Special Field Types in airtable2

library(airtable2)

Overview

Most Airtable fields map naturally to R atomic vectors: text becomes character, numbers become numeric, checkboxes become logical, and dates become Date or POSIXct. A handful of field types, however, carry structured data that cannot fit in a single scalar value per row.

airtable2 represents these as list-columns in the returned tibble — one list element per row. Each list-column is wrapped in a lightweight S3 class so that the data round-trips faithfully through read/write cycles and displays compactly in the tibble header and in the RStudio/Positron data viewer.

The six special types are:

Airtable field type	R S3 class	Pillar type tag
Multiple select	`air_multiselect`	`sel[]`
Linked records	`air_links`	`lnk[]`
Attachments	`air_attachments`	`att[]`
Single collaborator	`air_collaborator`	`collab`
Multiple collaborators	`air_collaborators`	`collabs`
Barcode	`air_barcode`	`barcode`

When you print a tibble that contains these columns, pillar uses the registered pillar_shaft and type_sum methods to show a one-line summary per cell and the short tag in the column header. The full structured data is always preserved in the list-column and can be written back with air_write() or air_upsert() unchanged.

If you want a plain, flat data frame instead, see Section 7 for air_simplify(), or the type-specific flatteners described throughout this vignette.

For the general workflow — authentication, reading, writing, syncing — see the Getting Started vignette.

1. Multi-select fields (`air_multiselect`)

Airtable’s Multiple select field lets each record carry zero or more selection options. air_read() returns these as an air_multiselect list-column, where each element is a character vector of selected options (or NULL for an empty selection).

# Suppose a "Projects" table has a "Tags" multiple-select field.
projects <- air_read("appXXXXXX", "Projects")
projects
#> # A tibble: 4 × 4
#>   airtable_id  airtable_created_time name         tags
#>   <chr>        <dttm>                <chr>        <sel[]>
#> 1 recAAAAAAAA  2024-01-10 09:00:00   Alpha        R, data, analysis
#> 2 recBBBBBBBB  2024-01-11 10:30:00   Beta         data
#> 3 recCCCCCCCC  2024-01-12 14:00:00   Gamma        NA
#> 4 recDDDDDDDD  2024-01-13 08:15:00   Delta        R, reporting

The sel[] tag in the column header indicates an air_multiselect list-column. Each cell shows the selections joined with ", " for display, but the underlying list element is a character vector:

# Look at the raw list element for the first row
projects$tags[[1]]
#> [1] "R"        "data"     "analysis"

# NULL for a row with no selection
projects$tags[[3]]
#> NULL

Flattening to a character vector

air_flatten_multiselect() collapses each element to a delimited string, giving one character value per row:

air_flatten_multiselect(projects$tags)
#> [1] "R, data, analysis" "data"              NA                  "R, reporting"

# Use a different delimiter
air_flatten_multiselect(projects$tags, sep = " | ")
#> [1] "R | data | analysis" "data"                NA                    "R | reporting"

This is useful for export to CSV or for passing to string-based tools.

Expanding back to a list-column

air_expand_multiselect() is the inverse: it splits delimited strings back into character vectors, ready to write to Airtable:

flat_tags <- c("R, data, analysis", "data", NA, "R, reporting")
air_expand_multiselect(flat_tags)
#> [[1]]
#> [1] "R"        "data"     "analysis"
#>
#> [[2]]
#> [1] "data"
#>
#> [[3]]
#> NULL
#>
#> [[4]]
#> [1] "R"         "reporting"

Round-tripping through write

Because air_write() and air_upsert() strip the air_* class before sending data to the API, you can pass the original air_multiselect list-column directly without any conversion:

# Modify one row's tags and write back
projects$tags[[3]] <- list("reporting") # was NULL
air_upsert(projects, "appXXXXXX", "Projects", merge_on = "name")

2. Linked records (`air_links`)

Airtable’s Link to another record field connects rows across tables. On read, each cell contains a character vector of record IDs (strings like "recXXXXXXXXXXXXXX"). airtable2 wraps these in an air_links list-column.

projects <- air_read("appXXXXXX", "Projects")
projects
#> # A tibble: 3 × 4
#>   airtable_id  name    owner_ids
#>   <chr>        <chr>   <lnk[]>
#> 1 recAAAAAAAA  Alpha   recPPPPPPPP
#> 2 recBBBBBBBB  Beta    [2 records]
#> 3 recCCCCCCCC  Gamma   NA

A single linked record is shown as the ID; multiple are shown as [N records]; an empty link field is NA.

