1 Install R packages
if (! require("remotes")) {
install.packages("remotes", repos = "https://cloud.r-project.org")
}
remotes::install_github("kwb-r/kwb.geosalz", dependencies = TRUE)2 Setup the project
2.1 Define paths
# Define paths and resolve placeholders
paths_list <- list(
servername = Sys.getenv("servername"),
root_server = "//<servername>/projekte$/GRW_Department/PROJECTS",
project = "geosalz",
root_project = "<root_server>/<project>",
monitoring = "<root_project>/Work-packages/AP2_Monitoring",
samples_bwb_dir = "<monitoring>/Beprobung",
version = "1.12",
samples_bwb = "<samples_bwb_dir>/Hydrochemie_Gesamt_Geosalz_v<version>.xlsx",
master_data = "<samples_bwb>",
isotopes_dir = "<monitoring>/Isotope/data",
isotopes = "<isotopes_dir>/Isotope.csv",
phreeqc_dir = "<samples_bwb_dir>/phreeqc",
phreeqc_txt = "<phreeqc_dir>/phreeqc_output_v%s.txt",
phreeqc_xlsx = "<phreeqc_dir>/phreeqc_output_v%s.xlsx"
)
paths <- kwb.utils::resolve(paths_list)
#paths <- kwb.utils::resolve(paths, root = "C:/projects")3 Data import
master_data <- kwb.geosalz::read_master_data(paths$master_data)
lab_bwb <- kwb.geosalz::read_lab_bwb(paths$samples_bwb, sheet = 'phreeqc_analysen')
lab_ufz <- kwb.geosalz::read_isotopes(paths$isotopes)
lab_data <- lab_bwb %>%
dplyr::mutate(probe_nr = as.character(.data$probe_nr)) %>%
dplyr::bind_rows(
lab_ufz %>%
dplyr::mutate(par_val_org = as.character(.data$par_val_org))
)
str(lab_data)
lab_data_master <- dplyr::left_join(lab_data, master_data, by = "messstelle")
str(lab_data_master)4 PhreeqC
4.1 Prepare PHREEQC Input File
phreeqc_input <- kwb.geosalz::get_phreeqc_data(lab_data_master)
phreeqc_input
phreeqc_input_wide <- kwb.geosalz::convert_phreeqc_input_to_wide(phreeqc_input)
phreeqc_input_wide
solutions_input <- kwb.geosalz::prepare_phreeqc_input(
phreeqc_input,
title = sprintf("Based on '%s'", paths$samples_bwb)
)Content of solutions_input is shown below:
4.2 Run PHREEQC
fs::dir_create(paths$phreeqc_dir)
### Load PHREEQC database "phreeqc.dat"
phreeqc::phrLoadDatabaseString(phreeqc::phreeqc.dat)
### Capture PHREEQC output normally sent to output file into a buffer
phreeqc::phrSetOutputStringsOn(TRUE)
## Run PHREEQC with "solutions_input"
phreeqc::phrRunString(input = solutions_input)
### Retrieves the phreeqc output as a character vector
phreeqc_output <- phreeqc::phrGetOutputStrings()
writeLines(phreeqc_output, paths$phreeqc_txt)Here is the content of phreeqc_output
4.3 Get Results
sims <- geosalz.phreeqc::read_simulations(phreeqc_output)
str(sims)
sims_list <- geosalz.phreeqc::convert_simulations_to_list(sims)
str(sims_list)
n_components <- length(names(sims_list))
masters_lab <- lab_data %>%
dplyr::count(
.data$probe_nr,
.data$probenahme_datum,
.data$messstelle
) %>%
dplyr::select(- .data$n) %>%
dplyr::left_join(master_data)
sims_list_master <- stats::setNames(
lapply(seq_len(n_components), function(i) {
if (i %in% 1:2) {
sims_list[[i]]
} else {
dplyr::left_join(
sims_list[[i]],
masters_lab,
by = c("solution_name" = "probe_nr")
)
}
}),
names(sims_list)
)
# View(sims_list_master$solution_composition)
# View(sims_list_master$description_of_solution)
# View(sims_list_master$redox_couples)
# View(sims_list_master$distribution_of_species)
# View(sims_list_master$saturation_indices)5 Export to Excel
export_data <- c(
list(
"master_data" = master_data,
"lab_bwb" = lab_bwb,
"lab_ufz" = lab_ufz,
"phreeqc_input" = phreeqc_input_wide
),
sims_list
)
openxlsx::write.xlsx(
x = export_data,
file = paths$phreeqc_xlsx,
overwrite = TRUE
)6 Session Info
Plattform
| name | value |
|---|---|
| version | R version 4.4.2 (2024-10-31 ucrt) |
| os | Windows Server 2022 x64 (build 20348) |
| system | x86_64, mingw32 |
| ui | RTerm |
| language | en |
| collate | English_United States.utf8 |
| ctype | English_United States.utf8 |
| tz | UTC |
| date | 2024-12-23 |
| pandoc | 3.1.11 @ C:/HOSTED1/windows/pandoc/31F3871.11/x64/PANDOC~1.11/ (via rmarkdown) |
Packages
#> package * version date (UTC) lib source
#> bslib 0.8.0 2024-07-29 [1] CRAN (R 4.4.1)
#> cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.0)
#> cli 3.6.3 2024-06-21 [1] RSPM (R 4.4.0)
#> desc 1.4.3 2023-12-10 [1] CRAN (R 4.4.0)
#> digest 0.6.37 2024-08-19 [1] RSPM (R 4.4.0)
#> dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
#> evaluate 1.0.1 2024-10-10 [1] CRAN (R 4.4.1)
#> fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
#> fs 1.6.5 2024-10-30 [1] CRAN (R 4.4.1)
#> generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
#> glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
#> htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
#> htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
#> jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.4.0)
#> jsonlite 1.8.9 2024-09-20 [1] RSPM (R 4.4.0)
#> knitr 1.49 2024-11-08 [1] RSPM (R 4.4.0)
#> kwb.utils 0.15.0 2024-04-25 [1] Github (kwb-r/kwb.utils@4415aa2)
#> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
#> pillar 1.10.0 2024-12-17 [1] CRAN (R 4.4.2)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
#> pkgdown 2.1.1 2024-09-17 [1] RSPM (R 4.4.0)
#> purrr 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
#> ragg 1.3.0 2024-03-13 [1] CRAN (R 4.4.0)
#> rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.0)
#> rmarkdown 2.29 2024-11-04 [1] CRAN (R 4.4.2)
#> sass 0.4.9 2024-03-15 [1] CRAN (R 4.4.0)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
#> systemfonts 1.1.0 2024-05-15 [1] CRAN (R 4.4.0)
#> textshaping 0.3.7 2023-10-09 [1] CRAN (R 4.4.0)
#> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
#> tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.0)
#> tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
#> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
#> xfun 0.49 2024-10-31 [1] CRAN (R 4.4.1)
#> yaml 2.3.10 2024-07-26 [1] RSPM (R 4.4.0)
#>
#> [1] D:/a/_temp/Library
#> [2] C:/R/library