Filter leading zeros from a data set.

Usage

filter_leading_zeros(data, obs_column = "confirm", by = NULL)

Arguments

data: A <data.frame> of disease reports (confirm) by date (date). confirm must be numeric and date must be in date format. Optionally, data can also have a logical accumulate column which indicates whether data should be added to the next data point. This is useful when modelling e.g. weekly incidence data. See also the fill_missing() function which helps add the accumulate column with the desired properties when dealing with non-daily data. If any accumulation is done this happens after truncation as specified by the truncation argument. If all entries of confirm are missing (NA) the returned estimates will represent the prior distributions.
obs_column: Character (default: "confirm"). If given, only the column specified here will be used for checking missingness. This is useful if using a data set that has multiple columns of hwich one of them corresponds to observations that are to be processed here.
by: Character vector. Name(s) of any additional column(s) where data processing should be done separately for each value in the column. This is useful when using data representing e.g. multiple geographies. If NULL (default) no such grouping is done.

Value

A data.table with leading zeros removed.

Examples

cases <- data.frame(
  date = as.Date("2020-01-01") + 0:10,
  confirm = c(0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
)
filter_leading_zeros(cases)
#>          date confirm
#>        <Date>   <num>
#> 1: 2020-01-03       1
#> 2: 2020-01-04       2
#> 3: 2020-01-05       3
#> 4: 2020-01-06       4
#> 5: 2020-01-07       5
#> 6: 2020-01-08       6
#> 7: 2020-01-09       7
#> 8: 2020-01-10       8
#> 9: 2020-01-11       9