Package 'dwc.wells'

Title: A Package for Condition Predictions for Drinking Water Wells
Description: This package allows to predict the condition of a drinking water well based on ML models. The models are trained with results from pump tests and a large set of input variables e.g. the well material, the age and the number of regenerations.
Authors: Mathias Riechel [aut] , Michael Rustler [aut, cre] , DWC [fnd], Kompetenzzentrum Wasser Berlin gGmbH (KWB) [cph]
Maintainer: Michael Rustler <[email protected]>
License: MIT + file LICENSE
Version: 0.2.0
Built: 2025-02-26 04:17:12 UTC
Source: https://github.com/KWB-R/dwc.wells

Help Index

Title

Description

Title

Usage

chi2.CramersV.test(data)

Arguments

data

data frame on which to perform Chi-2-test

Transfer Qs_rel into binary factor with low and high specific capacity

Description

Transfer Qs_rel into binary factor with low and high specific capacity

Usage

classify_Qs(x, split_point = 80, class_names = c("low", "high"))

Arguments

x

vector of Qs_rel values
split_point

threshold for classifying numeric Qs_rel values, default: 80
class_names

class names, default: c("low", "high")

Combined Pumptest and Q Monitoring Dataset

Description

Combined Pumptest and Q Monitoring Dataset

Usage

combine_pump_test_and_Q_monitoring_data(
  df_pump_tests_tidy,
  df_Q_monitoring,
  pump_test_vars
)

Arguments

df_pump_tests_tidy

df_pump_tests_tidy
df_Q_monitoring

df_Q_monitoring
pump_test_vars

default: get_pump_test_vars

Value

combined pumptest and Q monitoring dataset

plots Qs_rel vs. input variable as box plot (categorical input variable) or scatterplot (numerical input variable)

Description

plots Qs_rel vs. input variable as box plot (categorical input variable) or scatterplot (numerical input variable)

Usage

correlation_plot(df, x, y = "Qs_rel", title = gsub("_", " ", x))

Arguments

df

data frame
x

column name of x variable"
y

column name of y variable (default Qs_rel")
title

plot title

Get Path to File in This Package

Description

Get Path to File in This Package

Usage

extdata_file(...)

Arguments

...

parts of path passed to system.file

Fill up NA values with median of lookup table

Description

Fill up NA values with median of lookup table

Usage

fill_up_na_with_median_from_lookup(df, df_lookup, matching_id = "well_id")

Arguments

df

data frame with NA values
df_lookup

data frame to calculate median values
matching_id

column with ids for which median should be calculated

calculate absolute and relative frequencies of categorical varables

Description

calculate absolute and relative frequencies of categorical varables

Usage

frequency_table(x, perc_digits = 1, sort_freq = FALSE)

Arguments

x

vector with categorical variable
perc_digits

number of decimal digits for percentages, default = 1
sort_freq

sort according to frequency counts, logical, default: TRUE

Get Default Pump Test Variables

Description

Get Default Pump Test Variables

Usage

get_pump_test_vars()

Value

vector with column names of pump test variables

Examples

get_pump_test_vars()

Get W_static measurement data from Neubaupumpversuche, Kurzpumpversuche and other sources

Description

Get W_static measurement data from Neubaupumpversuche, Kurzpumpversuche and other sources

Usage

get_W_static_data(path, renamings, df_wells)

Arguments

path

path to static water level data (csv-file)
renamings

list with renamings
df_wells

data frame with prepared well data

Interpolate and fill up static water level

Description

Interpolate and fill up static water level

Usage

interpolate_and_fill(df, x_col, y_col, group_by_col, origin_col)

Arguments

df

data frame
x_col

x column, e.g. date, to be used for interpolation
y_col

y column, e.g. measured values, to be used for interpolation
group_by_col

grouping variable within which interpolation is done
origin_col

already existing or to be created column with type of value

Interpolates Qs time series data to a given time interval

Description

Interpolates Qs time series data to a given time interval

Usage

interpolate_Qs(df, interval_days = 1)

Arguments

df

data frame with date and Qs measurements
interval_days

interval for interpolation

load renaming table from original excel file

Description

load renaming table from original excel file

Usage

load_renamings_csv(infile)

Arguments

infile

full path to excel file

load renaming table from original excel file

Description

load renaming table from original excel file

Usage

load_renamings_excel(
  infile,
  sheet = "DATEN",
  old_name_col = "Feld",
  new_name_col = "Parametername-R"
)

