Access and download data on plant and animal populations from various databases through NatureCounts, a service managed by Bird Studies Canada.
You can install this developmental version of naturecounts
from GitHub
with the remotes package:
install.packages("remotes")
remotes::install_github("BirdStudiesCanada/naturecounts")
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")
#> collection nrecords
#> 1 ABATLAS1 123364
#> 2 ABATLAS2 201398
#> 3 ABBIRDRECS 357264
#> 4 BGRMM 476
#> 5 EBUTTERFLY 26192
#> 6 IMMP 43
#> 7 IMMP_A2 24
#> 8 IMMP_BW 19
#> 9 IMQC 761
#> 10 MEXU 3251
#> 11 MLMP 802
#> 12 MM 4343
#> 13 MONARCHWATCH 145665
#> 14 PMMM 5
#> 15 RCBIOTABASE 12598
#> 16 SAMPLE1 1000
#> 17 SAMPLE2 1000
#> 18 WMMM 8981
Use the show = "all"
argument to show counts for all collections
available (public or otherwise).
nc_count(show = "all") %>%
head()
#> collection nrecords
#> 1 ABATLAS1 123364
#> 2 ABATLAS2 201398
#> 3 ABBIRDRECS 357264
#> 4 ATOWLS 367
#> 5 BBS 5735895
#> 6 BBS50-CAN 3219534
Fetch all observations of moose which are available to user sample into a local data frame.
moose <- nc_data_dl(species = 133990, username = "sample")
#> Using filters: species (133990); fields_set (BMDE2.00-min)
#> Collecting available records...
#> collection nrecords
#> 1 RCBIOTABASE 2
#>
#> Downloading records for each collection:
#> RCBIOTABASE
#> Records 1 to 2 / 2
Alternatively, save the downloaded data as a SQLite database
(moose.nc
).
moose <- nc_data_dl(species = 133990, sql_db = "moose", username = "sample")
#> Using filters: species (133990); fields_set (BMDE2.00-min)
#> Collecting available records...
#> collection nrecords
#> 1 RCBIOTABASE 2
#>
#> Database 'moose.nc' does not exist, creating it...
#>
#> Downloading records for each collection:
#> RCBIOTABASE
#> Records 1 to 2 / 2
To 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")
moose <- nc_data_dl(species = 133990, username = "my_user_name")
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.