Create a `reactable`

table that highlights missing values and imputed values
with the same colors as `histMiss()`

`tableMiss(x, delimiter = "_imp")`

## Arguments

- x
a vector, matrix or `data.frame`

.

- delimiter
a character-vector to distinguish between variables and
imputation-indices for imputed variables (therefore, `x`

needs to have
`colnames()`

). If given, it is used to determine the corresponding
imputation-index for any imputed variable (a logical-vector indicating which
values of the variable have been imputed). If such imputation-indices are
found, they are used for highlighting and the colors are adjusted according
to the given colors for imputed variables (see `col`

).

## Examples

```
data(tao)
x_IMPUTED <- kNN(tao[, c("Air.Temp", "Humidity")])
#> Humidity Humidity
#> 71.6 94.8
#> Air.Temp Air.Temp
#> 21.42 28.50
tableMiss(x_IMPUTED[105:114, ])
x_IMPUTED[106, 2] <- NA
x_IMPUTED[105, 1] <- NA
x_IMPUTED[107, "Humidity_imp"] <- TRUE
tableMiss(x_IMPUTED[105:114, ])
```