Add a defined number to a numeric and log transform it

Frequently, light exposure data need to be log-transformed. Because light exposure data frequently also contain many zero-values, adding a small value avoids losing those observations. Must be applied with care and reported.

exp_zero_inflated() is the reverse function to log_zero_inflated().

Usage

log_zero_inflated(x, offset = 0.1, base = 10)

exp_zero_inflated(x, offset = 0.1, base = 10)

Arguments

x: A numeric vector
offset: the amount to add to x, by default 0.1
base: The logarithmic base, by default 10

Value

a transformed numeric vector

References

Johannes Zauner, Carolina Guidolin, Manuel Spitschan (2025) How to deal with darkness: Modelling and visualization of zero-inflated personal light exposure data on a logarithmic scale. bioRxiv. doi: https://doi.org/10.1101/2024.12.30.630669

Examples

c(0, 1, 10, 100, 1000, 10000) |> log_zero_inflated()
#> [1] -1.00000000  0.04139269  1.00432137  2.00043408  3.00004343  4.00000434

#For use in a function
sample.data.environment |> 
  dplyr::filter(Id == "Participant") |> 
  dplyr::group_by(Date = lubridate::wday(Datetime, label = TRUE, week_start = 1)) |> 
  dplyr::summarize(
  TAT250 = duration_above_threshold(log_zero_inflated(MEDI), 
                                    Datetime, 
                                    threshold = log_zero_inflated(250)
                                    )
                   )
#> # A tibble: 6 × 2
#>   Date  TAT250              
#>   <ord> <Duration>          
#> 1 Tue   5810s (~1.61 hours) 
#> 2 Wed   9960s (~2.77 hours) 
#> 3 Thu   16080s (~4.47 hours)
#> 4 Fri   14130s (~3.92 hours)
#> 5 Sat   26930s (~7.48 hours)
#> 6 Sun   25610s (~7.11 hours)
                   

#Calling exp_zero_inflated on data transformed with log_zero_inflated yields to the original result
c(0, 1, 10, 100, 1000, 10000) |> log_zero_inflated() |> exp_zero_inflated()
#> [1]     0     1    10   100  1000 10000