## ----setup, include=FALSE-----------------------------------------------------
library(sf)
library(geojsonsf)
library(dplyr)
library(neotoma2)

## ----setOpts, include = FALSE-------------------------------------------------
knitr::opts_chunk$set(eval = FALSE,
                      error = TRUE)

## ----setup_md, include=FALSE--------------------------------------------------
# safe_eval <- function(expr, fallback = "N/A") {
#   tryCatch(eval(expr, envir = .GlobalEnv), error = function(e) fallback)
# }

## ----getSiteBySiteID----------------------------------------------------------
# # Search for site by a single numeric ID:
# alex <- get_sites(24)
# alex
# 
# # Search for sites with multiple IDs using c():
# multiple_sites <- get_sites(c(24, 47))
# multiple_sites

## ----getsitename--------------------------------------------------------------
# alex <- get_sites(sitename = "Alexander Lake")
# alex

## ----sitewithwildcardname-----------------------------------------------------
# alex <- get_sites(sitename = 'Alex%')
# alex

## ----agebounds----------------------------------------------------------------
# # Note, we are using the `all_data = TRUE` flag here to avoid the default limit of 25 records, discussed below.
# # Because these queries are searching through every record they are slow and and are not
# # run in knitting this vignette.
# get_sites(ageof = 8200, all_data = TRUE) %>% length()
# get_sites(ageyounger = 5000, ageolder = 8000, all_data = TRUE) %>% length()
# get_sites(minage = 5000, maxage = 8000, all_data = TRUE) %>% length()

## ----extractElement-----------------------------------------------------------
# alex <- get_sites(sitename = "Alexander Lake")
# alex[[1]]$siteid

## ----showallNamesSite---------------------------------------------------------
# names(alex[[1]])
# 
# # Modify a value using $<- assignment:
# alex[[1]]$area
# alex[[1]]$area <- 100
# alex[[1]]$area
# 
# # Modify a value using [<- assignment:
# alex[[1]]["area"] <- 30
# alex[[1]]$area
# # alex[[1]][7] <- 30  This fails because the `Notes` field expects a character string.

## ----setsitefunction----------------------------------------------------------
# my_site <- set_site(sitename = "My Lake",
#                     geography = st_sf(a = 3, st_sfc(st_point(1:2))),
#                     description = "my lake",
#                     altitude = 30)
# my_site

## ----addtosites---------------------------------------------------------------
# # Add a new site that's been edited using set_site()
# longer_alex <- c(alex, my_site)
# # Or replace an element within the existing list of sites
# # with the newly created site.
# longer_alex[[2]] <- my_site
# 
# # Or append to the `sites` list with assignment:
# longer_alex[[3]] <- my_site

## ----getdatasetsbyid----------------------------------------------------------
# # Getting datasets by ID
# my_datasets <- get_datasets(c(5, 10, 15, 20))
# my_datasets

## ----getdatasetsbytype--------------------------------------------------------
# # Getting datasets by type
# my_pollen_datasets <- get_datasets(datasettype = "pollen", limit = 25)
# my_pollen_datasets

## ----all_data-----------------------------------------------------------------
# allSites_dt <- get_sites(datasettype = "diatom")
# allSites_dt_all <- get_sites(datasettype = "diatom", all_data = TRUE)
# 
# # Because we used the `all_data = TRUE` flag, there will be more sites
# # in allSites_dt_all, because it represents all sites containing diatom datasets.
# length(allSites_dt_all) > length(allSites_dt)

## ----boundingBox--------------------------------------------------------------
# brazil <- '{"type": "Polygon",
#             "coordinates": [[
#                 [-73.125, -9.102],
#                 [-56.953, -33.138],
#                 [-36.563, -7.711],
#                 [-68.203, 13.923],
#                 [-73.125, -9.102]
#               ]]}'
# 
# # We can make the geojson a spatial object if we want to use the
# # functionality of the `sf` package.
# brazil_sf <- geojsonsf::geojson_sf(brazil)
# 
# 
# brazil_datasets <- get_datasets(loc = brazil_sf)

## ----leafletBrazil------------------------------------------------------------
# plotLeaflet(brazil_datasets)

## ----filterBrazil-------------------------------------------------------------
# 
# brazil_dates <- neotoma2::filter(brazil_datasets,
#                                    datasettype == "geochronologic")
# # or:
# brazil_dates <- brazil_datasets %>%
#     neotoma2::filter(datasettype == "geochronologic")
# 
# # With boolean operators:
# brazil_space <- brazil_datasets %>% neotoma2::filter(lat > -18 & lat < -16)

## ----filterAndShowTaxa--------------------------------------------------------
# brazil <- '{"type": "Polygon",
#             "coordinates": [[
#                 [-73.125, -9.102],
#                 [-56.953, -33.138],
#                 [-36.563, -7.711],
#                 [-68.203, 13.923],
#                 [-73.125, -9.102]
#               ]]}'
# 
# # We can make the geojson a spatial object if we want to use the
# # functionality of the `sf` package.
# brazil_sf <- geojsonsf::geojson_sf(brazil)
# 
# brazil_records <- get_datasets(loc = brazil_sf, all_data=TRUE) %>%
#     neotoma2::filter(datasettype == "pollen" & age_range_young <= 1000 & age_range_old >= 10000) %>%
#     get_downloads()
# 
# 
# count_by_site <- samples(brazil_records) %>%
#   dplyr::filter(elementtype == "pollen" & units == "NISP") %>%
#   group_by(siteid, variablename) %>%
#   summarise(n = n()) %>%
#   group_by(variablename) %>%
#   summarise(n = n()) %>%
#   arrange(desc(n))
# 

## ----pubsbyid-----------------------------------------------------------------
# one <- get_publications(12)
# two <- get_publications(c(12, 14))

## ----showSinglePub------------------------------------------------------------
# two[[2]]

## ----fulltestPubSearch--------------------------------------------------------
# michPubs <- get_publications(search = "Michigan", limit = 2)

## ----nonsenseSearch-----------------------------------------------------------
# noise <- get_publications(search = "Canada Banada Nanada", limit = 5)

## ----getSecondPub-------------------------------------------------------------
# two[[1]]

## ----subsetPubs---------------------------------------------------------------
# # Select publications with Neotoma Publication IDs 1 - 10.
# pubArray <- get_publications(1:10)
# # Select the first five publications:
# subPub <- pubArray[[1:5]]
# subPub

## ----setNewPub----------------------------------------------------------------
# new_pub <- set_publications(
# articletitle = "Myrtle Lake: a late- and post-glacial pollen diagram from northern Minnesota",
# journal = "Canadian Journal of Botany",
# volume = 46)

## ----setPubValue--------------------------------------------------------------
# new_pub@pages <- "1397-1410"