Find missingness in each column and search for strings that might represent missing values

Finds the percent of NAs in a vector or in each column of a dataframe or matrix or in a vector. Possible mis-coded missing values are searched for and a warning issued if they are found.

missingness(
  d,
  return_df = TRUE,
  to_search = c("NA", "NAs", "na", "NaN", "?", "??", "nil", "NULL", " ", "")
)

Arguments

d	A data frame or matrix
return_df	If TRUE (default) a data frame is returned, which generally makes reading the output easier. If variable names are so long that the data frame gets wrapped poorly, set this to FALSE.
to_search	A vector of strings that might represent missingness. If found in d, a warning is issued.

Value

A data frame with two columns: variable names in d and the percent of entries in each variable that are missing.

See also

plot.missingness

Examples

d <- data.frame(x = c("a", "nil", "b"),
                y = c(1, NaN, 3),
                z = c(1:2, NA))
missingness(d)
#> Warning: Found these strings that may represent missing values: "nil". If they do represent missingness, replace them with NA with: `make_na(d, c("nil"))`
#> # A tibble: 3 x 2
#>   variable percent_missing
#> * <chr>              <dbl>
#> 1 x                    0  
#> 2 y                   33.3
#> 3 z                   33.3
missingness(d) %>% plot()
#> Warning: Found these strings that may represent missing values: "nil". If they do represent missingness, replace them with NA with: `make_na(d, c("nil"))`