Creating plots based on hydrochemical data

General note: the below vignette contains frozen output of 7 Nov 2025. This makes it possible to build the package with vignettes without access to the Watina data warehouse.

Retrieve chemical data from Watina

We use the get_chem() function to retrieve hydrochemical data. The example requests available hydrochemical data since 2024 from locations in the area ‘Kesterbeek’ and saves it locally (collect = TRUE):

watina <- connect_watina()
my_chem_data <-
  get_locs(watina, area_codes = "KES") %>%
  get_chem(watina, "1/1/2024", collect = TRUE)

my_chem_data
#> # A tibble: 272 x 10
#>    loc_code date       lab_project_id lab_sample_id chem_variable  value
#>    <chr>    <date>     <chr>          <chr>         <chr>          <dbl>
#>  1 KESP001  2024-07-04 0              42052         Al             0.050
#>  2 KESP001  2024-07-04 0              42052         Ca             109.881
#>  3 KESP001  2024-07-04 0              42052         Cl             11.349
#>  4 KESP001  2024-07-04 0              42052         CondF          599.000
#>  5 KESP001  2024-07-04 0              42052         CondL          606.700
#>  6 KESP001  2024-07-04 0              42052         Fe             2.788
#>  7 KESP001  2024-07-04 0              42052         HCO3           375.292
#>  8 KESP001  2024-07-04 0              42052         K              0.970
#>  9 KESP001  2024-07-04 0              42052         Mg             8.960
#> 10 KESP001  2024-07-04 0              42052         Mn             0.792
#> # … with 262 more rows, and 4 more variables: unit <chr>,
#> #   below_loq <lgl>, loq <dbl>, elneutr <dbl>

We will use this dataset to demonstrate the plotting options available in the Watina package.

Plot the ionic ratio against the electrical conductivity (Van Wirdum)

This diagram, devised by Geert Van Wirdum, shows the chemical similarity of a water sample to references samples such as atmocline water (rainwater), thalassocline water (seawater) and lithocline water (calcium rich, fresh groundwater). The diagram plots the ionic ratio, that is $[Ca^{2+}] / ([Ca^{2+}] + [Cl^{-}])$ (as equivalent concentrations), against the logarithm of the electrical conductivity at 25°C.

The electrical conductivity is available for most samples in Watina, but we need to compute the ionic ratio. We could do this manually for each sample but the watina package also contains a calculate_ir() function that can be applied directly to the output of the get_chem() function to calculate the ionic ratio:

my_chem_data_vanwirdum <- calculate_ir(my_chem_data)

The obtained dataset is a wide table with the following fields (namely all the fields from the input dataset and a new field ir that contains the calculated ionic ratio):

names(my_chem_data_vanwirdum)
#>  [1] "loc_code"       "date"           "lab_project_id" "lab_sample_id" 
#>  [5] "elneutr"        "chem_variable"  "value"          "unit"          
#>  [9] "below_loq"      "loq"            "ir"

Now we can create the diagram of the ionic ratio against the (log of) electrical conductivity at 25°C (the so called Van Wirdum diagram) with the ggplot_vanwirdum_background() function.

This function will create the background of a Van Wirdum diagram, with reference points for lithotrophic water (Li), atmotrophic water (At), thalassotrophic water (Th) and optionally molunotrophic water (Rh - polluted water as found in the Rhine).

# background with standard options:
ggplot_vanwirdum_background()

We can show the mixing contours of the reference points with a curve instead of a line and plot the reference point for polluted river water using respectively the contour and rhine arguments:

ggplot_vanwirdum_background(
  contour = "curve",
  rhine = TRUE
)

Now let’s add our own data for the valley of the Kesterbeek. In this example we plot 2 locations that were sampled twice in 2024:

# get the conductivity data
my_chem_data_vanwirdum <- my_chem_data_vanwirdum %>%
  filter(chem_variable == "CondL") %>% # CondL = conductivity in the lab
  select(loc_code, date, conductivity = value, ir)

# add your own data with EC as x and IR as y and format as you wish
ggplot_vanwirdum_background(
  contour = "curve",
  lang = "en",
  rhine = TRUE
) +
  geom_point(
    data = my_chem_data_vanwirdum %>% head(n = 4),
    aes(
      x = conductivity, y = ir,
      colour = loc_code,
      shape = as.factor(date)
    ),
    size = 3
  ) +
  scale_colour_manual(name = "Location", values = c("dodgerblue", "red")) +
  scale_shape_discrete(name = "Date")

All the usual options of ggplot2 are available to customize the layout of the plot.