The neotoma2 R Package
+ + +
+Neotoma Resources -
-The Neotoma Paleoecology
+ The Neotoma Paleoecology
Database is a domain-specific data resource containing millions of
fossil records from around the globe, covering the last 5.4 million
years. The Data in Neotoma is associated with sites, specific locations with
-lat/long coordinates. Within a site, there may be one or more collection
+lat/long coordinates. Within a site, there may be one or more collection
units – locations at which samples are physically collected
within the site. For example, an archaeological site
may have one or more collection units, pits within a
@@ -185,13 +4622,93 @@ Researchers often begin by searching for sites within a particular
study area, whether that is defined by geographic or political
@@ -205,7 +4722,2660 @@ The earlier The highest level object in Neotoma is the site.
Sites have spatial coordinates and, in many cases, additional metadata
related to lake parameters, or other site-specific properties. Sites can be searched using the Sites can be searched using the All sites in Neotoma have a unique numeric identifier. With the
If we’re looking for a site and we know its specific identifier, we
-can use the simplest implementation of Once you search for a site, the Often we do not know the particular Neotoma uses a Postgres Database to manage data. Postgres uses the
There are several ways of searching for sites using age parameters.
These are represented below: We can see how these age bounds differ: It is possible to pass all parameters ( Although the The The elements within a Using assignment, we can add information programmatically, for
example, by working interactively with a digital elevation model or
hydrographic data to obtain lake area measurements. Although not
currently implemented, the goal is to support direct upload of updated
information by users. As explained above, a If we have a set of sites that we are analyzing, we can add the new
site to the set of sites, either by appending it to the end, using
- This method allows us to begin modifying site information for
existing sites if we have updated knowledge about site properties. We can also use If you need to get to a deeper level of the sites object, you may
-want to look at the Getting the datasets by id is the easiest call, you can also pass a
vector of IDs or, if you already have a You can also retrieve datasets by type directly from the API. It can be computationally intensive to obtain the full set of records
for We can use that You can get the coordinates to create a GeoJson bounding box from here, or you can use
+ You can get the coordinates to create a GeoJson bounding box from here, or you can use
pre-existing objects within R, for example, country-level data within
the Accessing datasets by bounding box: Now we have an object called You can plot these findings! Sometimes we take a large number of records, do some analysis, and
then choose to select a subset. For example, we may want to select all
sites in a region, and then subset those by dataset type. If we want to
look at only the geochronological datasets from Brazil, we can start
-with the set of records returned from our The The Once we have the set of records we wish to examine, we then want to
recover the actual sample data. This will provide us with information
about the kinds of elements found at the site, within the dataset, their
sample ages, and their counts or measurements. To do this we use the
- Assuming we continue with our example from Brazil, we want to extract
records from the country, filter to only pollen records with samples
covering the last 10,000 years, and then look at the relative frequency
of taxa across sites. We might do something like this: In this code chunk we define the bounding polygon for our sites,
filter by time and dataset type, and then return the full records for
those sites. We get a Many Neotoma records have publications associated with them. The
The most simple case is a search for a publication based on one or
more publication IDs. Most people do not know the unique publication ID
of individual articles, but this provides a simple method to highlight
the way Neotoma retrieves and presents publication information. We can use a single publication ID or multiple IDs. In either case
the API returns the publication(s) and creates a new
From there we can then then subset and extract elements from the list
using the standard Will return the second publication in the list, corresponding to the
publication with We can also use search elements to search for publications. The
This results in a set of 2 publications from Neotoma, equal to the
Text matching in Neotoma is approximate, meaning it is a measure of
the overall similarity between the search string and the set of article
titles. This means that using a nonsense string may still return results
results: This returns a result set of length 5. This returns the (Neotoma) ID, the citation and the publication DOI
(if that is stored in Neotoma). We can get the first publication using
the standard The output will look similar to the output for Just as we can use the A Neotoma Resources
+neotoma2 R package simplifies some of the data
@@ -110,49 +4489,107 @@ Neotoma Resources
-
Resources
-
+Resources
-
+
-
-
-
-Neotoma Data Structure
-
+Neotoma Data Structure
Neotoma Data Structure
Neotoma Data Structureconstituent
database
.