The linked field stores only record IDs, not the values from the linked table. To retrieve the actual data you need to read the linked table separately and join.

Flattening links

air_flatten_links() collapses the ID vectors to delimited strings (it is a thin wrapper around air_flatten_multiselect()):

air_flatten_links(projects$owner_ids)
#> [1] "recPPPPPPPP"                NA             "recQQQQQQQQ, recRRRRRRRR"

Joining to resolve linked records

Use air_left_join(), air_inner_join(), or air_full_join() to fetch the linked table and join it with a local data frame in a single step. The by argument maps the column(s) to join on.

A common pattern is to first flatten the links to get one row per linked record, then join to the linked table:

# Read the "People" table that Projects links to
people <- air_read("appXXXXXX", "People")

# Flatten: one row per (project, person) pair
project_owners <- tidyr::unnest(projects, cols = owner_ids, keep_empty = TRUE)
project_owners
#> # A tibble: 4 × 3
#>   airtable_id  name    owner_ids
#>   <chr>        <chr>   <chr>
#> 1 recAAAAAAAA  Alpha   recPPPPPPPP
#> 2 recBBBBBBBB  Beta    recQQQQQQQQ
#> 3 recBBBBBBBB  Beta    recRRRRRRRR
#> 4 recCCCCCCCC  Gamma   NA

# Join on the record ID to get person names
dplyr::left_join(
  project_owners,
  dplyr::select(people, airtable_id, person_name = name),
  by = c("owner_ids" = "airtable_id")
)
#> # A tibble: 4 × 4
#>   airtable_id  name    owner_ids    person_name
#>   <chr>        <chr>   <chr>        <chr>
#> 1 recAAAAAAAA  Alpha   recPPPPPPPP  Alice
#> 2 recBBBBBBBB  Beta    recQQQQQQQQ  Bob
#> 3 recBBBBBBBB  Beta    recRRRRRRRR  Carol
#> 4 recCCCCCCCC  Gamma   NA           NA

You can also use air_left_join() if the join key is shared by name:

# air_left_join fetches the remote table and merges in one call
air_left_join(
  project_owners,
  "appXXXXXX",
  "People",
  by = c("owner_ids" = "airtable_id")
)

Re-linking records after a restore

A known limitation: linked records are stored by record ID, and IDs are assigned by Airtable and cannot be set on create. If you dump a base with air_dump() and restore it to a new base with air_restore(), the records get new IDs, so all air_links columns in the restored base point to the old IDs and the links are broken.

air_restore() handles this automatically by default (restore_linked_fields = TRUE): after inserting all records it remaps the old record IDs to the new ones using the insertion-order mapping and writes the link values back. If you need manual control (e.g. merging data from multiple sources), the Metadata and Backup vignette has a worked example using a natural key.

3. Attachments (`air_attachments`)

Airtable’s Attachments field holds one or more files per record. On read, each cell is a list of attachment objects, each with at minimum:

filename — the original file name
url — a temporary Airtable-hosted download URL
size — file size in bytes
type — MIME type

airtable2 wraps these in an air_attachments list-column.

Important: Airtable attachment URLs are temporary and expire after a short time (typically a few hours). Always re-fetch records before downloading if significant time has passed since the last air_read() call.

projects <- air_read("appXXXXXX", "Projects")
projects
#> # A tibble: 3 × 4
#>   airtable_id  name    docs
#>   <chr>        <chr>   <att[]>
#> 1 recAAAAAAAA  Alpha   brief.pdf
#> 2 recBBBBBBBB  Beta    report.docx +1
#> 3 recCCCCCCCC  Gamma   NA

The att[] tag marks the column as air_attachments. A single attachment shows its filename; multiple attachments show first_file.ext +N; an empty field is NA.