Arguments

infile

full path to excel file
sheet

sheet name
old_name_col

name of column with original variable names
new_name_col

name of column with new variable names

Input Data for Well Capacity Prediction

Description

A reduced dataset for well capacity prediction created with R script in /data-raw/model_data.R

Usage

model_data_reduced

Format

A data.frame with 6308 rows and 27 variables:

well_id

well id, for info

date

date of capacity measurement, for info

key

measurement key, e.g. operational_start, pump_test_1, pump_test_2, for info

Qs_rel

specific capacity of well relative to operational start condition, output

days_since_operational_start

days since operational start, redundant

well_age_years

years since operationa start, input, numeric

construction_year

year of well construction

screen_material

screen material

diameter

well diameter (mm)

drilling_method

drilling_method

admissible_discharge

allowed pumping rate

operational_start.Qs

initial Qs at construction

aquifer_coverage

confined / unconfined

W_static.sd

standard deviation of static water level

surface_water.distance

distance to surface water

n_rehab

number of well rehabilitations

time_since_rehab_years

time since last well rehabilitation in years

volume_m3_d.mean

mean daily abstraction volume (m3)

quality.EC

water quality: electical conductivity (us/cm)

quality.D0

water quality: dissolved oxygen (mg/l)

quality.Temp

water quality: temperature (C)

quality.pH

water quality: pH

quality.Redox

water quality: electical conductivity (us/cm)

quality.Fe_tot

water quality: dissolved oxygen (mg/l)

quality.Mn

water quality: Mn (mg/l)

quality.NO3

water quality: NO3 (mg/l)

quality.PO4

water quality: PO4 (mg/l)

quality.SO4

water quality: SO4 (mg/l)

quality.TSS

water quality: Total Suspended Solids (mg/l)

Paste percent sign to numbers

Description

Paste percent sign to numbers

Usage

paste_percent(x)

Arguments

x

numeric vector

plot frequency distribution of numerical variable

Description

plot frequency distribution of numerical variable

Usage

plot_distribution(
  Data,
  variable,
  binwidth = NULL,
  title,
  vertical_x_axis_labels = TRUE,
  boundary = 0
)

Arguments

Data

Data to be plotted
variable

variable
binwidth

binwidrh
title

plot title
vertical_x_axis_labels

should x-axis labels be ploted vertically (TRUE / FALSE)
boundary

left boundary of bars, default: 0

plot frequency distribution of factor variable

Description

plot frequency distribution of factor variable

Usage

plot_frequencies(
  Data,
  variable,
  title = variable,
  offset_perc_labels = 0.1,
  size_perc_labels = 3,
  vertical_x_axis_labels = TRUE
)

Arguments

Data

Data to be plotted
variable

variable
title

plot title
offset_perc_labels

distance of labels from bars
size_perc_labels

size of percent labels
vertical_x_axis_labels

should x-axis labels be ploted vertically (TRUE / FALSE)

prepare pump test data with one row per Qs-measurement + rehab history

Description

prepare pump test data with one row per Qs-measurement + rehab history

Usage

prepare_pump_test_data(path, renamings, df_wells, pump_test_vars)

Arguments

path

path to pump test data
renamings

list with renamings
df_wells

prepared data frame with well characteristics
pump_test_vars

default: get_pump_test_vars

Prepare pump test data in wide format

Description

Steps: i) read, rename and clean data, ii) correct wrong pump test dates, iii) fill up missing pump test dates, iv) get information for replaced wells, v) calculate Qs and Qs_rel, vi) determine action type, vii) select columns

Usage

prepare_pump_test_data_1(path, renamings, df_wells)

Arguments

path

path to pump test data
renamings

list with renamings
df_wells

prepared data frame with well characteristics

reformats untidy pump test data from wide into long format

Description

reformats untidy pump test data from wide into long format

Usage

prepare_pump_test_data_2(
  df_pump_tests_untidy,
  df_wells,
  pump_test_vars = get_pump_test_vars()
)

Arguments

df_pump_tests_untidy

pump test data in wide format
df_wells

prepared data frame with well characteristics
pump_test_vars

default: get_pump_test_vars

Prepare Quality Data

Description

Prepare Quality Data

Usage

prepare_quality_data(path, renamings)

Arguments

path

path
renamings

renamings

Value

prepared quality day

Prepare Volume Data

Description

Prepare Volume Data

Usage

prepare_volume_data(path, renamings, df_wells)

