impute_mean: Fills missing values with subgroup mean value
In dosgillespie/hseclean: Health Survey Data Wrangling

Description Usage Arguments Details Value Examples

Replaces any values < 0 with NA, calculates the subgroup mean, then replaces missing values with the subgroup mean.

impute_mean(
  data,
  var_names,
  remove_zeros = FALSE,
  strat_vars = c("year", "sex", "imd_quintile", "age_cat")
)

`data`	Data table - the Health Survey for England data
`var_names`	Character vector - the variable names to be imputed
`remove_zeros`	Logical - should zeros be treated as missing data
`strat_vars`	Character vector - the variables by which to stratify the subgroup means

If not all NAs can be imputed with the fine scale starting amount of stratification, imputation is attempted again, removing the stratification variable specified last.

Returns an updated version of data in which the variables specified have had their missing values imputed with the subgroup means.

## Not run: 

data <- read_2001()
data <- clean_age(data)
data <- clean_demographic(data)
data <- impute_mean(data, var_names = c("d7many"))


## End(Not run)