Background
One measurement chain consists of six sensors that are placed in one production well, but at different filter depths. In total, three measurement chains will be installed in three different production wells.
Table 1 below lists all individual sensors. The number of the sensor
within the well is given in column sensor_endnummer. The
identifier of the production well that the sensor is located in is given
in column brunnen_nummer.
Each sensor (type: WLF05, TODO: add-link-to-factsheet-on-kwb-cloud) measures two parameters with the following characteristics:
- electrical conductivity:
- measurement range: 0 - 20 mS
- measurement range detection: automatic
- typical accuracy: +/- 1.5 % of measurement value
- temperature:
- measurement range: 0 - 50 degree Celsius
- measurement resolution: 0.1 Kelvin
- typical accuracy: <= 0.1 Kelvin
The maximum temporal resolution that a sensor can provide is “every second”. Within the GeoSalz project, measurements are taken every 5 minutes. This should be sufficient to detect potential salinity shifts that are caused by changes in the pumping regime.
Data Management
Define Paths
# Define paths to directories, using <placeholder> replacements
paths <- kwb.utils::resolve(list(
# Local temporary directory
local_dir = kwb.geosalz:::temp_dir(),
# Target directory for downloaded measurement chain files (.csv)
download_dir = "<local_dir>/download",
# Local directory for aggregated data (.csv and .zip)
export_dir = "<local_dir>/export",
# KWB cloud directory to which data in "export_dir" is uploaded
upload_dir = "projects/GeoSalz/Monitoring/messketten",
# KWB cloud directory with latest BWB well operation data
well_operation = "<upload_dir>/BWB_Brunnen_Prozessdaten"
))
#> The directory "C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz" was created.
# Print all paths
paths
#> $local_dir
#> [1] "C:\\Users\\RUNNER~1\\AppData\\Local\\Temp/R_kwb.geosalz"
#>
#> $download_dir
#> [1] "C:\\Users\\RUNNER~1\\AppData\\Local\\Temp/R_kwb.geosalz/download"
#>
#> $export_dir
#> [1] "C:\\Users\\RUNNER~1\\AppData\\Local\\Temp/R_kwb.geosalz/export"
#>
#> $upload_dir
#> [1] "projects/GeoSalz/Monitoring/messketten"
#>
#> $well_operation
#> [1] "projects/GeoSalz/Monitoring/messketten/BWB_Brunnen_Prozessdaten"Define SFTP Login
Run usethis::edit_r_environ() to edit the
.Renviron file. The file defines environment variables that
are to be made accessible during an R session. Add the following three
rows, defining three more environment variables, to the file. The
environment variables are required to log in to the SFTP server from
which input data are downloaded.
MESSKETTEN_SERVER=<sftp_url>
MESSKETTEN_USER=<sftp_username>
MESSKETTEN_PASSWORD=<sftp_userpassword>Replace the placeholders <sftp_url>,
<sftp_username>,
<sftp_userpassword> with the URL, the user name, and
the password, respectively, that are required to get access to the SFTP
server.
Save the .Renviron file and restart the R session
(e.g. with “Session/Restart R” from the menu in RStudio) to make the
environment variables available in R.
In case that the SFTP login credentials are correct, the code below should work. It downloads the measurement chains data (i.e. parameters electrical conductivity and temperature) from the SFTP server to a user-defined directory on your local device.
Data Download
What files are available on the SFTP server?
# Metadata of measurement chains (see also Table 1 above)
metadata <- kwb.geosalz::get_measurementchains_metadata()
str(metadata)
#> spc_tbl_ [18 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#> $ galerie : chr [1:18] "K" "K" "K" "K" ...
#> $ brunnen_nummer : int [1:18] 9 9 9 9 9 9 10 10 10 10 ...
#> $ dn : int [1:18] 400 400 400 400 400 400 600 600 600 600 ...
#> $ einbau_pumpe : chr [1:18] "über Filter" "über Filter" "über Filter" "über Filter" ...
#> $ einbau_messkette : chr [1:18] "unter Pumpe" "unter Pumpe" "unter Pumpe" "unter Pumpe" ...
#> $ filteroberkante_muGOK : num [1:18] 21 21 21 21 21 21 11 11 11 11 ...
#> $ filterunterkante_muGOK: num [1:18] 27 27 27 27 27 27 18 18 18 18 ...
#> $ sensor_id : int [1:18] 4182018 4182017 4182016 4182015 4182014 4182013 4182006 4182005 4182004 4182003 ...
#> $ sensor_endnummer : int [1:18] 8 7 6 5 4 3 6 5 4 3 ...
