# Introduction to auk

eBird is an online tool for recording bird observations. Since its inception, nearly 500 million records of bird sightings (i.e. combinations of location, date, time, and bird species) have been collected, making eBird one of the largest citizen science projects in history and an extremely valuable resource for bird research and conservation. The full eBird database is packaged as a text file and available for download as the eBird Basic Dataset (EBD). Due to the large size of this dataset, it must be filtered to a smaller subset of desired observations before reading into R. This filtering is most efficiently done using AWK, a Unix utility and programming language for processing column formatted text data. This package acts as a front end for AWK, allowing users to filter eBird data before import into R.

This vignette is divided into three sections. The first section provides background on the eBird data and motivation for the development of this package. The second section outlines the use of `auk` for filtering text file to produce a presence-only dataset. The final section demonstrates how `auk` can be used to produce zero-filled, presence-absence (or more correctly presence–non-detection) data, a necessity for many modeling and analysis applications.

## Quick start

This package uses the command-line program AWK to extract subsets of the eBird Basic Dataset for use in R. This is a multi-step process:

1. Define a reference to the eBird data file.
2. Define a set of spatial, temporal, or taxonomic filters. Each type of filter corresponds to a different function, e.g. `auk_species` to filter by species. At this stage the filters are only set up, no actual filtering is done until the next step.
3. Filter the eBird data text file, producing a new text file with only the selected rows.
4. Import this text file into R as a data frame.

Because the eBird dataset is so large, step 3 typically takes several hours to run. Here’s a simple example that extract all Canada Jay records from within Canada.

    library(auk)
    # path to the ebird data file, here a sample included in the package
    # in practice, provide path to ebd, e.g. input_file <- "data/ebd_relFeb-2018.txt"
    input_file <- system.file("extdata/ebd-sample.txt", package = "auk")
    # output text file
    output_file <- "ebd_filtered_grja.txt"
    ebird_data <- input_file %>%
      # 1. reference file
      auk_ebd() %>%
      # 2. define filters
      auk_species(species = "Canada Jay") %>%
      auk_country(country = "Canada") %>%
      # 3. run filtering
      auk_filter(file = output_file) %>%
      # 4. read text file into r data frame
      read_ebd()

For those not familiar with the pipe operator (`%>%`), the above code could be rewritten:

    input_file <- system.file("extdata/ebd-sample.txt", package = "auk")
    output_file <- "ebd_filtered_grja.txt"
    ebd <- auk_ebd(input_file)
    ebd_filters <- auk_species(ebd, species = "Canada Jay")
    ebd_filters <- auk_country(ebd_filters, country = "Canada")
    ebd_filtered <- auk_filter(ebd_filters, file = output_file)
    ebd_df <- read_ebd(ebd_filtered)

## Background

### The eBird Basic Dataset

The eBird database currently contains nearly 500 million bird observations, and this rate of increase is accelerating as new users join eBird. These data are an extremely valuable tool both for basic science and conservation; however, given the sheer amount of data, accessing eBird data poses a unique challenge. Currently, access to the complete set of eBird observations is provided via the eBird Basic Dataset (EBD). This is a tab-separated text file, released quarterly, containing all validated bird sightings in the eBird database at the time of release. Each row corresponds to the sighting of a single species within a checklist and, in addition to the species and number of individuals reported, information is provided at the checklist level (location, time, date, search effort, etc.).

In addition, eBird provides a Sampling Event Data file that contains the checklist-level data for every valid checklist submitted to eBird, including checklists for which no species of birds were reported. In this file, each row corresponds to a checklist and only the checklist-level variables are included, not the associated bird data. While the eBird Basic Dataset provides presence-only data, it can be combined with the Sampling Event Data file to produce presence-absence data. This process is described below.

For full metadata on the both datasets, consult the documentation provided when the files are downloaded.

## `auk` vs. `rebird`

Those interested in eBird data may also want to consider `rebird`, an R package that provides an interface to the eBird APIs. The functions in `rebird` are mostly limited to accessing recent (i.e. within the last 30 days) observations, although `ebirdfreq()` does provide historical frequency of observation data. In contrast, `auk` gives access to the full set of ~ 500 million eBird observations. For most ecological applications, users will require `auk`; however, for some use cases, e.g. building tools for birders, `rebird` provides a quick and easy way to access data.

