Read and write to the seatable for draft BANC annotations

These functions use the logic and wrap some code from the flytable_.* functions in the fafbseg R package. banctable_set_token will obtain and store a permanent seatable user-level API token. banctable_query performs a SQL query against a banctable database. You can omit the base argument unless you have tables of the same name in different bases. banctable_base returns a base object (equivalent to a mysql database) which allows you to access one or more tables, logging in to the service if necessary. The returned base object give you full access to the Python Base API allowing a range of row/column manipulations. banctable_update_rows updates existing rows in a table, returning TRUE on success. banctable_append_rows appends new rows to a table. When bigdata=TRUE, rows are added directly to the big data backend using the /add-archived-rows/ endpoint. banctable_move_to_bigdata moves rows between normal backend and big data backend. When invert=FALSE (archive), it moves all rows from a specified view to big data storage. When invert=TRUE (unarchive), it moves specific rows by row_id from big data storage back to normal backend. Note: The big data backend must be enabled in your base for these functions to work.

Usage

banctable_query(
  sql = "SELECT * FROM banc_meta",
  limit = 200000L,
  base = NULL,
  python = FALSE,
  convert = TRUE,
  ac = NULL,
  token_name = "BANCTABLE_TOKEN",
  workspace_id = "57832",
  retries = 10,
  table.max = 10000L
)

banctable_set_token(
  user,
  pwd,
  url = "https://cloud.seatable.io/",
  token_name = "BANCTABLE_TOKEN"
)

banctable_login(
  url = "https://cloud.seatable.io/",
  token_name = "BANCTABLE_TOKEN"
)

banctable_update_rows(
  df,
  table,
  base = NULL,
  append_allowed = FALSE,
  chunksize = 1000L,
  workspace_id = "57832",
  token_name = "BANCTABLE_TOKEN",
  ...
)

banctable_move_to_bigdata(
  table = "banc_meta",
  base = "banc_meta",
  url = "https://cloud.seatable.io/",
  workspace_id = "57832",
  token_name = "BANCTABLE_TOKEN",
  view_name = "archive",
  view_id = NULL,
  where = NULL,
  invert = FALSE,
  row_ids = NULL
)

franken_meta(sql = "SELECT * FROM franken_meta", base = "cns_meta", ...)

banctable_append_rows(
  df,
  table,
  bigdata = FALSE,
  base = NULL,
  chunksize = 1000L,
  workspace_id = "57832",
  token_name = "BANCTABLE_TOKEN",
  ...
)

Arguments

sql

A SQL query string. See examples and seatable docs.

limit

An optional limit, which only applies if you do not specify a limit directly in the sql query. By default seatable limits SQL queries to 100 rows. We increase the limit to 100000 rows by default.

base

Character vector specifying the base

python

Logical. Whether to return a Python pandas DataFrame. The default of FALSE returns an R data.frame

convert

Expert use only: Whether or not to allow the Python seatable module to process raw output from the database. This is is principally for debugging purposes. NB this imposes a requirement of seatable_api >=2.4.0.

ac

A seatable connection object as returned by banctable_login.

token_name

The name of the token in your .Renviron file, should be BANCTABLE_TOKEN.

workspace_id

A numeric id specifying the workspace. Advanced use only

retries

if a request to the seatable API fails, the number of times to re-try with a 0.1 second pause.

table.max

the maximum number of rows to read from the seatable at one time, which is capped at 10000L by seatable.

user, pwd

banctable user and password used by banctable_set_token to obtain a token

url

Optional URL to the server

df

A data.frame containing the data to upload including an _id column that can identify each row in the remote table.

table

Character vector specifying a table foe which you want a base object.

append_allowed

Logical. Whether rows without row identifiers can be appended.

chunksize

To split large requests into smaller ones with max this many rows.

...

Additional arguments passed to the underlying parallel processing functions which might include cl=2 to specify a number of parallel jobs to run.

view_name

Character, the name of the view containing rows to archive (required for archive operation). Mutually exclusive with view_id.

view_id

Character, the ID of the view containing rows to archive (alternative to view_name). Mutually exclusive with view_name.

where

DEPRECATED. The API no longer supports WHERE clauses. Use view_name or view_id instead.

invert

Logical. If FALSE (default), archives rows from normal backend to big data backend (requires view_name or view_id). If TRUE, unarchives rows from big data backend back to normal backend (requires row_ids).

row_ids

Character vector of seatable row IDs. Required for unarchive operation (when invert=TRUE). These are the specific rows to move from big data backend back to normal backend. Use the table_id (not table_name) for unarchive operations.

bigdata

Logical. If TRUE, new rows are added directly to the big data backend using the /add-archived-rows/ API endpoint. If FALSE (default), rows are added to the normal backend. Note: The big data backend must be enabled in your base for this to work.

Value

a data.frame of results. There should be 0 rows if no rows matched query.

See also

fafbseg::flytable_query

Examples

if (FALSE) { # \dontrun{
# Do this once
banctable_set_token(user="MY_EMAIL_FOR_SEATABLE.com",
                    pwd="MY_SEATABLE_PASSWORD",
                    url="https://cloud.seatable.io/")

# Query a table:
banc.meta <- banctable_query()

# Archive rows to big data backend (requires a view):
banctable_move_to_bigdata(
  table = "banc_meta",
  base = "banc_meta",
  view_name = "optic_region_view"
)

# Alternative: use view_id instead of view_name:
banctable_move_to_bigdata(
  table = "banc_meta",
  view_id = "0000"
)

# Unarchive specific rows from big data backend:
banctable_move_to_bigdata(
  table = "banc_meta",
  invert = TRUE,
  row_ids = c("FoDxhChYQSycLm88JZ11RA", "AnotherRowId123")
)

# Append rows directly to big data backend:
new_data <- data.frame(
  root_id = c("720575940626768442", "720575940636821616"),
  cell_type = c("DNa02", "DNa02")
)
banctable_append_rows(
  df = new_data,
  table = "banc_meta",
  base = "banc_meta",
  bigdata = TRUE
)
} # }