ozonate_bromate_once function

Apply ozonate_bromatefunction within a data frame and output a data frame

This function allows ozonate_bromate to be added to a piped data frame. Its output is a data frame containing a bro3 column.

ozonate_bromate_once(
  df,
  input_water = "defined_water",
  dose = 0,
  time = 0,
  model = "Ozekin"
)

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 minutes.
• input_water: name of the column of water class data to be used as the input for this function. Default is "defined_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")

Returns

A data frame with updated bromate.

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.

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 %>%
  mutate(br = 50) %>%
  define_water_chain("raw") %>%
  ozonate_bromate_once(input_water = "raw", dose = 3, time = 8)

example_df <- water_df %>%
  mutate(br = 50) %>%
  define_water_chain("raw") %>%
  mutate(
    dose = c(seq(.5, 3, .5), seq(.5, 3, .5)),
    time = 10
  ) %>%
  ozonate_bromate_once(input_water = "raw")

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

# 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_once(input_water = "defined_water", dose = 4, time = 8)

# Optional: explicitly close multisession processing
plan(sequential)

See Also

ozonate_bromate