### Data access

To access eBird data, begin by creating an eBird account and signing in. Then visit the Download Data page. eBird data access is free; however, you will need to request access in order to obtain access to the EBD. Filling out the access request form allows eBird to keep track of the number of people using the data and obtain information on the applications for which the data are used

Once you have access to the data, proceed to the download page. There are two download options: prepackage download and custom download. Downloading the prepackaged option gives you access to the full global dataset. If you choose this route, you’ll likely want to download both the EBD (~ 25 GB) and corresponding Sampling Event Data (~ 2.5 GB). If you know you’re likely to only need data for a single species, or a small region, you can request a custom download be prepared consisting of only a subset of the data. This will result in significantly smaller files; however, note that custom requests that would result in huge numbers of checklists (e.g. all records from the US) won’t work. In either case, download and decompress the files.

### Example data

This package comes with two example datasets. The first is suitable for practicing filtering the EBD and producing presence-only data. It’s a sample of 400 records from the EBD. It contains data from North and Central America from 2010-2012 on 3 jay species: Canada Jay, Blue Jay, and Green Jay. It can be accessed with:

    library(auk)
    library(dplyr)
    system.file("extdata/ebd-sample.txt", package = "auk")

The second is suitable for producing zero-filled, presence-absence data. It contains every sighting from Singapore in the first half of 2012 of Collared Kingfisher, White-throated Kingfisher, and Blue-eared Kingfisher. The full Sampling Event Data file is also included, and contains all checklists from Singapore in the first half of 2012. These files can be accessed with:

    # ebd
    system.file("extdata/zerofill-ex_ebd.txt", package = "auk")
    # sampling event data
    system.file("extdata/zerofill-ex_sampling.txt", package = "auk")

Important note: in this vignette, `system.file()` is used to return the path to the example data included in this package. When using `auk` in practice, provide the path to the location of the EBD on your computer, this could be a relative path, e.g. `"data/ebd_relFeb-2018.txt"`, or an absolute path, e.g. `"~/ebird/ebd_relFeb-2018/ebd_relFeb-2018.txt"`.

### AWK

R typically works with objects in memory and, as a result, there is a hard limit on the size of objects that can be brought into R. Because eBird contains nearly 500 million sightings, the eBird Basic Dataset is an inherently large file (~150 GB uncompressed) and therefore impossible to manipulate directly in R. Thus it is generally necessary to create a subset of the file outside of R, then import this smaller subset for analysis.

AWK is a Unix utility and programming language for processing column formatted text data. It is highly flexible and extremely fast, making it a valuable tool for pre-processing the eBird data in order to create the smaller subset of data that is required. Users of the data can use AWK to produce a smaller file, subsetting the full text file taxonomically, spatially, or temporally, in order to produce a smaller file that can then be loaded in to R for visualization, analysis, and modelling.

Although AWK is a powerful tool, it has three disadvantages: it requires learning the syntax of a new language, it is only accessible via the command line, and it results in a portion of your workflow existing outside of R. This package is a wrapper for AWK specifically designed for filtering eBird data The goal is to ease the use of the this data by removing the hurdle of learning and using AWK.

Linux and Mac users should already have AWK installed on their machines, however, Windows uses will need to install Cygwin to gain access to AWK. Note that Cygwin should be installed in the default location (`C:/cygwin/bin/gawk.exe` or `C:/cygwin64/bin/gawk.exe`) in order for `auk` to work. To check that AWK is installed and can be found run `auk_getpath()`.

If AWK is installed in a non-standard location, or can’t be found by `auk`, you can manually set the path to AWK. To do so, set the `AWK_PATH` environment in your `.Renviron` file. For example, Mac and Linux users might add the following line:

    AWK_PATH=/usr/bin/awk

while Windows users might add:

    AWK_PATH=C:/cygwin64/bin/gawk.exe

### A note on versions

This package contains a current (as of the time of package release) version of the bird taxonomy used by eBird. This taxonomy determines the species that can be reported in eBird and therefore the species that users of `auk` can extract from the EBD. eBird releases an updated taxonomy once a year, typically in August, at which time `auk` will be updated to include the current taxonomy. When using `auk`, users should be careful to ensure that the version they’re using is in sync with the EBD file they’re working with. This is most easily accomplished by always using the most recent version of `auk` and the most recent release of the eBird Basic Dataset

## Presence data

The most common use of the eBird data is to produce a set of bird sightings, i.e. where and when was a given species seen. For example, this type of data could be used to produce a map of sighting locations, or to determine if a given bird has been seen in an area of interest. For more analytic work, such as species distribution modeling, presence and absence data are likely preferred (see Guillera-Arroita et al. 2015). Producing presence-absence data will be covered in the next section.

