Get normalised prescribing — get_normalised

Queries the normalised_prescribing table, with optional filtering by BNF codes and prescribing month. This is the table you'll usually want, rather than the raw prescribing table.

Usage

get_normalised_prescribing(
  con,
  bnf_codes = NULL,
  start_date = NULL,
  end_date = NULL
)

Arguments

con: A BigQuery connection from connect_bq().
bnf_codes: Character vector of BNF codes to keep, e.g. "0601022B0AAASAS". % anywhere in a code is a wildcard matching any characters, so "0404000M0%" keeps all methylphenidate presentations. Default NULL (no filter).
start_date: Earliest prescribing month to keep, as a Date or "YYYY-MM-DD" string. Default NULL (no filter).
end_date: Latest prescribing month to keep, as a Date or "YYYY-MM-DD" string. Default NULL (no filter).

Value

A query (tbl) of the normalised_prescribing table, to be downloaded with dplyr::collect().

Details

The result is a query, not data: nothing is downloaded until you call dplyr::collect(), and you can add further dplyr steps first. The full table is very large, so filter by BNF code and date range before collecting.

Examples

if (FALSE) { # \dontrun{
con <- connect_bq()

# Prescribing of two presentations between January and March 2023
get_normalised_prescribing(
  con,
  bnf_codes = c("0408010W0BBAKA1", "0601022B0AAASAS"),
  start_date = "2023-01-01",
  end_date = "2023-03-01"
) |>
  dplyr::collect()

# Prescribing of all methylphenidate presentations, using a wildcard
get_normalised_prescribing(
  con,
  bnf_codes = "0404000M0%",
  start_date = "2023-01-01",
  end_date = "2023-03-01"
) |>
  dplyr::collect()
} # }