Replaces NA values in tables

Replaces NA values in tables except for ones in time and geography columns that must be included in DfE official statistics. Get more guidance on Open Data Standards.

Usage

z_replace(data, replacement_alt = NULL, exclude_columns = NULL)

Arguments

data

name of the table that you want to replace NA values in

replacement_alt

optional - if you want the NA replacement value to be different to "z"

exclude_columns

optional - additional columns to exclude from NA replacement. Column names that match ones found in dfeR::geog_time_identifiers will always be excluded because any missing data for these columns need more explicit codes to explain why data is not available.

Value

table with "z" or an alternate replacement value instead of NA values for columns that are not for time or geography.

Details

Names of geography and time columns that are used in this function can be found in dfeR::geog_time_identifiers.

See also

geog_time_identifiers

Examples

# Create a table for the example

df <- data.frame(
  time_period = c(2022, 2022, 2022),
  time_identifier = c("Calendar year", "Calendar year", "Calendar year"),
  geographic_level = c("National", "Regional", "Regional"),
  country_code = c("E92000001", "E92000001", "E92000001"),
  country_name = c("England", "England", "England"),
  region_code = c(NA, "E12000001", "E12000002"),
  region_name = c(NA, "North East", "North West"),
  mystery_count = c(42, 25, NA)
)

z_replace(df)
#>   time_period time_identifier geographic_level country_code country_name
#> 1        2022   Calendar year         National    E92000001      England
#> 2        2022   Calendar year         Regional    E92000001      England
#> 3        2022   Calendar year         Regional    E92000001      England
#>   region_code region_name mystery_count
#> 1        <NA>        <NA>            42
#> 2   E12000001  North East            25
#> 3   E12000002  North West             z

# Use a different replacement value
z_replace(df, replacement_alt = "c")
#>   time_period time_identifier geographic_level country_code country_name
#> 1        2022   Calendar year         National    E92000001      England
#> 2        2022   Calendar year         Regional    E92000001      England
#> 3        2022   Calendar year         Regional    E92000001      England
#>   region_code region_name mystery_count
#> 1        <NA>        <NA>            42
#> 2   E12000001  North East            25
#> 3   E12000002  North West             c