#> $ einbau_sensor_muGOK : num [1:18] 21.5 22.5 23.5 24.5 25.5 26.5 11.5 12.7 13.9 15.1 ...
#> - attr(*, "spec")=
#> .. cols(
#> .. galerie = col_character(),
#> .. brunnen_nummer = col_integer(),
#> .. dn = col_integer(),
#> .. einbau_pumpe = col_character(),
#> .. einbau_messkette = col_character(),
#> .. filteroberkante_muGOK = col_double(),
#> .. filterunterkante_muGOK = col_double(),
#> .. sensor_id = col_integer(),
#> .. sensor_endnummer = col_integer(),
#> .. einbau_sensor_muGOK = col_double()
#> .. )
#> - attr(*, "problems")=<externalptr>
# Information on available measurement chain files on SFTP server
mc_files <- kwb.geosalz::get_measurementchains_files()
str(mc_files)
#> 'data.frame': 14093 obs. of 16 variables:
#> $ rights : chr "-rw-r--r--" "-rw-r--r--" "-rw-r--r--" "-rw-r--r--" ...
#> $ links : chr "1" "1" "1" "1" ...
#> $ ownername : chr "1011" "1011" "1011" "1011" ...
#> $ ownergroup : chr "1002" "1002" "1002" "1002" ...
#> $ filesize : chr "4559" "4559" "4559" "4559" ...
#> $ t1 : chr "Oct" "Oct" "Oct" "Oct" ...
#> $ t2 : chr "24" "24" "24" "25" ...
#> $ t3 : chr "2022" "2022" "2022" "2022" ...
#> $ sftp_path : chr "K09/4182013-2022-10-22-0606.csv" "K09/4182013-2022-10-23-0606.csv" "K09/4182013-2022-10-24-0606.csv" "K09/4182013-2022-10-25-0606.csv" ...
#> $ type : chr "file" "file" "file" "file" ...
#> $ galerie : chr "K" "K" "K" "K" ...
#> $ brunnen_nummer : int 9 9 9 9 9 9 9 9 9 9 ...
#> $ prefix : chr "" "" "" "" ...
#> $ sensor_id : int 4182013 4182013 4182013 4182013 4182013 4182013 4182013 4182013 4182013 4182013 ...
#> $ sensor_endnummer: int 3 3 3 3 3 3 3 3 3 3 ...
#> $ datum_uhrzeit : POSIXct, format: "2022-10-22 06:06:00" "2022-10-23 06:06:00" ...
head(mc_files)
#> rights links ownername ownergroup filesize t1 t2 t3
#> 1 -rw-r--r-- 1 1011 1002 4559 Oct 24 2022
#> 2 -rw-r--r-- 1 1011 1002 4559 Oct 24 2022
#> 3 -rw-r--r-- 1 1011 1002 4559 Oct 24 2022
#> 4 -rw-r--r-- 1 1011 1002 4559 Oct 25 2022
#> 5 -rw-r--r-- 1 1011 1002 4559 Oct 26 2022
#> 6 -rw-r--r-- 1 1011 1002 4559 Oct 27 2022
#> sftp_path type galerie brunnen_nummer prefix sensor_id
#> 1 K09/4182013-2022-10-22-0606.csv file K 9 4182013
#> 2 K09/4182013-2022-10-23-0606.csv file K 9 4182013
#> 3 K09/4182013-2022-10-24-0606.csv file K 9 4182013
#> 4 K09/4182013-2022-10-25-0606.csv file K 9 4182013
#> 5 K09/4182013-2022-10-26-0606.csv file K 9 4182013
#> 6 K09/4182013-2022-10-27-0606.csv file K 9 4182013
#> sensor_endnummer datum_uhrzeit
#> 1 3 2022-10-22 06:06:00
#> 2 3 2022-10-23 06:06:00
#> 3 3 2022-10-24 06:06:00
#> 4 3 2022-10-25 06:06:00
#> 5 3 2022-10-26 06:06:00
#> 6 3 2022-10-27 06:06:00
# All paths to files on the SFTP server
all_paths <- kwb.utils::selectColumns(mc_files, "sftp_path")What data are already available on the Nextcloud server?
# Download existing data from the Nextcloud server. NULL is returned if there is
# no file "mc_data.zip" in the Nextcloud folder
# "projects/GeoSalz/Monitoring/messketten"
old_data <- kwb.geosalz::get_measurement_chain_data_on_cloud()
#> The directory "~/../Downloads/nextcloud_3d4540527ad" was created.
