inst/doc/status.R

In ebirdst: Access and Analyze eBird Status and Trends Data Products

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(warning = FALSE, 
                      message = FALSE,
                      collapse = TRUE,
                      comment = "#>",
                      out.width = "100%",
                      fig.height = 4, 
                      fig.width = 7, 
                      fig.align = "center")
# only build vignettes locally and not for R CMD check
knitr::opts_chunk$set(eval = nzchar(Sys.getenv("BUILD_VIGNETTES")))

## ----access-------------------------------------------------------------------
#  library(dplyr)
#  library(sf)
#  library(terra)
#  library(ebirdst)
#  
#  # download a simplified example dataset for Yellow-bellied Sapsucker in Michigan
#  ebirdst_download_status(species = "yebsap-example", download_all = TRUE)

## ----species------------------------------------------------------------------
#  glimpse(ebirdst_runs)

## ----review-------------------------------------------------------------------
#  ebirdst_runs %>%
#    filter(species_code == "yebsap-example") %>%
#    glimpse()

## ----types_weekly-------------------------------------------------------------
#  # weekly, 27km res, median relative abundance
#  abd_lr <- load_raster("yebsap-example", product = "abundance",
#                        resolution = "27km")
#  
#  # weekly, 27km res, median proportion of population
#  prop_pop_lr <- load_raster("yebsap-example", product = "proportion-population",
#                        resolution = "27km")
#  
#  # weekly, 27km res, abundance confidence intervals
#  abd_lower <- load_raster("yebsap-example", product = "abundance", metric = "lower",
#                           resolution = "27km")
#  abd_upper <- load_raster("yebsap-example", product = "abundance", metric = "upper",
#                           resolution = "27km")

## ----types_weekly_dates-------------------------------------------------------
#  as.Date(names(abd_lr))

## ----types_seasonal-----------------------------------------------------------
#  # seasonal, 27km res, mean relative abundance
#  abd_seasonal_mean <- load_raster("yebsap-example", product = "abundance",
#                                   period = "seasonal", metric = "mean",
#                                   resolution = "27km")
#  # season that each layer corresponds to
#  names(abd_seasonal_mean)
#  # just the breeding season layer
#  abd_seasonal_mean[["breeding"]]
#  
#  # seasonal, 27km res, max occurrence
#  occ_seasonal_max <- load_raster("yebsap-example", product = "occurrence",
#                                  period = "seasonal", metric = "max",
#                                  resolution = "27km")

## ----types_fullyear-----------------------------------------------------------
#  # full year, 27km res, maximum relative abundance
#  abd_fy_max <- load_raster("yebsap-example", product = "abundance",
#                            period = "full-year", metric = "max",
#                            resolution = "27km")

## ----types_ranges-------------------------------------------------------------
#  # seasonal, 27km res, smoothed ranges
#  ranges <- load_ranges("yebsap-example", resolution = "27km")
#  ranges
#  
#  # subset to just the breeding season range using dplyr
#  range_breeding <- filter(ranges, season == "breeding")

## ----types_regional-----------------------------------------------------------
#  regional <- load_regional_stats("yebsap-example")
#  glimpse(regional)