Neotoma Data Structure
neotoma2 R package, and the way the elements are
related to one another. Individual boxes represent the major classes
(sites, site, collectionunits, etc.). Each box then has a list of the
@@ -220,9 +7390,8 @@ Neotoma Data Structure
-
Package Requirements
-
+Package Requirements
neotoma package tried to use base R as much
as possible. The neotoma2 package now draws primarily on
dplyr and purrr packages from the
@@ -232,48 +7401,45 @@ Package Requirements
-
Site Searches
-
+Site Searches
get_sites() function,
-or, can be created using the set_site() function. A single
+get_sites() function,
+or, can be created using the set_site() function. A single
site object is a special object in R, that can be combined
with other sites into a sites object. A sites
-object is effectively a list() of site objects
+object is effectively a list() of site objects
with special methods for printing, plotting and exporting
information.Finding Sites
-
+Finding Sites
neotoma2 package you can search for a site using the
-get_sites() function by its unique site id
+get_sites() function by its unique site id
(siteid), by name (sitename), by altitude
(altmin, altmax), by geopolitical name
(gpid), location (loc) or age bounds.get_sites(). Here we
+can use the simplest implementation of get_sites(). Here we
are searching for a site (Alexander Lake), where we know that the siteid
for the record in Neotoma is 24. We can get these siteids
-using the Neotoma
+using the Neotoma
Explorer web application, or if we have some familiarity with the
site records already.
-
# Search for site by a single numeric ID:
-alex <- get_sites(24)
-alex
-#> siteid sitename lat long altitude
-#> 24 Alexander Lake 53.33333 -60.58333 143
-
-# Search for sites with multiple IDs using c():
-multiple_sites <- get_sites(c(24, 47))
-multiple_sites
-#> siteid sitename lat long altitude
-#> 24 Alexander Lake 53.33333 -60.58333 143
-#> 47 Liberty 43.52000 -90.78000 305# Search for site by a single numeric ID:
+alex <- get_sites(24)
+alex
+#> siteid sitename lat long altitude
+#> 24 Alexander Lake 53.33333 -60.58333 143
+
+# Search for sites with multiple IDs using c():
+multiple_sites <- get_sites(c(24, 47))
+multiple_sites
+#> siteid sitename lat long altitude
+#> 24 Alexander Lake 53.33333 -60.58333 143
+#> 47 Liberty 43.52000 -90.78000 305neotoma2 R package makes
a call to the Neotoma Database, and returns a structured
sites object that contains metadata about the sites, and
@@ -281,8 +7447,83 @@ Finding Sites
-
get_sites() call, is a sites object containing
+
+get_sites() call, is a sites object containing
two individual site objects. Each site object
contains a collunits object with some limited metadata. The
top site appears to have two collection units, while the
@@ -290,43 +7531,168 @@ Finding Sites
-
Searching for Sites by Name
-
+Searching for Sites by Name
siteid. If we’re
looking for a site and we know its name or a part of its name, we can
search using the function with the sitename argument,
-get_site(sitename = 'XXX'), where 'XXX' is the
+get_site(sitename = 'XXX'), where 'XXX' is the
site name. This does not support multiple text strings (i.e., you can’t
-use c()).
-
alex <- get_sites(sitename = "Alexander Lake")
-alex
-#> siteid sitename lat long altitude
-#> 24 Alexander Lake 53.33333 -60.58333 143c()).
+alex <- get_sites(sitename = "Alexander Lake")
+alex
+#> siteid sitename lat long altitude
+#> 24 Alexander Lake 53.33333 -60.58333 143% sign as a general wildcard, so we can use the
% in the sitename argument operator to help us
find sites when we’re not sure the exact match. Note that the search is
case insensitive so a search for alex% or
Alex% will return the same results.
-
alex <- get_sites(sitename = 'Alex%')
-alex
-#> siteid sitename lat long altitude
-#> 24 Alexander Lake 53.33333 -60.58333 143
-#> 25 Alexis Lake 52.51667 -57.03333 200
-#> 4478 Alexander [3CN117] 35.25000 -92.61667 88
-#> 26226 Alexandra Lake 43.29030 -74.16966 354Searching for Sites by Age
-
+Searching for Sites by Age
ageof, ageyoung, ageold,
maxage and minage.Searching for Sites by Ageget_sites(minage = 5000, maxage = 8000).
