Package 'kwb.CrowdWater'

Title: Lake Indicators from CrowdWater Observations
Description: This package uses observations data of lakes collected by the CrowdWater App to derive semi-quantitative indictors for nutrients (trophic sate), biotop value, use of the lakes and water availability characteristics.
Authors: Malte Zamzow [aut, cre] (ORCID: <https://orcid.org/0000-0002-8748-038X>), Kompetenzzentrum Wasser Berlin gGmbH (KWB) [cph]
Maintainer: Malte Zamzow <[email protected]>
License: MIT + file LICENSE
Version: 0.0.0.9000
Built: 2026-06-14 19:39:49 UTC
Source: https://github.com/KWB-R/kwb.CrowdWater

Help Index

Add all the indicators to the lake properties table

Description

Add all the indicators to the lake properties table

Usage

add_ObservationIndicators(df_cw)

Arguments

df_cw

Dataframe of the CrowdWater lake properties, prepared by prepare_data()

Value

df_cw extended by 4 indicator columns

Assign Points of Multiple choice answer

Description

Used within the indicator functions

Usage

assign_points_MC(df, property, categories, points, NA_answers = "No answer")

Arguments

df

Prepared CrowdWater data frame
property

Character Value. One of the column names of df
categories

Character vector that lists the catetories of the property for which points are assigned
points

Numeric vector of the same length and order of the categories vector with the assigned points
NA_answers

Character vector defining one or more answers that are treated as NA values

Value

a list of

  • points: Numeric vector of the category points

  • valid_answer: Logical vector if all the required properties were observed

  • scoring_system: The points assigned to the categories

Assign Points of Single choice answer

Description

Used within the indicator functions

Usage

assign_points_SC(df, property, categories, points, NA_answers = "No answer")

Arguments

df

Prepared CrowdWater data frame
property

Character Value. One of the column names of df
categories

Character vector that lists the catetories of the property for which points are assigned
points

Numeric vector of the same length and order of the categories vector with the assigned points
NA_answers

Character vector defining one or more answers that are treated as NA values

Value

a list of

  • points: Numeric vector of the category points

  • valid_answer: Logical vector if all the required properties were observed

  • scoring_system: The points assigned to the categories

the Basemap used for all leaflet maps

Description

the Basemap used for all leaflet maps

Usage

base_map(
  longitude = 13.3987,
  latitude = 52.5048,
  zoom = 11,
  berlin_districts = TRUE
)

Arguments

longitude

Numeric value defining the longitude if WGS84
latitude

Numeric value defining the latitude if WGS84
zoom

The initial and minimum zoom level
berlin_districts

Logical value if berlin districts should be added

Value

A leaflet map

Regions Bounding Boxes

Description

Regions Bounding Boxes

Usage

bboxes

Format

A named list of regions defined by bounding boxes. The bounding boxes are numeric vectors of coordinates which define the top, bottom, left, and right coordinates of the box.(in WGS84)

Simple feature of the Berlin district boundaries

Description

Simple feature of the Berlin district boundaries

Usage

berlin_district_shp

Format

Besides the geometry, the Name of the district and its ID is given

This function returns the sum of categories per property of the given dataframe

Description

This function returns the sum of categories per property of the given dataframe

Usage

categories_sum(df, property, language = "english", includeNA = FALSE)

Arguments

df

Dataframe of the crowdwater lake properties prepared by prepare_data(), optionally filtered for one ROOT_ID
property

The property
language

A character string, defining the language of df. If unknown the function identify_language() can be used to find out.
includeNA

Logical if NA (No answer) data should be included

Value

A dataframe with the columns ROOT_ID, LATITUED, LONGITUDE and all the available categories per defined property. Besides the percentage of categories per site, the number of observations is given.