Arguments

path

path
renamings

renamings
df_wells

df_wells

Value

Prepared volume data

Heatmap / raster plot for Qs values over time with each well as one line

Description

Heatmap / raster plot for Qs values over time with each well as one line

Usage

Qs_heatmap_plot(
  df,
  colours,
  dummy_labels,
  date_limits,
  title,
  n_wells_per_page
)

Arguments

df

data frame with date, well_id, Qs_rel
colours

3 colours for low, middle and high colour limits
dummy_labels

dummy labels if there are less wells than expected
date_limits

vector with two date strings in format "yyyy-mm-dd"
title

plot title
n_wells_per_page

number of wells do be shown

read csv data file exported by Sebastian Schimmelpfennig from db2

Description

read csv data file exported by Sebastian Schimmelpfennig from db2

Usage

read_csv(
  file,
  header = TRUE,
  fileEncoding = "UTF-8",
  skip = 2,
  dec = ".",
  sep = "\t",
  na.strings = "(null)"
)

Arguments

file

path to csv file
header

logical, default = TRUE
fileEncoding

default = UTF-8
skip

number of rows to skip, default = 2
dec

decimal separator, default = '.'
sep

columns separator, default = 'tab'
na.strings

string that represents NA, default = "(null)"

read table from MS Access data base via odbc connection under 64-bit-R

Description

read table from MS Access data base via odbc connection under 64-bit-R

Usage

read_ms_access(path_db, tbl_name)

Arguments

path_db

full path to database
tbl_name

name of database table to be read

read table from MS Access data base; select and rename columns as defined in renamings table ('old_name' -> 'new_name')

Description

read table from MS Access data base; select and rename columns as defined in renamings table ('old_name' -> 'new_name')

Usage

read_select_rename(
  path_db,
  tbl_name,
  renamings,
  old_name_col = "old_name",
  new_name_col = "new_name"
)

Arguments

path_db

full path to database
tbl_name

name of database table to be read
renamings

name of data frame with renamings
old_name_col

name of column with original variable names
new_name_col

name of column with new variable names

rename values of a character vector according to renamings table

Description

rename values of a character vector according to renamings table

Usage

rename_values(
  x,
  renamings,
  old_name_col = "old_name",
  new_name_col = "new_name"
)

Arguments

x

character vector
renamings

data frame consisting of old and new names
old_name_col

name of column with original variable names
new_name_col

name of column with new variable names

Replace NAs with median

Description

Replace NAs with median

Usage

replace_na_with_median(x)

Arguments

x

vector, for which NA should be replaced

Save data frame in different formats: csv, RData, rds

Description

Save data frame in different formats: csv, RData, rds

Usage

save_data(Data, path, filename, formats = c("csv", "RData", "rds"))

Arguments

Data

data frame
path

out path for saving data
filename

core of file name
formats

export formats: "csv", "RData", "rds" or several using 'c'

scatterplot for comparing numeric predictions with observations

Description

scatterplot for comparing numeric predictions with observations

Usage

scatterplot(df_pred, lines_80perc = FALSE, alpha = 1, pointsize = 1)

Arguments

df_pred

data frame obtained with tidymodels::collect_predictions() with columns Qs_rel and .pred
lines_80perc

logical value; shout 80%-lines be drawn?; default = FALSE
alpha

alpha value for point of colours, default: 1
pointsize

size value for points, default: 1

selects and renames columns from a data frame according to a reference table

Description

selects and renames columns from a data frame according to a reference table

Usage

select_rename_cols(
  df,
  renamings,
  old_name_col = "old_name",
  new_name_col = "new_name"
)

Arguments

df

data frame with cols to be renamed
renamings

name of data frame with renamings
old_name_col

name of column with original variable names
new_name_col

name of column with new variable names

summarise factor levels with relative frequency below a threshold

Description

summarise factor levels with relative frequency below a threshold

Usage

summarise_marginal_factor_levels(x, perc_threshold, marginal_name)

Arguments

x

factor variable
perc_threshold

percentage threshold under which levels will be summarised
marginal_name

for new summary factor level

turn character into factor, sort factor levels and replace NA level

Description

turn character into factor, sort factor levels and replace NA level

Usage

tidy_factor(x, level_sorting = c("frequency", "alphabet")[1])

Arguments

x

character vector to be turned to factor
level_sorting

sorting of factor levels; two options: "frequency" (default) and "alphabet"; level "Unbekannt" is always always at the end