Package 'kwb.flusshygiene'

Bathing quality assessment (EU)

Description

Computes the quality assessment according to european bathing directive 2006/7/EC from E.Coli values. The four possible quality levels are: excellent, good, sufficient and poor

Usage

assess_bathing_quality_eu(e.coli, log = TRUE)

Arguments

e.coli	A numeric vector with e.coli values
log	logical. Are the values log-values?

Value

Returns a single factor with all quality levels

---

Build a Model for E.Coli

Description

Functions for modelbuilding \n build_model: takes the riverdata, handles the other functions and invokes stan_lm

Usage

build_model(riverdata, variables = ask_for_variables(riverdata),
  with_interaction = TRUE)

ask_for_variables(riverdata)

process_model_riverdata(riverdata, variables)

create_formula(variables, with_interaction = FALSE)

Arguments

riverdata	a list with riverdata (hygiene + physical data)
variables	character. Selected variables for the model
with_interaction	logical. Formula with interactions? Default set to TRUE

Details

Build the model from hygiene data and physical data like flow, rain, wwtp. Asks for user input to select variables. Computes the data.frame with data for hygiene and chosen variables and creates a formula of the form: Q*(K + R) while multiple Qs will be multiplied, multiple Ks and Rs will be added.

Value

Returns a model of the riverdata.

Returns a character-vector with the chosen model variables

Returns a data.frame with data for hygiene and chosen variables

Returns parsed model-formula. (Like model$formula)

Functions

• ask_for_variables: Internal function. Quite time consuming

• process_model_riverdata: Internal usage

• create_formula: Internal usage

Examples

## Not run: variables <- c("e.coli","q_havel",...)
lm(formula = eval(create_formula(variables)),
data = process_model_riverdata(riverdata, variables))
## End(Not run)

create_formula(c("log_e.coli","q_havel","ka_ruhleben","r_berlin"))
create_formula(c("e.coli","r_mitte","r_charlottenburg","r_spandau"))

---

Create a new Model

Description

Main function for creation, object handling and saving of new models. The models will be saved in a own subdirectory as r-objects as a side-effect. The models will not be returned. They have to be loaded by other functions.

Usage

build_new_model(river)

Arguments

river

character. river you want to build a model on

Value

This function returns merely a message what happend.

---

Set User Input for a List of Models

Description

Choose model asks the user inside the console for input. Options are Exit, New Model, or one of a list of existing models. If no integer number was presented by the user an ERROR message will be created but no ERROR will be thrown. This way this can be inserted inside a loop.

Usage

choose_model(rivermodels)

Arguments

rivermodels

A list of named models

Value

Returns the user input as character vector, or an ERROR message.

Examples

choose_model(list())
choose_model(list(fake_river_model = 1))

---

Scatterplotmatrix of similar Variables to E.Coli

Description

Takes similar named variables and produces a matrix of scatterplots and their correlation coefficients to E.Coli.

Usage

correlation_scatterplot(df, ...)

correlation_values(df, ...)

Arguments

df	data.frame with data for e.coli and chosen variables in lagdays
...	Arguments passed to stats::cor

Value

Plotting function. Returns a plot.

Returns correlation values.

Functions

• correlation_values: Internal function

Examples

correlation_values(data.frame(datum = rep("egal",10), e.coli = 1:10, var = 1:10), variable = "var")

---

Get MPN Confidence Intervals for E.Coli

Description

Lookup laboratory tables for MPN values for E.Coli to get upper and lower 0.95 confidence interval for the given values. If value is not directly found in table it will be generated by interpolating nearest neighbors.

Usage

get_mpn_ci(e.coli)

Arguments

e.coli

numeric. A vector for e.coli values

Value

A data.frame with 3 columns: e.coli, lo, up

Examples

## Not run: 
print(get_mpn_ci(c(15,30,35,60,61,71,120,1959,25000,369990)))

## End(Not run)

---

Get List of Paths used in the Flusshygiene Project

Description

Get List of Paths used in the Flusshygiene Project

Usage

get_paths(resolve = TRUE, ...)

Arguments

resolve	if TRUE (default) path placeholders are resolved
...	arguments passed to resolve if resolve is TRUE

Examples

## Not run: 
paths <- get_paths()

# Paths to the different work package folders
paths$ap2
paths$ap3
paths$ap4

# What tables are contained in the ODM database?
kwb.db::hsTables(paths$odm)

# Get all Flusshygiene data into one data frame
data <- kwb.ogre.model::get_lab_values(paths$odm)

## End(Not run)

---

Read existing River Data

Description

Read existing, preprocessed csv files with first column datetime and other columns variable information.

Usage

import_riverdata(path)

Arguments

path	character-string to a DATA_preprocessed_csv directory

Value

Returns a list of data.frames containing the river data

---

A package for creating and handling river hygiene models

Description

The kwb.flusshygiene package provides functions in three major categories: model handling, model creation and model prediction.

Model handling

river_model_prediction

is the main function in this package. It uses all of the following functions from within.

get_paths

reads a serverpath library for easy directory accessing

search_existing_models

searches saved R-objects in the river directories.

Model creation

build_new_model

again a overhead function for model creation. Asks the user whether or not the new model shall be saved as R-object.

import_riverdata

reads all river data from a directory.

build_model

is a small function handling model creation and invokes stan_lm for model building.

ask_for_variables

asks the user which variables shall be included in the model. Creates plots as a side effect.

process_model_riverdata

processes a data.frame with the necessary data for the data argument in stan_lm

create_formula

creates a hygiene formula out of the variables with the form e.coli ~ Q * (R + Ka)

Model prediction

predict_quality

is the overhead function for the prediction. It also invokes posterior_predict.stanreg

get_newdata

gathers the latest data for prediction.

print_latest

prints the prediction of the latest day.

plot_predicted_quality

plots a whole season with quality assessment.

