Read an EBD file

Source: R/read.R

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.

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 by auk_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(), unless unique = FALSE.

Methods (by class)

  • read_ebd(character): Filename of EBD.

  • read_ebd(auk_ebd): auk_ebd object output from auk_filter()

Functions

  • read_sampling(character): Filename of sampling event data file

  • read_sampling(auk_ebd): auk_ebd object output from auk_filter(). Must have had a sampling event data file set in the original call to auk_ebd().

  • read_sampling(auk_sampling): auk_sampling object output from auk_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…¹ last_…² taxon…³ categ…⁴ commo…⁵ scien…⁶ obser…⁷ breed…⁸
#>    <chr>        <chr>    <chr>     <dbl> <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 S6852862     URN:Cor… 2016-0…   20145 species Green … Cyanoc… 4       NA     
#>  2 S14432467    URN:Cor… 2013-0…   20145 species Green … Cyanoc… 2       NA     
#>  3 S39033556    URN:Cor… 2017-0…   20145 species Green … Cyanoc… 1       NA     
#>  4 S38303088    URN:Cor… 2017-0…   20145 species Green … Cyanoc… 1       NA     
#>  5 S14439180    URN:Cor… 2013-0…   20145 species Green … Cyanoc… 2       NA     
#>  6 S32118689    URN:Cor… 2016-1…   20145 species Green … Cyanoc… X       NA     
#>  7 S30663744    URN:Cor… 2016-1…   20145 species Green … Cyanoc… 6       NA     
#>  8 S39245968    URN:Cor… 2017-0…   20145 species Green … Cyanoc… 3       NA     
#>  9 S9816729     URN:Cor… 2012-0…   20145 species Green … Cyanoc… 3       NA     
#> 10 S30669718    URN:Cor… 2016-0…   20145 species Green … Cyanoc… 2       NA     
#> # … with 484 more rows, 36 more variables: 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>,
#> #   locality_id <chr>, locality_type <chr>, latitude <dbl>, longitude <dbl>,
#> #   observation_date <date>, time_observations_started <chr>,
#> #   observer_id <chr>, sampling_event_identifier <chr>, protocol_type <chr>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
# read a sampling event data file
x <- system.file("extdata/zerofill-ex_sampling.txt", package = "auk") %>%
  read_sampling()