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Pilot: MBR 4.0

Source: vignettes/mbr40.Rmd
mbr40.Rmd

Install R package 


# Install latest stable version from KWB-R R-Universe
options(repos = c(
    kwbr = 'https://kwb-r.r-universe.dev',
    CRAN = 'https://cloud.r-project.org'))


install.packages('kwb.pilot')

Setup Environment Variables

For downloading the latest (from Martin Systems webportal) and archived (from KWB Nextcloud) .tsv data files you need set the following environment variables
only once as shown below.

For doing so follow the steps below:

  1. Open RStudio and run usethis::edit_r_environ()

  2. In the opened window add the required environment variables

MBR40_URL = "url-to-download-pilot-plant-data"
NEXTCLOUD_URL = "https://<replace-with-dwc-cloud-url>"
NEXTCLOUD_USER = "<your-dwc-cloud-username>" # your username
NEXTCLOUD_PASSWORD = "your-nextcloud-app-password" ### see details below

To access the archived data you need to be a registered Nextcloud user with access to the folder projects/MBR4.0

For creating <your-nextcloud-app-password>:

  1. Finally you need to restart Rstudio and proceed with the code below:

Subsequently click Save and restart RStudio.

Run Shiny App

For running the shiny app it is needed to setup the environment variables as shown above and run the following code.

Latest Data

To use the latest data (downloads data from web) or if you run the app for the first time you need to run:

kwb.pilot::run_app("mbr4.0", use_live_data = TRUE)

Local Data

Once you have done the step above for one time, you can also just interactively load you datasets, which you downloaded during the command above by running:

kwb.pilot::run_app("mbr4.0", use_live_data = FALSE)

Define paths

For performing data aggregation and export to post-process the data in Excel in a wide format the workflow defined below has to be followed.

Option 1: At KWB

In paths_list you need to replace the dummy value kwb-server for the key servername.


#KWB server
paths_list <- list(
  servername = "kwb-server",
  root = "//<servername>/projekte$/WWT_Department/Projects/MBR 4.0/Exchange/",
  rawdata = "<root>/20_rawdata",
  processing = "<root>/21_processing",
  online =  "<rawdata>/online_data",
  export_dir = "<processing>/online-data"
  )

if(dir.exists(paths$online)) {
paths <- kwb.utils::resolve(paths_list)
}

Option 2: At Home 

paths_list <- list(
  root = "C:/kwb/projects/mbr4.0",
  rawdata = "<root>/20_rawdata",
  processing = "<root>/21_processing",
  online =  "<rawdata>/online_data",
  export_dir = "<processing>/online-data"
)

# Only execute "At Home" if not at "KWB" (i.e. KWB server path does not exist) 
fs::dir_create(paths$online, recurse = TRUE)
paths <- kwb.utils::resolve(paths_list)

Import 

library(kwb.pilot)

mbr4_data_raw <- kwb.pilot::read_mbr4()
str(mbr4_data_raw)

mbr4_data_tidy_raw <- kwb.pilot::tidy_mbr4_data(mbr4_data = mbr4_data_raw) 

str(mbr4_data_tidy_raw)

Aggregate 

mbr4_data_tidy_10min <- mbr4_data_tidy_raw %>%   
  kwb.pilot::group_datetime(by = 600) 

mbr4_data_tidy_hour <- mbr4_data_tidy_10min %>%
  kwb.pilot::group_datetime(by = 3600)

Export

CSV files

Raw and aggregated data will be exported as csv in wide format (by running kwb.pilot::long_to_wide()) as shown below:


kwb.pilot::export_data(df_long = mbr4_data_tidy_raw,
                       export_dir = paths$export_dir)

kwb.pilot::export_data(df_long = mbr4_data_tidy_list_10min,
                       export_dir = paths$export_dir)

kwb.pilot::export_data(df_long = mbr4_data_tidy_list_hour,
                       export_dir = paths$export_dir)

Finally you can check the datasets in the export folder by running:

kwb.utils::hsOpenWindowsExplorer(paths$export_dir)