### The `auk_ebd` object

This package uses an `auk_ebd` object to keep track of the input data file, any filters defined, and the output file that is produced after filtering has been executed. By keeping everything wrapped up in one object, the user can keep track of exactly what set of input data and filters produced any given output data. To set up the initial `auk_ebd` object, use `auk_ebd()`:

    ebd <- system.file("extdata/ebd-sample.txt", package = "auk") %>%
      auk_ebd()
    ebd
    #> Input
    #>   EBD: /private/var/folders/wf/y1p0nztd2ps95ppplsl5bjjr0000gs/T/RtmpBCe4Qj/Rinstdebc8747d20/auk/extdata/ebd-sample.txt
    #>
    #> Output
    #>   Filters not executed
    #>
    #> Filters
    #>   Species: all
    #>   Countries: all
    #>   States: all
    #>   Counties: all
    #>   BCRs: all
    #>   Bounding box: full extent
    #>   Years: all
    #>   Date: all
    #>   Start time: all
    #>   Last edited date: all
    #>   Protocol: all
    #>   Project code: all
    #>   Duration: all
    #>   Distance travelled: all
    #>   Records with breeding codes only: no
    #>   Exotic Codes: all
    #>   Complete checklists only: no

### Defining filters

`auk` uses a pipeline-based workflow for defining filters, which can then be compiled into an AWK script. Any of the following filters can be applied:

- `auk_species()`: filter by species using common or scientific names.
- `auk_country()`: filter by country using the standard English names or ISO 2-letter country codes.
- `auk_state()`: filter by state using the eBird state codes, see `?ebird_states`.
- `auk_bcr()`: filter by Bird Conservation Region (BCR) using BCR codes, see `?bcr_codes`.
- `auk_bbox()`: filter by spatial bounding box, i.e. a range of latitudes and longitudes in decimal degrees.
- `auk_date()`: filter to checklists from a range of dates. To extract observations from a range of dates, regardless of year, use the wildcard “`*`” in place of the year, e.g. `date = c("*-05-01", "*-06-30")` for observations from May and June of any year.
- `auk_last_edited()`: filter to checklists from a range of last edited dates, useful for extracting just new or recently edited data.
- `auk_protocol()`: filter to checklists that following a specific search protocol, either stationary, traveling, or casual.
- `auk_project()`: filter to checklists collected as part of a specific project (e.g. a breeding bird survey).
- `auk_time()`: filter to checklists started during a range of times-of-day.
- `auk_duration()`: filter to checklists with observation durations within a given range.
- `auk_distance()`: filter to checklists with distances travelled within a given range.
- `auk_breeding()`: only retain observations that have an associate breeding bird atlas code.
- `auk_complete()`: only retain checklists in which the observer has specified that they recorded all species seen or heard. It is necessary to retain only complete records for the creation of presence-absence data, because the “absence” information is inferred by the lack of reporting of a species on checklists.

Note that all of the functions listed above only modify the `auk_ebd` object, in order to define the filters. Once the filters have been defined, the filtering is actually conducted using `auk_filter()`.

    ebd_filters <- ebd %>%
      # species: common and scientific names can be mixed
      auk_species(species = c("Canada Jay", "Cyanocitta cristata")) %>%
      # country: codes and names can be mixed; case insensitive
      auk_country(country = c("US", "Canada", "mexico")) %>%
      # bbox: long and lat in decimal degrees
      # formatted as `c(lng_min, lat_min, lng_max, lat_max)`
      auk_bbox(bbox = c(-100, 37, -80, 52)) %>%
      # date: use standard ISO date format `"YYYY-MM-DD"`
      auk_date(date = c("2012-01-01", "2012-12-31")) %>%
      # time: 24h format
      auk_time(start_time = c("06:00", "09:00")) %>%
      # duration: length in minutes of checklists
      auk_duration(duration = c(0, 60)) %>%
      # complete: all species seen or heard are recorded
      auk_complete()
    ebd_filters
    #> Input
    #>   EBD: /private/var/folders/wf/y1p0nztd2ps95ppplsl5bjjr0000gs/T/RtmpBCe4Qj/Rinstdebc8747d20/auk/extdata/ebd-sample.txt
    #>
    #> Output
    #>   Filters not executed
    #>
    #> Filters
    #>   Species: Cyanocitta cristata, Perisoreus canadensis
    #>   Countries: CA, MX, US
    #>   States: all
    #>   Counties: all
    #>   BCRs: all
    #>   Bounding box: Lon -100 - -80; Lat 37 - 52
    #>   Years: all
    #>   Date: 2012-01-01 - 2012-12-31
    #>   Start time: 06:00-09:00
    #>   Last edited date: all
    #>   Protocol: all
    #>   Project code: all
    #>   Duration: 0-60 minutes
    #>   Distance travelled: all
    #>   Records with breeding codes only: no
    #>   Exotic Codes: all
    #>   Complete checklists only: yes

In all cases, extensive checks are performed to ensure filters are valid. For example, species are checked against the official eBird taxonomy and countries are checked using the `countrycode` package. This is particularly important because filtering is a time consuming process, so catching errors in advance can avoid several hours of wasted time.