Inspect a single row’s attachment metadata directly:

projects$docs[[2]]
#> [[1]]
#> [[1]]$id
#> [1] "attXXXXXXXXXXXXXX"
#> [[1]]$filename
#> [1] "report.docx"
#> [[1]]$url
#> [1] "https://dl.airtable.com/..."
#> [[1]]$size
#> [1] 42816
#> [[1]]$type
#> [1] "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
#>
#> [[2]]
#> [[2]]$filename
#> [1] "notes.txt"
#> ...

Flattening attachment metadata

air_flatten_attachments() extracts one field from each attachment object and collapses to a delimited string:

# Default: filenames
air_flatten_attachments(projects$docs)
#> [1] "brief.pdf"           "report.docx, notes.txt" NA

# Extract URLs instead
air_flatten_attachments(projects$docs, field = "url")
#> [1] "https://dl.airtable.com/..." "https://..." NA

Downloading attachments with `air_read_attachments()`

air_read_attachments() fetches all attachments for a given field and returns a flat tibble with one row per file. The dest argument controls the output format:

"meta" — not a valid value here; air_read_attachments() always downloads content. Use air_flatten_attachments() on the list-column if you only need metadata.
"blob" — download files as raw byte vectors stored in a blob list-column
"file" — write files to disk under dir/; returns a local_path column

# Download as raw bytes (in-memory)
blobs <- air_read_attachments(
  base_id = "appXXXXXX",
  table = "Projects",
  field = "docs",
  dest = "blob"
)
blobs
#> # A tibble: 3 × 6
#>   airtable_id  filename       url                    size    type           blob
#>   <chr>        <chr>          <chr>                 <int>    <chr>          <list>
#> 1 recAAAAAAAA  brief.pdf      https://dl.airtable…  18432    application/… <raw[18432]>
#> 2 recBBBBBBBB  report.docx    https://dl.airtable…  42816    application/… <raw[42816]>
#> 3 recBBBBBBBB  notes.txt      https://dl.airtable…   1024    text/plain    <raw[1024]>

# Save files to disk: one sub-directory per record
files <- air_read_attachments(
  base_id = "appXXXXXX",
  table = "Projects",
  field = "docs",
  dest = "file",
  dir = "downloads/"
)
files
#> # A tibble: 3 × 6
#>   airtable_id  filename     url                  size  type          local_path
#>   <chr>        <chr>        <chr>               <int>  <chr>         <chr>
#> 1 recAAAAAAAA  brief.pdf    https://…           18432  application/… downloads/brief.pdf
#> 2 recBBBBBBBB  report.docx  https://…           42816  application/… downloads/report.docx
#> 3 recBBBBBBBB  notes.txt    https://…            1024  text/plain    downloads/notes.txt

You can also control the attachment mode directly in air_read() using the attachments argument. This downloads files as part of the initial read and augments the attachment objects in the list-column with a content (blob) or local_path (file) field:

# Download attachments to disk while reading
projects <- air_read(
  "appXXXXXX",
  "Projects",
  attachments = "file",
  attachment_dir = "downloads/"
)
# Now each attachment object in projects$docs has a $local_path entry
projects$docs[[1]][[1]]$local_path
#> [1] "downloads/recAAAAAAAA/brief.pdf"

Parallel downloads

By default, airtable2 downloads attachments in parallel using httr2::req_perform_parallel() (up to 5 concurrent). You can control this with:

The parallel argument: TRUE to force on, FALSE to force sequential
The airtable2.parallel option: options(airtable2.parallel = FALSE)
The AIRTABLE2_PARALLEL environment variable: AIRTABLE2_PARALLEL=false

# Force sequential downloads (useful for debugging or strict rate limiting)
air_read_attachments(
  "appXXXXXX",
  "Projects",
  "docs",
  dest = "file",
  dir = "downloads/",
  parallel = FALSE
)

Uploading attachments with `air_write_attachments()`

air_write_attachments() uploads files to an existing attachment field. It takes a data frame with airtable_id and file_path columns:

to_upload <- data.frame(
  airtable_id = c("recAAAAAAAA", "recBBBBBBBB"),
  file_path = c("outputs/alpha_summary.pdf", "outputs/beta_report.docx")
)

air_write_attachments(
  base_id = "appXXXXXX",
  table = "Projects",
  field = "docs",
  data = to_upload
)
#> Uploading 2 attachments...
#> Upload complete.