-
# 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()# 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()ageof,
minage, maxage, ageyounger, . . .
), but it is likely that these will conflict and result in an empty set
@@ -357,11 +7722,10 @@ Searching for Sites by Age
Accessing
+sites metadata
-Accessing
sites metadatasites are structured using S4 objects (see
-Hadley Wickham’s S4
+Hadley Wickham’s S4
documentation), we’ve added helper functions to make accessing
elements easier for users.alex object is composed of several smaller objects
@@ -369,145 +7733,227 @@ Accessing
sites metadata[[ ]], placing the index of the desired object between the
brackets. Then we can also call the particular variable we want using
the $ symbol.
-
-
alex <- get_sites(sitename = "Alexander Lake")
-alex[[1]]$siteid
-#> [1] 24site are the same as the defined
-columns within the Neotoma ndb.sites
+columns within the Neotoma ndb.sites
table, with the exception of the collunits slot, which
contains the collection units and associated datasets that are found
within a site. You can see all the site slots using the
-names() function. You can select individual elements of a
+names() function. You can select individual elements of a
site, and you can assign values to these parameters:
-
names(alex[[1]])
-#> [1] "siteid" "sitename" "geography" "altitude" "geopolitical"
-#> [6] "area" "notes" "description" "collunits"
-
-# Modify a value using $<- assignment:
-alex[[1]]$area
-#> [1] NA
-alex[[1]]$area <- 100
-alex[[1]]$area
-#> [1] 100
-
-# Modify a value using [<- assignment:
-alex[[1]]["area"] <- 30
-# alex[[1]][7] <- 30 This fails because the `Notes` field expects a character string.names(alex[[1]])
+#> [1] "siteid" "sitename" "geography" "altitude" "geopolitical"
+#> [6] "area" "notes" "description" "collunits"
+
+# Modify a value using $<- assignment:
+alex[[1]]$area
+#> [1] NA
+alex[[1]]$area <- 100
+alex[[1]]$area
+#> [1] 100
+
+# Modify a value using [<- assignment:
+alex[[1]]["area"] <- 30
+# alex[[1]][7] <- 30 This fails because the `Notes` field expects a character string.Creating a Site
-
+Creating a Site
site is the fundamental unit of
the Neotoma Database. If you are working with your own data, you might
want to create a site object to allow it to interact with
other data within Neotoma. You can create a site with the
-set_site() function. It will ask you to provide important
+set_site() function. It will ask you to provide important
information such as sitename, lat, and
long attributes.
-
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
-#> siteid sitename lat long altitude
-#> e9cf20b7-bedc-40b0-a110-cfb4d36c0e87 My Lake 2 1 30my_site <- set_site(sitename = "My Lake",
+ geography = st_sf(a = 3, st_sfc(st_point(1:2))),
+ description = "my lake",
+ altitude = 30)
+my_site
+#> siteid sitename lat long altitude
+#> 24fcaf8f-239a-4c40-a221-c6e33f270404 My Lake 2 1 30c(), or by replacing a particular element using
+c(), or by replacing a particular element using
[[<-.
-
# 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# 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_siteset_sites() as a tool to update the
metadata associated with an existing site object:
-
# Update a value within an existing `sites` object:
-longer_alex[[3]] <- set_site(longer_alex[[3]],
- altitude = 3000)
-longer_alexDatasets
-
+Datasets
get_datasets() function. You can use
-get_datasets() using search parameters, or you can use it
+want to look at the get_datasets() function. You can use
+get_datasets() using search parameters, or you can use it
on an existing sites object, such as our prior
alex dataset.get_datasets() adds additional metadata to the
+get_datasets() adds additional metadata to the
site objects, letting us know which
datasettypes are associated with a site, and the dataset
sample locations at the site.
get_datasets() provides more
+
+get_datasets() provides more
complete metadata about a record, including the addition of
chronological information, and more complete metadata about the
-datasets, compared to the get_sites() call, shown above.
+datasets, compared to the get_sites() call, shown above.
The objects here are the same as above, but now have chronology
metadata, and contact metadata for the records. Note that there is still
no sample or taxonomic information about these records. This comes from
-the get_downloads() function.get_downloads() function.sites object, you
can pass a sites object.
