Wrangle child's age

Wrangle child's age for downstream analysis. This includes calculating age in months based on the date of data collection and the child's date of birth, and setting to NA the age values that are less than 6.0 and greater than or equal to 60.0 months old.

Usage

mw_wrangle_age(df, dos = NULL, dob = NULL, age, .decimals = 2)

Arguments

df

A dataset of class data.frame to wrangle age from.

dos

A vector of class Date for date of data collection from the df. Default is NULL.

dob

A vector of class Date for child's date of birth from the df. Default is NULL.

age

A vector of class numeric of child's age in months. In most cases this will be estimated using local event calendars; in some other cases it can be a mix of the former and the one based on the child's date of birth and the date of data collection.

.decimals

The number of decimals places to which the age should be rounded. Default is 2.

Value

A data.frame based on df. The variable age will be automatically filled in each row where age value was missing and both the child's date of birth and the date of data collection are available. Rows where age is less than 6.0 and greater than or equal to 60.0 months old will be set to NA. Additionally, a new variable for df named age_days, of class double, will be created.

Examples

## A sample data ----
df <- data.frame(
  surv_date = as.Date(c(
    "2023-01-01", "2023-01-01", "2023-01-01", "2023-01-01", "2023-01-01"
  )),
  birth_date = as.Date(c(
    "2019-01-01", NA, "2018-03-20", "2019-11-05", "2021-04-25"
  )),
  age = c(NA, 36, NA, NA, NA)
)

## Apply the function ----
mw_wrangle_age(
  df = df,
  dos = surv_date,
  dob = birth_date,
  age = age,
  .decimals = 3
)
#> # A tibble: 5 × 4
#>   surv_date  birth_date   age age_days
#>   <date>     <date>     <dbl>    <dbl>
#> 1 2023-01-01 2019-01-01  48      1461 
#> 2 2023-01-01 NA          36      1096.
#> 3 2023-01-01 2018-03-20  57.4    1748 
#> 4 2023-01-01 2019-11-05  37.9    1153 
#> 5 2023-01-01 2021-04-25  20.2     616