Title: Litter Analysis
Version: 1.0.1
Date: 2025-07-04
Description: Data sets on various litter types like beach litter, riverain litter, floating litter, and seafloor litter are rapidly growing. This package offers a simple user interface to analyse these litter data in a consistent and reproducible way. It also provides functions to facilitate several kinds of litter analysis, e.g., trend analysis, power analysis, and baseline analysis. Under the hood, these functions are also used by the user interface. See Schulz et al. (2019) <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are advised to run 'litteR' in 'RStudio'. See our vignette: Installation manual for 'RStudio' and 'litteR'.
Depends: R (≥ 4.0.0)
Imports: readr (≥ 1.3.1), stringr (≥ 1.4.0), dplyr (≥ 1.0.0), tidyselect (≥ 1.1.0), tidyr (≥ 1.1.0), fs (≥ 1.4.1), ggplot2 (≥ 3.3.1), purrr (≥ 0.3.4), rlang (≥ 0.4.6), yaml (≥ 2.2.1), rmarkdown (≥ 2.2), tcltk
Suggests: knitr, kableExtra, testthat (≥ 2.3.2)
License: GPL (≥ 3)
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-07-04 10:14:13 UTC; dennis
Author: Dennis Walvoort [aut, cre, cph], Willem van Loon [aut, cph], Rijkswaterstaat - The Netherlands [cph, fnd, dtc]
Maintainer: Dennis Walvoort <dennis.Walvoort@wur.nl>
Repository: CRAN
Date/Publication: 2025-07-04 10:30:02 UTC

Litter Analysis

Description

A tool for the analysis of various litter types, e.g., beach litter, riverain litter, floating litter, and seafloor litter.

Details

litter.png

The easiest way to get convenient with litteR is to create an empty project directory and fill it with example files by calling the function create_litter_project. The workhorse function in litteR is called litter. This function will start a simple user interface and lets you select an input file (*.csv) and a settings file (*.yaml). It will produce an HTML-report with litter analysis results according to the selected options in the settings file. See the package vignette for more details.

Author(s)

Maintainer: Dennis Walvoort dennis.Walvoort@wur.nl [copyright holder]

Authors:

Other contributors:

References

Schulz, Marcus, Dennis J.J. Walvoort, Jon Barry, David M. Fleet & Willem M.G.M. van Loon, 2019. Baseline and power analyses for the assessment of beach litter reductions in the European OSPAR region. Environmental Pollution 248:555-564 <doi:10.1016/j.envpol.2019.02.030>

Adjusted Boxplot Statistics

Description

Adjusted boxplot statistics according to Hubert & Vandervieren (2008). The upper whisker extends from the hinge to the largest value no further than the upper fence. Similarly, the lower whisker extends from the hinge to the smallest value no further than the lower fence. See Hubert & Vandervieren (2008, p.5191, Eq.5).

Usage

adj_boxplot_stats(x, ...)

## Default S3 method:
adj_boxplot_stats(x, ...)

Arguments

x

numeric vector

...

further arguments passed to or from other methods.

Value

Numeric vector consisting of respectively the lower whisker/fence, the first quartile/hinge, the median, the third quartile/hinge, and the upper whisker/fence.

Methods (by class)

References

Hubert, M., and E. Vandervieren, 2008. An adjusted boxplot for skewed distributions. Computational Statistics and Data Analysis 52:5186-5201 doi:10.1016/j.csda.2007.11.008

See Also

stat_adj_boxplot

Examples

adj_boxplot_stats(rlnorm(100))

Create Project Directory

Description

Fills an empty directory (path) with example files. If the path' argument is missing or NULL, a Tcl/Tk dialogue will be started.

Usage

create_litter_project(path = NULL)

Arguments

path

(Existing) directory name

Simple Logger

Description

Logger, in the spirit of loggers like log4j. Implemented logging levels are DEBUG, INFO, WARN, ERROR (in increasing order of specificity. Logging events can be filtered to show only events with a minimum specificity.

Usage

create_logger(con = stdout(), level = c("DEBUG", "INFO", "WARN", "ERROR"))

Arguments

con

connection to write logging data to

level

log only events of this level and those that are more specific (see details)

Value

Anonymous logging functions

Examples


logger <- create_logger(level = "INFO")
logger$info("starting specific computation")
logger$info("Today is {Sys.Date()}")

Coefficient of Variation

Description

Coefficient of Variation

Usage

cv(x, na.rm = FALSE)

Arguments

x

a numeric vector

na.rm

logical. Should missing values be removed?