#> Downloading remote.php/dav/files/hsonne/projects/GeoSalz/Monitoring/messketten/mc_data.zip ... ok. (0.78 secs)
#old_data <- NULLWhat files need to be downloaded from the SFTP server?
# Determine the SFTP paths of the files to be downloaded. Download all available
# files if there was no data on the Nextcloud yet. Otherwise, download only
# those files that have been added to the SFTP server since when this script was
# run the last time.
new_paths <- if (is.null(old_data)) {
all_paths
} else {
# Get SFTP paths from which data were originally retrieved
old_paths <- unique(kwb.utils::selectColumns(old_data, "file"))
# Determine the paths to the new files that need to be downloaded
setdiff(all_paths, old_paths)
}Download the new files from the SFTP server
# If there are new files to download, download them.
csv_files <- if (length(new_paths)) {
# Download only the new measurement chain files (.csv) from the SFTP server
kwb.geosalz::download_measurementchains_data(
sftp_paths = new_paths,
target_directory = paths$download_dir,
debug = TRUE
)
} # else NULL implicitly
#> The directory "C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/download" was created.
#> Download 36 measurement chains files (using 3 CPU cores) to C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/download ... ok. (35.28 secs)
str(csv_files)
#> chr [1:36] "C:\\Users\\RUNNER~1\\AppData\\Local\\Temp/R_kwb.geosalz/download/K09/4182013-2024-12-21-0506.csv" ...Data Import
The following code imports the downloaded measurement chains files (.csv) into R:
# Set the current data to the old data (may be NULL)
mc_data <- old_data
# If new files have been downloaded, import them into R
if (length(csv_files)) {
# Import csv files using multiple CPU cores
new_data <- kwb.geosalz::read_measurementchains_data(
csv_files,
run_parallel = TRUE,
debug = TRUE
)
# Combine the new data with the existing data and reorder
mc_data <- mc_data %>%
rbind(new_data) %>%
kwb.geosalz::order_measurement_chain_data()
}
#> Importing 36 measurement chains files (using 3 CPU cores) ... ok. (2.31 secs)
#> Filtering out 'lab' measurements before '2022-09-27 11:00:00' (installation in K10) ... ok. (0.00 secs)The following datasets were imported into R or already available in R:
mc_data_stats <- mc_data %>%
kwb.geosalz::get_measurmentchains_data_stats() %>%
dplyr::arrange(
.data$parameter,
dplyr::desc(.data$sensor_id)
)These cover the time period from 2022-09-27 11:00:00 to 2024-12-21 23:00:00 with a total of 199822 samples.
Data Export
debug <- TRUE
# Export "mc_data" to csv file
data_csv_path <- kwb.geosalz::write_measurementchains_data(
mc_data,
target_directory = paths$export_dir,
to_zip = FALSE,
debug = debug
)
#> Exporting provided dataset 'mc_data' to 'C:/Users/RUNNER~1/AppData/Local/Temp/R_kwb.geosalz/export/mc_data_20220927-110000TZ+01_20241221-230000TZ+01.csv' ... ok. (2.71 secs)
size_data_csv <- fs::file_size(data_csv_path)
# Export "mc_data" to zip file (~10x less disk space for test dataset)
data_zip_path <- kwb.geosalz::write_measurementchains_data(
mc_data,
target_directory = paths$export_dir,
to_zip = TRUE,
debug = debug
)
#> Exporting provided dataset 'mc_data' to 'C:/Users/RUNNER~1/AppData/Local/Temp/R_kwb.geosalz/export/mc_data_20220927-110000TZ+01_20241221-230000TZ+01.csv' ... ok. (2.70 secs)
#> Exporting provided dataset 'mc_data' to 'C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data.zip' and delete intermediate 'C:/Users/RUNNER~1/AppData/Local/Temp/R_kwb.geosalz/export/mc_data_20220927-110000TZ+01_20241221-230000TZ+01.csv' ... ok. (0.21 secs)
size_data_zip <- fs::file_size(data_zip_path)
size_data_csv / size_data_zip
#> 19.8
extract_data_timeperiod <- function(file) {
basename(file) %>%
kwb.utils::replaceFileExtension("") %>%
stringr::str_remove("^mc[-|_]data")
}
# Define helper function to write a csv file in a target directory
write_csv <- function(df, postfix = "", target_dir) {
fs::dir_create(target_dir)
name <- deparse(substitute(df))
file <- file.path(target_dir, paste0(name, postfix, ".csv"))
readr::write_csv(df, file)
file
}
postfix <- extract_data_timeperiod(data_zip_path)
target_dir <- paths$export_dir
# Export data and metadata to csv files
stats_path <- write_csv(mc_data_stats, postfix, target_dir)
metadata_path <- write_csv(metadata, postfix, target_dir)
files_path <- write_csv(mc_files, postfix, target_dir)
# Define function that plots data to a pdf file
plot_to_pdf <- function(mc_data, para, target_dir = ".", debug = TRUE) {
plots <- kwb.geosalz::plot_measurementchains(mc_data, para)
sapply(names(plots), function(name) {
path <- file.path(target_dir, sprintf("mc_data_%s.pdf", name))
kwb.utils::catAndRun(
sprintf("Writting '%s' to '%s'", name, path),
expr = {
kwb.utils::preparePdf(path, width.cm = 25, height.cm = 15)
on.exit(dev.off())
print(plots[[name]])
path
},
dbg = debug
)
})
}
pdf_files <- c(
plot_to_pdf(mc_data, "Leitfaehigkeit", paths$export_dir),
plot_to_pdf(mc_data, "Temperatur", paths$export_dir)
)
#> Writting 'Leitfaehigkeit_K10' to 'C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K10.pdf' ...
