calculate distribution metrics
# m_fisher_distill <- metrics_distill(fisher_ud)
#
# # download building footprints
# f_buildings <- file.path(tempdir(), "buildings.rds")
# download.file(paste0("https://github.com/mstrimas/colorist/raw/master/",
# "data-raw/buildings.rds"),
# f_buildings)
# buildings <- readRDS(f_buildings)
# unlink(f_buildings)
#
# # make a map
# map_fisher_distill <- map_single(m_fisher_distill, p_fisher,
# lambda_i = -5, lambda_s = 10) +
# geom_sf(data = streams, linetype = 6, color = "lightblue4", size = 0.25) +
# geom_sf(data = ponds, linetype = 6, color = "lightblue4", fill = "lightblue",
# size = 0.25) +
# geom_sf(data = roads, size = 0.25, color = alpha("black", 0.5)) +
# geom_sf(data = buildings, size = 0.25, color = alpha("black", 0.5)) +
# geom_sf(data = st_as_sfc(st_bbox(fisher_ud)), fill = NA, color = "black",
# size = 0.25)
#
# # show the map
# print(map_fisher_distill)
## ----fisher-legend------------------------------------------------------------
# # create a legend using default settings
# legend_timeline(p_fisher)
## ----fisher-legend-custom-----------------------------------------------------
# # change labels of legend
# l_fisher <- legend_timeline(p_fisher,
# time_labels = c("Apr 7", "Apr 15"),
# # intensity label
# label_i = "Peak use",
# # layer label
# label_l = "Night of peak use",
# # specificity labels
# label_s = c("Consistent use", "Occasional use", "Ephemeral use"))
#
# # show legend
# print(l_fisher)
## ----map-fisher, fig.width=8, fig.height=8------------------------------------
# # position legend to the left of the map
# map_fisher_distill +
# coord_sf(xlim = c(-4300, 2150)) +
# annotation_custom(ggplotGrob(l_fisher),
# xmin = -4400, xmax = -2500,
# ymin = 5100266, ymax = 5104666)
## ----elephants----------------------------------------------------------------
# # calculate metrics, choose a palette, make a series of maps
# m_elephants <- metrics_distill(elephant_ud)
# p_elephants <- palette_set(elephant_ud)
# map_elephants <- map_single(m_elephants, p_elephants, lambda_i = -5)
## ----elephants-legend---------------------------------------------------------
# # create a legend using default settings
# legend_set(p_elephants)
## ----elephant-legend-custom---------------------------------------------------
# # change labels of legend
# l_elephants <- legend_set(p_elephants,
# group_labels = names(elephant_ud),
# # intensity label
# label_i = "Intensity of use",
# # specificity label
# label_s = "Share of use",
# # specificty axis
# axis_s = c("equal", "sole"))
#
# # show legend
# print(l_elephants)
## ----elephants-map, fig.width=8, fig.height=6---------------------------------
# # position legend within map
# map_elephants +
# annotation_custom(ggplotGrob(l_elephants),
# xmin = -3000, xmax = 37000,
# ymin = -2347500, ymax = -2305000)
## ----fiespa-rgban-------------------------------------------------------------
# # ask for map as a raster stack
# s_fiesp <- map_single(m_fiespa, p_fiespa, return_type = "stack")
#
# # show stack information
# s_fiesp
## ----fiespa-rgban-raster------------------------------------------------------
# # plot layers using raster plot function
# plot(s_fiesp)
## ----fiespa-rgban-map---------------------------------------------------------
# # plot r, g, b, and alpha layers to reconstruct colorist map
# plotRGB(s_fiesp, 1, 2, 3, alpha = as.vector(s_fiesp[["alpha"]]))
## ----elephants-rgban-qgis, eval = FALSE---------------------------------------
# # ask for map as a RasterStack object
# s_elephants <- map_single(m_elephants, p_elephants, lambda_i = -5,
# return_type = "stack")
#
# # save RasterStack to R working directory
# writeRaster(s_elephants, "elephants_rgba.grd")
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