Value

coefficient of variation (numeric vector of length 1).

References

https://en.wikipedia.org/wiki/Coefficient_of_variation

Enumerate Objects

Description

Generic function for enumerating objects

Usage

enumerate(x, ...)

## S3 method for class 'numeric'
enumerate(x, ...)

Arguments

x

object to enumerate

...

further arguments passed to or from other methods.

Methods (by class)

See Also

enumerate.character

Enumerate Character Vector

Description

Collapsing a character vector of length n, to a character vector of length 1.

Usage

## S3 method for class 'character'
enumerate(x, ...)

Arguments

x

character vector

...

further arguments passed to or from other methods.

Value

character vector of length 1, with elements separated by a comma except for the last element which is prepended by "and".

Examples

enumerate("apples")
enumerate(c("apples", "oranges"))
enumerate(c("apples", "oranges", "pears"))

Convert Sequenized Output to Character String

Description

Convert Sequenized Output to Character String

Usage

## S3 method for class 'sequenized'
enumerate(x, ...)

Arguments

x

object of class sequenized.

...

further arguments passed to or from other methods.

Value

string representation (character vector of length 1) of a sequenized object

See Also

sequenize.integer

Check Write Permission

Description

Simple wrapper for file.access with mode=2

Usage

has_write_access(path)

Arguments

path

filename

Value

TRUE if write access, FALSE if not

Intercept

Description

Extract the intercept from object x.

Usage

intercept(x, ...)

Arguments

x

object

...

further arguments passed to or from other methods.

Value

estimate of the intercept (numeric vector of length 1).

Index of Dispersion

Description

A normalized measure of the dispersion of a probability distribution.

Usage

iod(x, na.rm = FALSE)

Arguments

x

a numeric vector

na.rm

logical. Should missing values be removed?

Value

index of dispersion (numeric vector of length 1).

References

https://en.wikipedia.org/wiki/Index_of_dispersion

Check Date Format

Description

Checks if the data format x complies with format.

Usage

is_date_format(x, format = "%Y-%m-%d")

Arguments

x

object of class character or Date

format

required date format (see strptime)

Value

TRUE if x complies with format, and FALSE otherwise.

Examples

is_date_format("2019-05-14", "%Y-%m-%d")

Test for Natural Numbers

Description

Test for natural numbers according to ISO 80000-2, that is the set {0, 1, 2, ...}

Usage

is_natural_number(x)

Arguments

x

numeric vector

Value

TRUE in case x is a natural number, FALSE otherwise.

Examples

stopifnot(!is_natural_number(3.1))
stopifnot(!is_natural_number(2.99))
stopifnot(is_natural_number(3))
stopifnot(all(is_natural_number(0:9)))
stopifnot(sum(is_natural_number(c(1, 2.5, 3))) == 2)

Mann-Kendall S Statistic

Description

Mann-Kendall S Statistic

Usage

kendall_s(x, t = seq_along(x))

Arguments

x

observations

t

time index

References

Gilbert, R.O., 1987. Statistical methods for environmental pollution monitoring.

See Also

kendall_var_s

Mann-Kendall Variance of S Statistic

Description

Mann-Kendall Variance of S Statistic

Usage

kendall_var_s(x, t = seq_along(x))

Arguments

x

observations

t

time index

References

Gilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitoring.

Van Belle and Hughes, 1984, Nonparametric Tests for Trend in Water Quality. Water Resources Research 20:127-136

List Duplicates

Description

Lists all duplicates as a list of tuples.

Usage

list_duplicates(x, ...)

## S3 method for class 'character'
list_duplicates(x, ...)

## S3 method for class 'tbl'
list_duplicates(x, ...)

## S3 method for class 'data.frame'
list_duplicates(x, ...)

Arguments

x

object of class character, tibble or data.frame.

...

further arguments passed to or from other methods.

Value

list of row numbers with duplicates

Methods (by class)

Examples

list_duplicates(c("a", "b", "c")) # list()
list_duplicates(c("a", "b", "a", "c")) # list(c(1, 3))

Graphical User Interface to the litteR-package

Description

Starts a graphical user interface for analysing litter data. A Tcl/Tk-dialogue will be started if one or more arguments are missing.

Usage

litter(filename = NULL)

Arguments

filename

name of file containing settings (see vignette for details)

Details

For details, see our vignette by typing: vignette("litter-manual")

Value

directory name (invisibly) where all results are stored.

Mann Kendall

Description

Performs Mann-Kendall non-parametric test for trend.