### Executing filters

Each of the functions described in the Defining filters section only defines a filter. Once all of the required filters have been set, `auk_filter()` should be used to compile them into an AWK script and execute it to produce an output file. So, as an example of bringing all of these steps together, the following commands will extract all Canada Jay and Blue Jay records from Canada and save the results to a tab-separated text file for subsequent use:

    output_file <- "ebd_filtered_blja-grja.txt"
    ebd_jays <- system.file("extdata/ebd-sample.txt", package = "auk") %>%
      auk_ebd() %>%
      auk_species(species = c("Canada Jay", "Cyanocitta cristata")) %>%
      auk_country(country = "Canada") %>%
      auk_filter(file = output_file)

Filtering the full EBD typically takes at least a couple hours, so set it running then go grab lunch!

### Reading

eBird Basic Dataset files can be read with `read_ebd()`. This is a wrapper around `readr::read_delim()`. `read_ebd()` uses `stringsAsFactors = FALSE`, `quote = ""`, sets column classes, and converts variable names to `snake_case`.

    system.file("extdata/ebd-sample.txt", package = "auk") %>%
      read_ebd() %>%
      glimpse()
    #> Rows: 398
    #> Columns: 48
    #> $ checklist_id              <chr> "G1131664", "G1131665", "G1158137", "G115813…
    #> $ global_unique_identifier  <chr> "URN:CornellLabOfOrnithology:EBIRD:OBS294400…
    #> $ last_edited_date          <chr> "2021-03-29 21:21:52.583259", "2020-02-01 20…
    #> $ taxonomic_order           <int> 20724, 20724, 20674, 20674, 20724, 20724, 20…
    #> $ category                  <chr> "species", "species", "species", "species", …
    #> $ taxon_concept_id          <chr> "avibase-361B447A", "avibase-361B447A", "avi…
    #> $ common_name               <chr> "Green Jay", "Green Jay", "Canada Jay", "Can…
    #> $ scientific_name           <chr> "Cyanocorax yncas", "Cyanocorax yncas", "Per…
    #> $ exotic_code               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ observation_count         <chr> "2", "6", "1", "1", "X", "3", "4", "3", "6",…
    #> $ breeding_code             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ breeding_category         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ behavior_code             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ age_sex                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ country                   <chr> "United States", "United States", "Canada", …
    #> $ country_code              <chr> "US", "US", "CA", "CA", "MX", "MX", "CA", "U…
    #> $ state                     <chr> "Texas", "Texas", "British Columbia", "Briti…
    #> $ state_code                <chr> "US-TX", "US-TX", "CA-BC", "CA-BC", "MX-NLE"…
    #> $ county                    <chr> "Zapata", "Starr", "Northern Rockies", "Nort…
    #> $ county_code               <chr> "US-TX-505", "US-TX-427", "CA-BC-NR", "CA-BC…
    #> $ iba_code                  <chr> NA, NA, NA, NA, "MX_69", NA, NA, NA, NA, NA,…
    #> $ bcr_code                  <int> 36, 36, 6, 6, 48, 36, 13, 10, 36, 48, 48, 8,…
    #> $ usfws_code                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ atlas_block               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ locality                  <chr> "Zapata Library / City Park (LTC 085)", "Fal…
    #> $ locality_id               <chr> "L846015", "L128962", "L343808", "L343808", …
    #> $ locality_type             <chr> "H", "H", "H", "H", "H", "H", "H", "H", "H",…
    #> $ latitude                  <dbl> 26.90220, 26.58361, 58.82617, 58.82617, 25.5…
    #> $ longitude                 <dbl> -99.27106, -99.14529, -122.90187, -122.90187…
    #> $ observation_date          <date> 2011-11-14, 2011-11-14, 2011-06-14, 2011-06…
    #> $ time_observations_started <chr> "06:45:00", "08:15:00", "10:30:00", "07:00:0…
    #> $ observer_id               <chr> "obsr554038", "obsr146271", "obsr12384", "ob…
    #> $ sampling_event_identifier <chr> "S21633922", "S9118288", "S22036612", "S2203…
    #> $ protocol_type             <chr> "Traveling", "Traveling", "Stationary", "Sta…
    #> $ protocol_code             <chr> "P22", "P22", "P21", "P21", "P22", "P22", "P…
    #> $ project_code              <chr> "EBIRD", "EBIRD", "EBIRD", "EBIRD", "EBIRD",…
    #> $ duration_minutes          <int> 30, 60, 60, 90, 90, 90, 90, 35, 60, 60, 75, …
    #> $ effort_distance_km        <dbl> 1.609, 3.219, NA, NA, 1.000, 1.500, NA, 3.21…
    #> $ effort_area_ha            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.04…
    #> $ number_observers          <int> 2, 2, 13, 13, 7, 2, 2, 5, 4, 5, 5, 2, 5, 10,…
    #> $ all_species_reported      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
    #> $ group_identifier          <chr> "G1131664", "G1131665", "G1158137", "G115813…
    #> $ has_media                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
    #> $ approved                  <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
    #> $ reviewed                  <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
    #> $ reason                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ trip_comments             <chr> NA, NA, "BCFO extension trip", "BCFO extensi…
    #> $ species_comments          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

