Apply a missingness mask to complete data

Sets every cell the mask flags to NA, leaving the complete data otherwise untouched, and keeps the mask alongside the result as a tidy component.

Usage

observed_data(data, missingness, id = NULL)

Arguments

data

Complete base data.frame, or a simulab_sim, with as many rows as missingness and in the same row order.

missingness

A logical mask returned by missingness_matrix(), with one row per row of data. Its columns that also name columns of data, other than those listed in id, are the targets, and each must be logical.

id

Identifier columns the mask carries that are never made missing. A character vector, or NULL (the default). Name the identifier columns of the mask here, or they are treated as targets and rejected for not being logical.

Value

A simulab_sim base data.frame with the columns and rows of data, the flagged cells of the target variables replaced by NA. The component missingness, reached with as.data.frame(x, what = "missingness"), is the mask in long form, one row per observation and target variable, with columns observation, variable and the logical missing.

Examples

data <- data.frame(id = 1:100, outcome = stats::rnorm(100))
mask <- missingness_matrix(
  data,
  specification = define_missingnesses(define_missingness("outcome", formula = "0.3")),
  seed = 1
)

result <- observed_data(data, missingness = mask, id = "id")
head(result)
#> <simulab_sim:observed_data> 6 rows x 2 columns
#>   id    outcome
#> 1  1         NA
#> 2  2 -1.1327594
#> 3  3  1.4899074
#> 4  4 -0.2482471
#> 5  5         NA
#> 6  6  0.4048710
#> 
#> Other tables: missingness. Read one with as.data.frame(x, what = "missingness").