biofilter_toc_chain function

Apply biofilter_toc within a dataframe and output a column of water class to be chained to other tidywater functions

This function allows biofilter_toc to be added to a piped data frame. Its output is a water class, and can therefore be used with "downstream" tidywater functions. TOC, DOC, and UV254 water slots will be updated based on input EBCT and whether the water is ozonated.

biofilter_toc_chain(
  df,
  input_water = "defined_water",
  output_water = "biofiltered_water",
  ebct = 0,
  ozonated = TRUE
)

Arguments

• df: a data frame containing a water class column, which has already been computed using define_water_chain. The df may include a column indicating the EBCT or whether the water is ozonated. and a column named for the set of coefficients to use.
• input_water: name of the column of Water class data to be used as the input for this function. Default is "defined_water".
• output_water: name of the output column storing updated parameters with the class, Water. Default is "biofiltered_water".
• ebct: The empty bed contact time (min) used for the biofilter
• ozonated: Logical; TRUE if the water is ozonated (default), FALSE otherwise

Returns

A data frame containing a water class column with updated DOC, TOC, and UV254 water slots.

Details

The data input comes from a water class column, as initialized in define_water_chain.

If the input data frame has column(s) named "ebct" or "ozonated", the function uses those as arguments. Note: The function can use either a column or the direct function arguments, not both.

For large datasets, using fn_once or fn_chain may take many minutes to run. These types of functions use the furrr package for the option to use parallel processing and speed things up. To initialize parallel processing, use plan(multisession) or plan(multicore) (depending on your operating system) prior to your piped code with the fn_once or fn_chain functions. Note, parallel processing is best used when your code block takes more than a minute to run, shorter run times will not benefit from parallel processing.

Examples

library(purrr)
library(furrr)
library(tidyr)
library(dplyr)

example_df <- water_df %>%
  define_water_chain() %>%
  biofilter_toc_chain(input_water = "defined_water", ebct = 10, ozonated = FALSE)

example_df <- water_df %>%
  define_water_chain() %>%
  mutate(
    ebct = c(10, 10, 10, 15, 15, 15, 20, 20, 20, 25, 25, 25),
    ozonated = c(rep(TRUE, 6), rep(FALSE, 6))
  ) %>%
  biofilter_toc_chain(input_water = "defined_water")

# Initialize parallel processing
plan(multisession, workers = 2) # Remove the workers argument to use all available compute
example_df <- water_df %>%
  define_water_chain() %>%
  biofilter_toc_chain(input_water = "defined_water", ebct = c(10, 20))

# Optional: explicitly close multisession processing
plan(sequential)

See Also

biofilter_toc