---
title: "Blending Multiple Waters"
# Blending water analysis using tidywater's helper functions
# More Practice with Helper Functions Through a Blending Analysis
description: "This vignette will show how to do a water blending analysis using some of tidywater's helper functions."
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{help_functions_blend_waters}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = ">#"
)
library(tidywater)
library(tidyr)
library(dplyr)
library(ggplot2)
library(furrr)
library(purrr)

# plan(multisession)
```

This vignette assumes a basic understanding of `define_water` and the S4 `water` class. See `vignette("intro", package = "tidywater")` for more information. Additionally, for more information on tidywater's `_chain` and `pluck_waters` functions, please see the `vignette("help_functions_chemdose_ph", package = "tidywater")`.

## Blending analysis setup
In this analysis, a hypothetical drinking water utility sources their water from a river and a lake, both of which have high hardness. The operators are investigating whether blending up to 5 MGD from two groundwater wells will reduce the total hardness below 200 mg/L as CaCO3. 

## Well setup
First, let's take a look at the available groundwater data from Well A and Well B. We use `define_water_chain` so that other models can be added to the dataframe.

```{r, warning=FALSE, echo=TRUE}
# Read in data from Wells A and B
raw_wells_water <- tibble(
  Well = c("A", "B"),
  ph = c(8, 9),
  alk = c(100, 150),
  temp = c(18, 19),
  ca = c(5, 10),
  cond = c(500, 900),
  tds = c(300, 500),
  na = c(100, 200),
  k = c(0, 20),
  cl = c(0, 30),
  so4 = c(0, 0)
) %>%
  define_water_chain() %>%
  balance_ions_chain()

raw_wells_water
```

It's always a good idea to verify our code is working properly. To make sure that our data was balanced using `balance_ions_chain`, we can plot our `water` class using `plot_ions`. The below example shows how to index a `water` class column: dataframe$water_class_column[[row_number]]


```{r, fig.width=7}
# Ion plot before balance_ions_chain was applied
raw_wells_water$defined_water[[1]] %>%
  plot_ions()
# Plot of balanced ions
raw_wells_water$balanced_water[[1]] %>%
  plot_ions()
```

Let's continue with our blending analysis. We're going to treat our two wells as a single groundwater source. Blending can be calculated as Well_A_ratio * Well_A concentration + Well_B_ratio * Well_B_concentration. This is fine for most parameters, but for pH and acid/base equilibrium species, blending is a little more complicated. Enter: `blend_waters`. This function blends waters as you'd expect, and does all the pH blending math for you. In the example below, we're going to be blending inefficiently. But don't worry, there will be a better blending example later.

To mix our two wells, we will blend row 1 of `balanced_water` with row 2 of `balanced_water`. This "vertical" blending is not efficient and will not be useful for large data frames. `water` objects cannot be pivoted, hence the row-to-row blending. In later examples, we will actually blend columns, which is more amenable to piped code chunks.

The `balanced_water` function takes 2 or more waters (must be of the `water` class), and corresponding ratios for each water. 

```{r, warning=FALSE}
# Blend "vertically": blends the data in well A's row with that of well B's.
# The pluck function from the purrr package is useful for indexing a water class column
### First, index the water column using the name or number of the column (ie "balanced_water" or 3 (column number))
### Next, index the row

blended_wells_water <- blend_waters(
  waters = c(
    pluck(raw_wells_water, "balanced_water", 1),
    pluck(raw_wells_water, 3, 2)
  ),
  ratios = c(.5, .5)
)
# outputs a water class object.
blended_wells_water
```

## Blending scenarios and finish source setup

We will create a data frame of the blend scenarios we will be modeling, in this case, we are varying flow rates from the different sources.

```{r, warning=FALSE}
# Assume wells can contribute up to 5 MGD each
groundwater <- tibble(Wells_flow = c(0, 2.5, 5))
# Blending scenarios and the resulting source water ratios
scenarios <- tibble(
  surface_flow = seq(2, 20, 2),
  River_flow = c(seq(2, 10, 2), rep(10, 5)),
  Lake_flow = c(rep(0, 5), seq(2, 10, 2)),
) %>%
  mutate(group = row_number()) %>%
  cross_join(groundwater) %>%
  mutate(
    total_flow = River_flow + Lake_flow + Wells_flow,
    River_ratio = River_flow / total_flow,
    Lake_ratio = Lake_flow / total_flow,
    Wells_ratio = Wells_flow / total_flow
  )
```

To finish blending our wells, we will transform the `blended_wells` `water` object into a data frame containing a `water` column. 

The river and lake sources don't require any mixing. We'll set up their raw data and balance the ions using `define_water_chain` to make a data frame with a `water` column. In `balance_ions_chain`, we are specifying the name of the output columns so we can use the different water sources later. Most of tidywater's `_chain` functions have the option to name the output column. Defaults vary depending on the `_chain` function. 

```{r, warning=FALSE}
Wells_water <- tibble(wells = c(blended_wells_water))

River_water <- tibble(
  ph = 7, temp = 20, alk = 200, tds = 950, cond = 1400,
  tot_hard = 300, na = 100, cl = 150, so4 = 200
) %>%
  define_water_chain() %>%
  balance_ions_chain(output_water = "river") %>%
  select(-defined_water)

Lake_water <- tibble(
  ph = 7.5, temp = 19, alk = 180, tds = 900, cond = 1000,
  tot_hard = 350, ca_hard = 250, na = 100, cl = 100, so4 = 150
) %>%
  define_water_chain() %>%
  balance_ions_chain(output_water = "lake") %>%
  select(-defined_water)
```

## Blending multiple sources

Now that we have our 3 sources defined, balanced, and cleaned up, we can blend them. This next code chunk showcases the power of working in a data frame. We'll use `blend_waters_chain`, the helper function for `blend_waters`. We already created `water` class columns above, so we'll use those column names in the `waters` argument. The ratios for each water source were calculated in the `scenarios` data frame. We'll pass the names of those ratio columns into the `ratio` argument. The ratios must always add up to 1, otherwise the function will not run.

```{r, warning=FALSE}
blend_water <- scenarios %>%
  cross_join(Wells_water) %>%
  cross_join(River_water) %>%
  cross_join(Lake_water) %>%
  blend_waters_chain(
    waters = c("wells", "river", "lake"),
    ratios = c("Wells_ratio", "River_ratio", "Lake_ratio")
  )
```

With all three source waters blended for each tested scenario, we can pull out a parameter of interest using `pluck_water`. Finally, we finish by plotting our parameter of interest with the `ggplot` package.

```{r, fig.width= 7}
plotting_data <- blend_water %>%
  pluck_water(input_water = "blended_water", "tot_hard")

# Plot the results!
ggplot(plotting_data, aes(x = total_flow, y = blended_water_tot_hard, color = as.character(Wells_flow))) +
  geom_point() +
  labs(
    y = "Hardness (mg/L as CaCO3)", color = "Well Flow (MGD)",
    x = "Total Plant Flow (MGD)"
  )
```

## Summary

In this tutorial, we learned how to use the `blend_waters` function to determine resulting water quality of multipled mixed sources. The function inputs `water` objects and their blending ratios, and outputs a new column storing updated parameters with the class `water`. 

We also got more practice using helper functions with the `_chain` suffix and also `pluck_water`. For more context on helper functions or to learn more about the `chemdose_ph` and `solvedose_ph` functions, please see `vignette("help_functions_chemdose_ph", package = "tidywater")`.