-
# Getting datasets by ID
-my_datasets <- get_datasets(c(5, 10, 15, 20))
-my_datasets
-#> siteid sitename lat long altitude
-#> 5 17/2 55.25000 -74.93333 335
-#> 10 Site 1 (Cohen unpublished) 30.83000 -82.33000 37
-#> 15 Aguilar -23.83333 -65.75000 4000
-#> 20 Akuvaara 69.12326 27.67406 170# Getting datasets by ID
+my_datasets <- get_datasets(c(5, 10, 15, 20))
+my_datasets
+#> siteid sitename lat long altitude
+#> 5 17/2 55.25000 -74.93333 335
+#> 10 Site 1 (Cohen unpublished) 30.83000 -82.33000 37
+#> 15 Aguilar -23.83333 -65.75000 4000
+#> 20 Akuvaara 69.12326 27.67406 170
-
# Getting datasets by type
-my_pollen_datasets <- get_datasets(datasettype = "pollen", limit = 25)
-my_pollen_datasets
-#> siteid sitename lat long altitude
-#> 7 Three Pines Bog 47.00000 -80.11667 294
-#> 8 Abalone Rocks Marsh 33.95639 -119.97667 0
-#> 9 Adange 43.30556 41.33333 1750
-#> 11 Konus Exposure, Adycha River 67.75000 135.58333 130
-#> 12 Ageröds Mosse 55.93329 13.42559 58
-#> 13 Aguas Calientes -23.08333 -67.40000 4210
-#> 14 Aguas Calientes 2 -23.50000 -67.58333 4210
-#> 15 Aguilar -23.83333 -65.75000 4000
-#> 16 Ahlenmoor 53.69908 8.74688 2
-#> 17 Ajata -18.25000 -69.20000 4700
-#> 18 South Soefje Bog 29.60000 -97.51694 97
-#> 19 Akulinin Exposure P1282 47.11667 138.55000 20
-#> 20 Akuvaara 69.12326 27.67406 170
-#> 21 Alazeya River Exposure, 8 m Terrace 68.50000 154.50000 40
-#> 22 Alazeya River Exposure, 9 m Terrace 64.33333 154.50000 40
-#> 24 Alexander Lake 53.33333 -60.58333 143
-#> 25 Alexis Lake 52.51667 -57.03333 200
-#> 27 Aliuk Pond 54.58333 -57.36667 25
-#> 29 Lake Allie 44.80156 -94.55982 328
-#> 30 Almora Lake 46.20611 -95.29361 437
-#> 31 Alut Lake 60.13667 152.31278 480
-#> 32 Amarete -15.23333 -68.98333 4000
-#> 33 Amba River Exposure 596 43.31667 131.81667 5
-#> 68 Amguema River Valley Exposure 1 67.75000 178.70000 175
-#> 69 Amguema River Valley Exposure 2 67.66667 178.60000 87# Getting datasets by type
+my_pollen_datasets <- get_datasets(datasettype = "pollen", limit = 25)
+my_pollen_datasets
+#> siteid sitename lat long altitude
+#> 7 Three Pines Bog 47.00000 -80.11667 294
+#> 8 Abalone Rocks Marsh 33.95639 -119.97667 0
+#> 9 Adange 43.30556 41.33333 1750
+#> 11 Konus Exposure, Adycha River 67.75000 135.58333 130
+#> 12 Ageröds Mosse 55.93329 13.42559 58
+#> 13 Aguas Calientes -23.08333 -67.40000 4210
+#> 14 Aguas Calientes 2 -23.50000 -67.58333 4210
+#> 15 Aguilar -23.83333 -65.75000 4000
+#> 16 Ahlenmoor 53.69908 8.74688 2
+#> 17 Ajata -18.25000 -69.20000 4700
+#> 18 South Soefje Bog 29.60000 -97.51694 97
+#> 19 Akulinin Exposure P1282 47.11667 138.55000 20
+#> 20 Akuvaara 69.12326 27.67406 170
+#> 21 Alazeya River Exposure, 8 m Terrace 68.50000 154.50000 40
+#> 22 Alazeya River Exposure, 9 m Terrace 64.33333 154.50000 40
+#> 24 Alexander Lake 53.33333 -60.58333 143
+#> 25 Alexis Lake 52.51667 -57.03333 200
+#> 27 Aliuk Pond 54.58333 -57.36667 25
+#> 29 Lake Allie 44.80156 -94.55982 328
+#> 30 Almora Lake 46.20611 -95.29361 437
+#> 31 Alut Lake 60.13667 152.31278 480
