Describes data available to subset and makes subset

This automates the process of:

1. Checking what data is available to create subsets

2. Make the subset

Usage

armadillo.subset(
  input_source = NULL,
  subset_def = NULL,
  source_project = NULL,
  source_folder = NULL,
  source_table = NULL,
  target_project = NULL,
  target_folder = NULL,
  target_table = NULL,
  target_vars = NULL,
  new_project = NULL,
  dry_run = NULL,
  strict = FALSE
)

Arguments

input_source

Character specifying how information about the target view is provided: choose 'subset_def' if providing a subset definition object, or 'arguments' if providing information directly.

subset_def

R object containing subset definition created by armadillo.subset_definition(). Compulsory if input_source = 'subset_def'

source_project

project from which to subset data

source_folder

folder from which to subset data. Compulsory if input_source = 'arguments'.

source_table

table from which to subset data. Compulsory if input_source = 'arguments'.

target_project

project to upload subset to. Will be created if it doesn't exist.

target_folder

folder to upload subset to. Will be created if it doesn't exist. Compulsory if input_source = 'arguments'.

target_table

table to upload subset to. Compulsory if input_source = 'arguments'.

target_vars

variables from `source_table` to include in the view. Compulsory if input_source = 'arguments'.

new_project

Deprecated: use target_project instead

dry_run

Defunct: previously enabgled dry-run to check which variables are missing

strict

Boolean specifying whether to create subset if one or more target variables do not exist in the target data. Option FALSE will throw and error, option TRUE (default) creates subset and return a warning

Value

missing variables provided in the subset definition

Examples

if (FALSE) { # \dontrun{
armadillo.subset(
  source_project = "gecko",
  target_project = "study1",
  subset_def = local_subset
)
} # }