calculate_corrosion_chain function

Apply calculate_corrosion to a dataframe and output a column of water class to be chained to other tidywater functions.

This function allows calculate_corrosion to be added to a piped data frame. Up to six additional columns will be added to the output water class column depending on what corrosion/scaling indices are selected: Aggressive index (AI), Ryznar index (RI), Langelier saturation index (LSI), Larson-Skold index (LI), chloride-to-sulfate mass ratio (CSMR) & calcium carbonate precipitation potential (CCPP).

calculate_corrosion_chain(
  df,
  input_water = "defined_water",
  output_water = "corrosion_indices",
  index = c("aggressive", "ryznar", "langelier", "ccpp", "larsonskold", "csmr"),
  form = "calcite"
)

Arguments

• df: a data frame containing a column, defined_water, which has already been computed using define_water, and a column named for each of the chemicals being dosed
• input_water: name of the column of water class data to be used as the input. Default is "defined_water".
• output_water: name of output column storing updated indices with the class, water. Default is "corrosion_indices".
• index: The indices to be calculated. Default calculates all six indices: "aggressive", "ryznar", "langelier", "ccpp", "larsonskold", "csmr" CCPP may not be able to be calculated sometimes, so it may be advantageous to leave this out of the function to avoid errors
• form: Form of calcium carbonate mineral to use for modelling solubility: "calcite" (default), "aragonite", or "vaterite"

Returns

A data frame containing a water class column with updated corrosion and scaling index slots.

Details

The data input comes from a water class column, initialized in define_water or balance_ions. The water class column to use in the function is specified in the input_water argument (default input water is "defined_water". The name of the output water class column defaults to "corrosion_indices", but may be altered using the output_water argument.

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 %>%
  slice_head(n = 2) %>% # used to make example run faster
  define_water_chain() %>%
  calculate_corrosion_chain()

example_df <- water_df %>%
  slice_head(n = 2) %>% # used to make example run faster
  define_water_chain() %>%
  calculate_corrosion_chain(index = c("aggressive", "ccpp"))

# Initialize parallel processing
plan(multisession, workers = 2) # Remove the workers argument to use all available compute
example_df <- water_df %>%
  define_water_chain() %>%
  calculate_corrosion_chain(index = c("aggressive", "ccpp"))

# Optional: explicitly close multisession processing
plan(sequential)

See Also

calculate_corrosion