Generate a table of

Create a table of missing values along with percentages. It may be undesirable to count a variable if it is dependent on another one. This can be accounted for here by using a named vector where the name is the dependent variable and the value is the indpendent variable. c("B" = "A") can be thought of here as "B depends on A". In other words, each entry in B will only be evaluated if A is not missing. If the input is a labeled data frame, the final output will include a column of labels.

missing_tbl(data, depends = NA)

Arguments

data	A tbl.
depends	A named vector representing variable dependencies. For example, c("B" = "A", "C" = "B") can be read as "B depends on A and C depends on B". If a value for B is missing, it will only be counted if A is not missing, and similarly for C and B.

Examples

data <- tibble::tribble(
  ~A, ~B, ~C,
  1, 2, 3,
  NA, NA, 6,
  7, NA, 9
)

data
#> # A tibble: 3 x 3
#>       A     B     C
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3
#> 2    NA    NA     6
#> 3     7    NA     9

missing_tbl(data)
#>   Variable N Non-Missing   Missing
#> 1        A 3   2.0 (67%) 1.0 (33%)
#> 2        B 3   1.0 (33%) 2.0 (67%)
#> 3        C 3  3.0 (100%)  0.0 (0%)

missing_tbl(data, depends = c("B" = "A"))
#>   Variable Depends   N Non-Missing   Missing
#> 1        A    <NA> 3.0   2.0 (67%) 1.0 (33%)
#> 2        B       A 2.0   1.0 (50%) 1.0 (50%)
#> 3        C    <NA> 3.0  3.0 (100%)  0.0 (0%)

Generate a table of "missingness"

Arguments

Examples