Collect data from a parquet, feather or sqlite query and normalize cansim table output

Usage

collect_and_normalize(
  connection,
  replacement_value = "val_norm",
  normalize_percent = TRUE,
  default_month = "07",
  default_day = "01",
  factors = TRUE,
  strip_classification_code = FALSE,
  disconnect = FALSE
)

Arguments

connection: A connection to a local arrow connection as returned by get_cansim_connection, possibly with filters or other dplyr verbs applied
replacement_value: (Optional) the name of the column the manipulated value should be returned in. Defaults to adding the `val_norm` value field.
normalize_percent: (Optional) When true (the default) normalizes percentages by changing them to rates
default_month: The default month that should be used when creating Date objects for annual data (default set to "07")
default_day: The default day of the month that should be used when creating Date objects for monthly data (default set to "01")
factors: (Optional) Logical value indicating if dimensions should be converted to factors. (Default set to FALSE).
strip_classification_code: (Optional) Logical value indicating if classification code should be stripped from names. (Default set to false).
disconnect: (Optional) Only used when format is sqlite. Logical value to indicate if the SQLite database connection should be disconnected. (Default is FALSE)

Value

A tibble with the collected and normalized data

Examples

if (FALSE) { # \dontrun{
library(dplyr)

con <- get_cansim_connection("34-10-0013")
data <- con %>%
  filter(GEO=="Ontario") %>%
  collect_and_normalize()

} # }