Generate custom table queries with the table name and filter arguments.
nc_query_table(
table = NULL,
...,
username = NULL,
timeout = 120,
verbose = FALSE
)Character. Table to query (see details)
Name/value pairs for custom queries/filters (see details)
Character vector. Username for http://naturecounts.ca. If provided, the user will be prompted for a password. If left NULL, only public collections will be returned.
Numeric. Number of seconds before connecting to the server times out.
Logical. Show messages?
data.frame()
nc_query_table(username = "sample") for available options
# What tables are available? What 'filters' do they take? Are any 'required'?
nc_query_table(username = "sample")
#> table_name filters required
#> 1 bmde_filter_bad_dates project_id,SiteCode,species_id NA
#> 2 vwResultsRnest <NA> NA
#> 3 vwSpeciesLifeHistory <NA> NA
# Query the bmdefilter_bad_dates table
d <- nc_query_table(table = "bmde_filter_bad_dates", username = "sample")
head(d)
#> survey_month species_id period comments project_id SiteCode
#> 1 0 0 spring beaver damage 1005 ACBO
#> 2 0 0 spring beaver damage 1005 ACBO
#> 3 0 0 spring missing 1005 BBO
#> 4 0 0 spring missing 1005 BBO
#> 5 0 0 0 covid disruption 1005 BBO
#> 6 0 7680 0 began NSWO surveys in 2000 1005 BBO
#> survey_year start_year end_year survey_day
#> 1 2019 0 0 0
#> 2 2022 0 0 0
#> 3 2002 0 0 0
#> 4 2019 0 0 0
#> 5 2020 0 0 0
#> 6 0 0 1999 0
# Filter our query
d <- nc_query_table(table = "bmde_filter_bad_dates",
SiteCode = "DMBO", username = "sample")
d
#> survey_month species_id period comments
#> 1 1 0 0 Changed sites in spring in 2006 - new site
#> 2 2 0 0 Changed sites in spring in 2006 - new site
#> 3 3 0 0 Changed sites in spring in 2006 - new site
#> 4 4 0 0 Changed sites in spring in 2006 - new site
#> 5 5 0 0 Changed sites in spring in 2006 - new site
#> 6 6 0 0 Changed sites in spring in 2006 - new site
#> 7 0 0 0 Oak Hammock Marsh covid dates
#> project_id SiteCode survey_year start_year end_year survey_day
#> 1 1005 DMBO 0 0 2005 0
#> 2 1005 DMBO 0 0 2005 0
#> 3 1005 DMBO 0 0 2005 0
#> 4 1005 DMBO 0 0 2005 0
#> 5 1005 DMBO 0 0 2005 0
#> 6 1005 DMBO 0 0 2005 0
#> 7 1005 DMBO 2020 0 0 0
# Filter our query
d <- nc_query_table(table = "bmde_filter_bad_dates",
species_id = 15770, username = "sample")
# Want more than one species? Either filter after, or combine two queries
# Filter after
library(dplyr)
d <- nc_query_table(table = "bmde_filter_bad_dates", username = "sample")
d <- filter(d, species_id %in% c(15770, 9750))
# Combine two queries
d1 <- nc_query_table(table = "bmde_filter_bad_dates",
species_id = 15770, username = "sample")
d2 <- nc_query_table(table = "bmde_filter_bad_dates",
species_id = 9750, username = "sample")
d <- rbind(d1, d2)