Function reference • dwc.wells

Datasets

Datasets for statistical analysis and predicting well capacity

model_data_reduced: Input Data for Well Capacity Prediction

Data preparation

Functions for preparing / cleaning data

read_csv(): read csv data file exported by Sebastian Schimmelpfennig from db2

read_ms_access(): read table from MS Access data base via odbc connection under 64-bit-R

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

rename_values(): rename values of a character vector according to renamings table

select_rename_cols(): selects and renames columns from a data frame according to a reference table

classify_Qs(): Transfer Qs_rel into binary factor with low and high specific capacity

combine_pump_test_and_Q_monitoring_data(): Combined Pumptest and Q Monitoring Dataset

extdata_file(): Get Path to File in This Package

replace_na_with_median(): Replace NAs with median

fill_up_na_with_median_from_lookup(): Fill up NA values with median of lookup table

get_pump_test_vars(): Get Default Pump Test Variables

get_W_static_data(): Get W_static measurement data from Neubaupumpversuche, Kurzpumpversuche and other sources

interpolate_and_fill(): Interpolate and fill up static water level

interpolate_Qs(): Interpolates Qs time series data to a given time interval

load_renamings_csv(): Load renaming table from CSV file

load_renamings_excel(): load renaming table from original excel file

prepare_pump_test_data(): prepare pump test data with one row per Qs-measurement + rehab history

prepare_pump_test_data_1(): Prepare pump test data in wide format

prepare_pump_test_data_2(): reformats untidy pump test data from wide into long format

prepare_quality_data(): Prepare Quality Data

prepare_volume_data(): Prepare Volume Data

summarise_marginal_factor_levels(): summarise factor levels with relative frequency below a threshold

tidy_factor(): turn character into factor, sort factor levels and replace NA level

Data analyis

Functions for statistical analysis feature importance ranking

chi2.CramersV.test(): Title

frequency_table(): calculate absolute and relative frequencies of categorical varables

Data visualisation

Functions for plotting

Qs_heatmap_plot(): Heatmap / raster plot for Qs values over time with each well as one line

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

plot_distribution(): plot frequency distribution of numerical variable

plot_frequencies(): plot frequency distribution of factor variable

scatterplot(): scatterplot for comparing numeric predictions with observations

paste_percent(): Paste percent sign to numbers

Export data

Functions for exporting data

save_data(): Save data frame in different formats: csv, RData, rds

Scripts

folder “scripts” contains all scripts for data preparation, data analysis, ml modelling (mainly relying on functions from R package “tidymodels”) and plotting