#> ok. (0.86 secs)
#> Writting 'Leitfaehigkeit_K13' to 'C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K13.pdf' ...
#> ok. (0.74 secs)
#> Writting 'Leitfaehigkeit_K09' to 'C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K09.pdf' ...
#> ok. (0.63 secs)
#> Writting 'Temperatur_K10' to 'C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K10.pdf' ...
#> ok. (0.77 secs)
#> Writting 'Temperatur_K13' to 'C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K13.pdf' ...
#> ok. (0.59 secs)
#> Writting 'Temperatur_K09' to 'C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K09.pdf' ...
#> ok. (0.75 secs)
data_zip_path
#> [1] "C:\\Users\\RUNNER~1\\AppData\\Local\\Temp/R_kwb.geosalz/export/mc_data.zip"The file size of the mc_data_20220927-110000TZ+01_20241221-230000TZ+01.csv file is 19.8 times larger compared to compressing it into mc_data.zip.
Data Upload
In order to automate the data management process the exported data these data will be uploaded to a restricted shared folder on the KWB cloud. For doing so the following environment variables need to be defined in case the code below should be run from a client computer. However, as these variables are defined in a GitHub actions workflow there is no need to do this locally.
NEXTCLOUD_URL = "https://<replace-with-kwb-cloud-url>"
NEXTCLOUD_USER = "<your-kwb-cloud-username>" # your username
NEXTCLOUD_PASSWORD = "your-nextcloud-app-password" ### see details belowSubsequently the following code is run for uploading the data:
# List paths of files to upload explicitly
upload_files <- kwb.geosalz:::exclude_missing_files(c(
stats_path,
pdf_files,
metadata_path,
files_path,
data_zip_path
))
for (file in upload_files) {
kwb.utils::catAndRun(
messageText = paste("Uploading file", file),
expr = try(kwb.nextcloud::upload_file(
file = file,
target_path = paths$upload_dir
)),
dbg = TRUE
)
}
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_stats.csv ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_stats.csv
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (1.61 secs)
#> ok. (1.94 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K10.pdf ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K10.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (2.93 secs)
#> ok. (3.25 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K13.pdf ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K13.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (1.24 secs)
#> ok. (1.56 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K09.pdf ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Leitfaehigkeit_K09.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (1.16 secs)
#> ok. (1.49 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K10.pdf ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K10.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (1.22 secs)
#> ok. (1.54 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K13.pdf ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K13.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (1.15 secs)
#> ok. (1.46 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K09.pdf ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data_Temperatur_K09.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (1.52 secs)
#> ok. (1.85 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/metadata.csv ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/metadata.csv
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (0.73 secs)
#> ok. (1.04 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_files.csv ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_files.csv
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (1.24 secs)
#> ok. (1.56 secs)
#> Uploading file C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data.zip ... Uploading
#> C:\Users\RUNNER~1\AppData\Local\Temp/R_kwb.geosalz/export/mc_data.zip