Usage

mann_kendall(x, t = seq_along(x), type = c("both", "increasing", "decreasing"))

## S3 method for class 'mann_kendall'
test_statistic(x, ...)

## S3 method for class 'mann_kendall'
p_value(x, ...)

Arguments

x

numeric vector representing a time-series.

t

time index (a numeric vector, or a vector of class Date).

type

direction to test (both, increasing, or decreasing).

...

further arguments passed to or from other methods.

Value

object of class Mann-Kendall.

Methods (by generic)

See Also

test_statistic, p_value, cor.test, regional_kendall

Examples


# create mann_kendall object
mk <- mann_kendall(c(9, 4, 7, 5, 3), type = "decreasing")
mk <- mann_kendall(
  x = c(9, 4, 7, 5, 3),
  t = c(1, 3, 2, 5, 9),
  type = "decreasing")

# get test statistic tau
test_statistic(mk)

# get p-value
p_value(mk)

Medcouple

Description

Robust statistic that quantifies the skewness of univariate distributions.

Usage

medcouple(x, ...)

## Default S3 method:
medcouple(x, ...)

Arguments

x

numeric vector

...

further arguments passed to or from other methods.

Value

medcouple (numeric vector of length 1).

Methods (by class)

Note

This is a naive, but robust en simple implementation. For a more efficient implementation see package robustbase and the references section below.

References

Brys, G., M. Hubert, A. Struyf, 2004. A Robust Measure of Skewness. Journal of Computational and Graphical Statistics 13: 996-1017. doi:10.1198/106186004X12632.

p-value

Description

Extract p-value.

Usage

p_value(x, ...)

Arguments

x

object

...

further arguments passed to or from other methods.

Value

p-value of a test (numeric vector of length 1).

Read Litter Data

Description

Reads litter data from various formats. Currently only the OSPAR data snapshot format, and a wide format are supported. See the package vignette for more details.

Usage

read_litter(filename, logger = create_logger(level = "INFO"), type_names)

Arguments

filename

name of litter file

logger

optional logger object (see create_logger)

type_names

character vector of allowed type_names

Value

tibble with litter data in long format

Read Type Names

Description

Read the file that links type names to group codes See the package vignette for more details.

Usage

read_litter_types(filename, logger = create_logger(level = "INFO"))

Arguments

filename

name of type file

logger

optional logger object (see create_logger)

Value

tibble with look-up-table of type names and group codes

Read Settings File

Description

Reads settings file. See tutorial for its format.

Usage

read_settings(filename, logger = create_logger(level = "INFO"))

Arguments

filename

name of litter file

logger

optional logger object (see create_logger)

Value

validated settings file

Sample From an ECDF

Description

Type stable implementation of an Empirical Cumulative Distribution Function (ECDF) sampler.

Usage

recdf(x, n)

Arguments

x

numeric vector

n

number of draws

Value

vector of n elements of the same type as x

See Also

ecdf

Examples

recdf(1:5, 10)

Regional Kendall Test for Trend

Description

Performs Regional Kendall non-parametric test for trend.

Usage

regional_kendall(
  x,
  t = seq_along(x),
  r = rep.int(1, length(x)),
  type = c("both", "increasing", "decreasing")
)

## S3 method for class 'regional_kendall'
test_statistic(x, ...)

## S3 method for class 'regional_kendall'
p_value(x, ...)

Arguments

x

observations

t

time index

r

region index

type

direction to test (both, increasing, or decreasing).

...

further arguments passed to or from other methods.

Methods (by generic)

References

Gilbert, R.O., 1987. Statistical methods for environmental pollution monitoring.

See Also

mann_kendall

Relative Median Absolute Deviation

Description

This is the Median Absolute Deviation divided by the median and is similar to the coefficient of variation.

Usage

rmad(x, na.rm = FALSE)

Arguments

x

a numeric vector

na.rm

logical. Should missing values be removed?

Value

Relative median absolute deviation (numeric vector of length 1).

References

https://en.wikipedia.org/wiki/Median_absolute_deviation

Rolling Statistics

Description

Applies function fun within a rolling (moving) window of size w to vector numeric vector x.

Usage

roll(x, w = 3, fun = mean)

Arguments

x

numeric vector (time-series)

w

width of moving window

fun

function to be applied

Value

vector of length length(x)-w

Sequenize Objects

Description

Generic function for sequenizing objects

Usage

sequenize(x, ...)

Arguments

x

object to sequenize

...

further arguments passed to or from other methods.

