Package 'kwb.waterParcel'

Check for unique units per parameter

Description

Check for unique units per parameter

Usage

check_for_units(df_in)

Arguments

df_in

Data frame loaded with load_lab_data()

Value

Returns either a warning or a confirmation

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Removes all samples without complete tracer data

Description

Removes all samples without complete tracer data

Usage

discard_na_samples(MCS_input)

Arguments

MCS_input

A list of tracer concentrations per sample

Value

A list of tracer concentrations per sample

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Random concentrations in all samples based on measured data and assumed deviation

Description

Random concentrations in all samples based on measured data and assumed deviation

Usage

get_all_sample_concentrations(
  input_table,
  predictor_samples,
  tracer_names,
  MCS_runs,
  rel_deviation
)

Arguments

input_table	Data frame reshaped by function reshape_table()
predictor_samples	A character vector of names of samples used for predicting the substance concentration of the remaining samples.
tracer_names	A vector of character strings defining the tracers used
MCS_runs	The number of random samples drawn (Monte-Carlo-Simulation runs)
rel_deviation	Either one numeric value between 0 and 1 defining the standard deviation relative to the measured value for all tracers or a vector of the same length as 'tracer_names' defining a deviation for each tracer

Value

A List of data frames with columns corresponding to the tracers and rows correspoding to the Monte Carlo runs. The predictor samples are the first three entries and are named "A", "B" and "C"

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Find minimum and maximum of the tracers in all samples (including MCS)

Description

Find minimum and maximum of the tracers in all samples (including MCS)

Usage

get_concentration_ranges(MCS_input)

Arguments

MCS_input

A list of sample Data frames created by get_all_sample_concentrations()

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Random concentrations in sample based on measured data and assumed deviation

Description

Random concentrations in sample based on measured data and assumed deviation

Usage

get_varrying_sample_concentration(
  input_table,
  sample_name,
  tracer_names,
  MCS_runs,
  rel_deviation
)

Arguments

input_table	Data frame reshaped by function reshape_table()
sample_name	A character string defining the sample
tracer_names	A vector of character strings defining the tracers used
MCS_runs	The number of random samples drawn (Monte-Carlo-Simulation runs)
rel_deviation	Either one numeric value between 0 and 1 defining the standard deviation relative to the measured value for all tracers or a vector of the same length as 'tracer_names' defining a deviation for each tracer

Value

A data frame, columns corresponding to the tracers and rows correspoding to the runs.

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Loads CSV-Data table

Description

The CSV table must contain one row per measurement and standard column names as defined in the details section

Usage

load_lab_data(path, file)

Arguments

path	The filepath
file	The filename including the ".csv" ending

Details

Required table column names:

• sample_name: unique ID of the sample (at least unique per sampling campaign)
  

• sampling_campaign: unique ID of the sampling campaign
  

• tBeg: starting time of the sample (format: "YYYY-mm-dd HH:MM:SS")
  

• tEnd: ending time of the sample (format: "YYYY-mm-dd HH:MM:SS")
  

• operator: definition of LOD-relation (either "<", ">" or empty)
  

• value: measured value
  

• parameter: unique name of the measured parameter (this parameter is used column name after reshaping the table, so it should not start with a number or contain any special symbols or spaces)
  

• unit: the unit of the measured value

Value

A data frame corresding to the csv file, with date columns specified as POSIXct

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Transforms tracer concentrations into normalized values between 0 and 1

Description

Transforms tracer concentrations into normalized values between 0 and 1

Usage

normalize_MCS_input(MCS_input)

Arguments

MCS_input

A list of sample Data frames created by get_all_sample_concentrations()

Details

Normalization per tracer by equation:
(c_i - min(c)) / (max(c) - min(c))
This results in values between 0 (equals the overall minimal value of the tracer within all samples) and 1 (equals the overall maximal value of the tracer within all samples).

Value

Same structured list as the input with normalized values instead of absolute concentrations

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Reshape Data to Parameter-as-Columns-Structure

Description

This function keeps the columns "sample_name", "sampling_campaign", "tBeg", "tEnd" and adds one column per parameter. The operators are taken into account.

Usage

reshape_table(df_in, sampling_campaigns, bLOD = "na", aLOD = "na")

Arguments

df_in	Data frame loaded with load_lab_data()
sampling_campaigns	One or more sampling Campaign IDs to be considered
bLOD	Character string defining the handling of operator "<" (below limit of detection). Either "half" for dividing the value by 2, "zero" or "na" for setting the value to 0 or NA, respectively.
aLOD	Character string defining the handling of operator ">" (above limit of detection). Either "double" for multiplying the value by 2, "use_limit" for using the limit value or "na" for setting the value to NA.

Value

A data frame of all data per sample in a row, arranged by the starting time of the sample.

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