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (0.82 secs)
#> ok. (1.14 secs)Download Well Operation Data from Cloud
mc_dat <- mc_data %>%
dplyr::left_join(metadata[,c("sensor_id", "einbau_sensor_muGOK")], by = "sensor_id") %>%
dplyr::left_join(mc_files %>% dplyr::select(sftp_path, galerie, brunnen_nummer),
by = c(file = "sftp_path"))
well_op_file <- kwb.nextcloud::list_files(path = paths$well_operation,
full_info = TRUE) %>%
dplyr::filter(lastmodified == max(lastmodified))
#> Listing projects/GeoSalz/Monitoring/messketten/BWB_Brunnen_Prozessdaten
xlsx_file <- kwb.nextcloud::download_files(hrefs = well_op_file$href,
target_dir = paths$export_dir)
#> Downloading /remote.php/dav/files/hsonne/projects/GeoSalz/Monitoring/messketten/BWB_Brunnen_Prozessdaten/FRI%20-%20K%20-%20Brunnenlaufzeiten%20und%20Q%20(Export%20f%c3%bcr%20KWB)2024-11-18.xlsx ... ok. (0.78 secs)
well_op_data <- readxl::read_xlsx(path = xlsx_file) %>%
janitor::clean_names() %>%
dplyr::filter(.data$menge_summe_m3 < 2000)
separate_name_der_messstelle_gms <- function(string) {
tibble::tibble(
wasserwerk = stringr::str_sub(string, 1L, 3L),
galerie = stringr::str_sub(string, 4L, 4L) %>% toupper(),
brunnen_nummer = stringr::str_sub(string, 5L, 9L) %>%
stringr::str_remove_all(pattern = "-") %>%
as.integer(),
unbekannter_buchstabe = stringr::str_sub(string, 10L, 10L) %>%
stringr::str_remove_all(pattern = "-") %>%
as.character(),
brunnen_baujahr = stringr::str_sub(string, 12L, 15L) %>%
stringr::str_remove_all(pattern = "-") %>%
as.integer(),
brunnen_bauart = stringr::str_sub(string, 16L, 16L) %>%
stringr::str_remove_all(pattern = "-") %>%
as.character()
)
}
well_op_data_meta <- well_op_data %>%
dplyr::bind_cols(separate_name_der_messstelle_gms(well_op_data$name_der_messstelle_gms))Make combined EC and well operation plot
and upload on cloud.
well_ids <- c(9,10,13)
pdf_names <- sprintf("mc_and_q_well-%02d.pdf", well_ids)
target_dir <- "."
para <- "Leitfaehigkeit"
debug <- TRUE
### Make pdf for each well
pdf_files <- sapply(well_ids, function(well_id) {
path <- file.path(target_dir, sprintf("mc-%s_and_abstraction_well-%02d.pdf",
para,
well_id))
kwb.utils::catAndRun(
sprintf("Writting '%s' to '%s'", well_id, path),
expr = {
kwb.utils::preparePdf(path, landscape = TRUE)
on.exit(dev.off())
print(
kwb.geosalz::plot_measurementchain_and_well_operation(
mc_dat = mc_dat,
well_op_data_meta = well_op_data_meta,
brunnen_nr = well_id,
para = para,
date_min = as.Date("2023-05-10")))
path
},
dbg = debug
)
})
#> Writting '9' to './mc-Leitfaehigkeit_and_abstraction_well-09.pdf' ...
#> Warning: Removed 16530 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> ok. (0.74 secs)
#> Writting '10' to './mc-Leitfaehigkeit_and_abstraction_well-10.pdf' ...
#> Warning: Removed 26112 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> ok. (0.70 secs)
#> Writting '13' to './mc-Leitfaehigkeit_and_abstraction_well-13.pdf' ...
#> Warning: Removed 14226 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> ok. (0.69 secs)
### Upload pdf files on cloud
for (file in pdf_files) {
kwb.utils::catAndRun(
messageText = paste("Uploading file", file),
expr = try(kwb.nextcloud::upload_file(
file = file,
target_path = paths$upload_dir
)),
dbg = TRUE
)}
#> Uploading file ./mc-Leitfaehigkeit_and_abstraction_well-09.pdf ... Uploading
#> ./mc-Leitfaehigkeit_and_abstraction_well-09.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (0.88 secs)
#> ok. (1.19 secs)
#> Uploading file ./mc-Leitfaehigkeit_and_abstraction_well-10.pdf ... Uploading
#> ./mc-Leitfaehigkeit_and_abstraction_well-10.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (0.83 secs)
#> ok. (1.17 secs)
#> Uploading file ./mc-Leitfaehigkeit_and_abstraction_well-13.pdf ... Uploading
#> ./mc-Leitfaehigkeit_and_abstraction_well-13.pdf
#> to
#> projects/GeoSalz/Monitoring/messketten ... ok. (0.74 secs)
#> ok. (1.06 secs)