Note that uploads add attachments to a record; they do not replace existing ones. Use air_sync_attachments() if you want to avoid duplicates.

Diff-based sync with `air_sync_attachments()`

air_sync_attachments() compares the filenames in your local data with those already in Airtable, uploading only files that are not already present. This avoids duplicate uploads on repeated runs:

local_files <- data.frame(
  project_name = c("Alpha", "Alpha", "Beta"),
  file_path = c(
    "outputs/alpha_summary.pdf",
    "outputs/alpha_data.csv",
    "outputs/beta_report.docx"
  )
)

air_sync_attachments(
  base_id = "appXXXXXX",
  table = "Projects",
  field = "docs",
  data = local_files,
  key = "project_name" # column in both local_files and the Airtable table
)
#> Attachment sync: 2 uploaded, 1 skipped.

Stable preview URLs and the temporary-URL caveat

The url field in each attachment object is temporary: Airtable generates a signed airtableusercontent.com URL that expires approximately two hours after the API call. Never save this URL to a database or share it as a permanent link — it will stop working shortly after you fetch it.

The function air_attachment_preview_url() always returns NA_character_. This is intentional and correct. Airtable does not expose stable viewer URLs through the REST API. The stable URL — the one hosted on airtable.com rather than airtableusercontent.com — can only be obtained by opening the attachment in the Airtable web app and copying the address bar URL.

# The url field in each attachment expires after ~2 hours:
projects$Files[[1]][[1]]$url
#> [1] "https://v5.airtableusercontent.com/v3/..."  # <- expires soon

# air_attachment_preview_url() always returns NA — this is correct behaviour:
air_attachment_preview_url(projects$Files[[1]][[1]])
#> [1] NA
#> i Airtable does not expose stable viewer URLs through the REST API.
#> i To get a permanent shareable link: open the record in the Airtable web app,
#>   click the attachment, and copy the URL from your browser's address bar.
#>   That URL (from airtable.com) does not expire.

Workaround for a stable link: navigate to the record in the Airtable web app, click the attachment thumbnail to open the viewer, and copy the URL from your browser’s address bar. That URL originates from airtable.com and does not expire.

4. Collaborator fields (`air_collaborator` / `air_collaborators`)

Airtable has two collaborator field types:

Collaborator (collaborator) — a single workspace member. Also used for the built-in Created by and Last modified by fields.
Multiple collaborators (multipleCollaborators) — zero or more workspace members.

Each collaborator object has three fields: id (Airtable user ID), email, and name.

Single-collaborator fields become an air_collaborator list-column (type tag collab). Multi-collaborator fields become an air_collaborators list-column (type tag collabs).

tasks <- air_read("appXXXXXX", "Tasks")
tasks
#> # A tibble: 3 × 5
#>   airtable_id  title         assigned_to            reviewers
#>   <chr>        <chr>         <collab>               <collabs>
#> 1 recAAAAAAAA  Write tests   Alice <[email protected]>   Bob <[email protected]>
#> 2 recBBBBBBBB  Review PR     Bob <[email protected]>       Alice <[email protected]> +1
#> 3 recCCCCCCCC  Deploy        NA                     NA

# Single collaborator: a named list
tasks$assigned_to[[1]]
#> $id
#> [1] "usrXXXXXXXXXXXXXX"
#> $email
#> [1] "[email protected]"
#> $name
#> [1] "Alice"

Flattening collaborators

air_flatten_collaborator() converts each collaborator to a formatted string. The format argument is a glue-style template with access to id, email, and name:

# Default: "Name <email>"
air_flatten_collaborator(tasks$assigned_to)
#> [1] "Alice <[email protected]>" "Bob <[email protected]>"     NA

# Just the name
air_flatten_collaborator(tasks$assigned_to, format = "{name}")
#> [1] "Alice" "Bob"   NA

# Just the ID
air_flatten_collaborator(tasks$assigned_to, format = "{id}")
#> [1] "usrXXXXXXXXXXXXXX" "usrYYYYYYYYYYYYYY" NA

Expanding strings back to collaborator list-columns

air_expand_collaborator() parses formatted strings back into named lists. The pattern argument is a regex with two capture groups (name, email):

strings <- c("Alice <[email protected]>", "Bob <[email protected]>", NA)
air_expand_collaborator(strings)
#> [[1]]
#> $name
#> [1] "Alice"
#> $email
#> [1] "[email protected]"
#>
#> [[2]]
#> $name
#> [1] "Bob"
#> $email
#> [1] "[email protected]"
#>
#> [[3]]
#> NULL

When writing collaborator values back to Airtable, pass the id field — the API identifies users by their Airtable user ID, not by name or email.

