inst/doc/neotoma2-package.R

In neotoma2: Working with the Neotoma Paleoecology Database

## ----setOpts, include = FALSE-------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

# Check if the API is available
# otherwise, set up eval=False
api_available <- FALSE
try({
  res = pingNeotoma()
  if(res$status_code==200){
    api_available <- TRUE
  }
}, silent = TRUE)

knitr::knit_hooks$set(eval = function(before, options, envir) {
  if(!api_available) 
    return(FALSE)
  options$eval  # fallback to original
})

# Set global chunk options
knitr::opts_chunk$set(eval = TRUE)

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

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

## ----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, eval=FALSE----------------------------------------------------
# # 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]][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, eval=FALSE-----------------------------------------------------
# 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 <- tryCatch({
  get_datasets(loc = brazil_sf)
}, error = function(e) {
  message("Failed to retrieve datasets for Brazil: ", e$message)
  NULL
})

## ----leafletBrazil------------------------------------------------------------
if (!is.null(brazil_datasets)) {
  plotLeaflet(brazil_datasets)
} else {
  cat("Datasets could not be retrieved due to an API error. Please try again later.")
}

## ----filterBrazil-------------------------------------------------------------

if (!is.null(brazil_datasets)) {
  brazil_dates <- neotoma2::filter(brazil_datasets,
                                   datasettype == "geochronologic")
} else {
  cat("Datasets could not be filtered due to a previous API error. Please try again later.")
}

# or:
if (!is.null(brazil_datasets)) {
  brazil_dates <- brazil_datasets %>%
    neotoma2::filter(datasettype == "geochronologic")
} else {
  cat("Datasets could not be filtered due to a previous API error. Please try again later.")
}

# With boolean operators:
if (!is.null(brazil_datasets)) {
  brazil_space <- brazil_datasets %>% neotoma2::filter(lat > -18 & lat < -16)
} else {
  cat("Datasets could not be filtered due to a previous API error. Please try again later.")
}

## ----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 <- tryCatch({
  get_datasets(loc = brazil_sf) %>%
    neotoma2::filter(datasettype == "pollen" & age_range_young <= 1000 & age_range_old >= 10000) %>%
    get_downloads(verbose = FALSE)
}, error = function(e) {
  message("Failed to retrieve records for Brazil: ", e$message)
  NULL
})

if (!is.null(brazil_records)) {
  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))
} else {
  cat("Records could not be retrieved due to an API error. Please try again later.")
}

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

## ----showSinglePub, eval=FALSE------------------------------------------------
# two[[2]]

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

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

## ----getSecondPub, eval=FALSE-------------------------------------------------
# two[[1]]

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

## ----setNewPub, eval=FALSE----------------------------------------------------
# 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, eval=FALSE--------------------------------------------------
# new_pub@pages <- "1397-1410"