Access and download data on plant and animal populations from various databases through NatureCounts, a service managed by Birds Canada.
See tutorials, documentation and articles on the naturecounts package Website
You can install the main version of naturecounts from our R-Universe
install.packages("naturecounts",
repos = c(birdscanada = 'https://birdscanada.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))library(naturecounts)Use the nc_count() function to return collections and the number of
observations in each for which you have access (here returns all
collections associated with username sample).
nc_count(username = "sample")
#> # A tibble: 2 × 4
#> collection akn_level access nrecords
#> <chr> <int> <chr> <int>
#> 1 SAMPLE1 0 full 991
#> 2 SAMPLE2 0 full 995Use the show = "all" argument to show counts for all collections
available (public or otherwise).
nc_count(show = "all") %>%
head()
#> # A tibble: 6 × 4
#> collection akn_level access nrecords
#> <chr> <int> <chr> <int>
#> 1 ABATLAS1 5 full 123364
#> 2 ABATLAS2 5 full 201357
#> 3 ABBIRDRECS 5 full 357264
#> 4 ATBANS 3 by request 267
#> 5 ATOWLS 4 by request 33964
#> 6 BBS 5 full 5735895Fetch all observations of bittern which are available to user sample into a local data frame.
First find the species id
search_species("American Bittern")
#> # A tibble: 1 × 5
#> species_id scientific_name english_name french_name taxon_group
#> <int> <chr> <chr> <chr> <chr>
#> 1 2490 Botaurus lentiginosus American Bittern Butor d'Amérique BIRDSUse this id with nc_data_dl(). The info parameter is a short
description of what the data is being downloaded for.
bittern <- nc_data_dl(species = 2490, username = "sample",
info = "readme_example")
#> Using filters: species (2490); fields_set (BMDE2.00-min)
#> Collecting available records...
#> collection nrecords
#> 1 SAMPLE1 1
#> Total records: 1
#>
#> Downloading records for each collection:
#> SAMPLE1
#> Records 1 to 1 / 1Alternatively, save the downloaded data as a SQLite database
(bittern).
bittern <- nc_data_dl(species = 2490, sql_db = "bittern", username = "sample",
info = "readme_example")
#> Using filters: species (2490); fields_set (BMDE2.00-min)
#> Collecting available records...
#> collection nrecords
#> 1 SAMPLE1 1
#> Total records: 1
#>
#> Database 'bittern.nc' does not exist, creating it...
#>
#> Downloading records for each collection:
#> SAMPLE1
#> Records 1 to 1 / 1To access private/semi-public projects/collections you must sign
up for a free
NatureCounts account and
register for
the projects you’d like to access. Once registered, you can use the
username argument (you will be prompted for a password) for both
nc_count() and nc_data_dl(), which will then return a different set
of records.
nc_count(username = "my_user_name")
bittern <- nc_data_dl(species = 2490, username = "my_user_name", info = "readme_example")nc_count() and nc_data_dl() have a variety of arguments that allow
you to filter the counts/data prior to downloading. These options
include collections, species, years, doy (day-of-year),
region, and site_type (users can specify up to 3 of these). For
nc_data_dl() you have the additional arguments fields_set and
fields with which you can customize which fields/columns to include in
your download.
See the function examples
(nc_count(),
nc_data_dl())
the following articles for more information on these filters:
- Collections
- Species Codes
- Regional Codes
- IBAs and BCRs (regions)
- Using spatial data to filter observations
We also have an article on post-filtering your data
NatureCounts includes a great deal of metadata which can be accessed
through the functions with the meta_ prefix. See the Meta
Documentation
for specifics.