## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(warning = FALSE, 
                      message = FALSE,
                      collapse = TRUE,
                      comment = "#>",
                      out.width = "\\textwidth", 
                      fig.height = 4, 
                      fig.width = 7, 
                      fig.align = "center",
                      dpi = 300)
# only build vignettes locally and not for R CMD check
knitr::opts_chunk$set(eval = nzchar(Sys.getenv("BUILD_VIGNETTES")))

## ----fiespa-data--------------------------------------------------------------
#  library(sf)
#  library(ggplot2)
#  library(colorist)
#  
#  # load data
#  data("fiespa_occ")
#  fiespa_occ

## ----fiespa-metrics-----------------------------------------------------------
#  # pull information from the stack
#  m1 <- metrics_pull(fiespa_occ)
#  m1

## ----fiespa-palette-----------------------------------------------------------
#  # generate a color palette
#  p1 <- palette_timecycle(fiespa_occ)
#  head(p1)

## ----fiespa-mapmult-----------------------------------------------------------
#  # map each of the layers
#  map_multiples(m1, p1, ncol = 4, labels = names(fiespa_occ))

## ----fiespa-mapsing-----------------------------------------------------------
#  # map one layer
#  map_single(m1, p1, layer = 6)

## ----fiespa-distill-----------------------------------------------------------
#  # distill distribution information across layers
#  m1_distill <- metrics_distill(fiespa_occ)
#  
#  # visualize distilled information on a single map
#  map_single(m1_distill, p1)

## ----fielsp-legend------------------------------------------------------------
#  # generate a legend
#  legend_timecycle(p1, origin_label = "Jan 1")

## ----fisher-data--------------------------------------------------------------
#  # loda data
#  data("fisher_ud")
#  fisher_ud

## ----fisher-map---------------------------------------------------------------
#  # pull information from the stack
#  m2 <- metrics_pull(fisher_ud)
#  
#  # generate a color palette
#  p2 <- palette_timeline(fisher_ud)
#  
#  # map each of the layers
#  map_multiples(m2, p2)

## ----fisher-lambda_i----------------------------------------------------------
#  # map each of the layers and adjust visual weights
#  map_multiples(m2, p2, lambda_i = -5)

## ----fisher-distill-----------------------------------------------------------
#  # distill distribution information across layers
#  m2_distill <- metrics_distill(fisher_ud)
#  
#  # visualize distilled information on a single map
#  map_single(m2_distill, p2, lambda_i = -5)

## ----fisher-legend------------------------------------------------------------
#  # generate a legend
#  legend_timeline(p2, time_labels = c("April 7", "April 15"))

## ----elephant-pull------------------------------------------------------------
#  # load data
#  data("elephant_ud")
#  
#  # pull information from the stack
#  m3 <- metrics_pull(elephant_ud)
#  
#  # assign a color palette
#  p3 <- palette_set(elephant_ud)
#  
#  # generate maps for each individual
#  map_multiples(m3, p3, ncol = 2, lambda_i = -5, labels = names(elephant_ud))

## ----elephant-distill---------------------------------------------------------
#  # distill distribution information across individuals
#  m3_distill <- metrics_distill(elephant_ud)
#  
#  # visualize distilled information on a single map
#  map_single(m3_distill, p3, lambda_i = -5)
#  # generate a legend
#  legend_set(p3, group_labels = names(elephant_ud))

## ----elephant-sfdl, eval = FALSE----------------------------------------------
#  # download data to a temp directory
#  url <- "https://github.com/mstrimas/colorist/raw/master/data-raw/"
#  f <- file.path(tempdir(), "etosha-features.gpkg")
#  download.file(paste0(url, basename(f)), f)

## ----elephant-sfpath, echo = FALSE--------------------------------------------
#  f <- "../data-raw/etosha-features.gpkg"

## ----elepaphant-sf------------------------------------------------------------
#  pans <- read_sf(f, layer = "pans") %>%
#    st_transform(crs = st_crs(elephant_ud))
#  
#  waterholes <- read_sf(f, layer = "waterholes") %>%
#    st_transform(crs = st_crs(elephant_ud))
#  
#  park <- read_sf(f, layer = "etosha") %>%
#    st_transform(crs = st_crs(elephant_ud))
#  
#  roads <- read_sf(f, layer = "roads") %>%
#    st_transform(crs = st_crs(elephant_ud))

## ----elephant-pretty, fig.width = 6, fig.height = 3.5-------------------------
#  # visualize both distributions on a single map and add environmental data
#  elephant_map <- map_single(m3_distill, p3, lambda_i = -5) +
#    geom_sf(data = pans, alpha = 0.2, size = 0.15, color = "gray40") +
#    geom_sf(data = waterholes, size = 0.25) +
#    geom_sf(data = park, size = 3, fill = NA, color = alpha("gray60", 0.2)) +
#    geom_sf(data = park, size = 0.2, fill = NA, color = "gray20", linetype = 6) +
#    ggtitle("Two Elephants in Etosha National Park")
#  
#  # show the map
#  elephant_map

## ----elephant-save, eval = FALSE----------------------------------------------
#  # save the map
#  ggsave(plot = elephant_map,
#         filename = "afrele_map_singles.png",
#         width = 6,
#         height = 3.5,
#         dpi = 600)