Read an eBird Basic Dataset file using readr::read_delim()
. read_ebd()
reads the EBD itself, while read_sampling()` reads a sampling event data
file.
Usage
read_ebd(x, sep = "\t", unique = TRUE, rollup = TRUE)
# S3 method for character
read_ebd(x, sep = "\t", unique = TRUE, rollup = TRUE)
# S3 method for auk_ebd
read_ebd(x, sep = "\t", unique = TRUE, rollup = TRUE)
read_sampling(x, sep = "\t", unique = TRUE)
# S3 method for character
read_sampling(x, sep = "\t", unique = TRUE)
# S3 method for auk_ebd
read_sampling(x, sep = "\t", unique = TRUE)
# S3 method for auk_sampling
read_sampling(x, sep = "\t", unique = TRUE)
Arguments
- x
filename or
auk_ebd
object with associated output files as created byauk_filter()
.- sep
character; single character used to separate fields within a row.
- unique
logical; should duplicate grouped checklists be removed. If
unique = TRUE
,auk_unique()
is called on the EBD before returning.- rollup
logical; should taxonomic rollup to species level be applied. If
rollup = TRUE
,auk_rollup()
is called on the EBD before returning. Note that this process can be time consuming for large files, try turning rollup off if reading is taking too long.
Value
A data frame of EBD observations. An additional column,
checklist_id
, is added to output files if unique = TRUE
, that uniquely
identifies the checklist from which the observation came. This field is
equal to sampling_event_identifier
for non-group checklists, and
group_identifier
for group checklists.
Details
This functions performs the following processing steps:
Data types for columns are manually set based on column names used in the February 2017 EBD. If variables are added or names are changed in later releases, any new variables will have data types inferred by the import function used.
Variables names are converted to
snake_case
.Duplicate observations resulting from group checklists are removed using
auk_unique()
, unlessunique = FALSE
.
Methods (by class)
read_ebd(character)
: Filename of EBD.read_ebd(auk_ebd)
:auk_ebd
object output fromauk_filter()
Functions
read_sampling(character)
: Filename of sampling event data fileread_sampling(auk_ebd)
:auk_ebd
object output fromauk_filter()
. Must have had a sampling event data file set in the original call toauk_ebd()
.read_sampling(auk_sampling)
:auk_sampling
object output fromauk_filter()
.
See also
Other import:
auk_zerofill()
Examples
f <- system.file("extdata/ebd-sample.txt", package = "auk")
read_ebd(f)
#> # A tibble: 494 × 45
#> checklist_id global_unique_identi…¹ last_edited_date taxonomic_order category
#> <chr> <chr> <chr> <dbl> <chr>
#> 1 S6852862 URN:CornellLabOfOrnit… 2016-02-22 14:5… 20145 species
#> 2 S14432467 URN:CornellLabOfOrnit… 2013-06-16 17:3… 20145 species
#> 3 S39033556 URN:CornellLabOfOrnit… 2017-09-06 13:1… 20145 species
#> 4 S38303088 URN:CornellLabOfOrnit… 2017-07-24 15:1… 20145 species
#> 5 S14439180 URN:CornellLabOfOrnit… 2013-06-16 22:2… 20145 species
#> 6 S32118689 URN:CornellLabOfOrnit… 2016-10-19 20:3… 20145 species
#> 7 S30663744 URN:CornellLabOfOrnit… 2016-11-16 13:5… 20145 species
#> 8 S39245968 URN:CornellLabOfOrnit… 2017-09-18 05:2… 20145 species
#> 9 S9816729 URN:CornellLabOfOrnit… 2012-02-12 17:2… 20145 species
#> 10 S30669718 URN:CornellLabOfOrnit… 2016-07-13 13:0… 20145 species
#> # ℹ 484 more rows
#> # ℹ abbreviated name: ¹global_unique_identifier
#> # ℹ 40 more variables: common_name <chr>, scientific_name <chr>,
#> # observation_count <chr>, breeding_code <chr>, breeding_category <chr>,
#> # age_sex <chr>, country <chr>, country_code <chr>, state <chr>,
#> # state_code <chr>, county <chr>, county_code <chr>, iba_code <chr>,
#> # bcr_code <int>, usfws_code <chr>, atlas_block <chr>, locality <chr>, …
# read a sampling event data file
x <- system.file("extdata/zerofill-ex_sampling.txt", package = "auk") %>%
read_sampling()