`auk_filter()` returns an `auk_ebd` object with the output file paths stored in it. This `auk_ebd` object can then be passed directly to `auk_read()`, allowing for a complete pipeline. For example, we can create an `auk_ebd` object, define filters, filter with AWK, and read in the results all at once.

    output_file <- "ebd_filtered_blja-grja.txt"
    ebd_df <- system.file("extdata/ebd-sample.txt", package = "auk") %>%
      auk_ebd() %>%
      auk_species(species = c("Canada Jay", "Cyanocitta cristata")) %>%
      auk_country(country = "Canada") %>%
      auk_filter(file = output_file) %>%
      read_ebd()

### Saving the AWK command

The AWK script can be saved for future reference by providing an output filename to `awk_file`. In addition, by setting `execute = FALSE` the AWK script will be generated but not run.

    awk_script <- system.file("extdata/ebd-sample.txt", package = "auk") %>%
      auk_ebd() %>%
      auk_species(species = c("Canada Jay", "Cyanocitta cristata")) %>%
      auk_country(country = "Canada") %>%
      auk_filter(awk_file = "awk-script.txt", execute = FALSE)
    # read back in and prepare for printing
    awk_file <- readLines(awk_script)
    unlink("awk-script.txt")
    awk_file[!grepl("^[[:space:]]*$", awk_file)] %>%
      paste0(collapse = "\n") %>%
      cat()
    #> BEGIN {
    #>   FS = OFS = "   "
    #>     split("Cyanocitta cristata   Perisoreus canadensis", speciesValues, "    ")
    #>     for (i in speciesValues) species[speciesValues[i]] = 1
    #>     split("CA", countryValues, " ")
    #>     for (i in countryValues) countries[countryValues[i]] = 1
    #> }
    #> {
    #>   keep = 1
    #>   # filters
    #>   if (keep == 1 && ($7 in species)) {
    #>     keep = 1
    #>   } else {
    #>     keep = 0
    #>   }
    #>   if (keep == 1 && ($17 in countries)) {
    #>     keep = 1
    #>   } else {
    #>     keep = 0
    #>   }
    #>   # keeps header
    #>   if (NR == 1) {
    #>     keep = 1
    #>   }
    #>   if (keep == 1) {
    #>     print $0
    #>   }
    #> }

### Group checklists

eBird allows observers birding together to share checklists. This process creates a new copy of the original checklist for each observer with whom the original checklist was shared; these copies can then be tweaked to add or remove some species that weren’t seen by the entire group, or altering the sampling-event data. For most applications, it’s best to remove these duplicate (or near-duplicate) checklists. `auk_unique()` removes duplicates resulting from group checklists by selecting the observation with the lowest `sampling_event_identifier` (a unique ID for each checklist); this is the original checklists from which shared copies were generated. In addition to removing duplicates, a `checklist_id` field is added, which is equal to the `sampling_event_identifier` for non-group checklists and the `group_identifier` for grouped checklists. After running `auk_unique()`, every species will have a single entry for each `checklist_id`.

`read_ebd()` automatically runs `auk_unique()`, however, we can use `unique = FALSE` then manually run `auk_unique()`.

