chemdose_chlordecay_once: Apply `chemdose_chlordecay`function within a data frame and output a data frame

Description

This function allows chemdose_chlordecay to be added to a piped data frame. Its output is a data frame containing columns for free_chlorine or combined_chlorine (depending on chlorine type).

Usage

chemdose_chlordecay_once(
  df,
  input_water = "defined_water",
  cl2_dose = 0,
  time = 0,
  treatment = "raw",
  cl_type = "chlorine"
)

Value

A data frame with updated chlorine residuals.

Arguments

df: a data frame containing a water class column, which has already been computed using define_water_once. The df may include a column named for the applied chlorine dose (cl2), and a column for time in hours.
input_water: name of the column of water class data to be used as the input for this function. Default is "defined_water".
cl2_dose: Applied chlorine or chloramine dose (mg/L as cl2). Model results are valid for doses between 0.995 and 41.7 mg/L for raw water, and for doses between 1.11 and 24.7 mg/L for coagulated water.
time: Reaction time (hours). Chlorine decay model results are valid for reaction times between 0.25 and 120 hours. Chloramine decay model does not have specified boundary conditions.
treatment: Type of treatment applied to the water. Options include "raw" for no treatment (default), "coag" for water that has been coagulated or softened.
cl_type: Type of chlorination applied, either "chlorine" (default) or "chloramine".

Details

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

If the input data frame has a chlorine dose column (cl2) or time column (time), the function will use those columns. Note: The function can only take cl2 and time inputs as EITHER a column or as 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

Run this code


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

example_df <- water_df %>%
  mutate(br = 50) %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  chemdose_chlordecay_once(input_water = "balanced_water", cl2_dose = 4, time = 8)

example_df <- water_df %>%
  mutate(br = 50) %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  mutate(
    cl2_dose = seq(2, 24, 2),
    time = 30
  ) %>%
  chemdose_chlordecay_once(input_water = "balanced_water")

example_df <- water_df %>%
  mutate(br = 80) %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  mutate(time = 8) %>%
  chemdose_chlordecay_once(
    input_water = "balanced_water", cl2_dose = 6, treatment = "coag",
    cl_type = "chloramine"
  )
# \donttest{
# Initialize parallel processing
plan(multisession, workers = 2) # Remove the workers argument to use all available compute
example_df <- water_df %>%
  mutate(br = 50) %>%
  define_water_chain() %>%
  balance_ions_chain() %>%
  chemdose_chlordecay_once(input_water = "balanced_water", cl2_dose = 4, time = 8)

# Optional: explicitly close multisession processing
plan(sequential)
# }