+#> 32 Amarete -15.23333 -68.98333 4000
+#> 33 Amba River Exposure 596 43.31667 131.81667 5
+#> 68 Amguema River Valley Exposure 1 67.75000 178.70000 175
+#> 69 Amguema River Valley Exposure 2 67.66667 178.60000 87sites or datasets. By default the
limit for all queries is 25. The default
@@ -518,89 +7964,83 @@ Datasets
all_data = TRUE in R in the following
way:
-
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)Spatial Searches
-
-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)Spatial Searches
+spData package:
-
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
-#> siteid sitename lat long altitude
-#> 32 Amarete -15.23333 -68.98333 4000
-#> 211 Lago do Aquiri -3.16667 -44.98333 10
-#> 323 Cala Conto -17.56667 -65.93333 2700
-#> 347 Chacaltaya 1 -16.36667 -68.15000 4750
-#> 348 Chacaltaya 2 -16.36667 -68.15000 4350
-#> 523 Cumbre Unduavi -16.35000 -68.04167 4620
-#> 532 Lagoa das Patas 0.26667 -66.68333 300
-#> 843 Serra Campos Gerais -24.66667 -50.21667 1200
-#> 1406 Katantica -14.80000 -69.18333 4820
-#> 1994 Río Kaluyo -16.43333 -68.13333 4070
-#> 2827 Wasa Mayu -17.53333 -65.81667 2720
-#> 9685 Laguna Granja -13.26341 -63.71025 138
-#> 9686 Laguna Orícore -13.34833 -63.52796 139
-#> 11923 Lake Valencia 10.16576 -67.76755 410
-#> 13964 Lake Chalalán -14.42868 -67.92125 330
-#> 526 Lagoa da Curuça -0.76667 -47.85000 35
-#> 1717 Monte Blanco -17.02500 -67.35000 4780
-#> 2248 Lagoa Campestre de Salitre -19.00000 -46.76667 980
-#> 11877 Lake Consuelo -13.95125 -68.99162 1333brazil <- '{"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
+#> siteid sitename lat long altitude
+#> 32 Amarete -15.23333 -68.98333 4000
+#> 211 Lago do Aquiri -3.16667 -44.98333 10
+#> 323 Cala Conto -17.56667 -65.93333 2700
+#> 347 Chacaltaya 1 -16.36667 -68.15000 4750
+#> 348 Chacaltaya 2 -16.36667 -68.15000 4350
+#> 523 Cumbre Unduavi -16.35000 -68.04167 4620
+#> 532 Lagoa das Patas 0.26667 -66.68333 300
+#> 843 Serra Campos Gerais -24.66667 -50.21667 1200
+#> 1406 Katantica -14.80000 -69.18333 4820
+#> 1994 Río Kaluyo -16.43333 -68.13333 4070
+#> 2827 Wasa Mayu -17.53333 -65.81667 2720
+#> 9685 Laguna Granja -13.26341 -63.71025 138
+#> 9686 Laguna Orícore -13.34833 -63.52796 139
+#> 11923 Lake Valencia 10.16576 -67.76755 410
+#> 13964 Lake Chalalán -14.42868 -67.92125 330
+#> 526 Lagoa da Curuça -0.76667 -47.85000 35
+#> 1717 Monte Blanco -17.02500 -67.35000 4780
+#> 2248 Lagoa Campestre de Salitre -19.00000 -46.76667 980
+#> 11877 Lake Consuelo -13.95125 -68.99162 1333brazil_datasets that
contains 19.
-
plotLeaflet(brazil_datasets)Filtering Records
-
+Filtering Records
get_datasets()
+with the set of records returned from our get_datasets()
query, and then use the filter function in
neotoma2 to select only those datasets that are
geochronologic:
-
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)filter() function takes as the first argument, a
+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)filter() function takes as the first argument, a
datasets object, followed by the criteria we want to use to filter.