    # read in an ebd file and don't automatically remove duplicates
    ebd_dupes <- system.file("extdata/ebd-sample.txt", package = "auk") %>%
      read_ebd(unique = FALSE)
    # remove duplicates
    ebd_unique <- auk_unique(ebd_dupes)
    # compare number of rows
    nrow(ebd_dupes)
    #> [1] 400
    nrow(ebd_unique)
    #> [1] 398

### Taxonomic rollup

The eBird Basic Dataset includes both true species and other taxa, including domestics, hybrids, subspecies, “spuhs”, and recognizable forms. In some cases, a checklist may contain multiple records for the same species, for example, both Audubon’s and Myrtle Yellow-rumped Warblers, as well as some records that are not resolvable to species, for example, “warbler sp.”. For most use cases, a single record for each species on each checklist is desired. The function `ebd_rollup()` addresses these cases by removing taxa not identifiable to species and rolling up taxa identified below species level to a single record for each species in each checklist.

    # read in sample data without rolling up
    ebd <- system.file("extdata/ebd-rollup-ex.txt", package = "auk") %>%
      read_ebd(rollup = FALSE)
    # apply roll up
    ebd_ru <- auk_rollup(ebd)

    # all taxa not identifiable to species are dropped
    # taxa below species have been rolled up to species
    unique(ebd$category)
    #> [1] "domestic"   "form"       "hybrid"     "intergrade" "slash"
    #> [6] "spuh"       "species"    "issf"
    unique(ebd_ru$category)
    #> [1] "species"

    # yellow-rump warbler subspecies rollup
    # without rollup, there are three observations
    ebd %>%
      filter(common_name == "Yellow-rumped Warbler") %>%
      select(checklist_id, category, common_name, subspecies_common_name,
             observation_count)
    #> # A tibble: 4 × 5
    #>   checklist_id category   common_name   subspecies_common_name observation_count
    #>   <chr>        <chr>      <chr>         <chr>                  <chr>
    #> 1 S44943108    intergrade Yellow-rumpe… Yellow-rumped Warbler… 1
    #> 2 S129851825   species    Yellow-rumpe… <NA>                   1
    #> 3 S129851825   issf       Yellow-rumpe… Yellow-rumped Warbler… 1
    #> 4 S129851825   issf       Yellow-rumpe… Yellow-rumped Warbler… 2
    # with rollup, they have been combined
    ebd_ru %>%
      filter(common_name == "Yellow-rumped Warbler") %>%
      select(checklist_id, category, common_name, observation_count)
    #> # A tibble: 2 × 4
    #>   checklist_id category common_name           observation_count
    #>   <chr>        <chr>    <chr>                 <chr>
    #> 1 S129851825   species  Yellow-rumped Warbler 4
    #> 2 S44943108    species  Yellow-rumped Warbler 1

By default, `read_ebd()` calls `ebd_rollup()` when importing an eBird Basic Dataset file. To avoid this, and retain subspecies, use `read_ebd(rollup = FALSE)`.

## Zero-filled, presence-absence data

For many applications, presence-only data are sufficient; however, for modeling and analysis, presence-absence data are required. eBird observers only explicitly collect presence data, but they have the option of flagging their checklist as “complete” meaning that they are reporting all the species they saw or heard, and identified. Therefore, given a list of positive sightings (the basic dataset) and a list of all checklists (the sampling event data) it is possible to infer absences by filling zeros for all species not explicitly reported. This section of the vignette describes functions for producing zero-filled, presence-absence data.

### Filtering

When preparing to create zero-filled data, the eBird Basic Dataset and sampling event data must be filtered to the same set of checklists to ensure consistency. To ensure these two datasets are synced, provide both to `auk_ebd`, then filter as described in the previous section. This will ensure that all the filters applied to the ebd (except species) will be applied to the sampling event data so that we’ll be working with the same set of checklists. It is critical that `auk_compete()` is called, since complete checklists are a requirement for zero-filling.

For example, the following filters to only include sightings of Collared Kingfisher between 6 and 10am:

    # to produce zero-filled data, provide an EBD and sampling event data file
    f_ebd <- system.file("extdata/zerofill-ex_ebd.txt", package = "auk")
    f_smp <- system.file("extdata/zerofill-ex_sampling.txt", package = "auk")
    filters <- auk_ebd(f_ebd, file_sampling = f_smp) %>%
      auk_species("Collared Kingfisher") %>%
      auk_time(c("06:00", "10:00")) %>%
      auk_complete()
    filters
    #> Input
    #>   EBD: /private/var/folders/wf/y1p0nztd2ps95ppplsl5bjjr0000gs/T/RtmpBCe4Qj/Rinstdebc8747d20/auk/extdata/zerofill-ex_ebd.txt
    #>   Sampling events: /private/var/folders/wf/y1p0nztd2ps95ppplsl5bjjr0000gs/T/RtmpBCe4Qj/Rinstdebc8747d20/auk/extdata/zerofill-ex_sampling.txt
    #>
    #> Output
    #>   Filters not executed
    #>
    #> Filters
    #>   Species: Todiramphus chloris
    #>   Countries: all
    #>   States: all
    #>   Counties: all
    #>   BCRs: all
    #>   Bounding box: full extent
    #>   Years: all
    #>   Date: all
    #>   Start time: 06:00-10:00
    #>   Last edited date: all
    #>   Protocol: all
    #>   Project code: all
    #>   Duration: all
    #>   Distance travelled: all
    #>   Records with breeding codes only: no
    #>   Exotic Codes: all
    #>   Complete checklists only: yes

