Package 'rairtable'

Create a new airtable object

Description

Creates an S3 airtable object, which serves as a pointer for rairtable functions

Usage

airtable(
  table,
  base,
  view = NULL,
  api_url = "https://api.airtable.com",
  api_version = 0
)

Arguments

table	Table name in Airtable
base	Airtable base containing table. A base functions like a schema in a traditional database. You can retrieve the base ID from the API documentation.
view	Optional view of data to read
api_url	API endpoint to connect to. Can be changed for API integrations that require custom endpoint
api_version	Version of API to use. Defaults to 0 (the current version as of Fall 2021)

Value

An airtable object

Examples

## Not run: 
table <- airtable("Table 1", "appXXXXXXXXXXXXX")

## End(Not run)

---

Delete airtable records

Description

Delete records in an Airtable table based on their Airtable record ID.

Usage

delete_records(
  data,
  airtable,
  airtable_id_col = "airtable_record_id",
  safely = TRUE,
  batch_size = 10
)

Arguments

data	A data frame containing records to delete
airtable	An airtable object
airtable_id_col	Column containing Airtable record IDs. Not required if record IDs are stored in row names as returned from read_airtable.
safely	If TRUE, ask for confirmation before executing DELETE request
batch_size	Number of requests to send at a time. Maximum of 10.

Value

A vector of IDs deleted

---

Insert records into an Airtable table

Description

Insert rows into an Airtable table. Requires that data names and types exactly match column names and types in Airtable. Violating this assumption will return a 422 Unprocessable Entity error. Supports batch insert and parallel JSON encoding (recommended for large tables).

Usage

insert_records(
  data,
  airtable,
  typecast = FALSE,
  parallel = FALSE,
  batch_size = 10
)

Arguments

data	A dataframe containing records to insert
airtable	An airtable object
typecast	If TRUE, values will be converted to match the base if possible. Set to TRUE to add new values to a multi select field.
parallel	If TRUE, use parallel processing for encoding large tables
batch_size	Number of records per request to insert. Maximum of 10

Value

A dataframe (invisibly) of the input data, to be stored as an object or piped into further 'dplyr' functions

---

Read table from Airtable

Description

Connect to and read values from an Airtable table.

Usage

read_airtable(airtable, fields = NULL, id_to_col = TRUE, max_rows = 50000)

Arguments

airtable	An airtable object
fields	An optional list of fields to select.
id_to_col	If TRUE, store airtable ID as a column rather than as row names
max_rows	Optional maximum number of rows to read

Value

A dataframe containing the data read from the specified 'Airtable' table

---

Set or install Airtable API key

Description

Set Airtable API key as an environment variable, and optionally install the API key to your .Renviron file for future use.

Usage

set_airtable_api_key(key, install = FALSE)

Arguments

key	A valid Airtable API key
install	Add your API key to .Renviron for future sessions. Optionally overwrite an existing Airtable API key.

Value

No return value, called for side effects

Examples

## Not run: 
airtable_api_key("XXXXXXXXXX", install = TRUE)

## End(Not run)

---

Update Airtable records

Description

Update one or more columns of data in an Airtable table. Supports batch updates and parallel JSON encoding (recommended for large tables).

Usage

update_records(
  data,
  airtable,
  columns = dplyr::everything(),
  airtable_id_col = "airtable_record_id",
  safely = TRUE,
  parallel = FALSE,
  batch_size = 10
)

Arguments

data	A dataframe containing the records and fields to update
airtable	An airtable object
columns	Columns in the data to update on Airtable. Can be a vector of character strings, unquoted column names, or a dplyr tidyselect helper like starts_with(), ends_with() or everything(). Defaults to dplyr::everything()
airtable_id_col	Column containing Airtable record IDs. Not required if record IDs are stored in row names as returned from read_airtable
safely	If TRUE, confirm number and names of columns to update and number of rows before executing update.
parallel	If TRUE use parallel processing for encoding large tables
batch_size	Number of records to update per request. Maximum of 10

Value

A dataframe (invisibly) of the input data, to be stored as an object or piped into further 'dplyr' functions

---