Normalize retrieved data table values to appropriate scales

Facilitates working with Statistics Canada data table values retrieved using the package by setting all units to counts/dollars instead of millions, etc. If "replacement_value" is not set, it will replace the VALUE field with normalized values and drop the scale column. Otherwise it will keep the scale columns and create a new column named replacement_value with the normalized value. It will attempt to parse the REF_DATE field and create an R date variable. This is currently experimental.

Usage

normalize_cansim_values(
  data,
  replacement_value = "val_norm",
  normalize_percent = TRUE,
  default_month = "01",
  default_day = "01",
  factors = TRUE,
  strip_classification_code = FALSE,
  cansimTableNumber = NULL,
  internal = FALSE
)

Arguments

data: A retrieved data table as returned from get_cansim() pr get_cansim_ndm()
replacement_value: (Optional) the name of the column the manipulated value should be returned in. Defaults to "val_norm"
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 "01")
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 TRUE).
strip_classification_code: (strip_classification_code) Logical value indicating if classification code should be stripped from names. (Default set to FALSE, if factors=TRUE this is overridden and set to TRUE).
cansimTableNumber: (Optional) Only needed when operating on results of SQLite connections.
internal: (Optional) Flag to indicate that this function is called internally.

Value

Returns a tibble with with adjusted values.

Examples

if (FALSE) { # \dontrun{
cansim_table <- get_cansim("34-10-0013")
normalize_cansim_values(cansim_table)
} # }