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Summarise missingness in key ES surveillance variables

Source: R/clean_es.R
es_missingness.Rd

A tidy, in-memory replacement for the side-file missingness export: returns one row per variable with the count and percentage of missing values, so a caller can inspect or write it however they like.

Usage

es_missingness(data, vars = NULL)

Arguments

data

A cleaned ES data frame.

vars

Character vector of columns to summarise. Default NULL uses the key ES surveillance fields that are present in data.

Value

A tibble with columns variable, n, n_missing and pct_missing, ordered most-missing first.

Examples

es <- data.frame(
  collection_date = as.Date(c("2024-01-01", NA)),
  adm0 = c("CHAD", "CHAD"),
  classification_all = c(NA, "NEGATIVE")
)
es_missingness(es)
#> # A tibble: 3 × 4
#>   variable               n n_missing pct_missing
#>   <chr>              <int>     <int>       <dbl>
#> 1 collection_date        2         1          50
#> 2 classification_all     2         1          50
#> 3 adm0                   2         0           0