As with presence-only data, call `auk_filter()` to actually run AWK. Output files must be provided for both the EBD and sampling event data.

    ## ebd_sed_filtered <- auk_filter(filters,
    ##                                file = "ebd-filtered.txt",
    ##                                file_sampling = "sampling-filtered.txt")
    ebd_sed_filtered
    #> Input
    #>   EBD: /private/var/folders/wf/y1p0nztd2ps95ppplsl5bjjr0000gs/T/RtmpBCe4Qj/Rinstdebc8747d20/auk/extdata/zerofill-ex_ebd.txt
    #>   Sampling events: /private/var/folders/wf/y1p0nztd2ps95ppplsl5bjjr0000gs/T/RtmpBCe4Qj/Rinstdebc8747d20/auk/extdata/zerofill-ex_sampling.txt
    #>
    #> Output
    #>   EBD: ebd-filtered.txt
    #>   Sampling events: sampling-filtered.txt
    #>
    #> Filters
    #>   Species: Todiramphus chloris
    #>   Countries: all
    #>   States: all
    #>   Counties: all
    #>   BCRs: all
    #>   Bounding box: full extent
    #>   Years: all
    #>   Date: all
    #>   Start time: 06:00-10:00
    #>   Last edited date: all
    #>   Protocol: all
    #>   Project code: all
    #>   Duration: all
    #>   Distance travelled: all
    #>   Records with breeding codes only: no
    #>   Exotic Codes: all
    #>   Complete checklists only: yes

### Reading and zero-filling

The filtered datasets can now be combined into a zero-filled, presence-absence dataset using `auk_zerofill()`.

    ## ebd_zf <- auk_zerofill(ebd_sed_filtered)
    ebd_zf
    #> Zero-filled EBD: 131 unique checklists, for 1 species.

Filenames or data frames of the basic dataset and sampling event data can also be passed to `auk_zerofill()`; see the documentation for these cases. By default, `auk_zerofill()` returns an `auk_zerofill` object consisting of two data frames that can be linked by a common `checklist_id` field:

- `ebd_zf$sampling_events` contains the checklist information
- `ebd_zf$observations` contains the species counts and a binary presence-absence variable