Current supported criteria includes:
@@ -617,63 +8057,158 @@
Filtering Records
-
Sample and Taxonomic data
-
+Sample and Taxonomic data
get_downloads() call. Note, as before, we are returning a
+get_downloads() call. Note, as before, we are returning a
sites objects, but this time with the most complete
metadata.
get_downloads() returns a
+
+get_downloads() returns a
sites object, but one that contains dataset objects with
filled samples slots. The samples slot is
often very large relative to the other metadata associated with
sites, and so it is commonly held back until a direct
request is provided. Helper functions at the sites level
-can pull out sample data once get_downloads()
+can pull out sample data once get_downloads()
has been called.
-
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)
-#> [1] "data/downloads?datasetid=356,537,547,872,2069,18100,21537"
-
-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))
-#> `summarise()` has grouped output by 'siteid'. You can override using the
-#> `.groups` argument.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))
+#> `summarise()` has grouped output by 'siteid'. You can override using the
+#> `.groups` argument.sites object with dataset and sample
-information (because we used get_downloads()). We execute
-the samples() function to extract all the samples from the
+information (because we used get_downloads()). We execute
+the samples() function to extract all the samples from the
sites objects, and then filter the resulting
data.frame to pull only pollen (a pollen dataset may
contain spores and other elements that are not, strictly speaking,
@@ -686,51 +8221,44 @@ Sample and Taxonomic dataarrange() so that the records show the most common taxa
first in the resulting variable
count_by_site.
Publications
-
+Publications
publication object (and the publications
-collection) provide the opportunity to do this. The publication
+collection) provide the opportunity to do this. The publication
table in Neotoma contains an extensive number of fields. The methods for
publications in the neotoma2 package provide
us with tools to retrieve publication data from Neotoma, to set and
manipulate publication data locally, and to retrieve publication data
from external sources (e.g., using a DOI).
-
+get_publications() from Neotoma
-get_publications() from NeotomaGet Publication By ID
-
+Get Publication By ID
publications object (which consists of multiple individual
publications).
-
one <- get_publications(12)
-two <- get_publications(c(12, 14))[[ format. For example:
-
two[[2]]
-#> publicationid
-#> 1 14
-#> citation
-#> 1 Baker, R.G., R.S. Rhodes II, D.P. Schwert, A.C. Ashworth, T.J. Frest, G.R. Hallberg, and J.A. Janssens. 1986. A full- glacial biota from southeastern Iowa, USA. Journal of Quaternary Science 1(2):91-107.
-#> doi
-#> 1 <NA>two[[2]]
+#> publicationid
+#> 1 14
+#> citation
+#> 1 Baker, R.G., R.S. Rhodes II, D.P. Schwert, A.C. Ashworth, T.J. Frest, G.R. Hallberg, and J.A. Janssens. 1986. A full- glacial biota from southeastern Iowa, USA. Journal of Quaternary Science 1(2):91-107.
+#> doi
+#> 1 10.1002/jqs.3390010202publicationid 14 in this case.Get Publication using Search
-
+Get Publication using Search
get_publication method uses the Neotoma API to search for
publications. We can search using the following properties:Get Publication using Search
-
michPubs <- get_publications(search = "Michigan", limit = 2)limit. If the number of matching publications is less than
-the limit then the length() will be smaller.length() will be smaller.
-
noise <- get_publications(search = "Canada Banada Nanada", limit = 5)[[ nomenclature:
-
two[[1]]
-#> publicationid
-#> 1 12
-#> citation
-#> 1 King, J.E. 1973. Late Pleistocene palynology and biogeography of the western Missouri Ozarks. Ecological Monographs 43:539-565.
-#> doi
-#> 1 <NA>two[[1]]
+#> publicationid
+#> 1 12
+#> citation
+#> 1 King, J.E. 1973. Late Pleistocene palynology and biogeography of the western Missouri Ozarks. Ecological Monographs 43:539-565.
+#> doi
+#> 1 <NA>two
above, however you will see that only a single result will be returned
and the class (for a single publication) will be of type
@@ -775,190 +8300,149 @@ Get Publication using Search
-
# Select publications with Neotoma Publication IDs 1 - 10.
-pubArray <- get_publications(1:10)
-# Select the first five publications:
-subPub <- pubArray[[1:5]]
-subPub
-#> publicationid
-#> 1 1
-#> 2 2
-#> 3 3
-#> 4 4
-#> 5 5
-#> citation
-#> 1 International Organization for Standardization. 2006. Codes for the representation of names of countries and their subdivisions — Part 1: Country codes. ISO 3166-1, Geneva, Switzerland.
