pac_toc_chain function

Apply pac_toc within a data frame and output a column of water class to be chained to other tidywater functions PAC = powdered activated carbon

This function allows pac_toc to be added to a piped data frame. Its output is a water class, and can therefore be used with "downstream" tidywater functions.

Source

See references list at: https://github.com/BrownandCaldwell-Public/tidywater/wiki/References

CHO(2007)

pac_toc_chain(
  df,
  input_water = "defined_water",
  output_water = "pac_water",
  dose = 0,
  time = 0,
  type = "bituminous"
)

Arguments

• df: a data frame containing a water class column, which has already been computed using define_water_chain. The df may include columns named for the dose, time, and type
• 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 "pac_water".
• dose: Applied PAC dose (mg/L). Model results are valid for doses concentrations between 5 and 30 mg/L.
• time: Contact time (minutes). Model results are valid for reaction times between 10 and 1440 minutes
• type: Type of PAC applied, either "bituminous", "lignite", "wood".

Returns

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

Details

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

If the input data frame has a dose, time or type column, the function will use those columns. Note: The function can only take dose, time, and type inputs as EITHER a column or from the function arguments, not both.

tidywater functions cannot be added after this function because they require a water class input.

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("raw") %>%
  pac_toc_chain(input_water = "raw", dose = 10, time = 20)

example_df <- water_df %>%
  define_water_chain("raw") %>%
  mutate(dose = seq(11, 22, 1), time = 30) %>%
  pac_toc_chain(input_water = "raw")

example_df <- water_df %>%
  define_water_chain("raw") %>%
  mutate(time = 8) %>%
  pac_toc_chain(
    input_water = "raw", dose = 6, type = "wood"
  )

# Initialize parallel processing
plan(multisession, workers = 2) # Remove the workers argument to use all available compute
example_df <- water_df %>%
  define_water_chain("raw") %>%
  pac_toc_chain(input_water = "raw", dose = 4, time = 8)

# Optional: explicitly close multisession processing
plan(sequential)

See Also

pac_toc