    head(ebd_zf$observations)
    #> # A tibble: 6 × 8
    #>   checklist_id scientific_name     breeding_code breeding_category behavior_code
    #>   <chr>        <chr>               <chr>         <chr>             <chr>
    #> 1 G2470228     Todiramphus chloris <NA>          <NA>              <NA>
    #> 2 G366411      Todiramphus chloris <NA>          <NA>              <NA>
    #> 3 S10006552    Todiramphus chloris <NA>          <NA>              <NA>
    #> 4 S10006731    Todiramphus chloris <NA>          <NA>              <NA>
    #> 5 S10006786    Todiramphus chloris <NA>          <NA>              <NA>
    #> 6 S10011787    Todiramphus chloris <NA>          <NA>              <NA>
    #> # ℹ 3 more variables: age_sex <chr>, observation_count <chr>,
    #> #   species_observed <lgl>
    glimpse(ebd_zf$sampling_events)
    #> Rows: 131
    #> Columns: 31
    #> $ checklist_id              <chr> "S9843037", "S34396450", "S9589770", "S16642…
    #> $ last_edited_date          <chr> "2022-01-13 07:47:42.702684", "2022-01-13 07…
    #> $ country                   <chr> "Singapore", "Singapore", "Singapore", "Sing…
    #> $ country_code              <chr> "SG", "SG", "SG", "SG", "SG", "SG", "SG", "S…
    #> $ state                     <chr> "Singapore", "Singapore", "Singapore", "Sing…
    #> $ state_code                <chr> "SG-", "SG-", "SG-", "SG-", "SG-", "SG-", "S…
    #> $ county                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ county_code               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ iba_code                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ bcr_code                  <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ usfws_code                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ atlas_block               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ locality                  <chr> "Pulau Ubin", "Pulau Ubin", "Pulau Ubin", "P…
    #> $ locality_id               <chr> "L1055540", "L1055540", "L1055540", "L105554…
    #> $ locality_type             <chr> "H", "H", "H", "H", "H", "H", "H", "H", "H",…
    #> $ latitude                  <dbl> 1.403608, 1.403608, 1.403608, 1.403608, 1.35…
    #> $ longitude                 <dbl> 103.9688, 103.9688, 103.9688, 103.9688, 103.…
    #> $ observation_date          <date> 2012-02-16, 2012-06-23, 2012-01-15, 2012-01…
    #> $ time_observations_started <chr> "08:00:00", "09:00:00", "08:00:00", "08:00:0…
    #> $ observer_id               <chr> "obs204697", "obs816783", "obs205759", "obs4…
    #> $ sampling_event_identifier <chr> "S9843037", "S34396450", "S9589770", "S16642…
    #> $ protocol_type             <chr> "Traveling", "Traveling", "Traveling", "Trav…
    #> $ protocol_code             <chr> "P22", "P22", "P22", "P22", "P21", "P22", "P…
    #> $ project_code              <chr> "EBIRD", "EBIRD", "EBIRD_CAN", "EBIRD_AU", "…
    #> $ duration_minutes          <int> 300, 180, 300, 510, 23, 195, 150, 105, 190, …
    #> $ effort_distance_km        <dbl> 1.609, 4.000, 3.000, 10.000, NA, 3.000, 3.00…
    #> $ effort_area_ha            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ number_observers          <int> 2, 2, 6, 1, 2, 3, 12, 3, 1, 1, 3, 3, 2, 1, 1…
    #> $ all_species_reported      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
    #> $ group_identifier          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ trip_comments             <chr> "With Ailin Chuah on day trip", NA, NA, "Spe…

This format is efficient for storage because the checklist information isn’t duplicated, however, a single flat data frame is often required for analysis. To collapse the two data frames together use `collapse_zerofill()`, or call `auk_zerofill()` with `collapse = TRUE`.

    ## ebd_zf_df <- auk_zerofill(ebd_filtered, collapse = TRUE)
    ebd_zf_df <- collapse_zerofill(ebd_zf)
    class(ebd_zf_df)
    #> [1] "tbl_df"     "tbl"        "data.frame"
    ebd_zf_df
    #> # A tibble: 131 × 38
    #>    checklist_id last_edited_date    country country_code state state_code county
    #>    <chr>        <chr>               <chr>   <chr>        <chr> <chr>      <chr>
    #>  1 S9843037     2022-01-13 07:47:4… Singap… SG           Sing… SG-        <NA>
    #>  2 S34396450    2022-01-13 07:47:4… Singap… SG           Sing… SG-        <NA>
    #>  3 S9589770     2023-01-31 19:39:0… Singap… SG           Sing… SG-        <NA>
    #>  4 S16642917    2023-01-31 19:39:0… Singap… SG           Sing… SG-        <NA>
    #>  5 S10410041    2013-10-14 16:08:30 Singap… SG           Sing… SG-        <NA>
    #>  6 S10366236    2021-04-02 03:48:3… Singap… SG           Sing… SG-        <NA>
    #>  7 S34396153    2021-08-03 11:08:2… Singap… SG           Sing… SG-        <NA>
    #>  8 S9760550     2022-03-12 04:34:1… Singap… SG           Sing… SG-        <NA>
    #>  9 S16899954    2022-03-02 06:47:0… Singap… SG           Sing… SG-        <NA>
    #> 10 S10920563    2019-08-10 00:41:22 Singap… SG           Sing… SG-        <NA>
    #> # ℹ 121 more rows
    #> # ℹ 31 more variables: 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>,
    #> #   protocol_code <chr>, project_code <chr>, duration_minutes <int>, …

## Acknowledgements

This package is based on the AWK scripts provided in a presentation given by Wesley Hochachka, Daniel Fink, Tom Auer, and Frank La Sorte at the 2016 NAOC eBird Data Workshop on August 15, 2016.

`auk` benefited significantly from the rOpenSci review process, including helpful suggestions from Auriel Fournier and Edmund Hart.

## References

    eBird Basic Dataset. Version: ebd_relFeb-2018. Cornell Lab of Ornithology, Ithaca, New York. May 2013.

    Guillera-Arroita, G., J.J. Lahoz-Monfort, J. Elith, A. Gordon, H. Kujala, P.E. Lentini, M.A. McCarthy, R. Tingley, and B.A. Wintle. 2015. Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography 24:276-292.