5. Barcode fields (`air_barcode`)

Airtable’s Barcode field stores a scanned barcode value alongside its symbology (e.g., QR code, EAN-13, Code 128). Each row’s value is a named list with:

text — the decoded barcode string
type — the barcode symbology (may be absent)

inventory <- air_read("appXXXXXX", "Inventory")
inventory
#> # A tibble: 3 × 4
#>   airtable_id  item         barcode
#>   <chr>        <chr>        <barcode>
#> 1 recAAAAAAAA  Widget A     9780201379624 (EAN13)
#> 2 recBBBBBBBB  Widget B     https://example.com/p/456 (QR)
#> 3 recCCCCCCCC  Widget C     NA

inventory$barcode[[1]]
#> $text
#> [1] "9780201379624"
#> $type
#> [1] "EAN13"

There is no dedicated air_flatten_barcode() function. Use air_simplify() (described in the next section) or extract fields manually:

# Extract just the barcode text
vapply(inventory$barcode, function(b) b$text %||% NA_character_, character(1))
#> [1] "9780201379624"               "https://example.com/p/456"   NA

6. `air_simplify()` — the kitchen-sink flattener

air_simplify() processes every list-column in a tibble at once, converting each to its simplest character representation using the appropriate type-specific flattener. It is the quickest way to get a fully flat, all-atomic data frame for export or display:

projects <- air_read("appXXXXXX", "Projects")

# Before: list-columns with air_* classes
str(projects$tags)
#>  air_multiselect [1:4] ...

flat <- air_simplify(projects)
flat
#> # A tibble: 4 × 4
#>   airtable_id  name    tags                owner_ids
#>   <chr>        <chr>   <chr>               <chr>
#> 1 recAAAAAAAA  Alpha   R, data, analysis   recPPPPPPPP
#> 2 recBBBBBBBB  Beta    data                recQQQQQQQQ, recRRRRRRRR
#> 3 recCCCCCCCC  Gamma   NA                  NA
#> 4 recDDDDDDDD  Delta   R, reporting        NA

# After: plain character columns
str(flat$tags)
#>  chr [1:4] "R, data, analysis" "data" NA "R, reporting"

When a schema is available (passed as the schema argument), air_simplify() uses the field type to pick the right flattener. Without a schema, it falls back to a generic paste(format(v), collapse = ", ") for each list element.

# at_get_schema() returns all tables; find the one named "Projects"
all_tables <- at_get_schema("appXXXXXX")
projects_table <- Filter(function(t) t$name == "Projects", all_tables)[[1]]
schema <- projects_table$fields
flat <- air_simplify(projects, schema = schema)

When to use `air_simplify()` vs. the type-specific flatteners

Use air_simplify() when you want a quick flat snapshot of the whole table, e.g., to export to CSV or to inspect data interactively.
Use type-specific flatteners (air_flatten_multiselect(), air_flatten_links(), air_flatten_attachments(), air_flatten_collaborator()) when you need control over delimiters, output format, or want to work with individual columns in a pipeline.
Avoid air_simplify() before writing data back to Airtable. The original air_* list-columns round-trip correctly; flattened strings do not (you would need air_expand_multiselect() etc. to convert them back).

Special Field Types in airtable2

Overview

1. Multi-select fields (air_multiselect)

Flattening to a character vector

Expanding back to a list-column

Round-tripping through write

2. Linked records (air_links)

Flattening links

Joining to resolve linked records

Re-linking records after a restore

3. Attachments (air_attachments)

Flattening attachment metadata

Downloading attachments with air_read_attachments()

Parallel downloads

Uploading attachments with air_write_attachments()

Diff-based sync with air_sync_attachments()