See Also

sequenize.integer

Sequenize Integer Sequence

Description

Compression of integer sequences to 'start-end' notation. For instance c(1:5, 8:9) becomes "1-5, 8-9".

Usage

## S3 method for class 'integer'
sequenize(x, ...)

Arguments

x

vector of integers.

...

further arguments passed to or from other methods.

Value

object of class sequenized

Note

The elements of x should be unique and in ascending order.

Examples

sequenize(c(1:4, 8:9))

Slope

Description

Extract slope.

Usage

slope(x, ...)

Arguments

x

object

...

further arguments passed to or from other methods.

Value

estimate of the slope (numeric vector of length 1).

Adjusted Boxplot Statistics for ggplot2

Description

Computes adjusted boxplot statistics to be used by ggplot2. See Hubert & Vandervieren (2008, p.5191, Eq.5).

Usage

stat_adj_boxplot()

stat_adj_boxplot_outlier()

Functions

References

Hubert, M., and E. Vandervieren, 2008. An adjusted boxplot for skewed distributions. Computational Statistics and Data Analysis 52:5186-5201 doi:10.1016/j.csda.2007.11.008

See Also

adj_boxplot_stats, stat_adj_boxplot_outlier

Examples

library(ggplot2)

d <- data.frame(x = gl(2, 50), y = rnorm(100))
ggplot(data = d, mapping = aes(x = x, y = y)) +
   stat_adj_boxplot()

Test Statistic

Description

Extract test_statistic.

Usage

test_statistic(x, ...)

Arguments

x

object

...

further arguments passed to or from other methods.

Value

test statistic of a test (numeric vector of length 1).

See Also

test_statistic.wilcoxon, test_statistic.mann_kendall

Theil Sen Slope Estimator

Description

Theil Sen Slope Estimator

Usage

theil_sen(x, y, ...)

## S3 method for class 'theil_sen'
slope(x, ...)

## S3 method for class 'theil_sen'
intercept(x, ...)

Arguments

x

time vector (numeric, or Date).

y

numeric value.

...

further arguments passed to or from other methods.

Value

object of class Theil_Sen.

Methods (by generic)

References

https://en.wikipedia.org/wiki/Theil-Sen_estimator

Examples


# create theil_sen object
ts <- theil_sen(1:5, c(1, 2, 3, 5, 9))

# get slope
slope(ts)

# get intercept
intercept(ts)

Tukey's Trimean

Description

Robust centrality measure estimated as the weighted average of the three quartiles: (Q_1 + 2Q_2 + Q_3) / 4, where Q_1, Q_2 and Q_3 are the first, second and third quartiles respectively.

Usage

trimean(x, ...)

## Default S3 method:
trimean(x, ...)

Arguments

x

numeric vector

...

further arguments passed to or from other methods.

Value

trimean (numeric value of length 1).

Methods (by class)

References

https://en.wikipedia.org/wiki/Trimean

Examples

stopifnot(trimean(0:100) == mean(0:100))
stopifnot(trimean(0:100) == median(0:100))

Validation of LitteR File Formats

Description

Generic function for validation of file formats.

Usage

validate(x, ...)

## S3 method for class 'litter'
validate(x, type_names, logger = create_logger(level = "INFO"), ...)

## S3 method for class 'litter_types'
validate(x, logger = create_logger(level = "INFO"), ...)

## S3 method for class 'settings'
validate(x, logger = create_logger(level = "INFO"), ...)

Arguments

x

object to validate

...

further arguments passed to or from other methods.

type_names

character vector of permissible types

logger

optional logger object (see create_logger)

Value

validated object of class wide

validated object of class litter_types

validated settings (list)

Methods (by class)

Wilcoxon Test

Description

Constructor for a Wilcoxon test (simple wrapper for wilcox.test).

Usage

wilcoxon(x, type = c("both", "greater", "less"), mu = 0)

## S3 method for class 'wilcoxon'
test_statistic(x, ...)

## S3 method for class 'wilcoxon'
p_value(x, ...)

Arguments

x

numeric vector representing a time-series.

type

direction to test (both, increasing, or decreasing).

mu

baseline value (null hypothesis)

...

further arguments passed to or from other methods.

Value

object of class wilcoxon.

Methods (by generic)

See Also

wilcox.test, p_value, test_statistic

Examples


# create wilcoxon object
w <- wilcoxon(c(9, 4, 7, 5, 3), type = "less")

# get test statistic V
test_statistic(w)

# get p-value
p_value(w)