ozonate_bromate_chain: Apply `ozonate_bromate` within a data frame and output a column of `water` class to be chained to other tidywater functions

Description

This function allows ozonate_bromate to be added to a piped data frame. Its output is a `water` class, and can therefore be used with "downstream" tidywater functions. The bro3 slot will be updated.

Usage

ozonate_bromate_chain(
  df,
  input_water = "defined_water",
  output_water = "ozonated_water",
  dose = 0,
  time = 0,
  model = "Ozekin"
)

Value

A data frame containing a water class column with updated bro3.

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 named for the applied ozone dose (dose), and a column for time in minutes.
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 "ozonated_water".
dose: Applied ozone dose (mg/L as O3). Results typically valid for 1-10 mg/L, but varies depending on model.
time: Reaction time (minutes). Results typically valid for 1-120 minutes, but varies depending on model.
model: Model to apply, defaults to "Ozekin". One of c("Ozekin", "Sohn", "Song", "Galey", "Siddiqui")

Details

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

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

Run this code


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

example_df <- water_df %>%
  mutate(br = 50) %>%
  define_water_chain() %>%
  ozonate_bromate_chain(dose = 4, time = 8)

example_df <- water_df %>%
  mutate(br = 50) %>%
  define_water_chain() %>%
  mutate(
    dose = c(seq(.5, 3, .5), seq(.5, 3, .5)),
    time = 30
  ) %>%
  ozonate_bromate_chain()

example_df <- water_df %>%
  mutate(br = 80) %>%
  define_water_chain() %>%
  mutate(time = 8) %>%
  ozonate_bromate_chain(
    dose = 6, model = "Sohn"
  )

# \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() %>%
  ozonate_bromate_chain(dose = 4, time = 8)

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