Adds additional information to legend values

Description

Adds additional information to legend values

Usage

create_legend_labels(
  lv,
  low_label = NULL,
  high_label = NULL,
  increase_bounds = TRUE
)

Arguments

lv

legend values as returned by indicators_to_colors() in the legend_table
low_label

Additional lable for the lowest value
high_label

Additional lable for the highest value
increase_bounds

Add greater or equal / less or equal than sign to the highest /lowest legend value

Value

extended legend labels

Calculate all indicators for all recorded lakes

Description

Calculate all indicators for all recorded lakes

Usage

get_LakeIndicators(df_cw)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()

Value

A dataframe that contains columns of the root id, coordinates, the indicator values and number of observations as well as the date of the most recent observation

Function identifies the language based on category names

Description

Function identifies the language based on category names

Usage

identify_language(df_cw)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()

Value

A character string of the language

Calculation of the CrowdWater Biotope Indicator

Description

The indicator is calculated per observation

Usage

indicator_biotope(df_cw, return_scoring_system = FALSE)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()
return_scoring_system

If TRUE the scoring system is returned instead of the indicator vector.

Value

Either a numeric vector of the indicator points, or a description of the scoring system. This is a list of properties and categories which contribute to the indicator.

Calculation of the CrowdWater Nutrient Indicator

Description

The indicator is calculated per observation

Usage

indicator_nutrients(df_cw, return_scoring_system = FALSE)

Arguments

df_cw

Dataframe of the prepared crowdwater data
return_scoring_system

If TRUE the scoring system is returned instead of the indicator vector.

Value

Either a numeric vector of the indicator points, or a description of the scoring system. This is a list of properties and categories which contribute to the indicator.

Calculation of the CrowdWater Usage Indicator

Description

The indicator is calculated per observation

Usage

indicator_usage(df_cw, return_scoring_system = FALSE)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()
return_scoring_system

If TRUE the scoring system is returned instead of the indicator vector.

Value

Either a numeric vector of the indicator points, or a description of the scoring system. This is a list of properties and categories which contribute to the indicator.

Calculation of the CrowdWater Biotope Indicator

Description

The indicator is calculated per observation

Usage

indicator_waterStress(df_cw, return_scoring_system = FALSE)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()
return_scoring_system

If TRUE the scoring system is returned instead of the indicator vector.

Details

While most citizens are not able to tell if the lake experiences water stress by the observation at one point of time, there might be citizens who know the water body for a long time. The more observations include this information the more certain it is. Some citizen may only know about water level changes but cannot answer if the lake dries out. This is why in contrast to the other indicators, the points are counted even if one question remains unknown or unanswered.

Value

Either a numeric vector of the indicator points, or a description of the scoring system. This is a list of properties and categories which contribute to the indicator.

Transformation of numeric values into colors

Description

Transformation of numeric values into colors

Usage

indicators_to_colors(
  v_indicator,
  v_worst,
  v_neutral,
  v_best,
  col_worst = "#E6550D",
  col_neutral = "#EFF3FB",
  col_best = "#08306B",
  col_NA = "#00000000",
  resolution_bad = 0.1,
  resolution_good = 0.1,
  resolution_legend = 1
)

Arguments

v_indicator

Numeric Vector of indicator data
v_worst

Worst possible value
v_neutral

Neutral value
v_best

Best possible value
col_worst

Color for worst value as hexadecimal string of the form "#rrggbb"
col_neutral

Color for worst value as hexadecimal string of the form "#rrggbb"
col_best

Color for worst value as hexadecimal string of the form "#rrggbb"
col_NA

Color for NA values (Transparent by default)
resolution_bad

the resolution between neutral and worst value
resolution_good

The resolution between netral and best value
resolution_legend

The resolution of the legend values

Value

Vector of colors corresponding to the indicator values

From single observation to average site indicators

Description

From single observation to average site indicators

Usage

lake_indicator(df_cw, indicator_type)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()
indicator_type

A character defining the indicator. Either "nutrients" "biotope", "usage" or "waterstress"

Value

A data frame of 5 columns (ROOT_ID, LATITUDE, LONGITUDE and the indicator value and the number of observations)