-#> 2 United States Central Intelligence Agency. 2004. CIA World Factbook. Washington, DC, USA.
-#> 3 Davis, M.B. 1978. Climatic interpretation of pollen in Quaternary sediments. Pages 35-51 in D. Walker and J.C. Guppy, editors. Biology and Quaternary environments. Australian Academy of Science, Canberra City, Australia.
-#> 4 Nanavati, W., C. Whitlock, V. Outes, and G. Villarosa. 2011. Diatom-inferred wind activity at Lac du Sommet, southern Québec, Canada: A multiproxy paleoclimate reconstruction based on diatoms, chironomids, and pollen for the past 9500 years. The Holocene 21(6):925-938. http://hol.sagepub.com/content/21/6/925.full.pdf+html. [DOI: 10.1177/0959683611400199]
-#> 5 Janssen, C.R. 1968. Myrtle Lake: a late- and post-glacial pollen diagram from northern Minnesota. Canadian Journal of Botany 46(11):1397-1410. [DOI: 10.1139/b68-190]
-#> doi
-#> 1 <NA>
-#> 2 <NA>
-#> 3 <NA>
-#> 4 10.1177/0959683611400199
-#> 5 10.1139/b68-190# Select publications with Neotoma Publication IDs 1 - 10.
+pubArray <- get_publications(1:10)
+# Select the first five publications:
+subPub <- pubArray[[1:5]]
+subPub
+#> publicationid
+#> 1 1
+#> 2 2
+#> 3 3
+#> 4 4
+#> 5 5
+#> citation
+#> 1 International Organization for Standardization. 2006. Codes for the representation of names of countries and their subdivisions — Part 1: Country codes. ISO 3166-1, Geneva, Switzerland.
+#> 2 United States Central Intelligence Agency. 2004. CIA World Factbook. Washington, DC, USA.
+#> 3 Davis, M.B. 1978. Climatic interpretation of pollen in Quaternary sediments. Pages 35-51 in D. Walker and J.C. Guppy, editors. Biology and Quaternary environments. Australian Academy of Science, Canberra City, Australia.
+#> 4 Nanavati, W., C. Whitlock, V. Outes, and G. Villarosa. 2011. Diatom-inferred wind activity at Lac du Sommet, southern Québec, Canada: A multiproxy paleoclimate reconstruction based on diatoms, chironomids, and pollen for the past 9500 years. The Holocene 21(6):925-938. http://hol.sagepub.com/content/21/6/925.full.pdf+html. [DOI: 10.1177/0959683611400199]
+#> 5 Janssen, C.R. 1968. Myrtle Lake: a late- and post-glacial pollen diagram from northern Minnesota. Canadian Journal of Botany 46(11):1397-1410. [DOI: 10.1139/b68-190]
+#> doi
+#> 1 <NA>
+#> 2 <NA>
+#> 3 <NA>
+#> 4 10.1177/0959683611400199
+#> 5 10.1139/b68-190Create (or Import) New Publications
-
+Create (or Import) New Publications
set_sites() function to set new
site information, we can also create new publication information using
-set_publications(). With set_publications()
+set_publications(). With set_publications()
you can enter as much or as little of the article metadata as you’d
like, but it’s designed (in part) to use the CrossRef API to return
information from a DOI.
-
new_pub <- set_publications(
- articletitle = "Myrtle Lake: a late- and post-glacial pollen diagram from northern Minnesota",
- journal = "Canadian Journal of Botany",
- volume = 46)new_pub <- set_publications(
+ articletitle = "Myrtle Lake: a late- and post-glacial pollen diagram from northern Minnesota",
+ journal = "Canadian Journal of Botany",
+ volume = 46)publication has a large number of slots that can be
defined. These may be left blank, they may be set directly after the
publication is defined:
-
new_pub@pages <- "1397-1410"Workshops and Code Examples
-
+Workshops and Code Examples
+
-
-
-
-
-