Nothing
## ----setOpts, include = FALSE-------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup, include=FALSE-----------------------------------------------------
library(sf)
library(geojsonsf)
library(dplyr)
library(neotoma2)
## ----getSiteBySiteID----------------------------------------------------------
# Search for site by a single numeric ID:
alex <- get_sites(24)
alex
# Search for sites with multiple IDs using c():
multiple_sites <- get_sites(c(24, 47))
multiple_sites
## ----getsitename--------------------------------------------------------------
alex <- get_sites(sitename = "Alexander Lake")
alex
## ----sitewithwildcardname-----------------------------------------------------
alex <- get_sites(sitename = 'Alex%')
alex
## ----agebounds, eval=FALSE----------------------------------------------------
# # Note, we are using the `all_data = TRUE` flag here to avoid the default limit of 25 records, discussed below.
# # Because these queries are searching through every record they are slow and and are not
# # run in knitting this vignette.
# get_sites(ageof = 8200, all_data = TRUE) %>% length()
# get_sites(ageyounger = 5000, ageolder = 8000, all_data = TRUE) %>% length()
# get_sites(minage = 5000, maxage = 8000, all_data = TRUE) %>% length()
## ----extractElement-----------------------------------------------------------
alex <- get_sites(sitename = "Alexander Lake")
alex[[1]]$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 <- get_datasets(loc = brazil_sf)
brazil_datasets
## ----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) %>%
neotoma2::filter(datasettype == "pollen" & age_range_young <= 1000 & age_range_old >= 10000) %>%
get_downloads(verbose = FALSE)
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"
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