Utility functions

unroll_physical_data

unrolls a list with data with lagday combinations to 5 days (default)

correlation_scatterplot

plots a scatterplot matrix of the unrolled physical data together with correlation values.

Plotting functions

plot_stan_model

plots a stan_lm posterior predction with quality assessment.

plot_data_overview

plot data overview

plot_hygiene_overview

a statistical hygiene data overview

plot_q_overview

plot a overview of all q values

plot_rain_overview

plot a monthly overview of all rain gauges

---

Plot Data Timeline Overview

Description

Creates a plot with segments or points of the data availability.

Usage

plot_data_overview(riverdata, type = "segment")

Arguments

riverdata	A list of hygiene and physical data of the river
type	Either "segment" or "point" for more precise information

Value

Returns a plot

---

Plot Hygiene Overview

Description

Creates a plot with three graphs: Histogramm of all e.coli values, a density curve of the last 16 values, and a boxplot of all values again

Usage

plot_hygiene_overview(hygiene_df)

Arguments

hygiene_df

A data.frame with the hygiene data of a given river

Value

Returns a plot

---

Plot Quality

Description

Window function for plot_stan_model

Usage

plot_predicted_quality(model, prediction, ...)

Arguments

model	stan.lm model for the river
prediction	list of season, ppd of predcit and ppd of means
...	Further parameter passed to plot.default

Value

Plotting function. Returns a plot.

---

Plot Flowing Conditions

Description

Creates a plot with the standard flowing conditions over the year. The data of all years will be taken into account.

Usage

plot_q_overview(q_df)

Arguments

q_df	The data.frame with 2 columns: datum and q

Value

Returns a plot

---

Plot Monthly Rain Summary

Description

Creates a plot with a monthly summary overview over the different rain sites

Usage

plot_rain_overview(df)

Arguments

df	A data frame with different rain gauges.

Value

Returns a plot

---

Plot Model Prediction with Quality Assessment

Description

Plots a sample of posterior predictions and means. Furthermore colours an hygiene quality assessment as background (see EU Bathing Water Directive) Dark blue means excellent quality. Steelblue means good quality. Yellow means sufficient quality. Red means insufficient quality.

Usage

plot_stan_model(timestamp, predict, linpred, log = FALSE, q90, q95,
  nlines = 250, nlinesCenter = 100, ...)

Arguments

timestamp	POSIX. The x-axis timestamp
predict	ppd. The posterior prediction of the model
linpred	ppd. The linpred (predicted means) of the model
log	logical. Is E.Coli log01-transformed?
q90	numeric. The 90. percentile of predict.
q95	numeric. The 95. precentile of predict.
nlines	numeric. How many lines for posterion predictions?
nlinesCenter	numeric. How many lines for predicted means?
...	Further parameters for plot.default

Value

Plotting function. Returns a plot.

---

Predict Hygiene Quality

Description

Main function for invoking and object handling. E.Coli hygiene models will be used to predict hygiene quality on differnt scopes.

Usage

predict_quality(model, river_dir, output = "season")

get_newdata(variables, river_dir)

print_latest(model, newdata)

get_latest_season(newdata)

Arguments

model	stan_lm. A model of e.coli concentration in given river
river_dir	character. Path to river-data for up-to-date predictions.
output	character. "season" will return a list with prediction, "latest" will return console output
variables	character. A vector with all variables used in the model
newdata	data.frame with physical data used in the model

Value

Returns a list of physical data and prediction and linpred from model

Returns a data.frame with the merged data found

Functions

• get_newdata: Internal Usage

• print_latest: Internal Usage

• get_latest_season: Internal Usage

---

Read Data for ODM Tables

Description

Read data for ODM tables from CSV files stored in the package

Usage

readTableData(sourcedir = system.file("extdata", "ODM", package =
  "kwb.flusshygiene"))

Arguments

sourcedir

path to input directory

Value

list of data frames

---

Programm for Model Handling and Prediction

Description

This function is the front-end for model search on the server, model building with existing data, or prediction with new data. It invokes all other functions and handles their objects. It is a main function.

Usage

river_model_prediction(river)

search_existing_models(river_dir)

Arguments

river	character. The desired river, like "isar".
river_dir	character. Path to server and river directory

Value

(invisible) The data.frame returned by the prediction plus a date column for easy plotting.

Returns a list with the existing models for that river (empty if no model was found).

Functions

• search_existing_models: directory searching

Examples

river_model_prediction(river = "isar")



serverpath <- "//poseidon/projekte$/SUW_Department/Projects/FLUSSHYGIENE/Data-Work packages/Daten"
river_dir <- search_existing_river_dir(river = "isar", server = serverpath)
search_existing_models(river_dir = river_dir)

---

Unroll Lagdays of Data

Description

Unrolls the lagdays of data.frames.

Usage

unroll_physical_data(physical_data)

unroll_lagdays(df, n = 5)

Arguments

physical_data	list of river data (without hygiene)
df	data.frame of 2 columns: datum and var
n	numeric. unto to which day shall be lagged behind?

Value

Returns a list of data.frames for each variable. The data.frames contain the unrolled lagdays (with maxday = 5, length(df) == 17)

Functions

• unroll_lagdays: Internal usage mostly

Examples

df1 <- data.frame(datum = rep("egal", 25), var = 1:25)
df2 <- data.frame(datum = rep("egal", 25), var2 = 51:75, var3 = 101:125)
unroll_lagdays(df1)
summary(unroll_physical_data(list(var1 = df1, var2 = df2)))

---