define_water_once
First, let’s take a look at the available groundwater data from Well
A and Well B. Notice the use of define_water_once.
# Example of how to use a tidywater "_once" function to define multiple waters in a single data frame
data <- 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_once()
data
># Well ph temp alk tds cond tot_hard kw alk_eq na
># 1 A 8 18 100 300 468.75 19.21002 6.309573e-15 0.001998264 0.004349761
># 2 B 9 19 150 500 781.25 38.42004 6.309573e-15 0.002997395 0.008699522
># ca mg k cl so4 hco3
># 1 0.0001247567 0.0000671767 0.0000000000 0.0000000000 0 0.001974750
># 2 0.0002495134 0.0001343534 0.0005115312 0.0008461907 0 0.002647881
># co3 h2po4 hpo4 po4 ocl h oh tot_po4 tot_ocl tot_nh3
># 1 1.144647e-05 0 0 0 0 1e-08 6.309573e-07 0 0 0
># 2 1.716027e-04 0 0 0 0 1e-09 6.309573e-06 0 0 0
># tot_co3 is nh4 applied_treatment estimated
># 1 0.002026005 0.0075 0 defined _tothard_mg_cond
># 2 0.002824530 0.0125 0 defined _tothard_mg_cond
define_water_once is the first helper function we’ll
cover. This function does exactly what define_water does,
but applies it to a data frame input. This means once your data has the
proper column names, define_water_once can add the
parameters to each water slot, calculate the carbonate
balance (and other relevant calculations), and outputs all parameters in
the water class as a data frame. Any function with the
_once suffix in tidywater can be used in a piped code
block. However, tidywater functions cannot be used directly downstream
of these types of functions because the data is no longer in a
water class format.
define_water_chain
So what if you want to chain more tidywater functions together? In
that case, you can use define_water_chain. This function
takes a dataframe input, then outputs all parameters in a
water class column. This is true for all tidywater
functions with the _chain suffix. _chain
functions are handy in a piped code block where you’ll need to use many
tidywater functions, such as chemdose_ph,
solvedose_alk, etc. Most tidywater functions have a
_chain or _once option.
# 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
># defined_water Well
># 1 <S4 class 'water' [package "tidywater"] with 63 slots> A
># 2 <S4 class 'water' [package "tidywater"] with 63 slots> B
># balanced_water
># 1 <S4 class 'water' [package "tidywater"] with 63 slots>
># 2 <S4 class 'water' [package "tidywater"] with 63 slots>
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$water_class_column[[row_number]]
# Ion plot before balance_ions_chain was applied
raw_wells_water$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.
blend_waters
To blend 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.
# 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
># pH (unitless): 8.73
># Temperature (deg C): 18.5
># Alkalinity (mg/L CaCO3): 125
># Use summary functions or slot names to view other parameters.
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.
# Assume wells can contribute 2.5 MGD each
groundwater <- tibble(Wells_flow = c(0, 2.5, 5))
# Blending scenarios and the resulting source water ratios
scenarios <- tibble(
surface_flow = seq(1, 20, 1),
River_flow = c(seq(1, 10, 1), rep(10, 10)),
Lake_flow = c(rep(0, 10), seq(1, 10, 1)),
group = seq(1, 20, 1)
) %>%
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.
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)
blend_waters_chain
Now that we have our 3 sources defined, balanced, and cleaned up, we
can blend them. This next code chunk showcases the power of tidywater.
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.
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")
)
pluck_water
With all three source waters blended for each tested scenario, we can
pull out a parameter of interest. In this case, we’re investigating how
much we can dilute the total hardness. This brings us to our final
helper function for this vignette: pluck_water. This
function uses purrr::pluck to create a new column for one
selected parameter from a water class object. You can
choose which water column to pluck from using the
input_water argument. Next, select the parameter of
interest (which must match the water slot’s name). Finally, the output
column’s name will default to the name of the plucked parameter, but
there is an option to name it yourself using the
output_column argument. If you want to view all the
parameters as separate columns, it is better to use a _once
function instead of _chain (i.e., we could have used
blend_water_once above).
plotting_data <- blend_water %>%
pluck_water(input_water = "blended_water", "tot_hard") %>%
# Flag scenarios for plotting
mutate(Flagged = case_when(blended_water_tot_hard > 200 ~ "FLAG: Hardness > 200 mg/L CaCO3", TRUE ~ "Not Flagged"))
# Plot the results!
ggplot(plotting_data, aes(x = total_flow, y = blended_water_tot_hard, color = as.character(Wells_flow), shape = Flagged)) +
geom_point() +
scale_shape_manual(values = c(4, 16)) +
labs(
y = "Hardness (mg/L as CaCO3)", color = "Contributions from new wells (MGD)",
shape = "Scenario Flags", x = "Total Plant Flow (MGD)"
) +
theme_bw() +
theme(
legend.position = "bottom",
legend.box = "vertical",
legend.margin = margin(-5, 0, 0, 0)
)
Speed it up
As you use more tidywater helper functions with larger data sets,
you’ll notice the code can take a few minutes to run. All helper
functions use functions from the
furrr package. To reduce
processing time, you can activate furrr’s parallel
processing power by using plan() at the beginning of your
script. plan() depends on what type of operating system you
have, more info on that in the
Controlling How Futures are
Resolved table.
# For most operating systems, especially Windows, use this at the beginning of your script
# We recommend revmoving the `workers` argument to use your computer's full power.
plan(multisession, workers = 2)
# rest of script
# At the end of the script, here's an option to explicitly close the multisession processing
plan(sequential)