Fill missing values in hierarchical variables

Replace NA values in one column conditional on the same row having a non-NA value in a different column.

The user supplies a vector of columns from which the pairs will be extracted with a rolling window. For example vars <- c("A", "B", "C") will generate 2 pairs ("A", "B") and ("B", "C"). Therefore the order of the variables matters.

In each pair the second column B will be filled if A is not missing. One can choose the value to fill with:

• "Any {colname}", in this case evaluating to "Any B" is the default.

• Any other value. For example "Any event" for an adverse effects table.

• the value of pair's first column. In this case, the value of A.

Usage

prep_hierarchical_fill(
  df,
  vars,
  fill = "Any {colname}",
  fill_from_left = FALSE
)

Arguments

df

(data.frame)

vars

(character) a vector of variables to generate pairs from.

fill

(character) value to replace with. Defaults to "Any {colname}", in which case colname will be replaced with the name of the column.

fill_from_left

(logical) indicating whether to fill from the left (first) column in the pair. Defaults to FALSE. If TRUE it takes precedence over fill.

Value

a data.frame with the same columns as the input, but in which some the desired columns have been filled pairwise.

Examples

df <- data.frame(
  x = c(1, 2, NA),
  y = c("a", NA, "b"),
  z = rep(NA, 3)
)

prep_hierarchical_fill(
  df,
  vars = c("x", "y")
)
#>    x     y  z
#> 1  1     a NA
#> 2  2 Any y NA
#> 3 NA     b NA

prep_hierarchical_fill(
  df,
  vars = c("x", "y"),
  fill = "foo"
)
#>    x   y  z
#> 1  1   a NA
#> 2  2 foo NA
#> 3 NA   b NA

prep_hierarchical_fill(
  df,
  vars = c("x", "y", "z"),
  fill_from_left = TRUE
)
#>    x y z
#> 1  1 a a
#> 2  2 2 2
#> 3 NA b b