Returnes only the latest observations per lake

Description

Returnes only the latest observations per lake

Usage

latest_observations(df_cw)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()

Value

A dataframe in the same shape as the lake properties dataframe with all historical observations removed

Create an interactive leaflet map which contains all the information of the last observation

Description

The leaflet map has two layers. 1) all the observered properties 2) the corresponding image of that observation

Usage

leaflet_indicator(cw_indicators, language = "german")

Arguments

cw_indicators

Dataframe of the crowdwater indicators per lake created by get_LakeIndicators()
language

A character string, defining the language if the map

Value

A leflet map

Create an interactive leaflet map which contains the days passed since the last observations

Description

Create an interactive leaflet map which contains the days passed since the last observations

Usage

leaflet_last_observation_days(df_cw, language = "german")

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()
language

A character string, defining the language if the map

Value

A leaflet map

Create an interactive leaflet map which contains all the information of the last observation

Description

The leaflet map has two layers. 1) all the observered properties 2) the corresponding image of that observation

Usage

leaflet_last_observation_info(df_cw, language = "german")

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()
language

A character string, defining the language if the map

Value

A leflet map

Merge two lake indicator tables

Description

Merge two lake indicator tables

Usage

merge_LakeIndicators(x, y)

Arguments

x

indicator table created by lake_indicator()
y

indicator table created by lake_indicator()

Value

merged table

CrowdWater lake data preparation

Description

The downloaded csv file is filtered for lake specific columns, for regions and for validated observations. The observed properties are named correctly in English.

Usage

prepare_data(path, file, region = NULL, validated_only = TRUE)

Arguments

path

The filepath
file

The filename including the ".csv" ending
region

Either a character defining a region which is available in or a numeric vector of coordinates in WGS84 shaping a rectangle by "top", "bottom", "left" and "right" coordinate.
validated_only

Logical if only checked observations are selected

Value

A list of two data frames. The first one contains data on the observered proberties, the second data frame contains data of the phyiscale category.

List of CrowdWater Properties and Categories

Description

List of CrowdWater Properties and Categories

Usage

props_and_cats

Format

A named list where list names correspond to the crowdwater properties (column names of the table). For each property a data frame of categories is given, which includes the translation into German or English, the order of categories if applicable (if not -> NA) and the type of answers

Replaces multiple separated category names by new names

Description

Multiple choice answers per observation are combined a one cell of the table and seperated by a comma.

Usage

renameMultipleChoice(v, oldNames, newNames, sep = ", ")

Arguments

v

Character vector of categories
oldNames

Character vector listing the old names of the categories
newNames

Characteri vector listing the new names of the categories in the same order as the oldNames vector
sep

The seperator that is used in a cell between categories of the same observation

Value

Character vector of renamed categories

Replaces multiple category names by new names

Description

Replaces multiple category names by new names

Usage

renameSingleChoice(v, oldNames, newNames)

Arguments

v

Character vector of categories
oldNames

Character vector listing the old names of the categories
newNames

Characteri vector listing the new names of the categories in the same order as the oldNames vector

Value

Character vector of renamed categories

Save a leaflet as html

Description

By adding content to the html it is assured that icons are scaled according to the device. In this way the map can be used by mobile devices.

Usage

save_leaflet(x, file)

Arguments

x

Leaflet object to be saved
file

Filename including path

Returnes only the latest observations per lake

Description

Returnes only the latest observations per lake

Usage

site_category_percentage(df_cw, includeNA = FALSE)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()
includeNA

Logical if NA (No answer) data should be included

Value

A dataframe in the same shape as the lake properties dataframe with all historical observations removed

From single observation to average site indicators

Description

From single observation to average site indicators

Usage

tranlsate_categories(df_cw, from, to)

Arguments

df_cw

Dataframe of the crowdwater lake properties prepared by prepare_data()
from

Character string, defining the language of df_cw
to

Character string, defining the language to translate in

Value

df_cw translated