Litter Analysis
Description
A tool for the analysis of various litter types, e.g., beach litter, riverain litter, floating litter, and seafloor litter.
Details
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 [email protected] [copyright holder]
Authors:
-
Willem van Loon [email protected] [copyright holder]
Other contributors:
-
Rijkswaterstaat - The Netherlands [copyright holder, funder, data contributor]
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, ...)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)
-
adj_boxplot_stats(default): Adjusted Boxplot Statistics
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
Examples
adj_boxplot_stats(rlnorm(100))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)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"))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()}")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)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, ...)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)
-
enumerate(numeric): enumeratenumericvector.
See Also
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, ...)## 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"))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, ...)## S3 method for class 'sequenized' enumerate(x, ...)
Arguments
x |
object of class |
... |
further arguments passed to or from other methods. |
Value
string representation (character vector of length 1) of a sequenized object
See Also
Check Write Permission
Description
Simple wrapper for file.access with mode=2
Usage
has_write_access(path)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, ...)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)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")is_date_format(x, format = "%Y-%m-%d")
Arguments
x |
object of class |
format |
required date format (see |
Value
TRUE if x complies with format,
and FALSE otherwise.
Examples
is_date_format("2019-05-14", "%Y-%m-%d")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)is_natural_number(x)
Arguments
x |
|
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)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))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
Mann-Kendall Variance of S Statistic
Description
Mann-Kendall Variance of S Statistic
Usage
kendall_var_s(x, t = seq_along(x))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, ...)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 |
... |
further arguments passed to or from other methods. |
Value
list of row numbers with duplicates
Methods (by class)
-
list_duplicates(character): list duplicates for acharactervector. -
list_duplicates(tbl): lists duplicates for atibble. -
list_duplicates(data.frame): lists duplicates for adata.frame.
Examples
list_duplicates(c("a", "b", "c")) # list() list_duplicates(c("a", "b", "a", "c")) # list(c(1, 3))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)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, ...)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 |
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)
-
test_statistic(mann_kendall): Extracts Mann Kendall tau -
p_value(mann_kendall): Extract p-value
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)# 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, ...)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)
-
medcouple(default): default method
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, ...)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)read_litter(filename, logger = create_logger(level = "INFO"), type_names)
Arguments
filename |
name of litter file |
logger |
optional logger object (see |
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"))read_litter_types(filename, logger = create_logger(level = "INFO"))
Arguments
filename |
name of type file |
logger |
optional logger object (see |
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"))read_settings(filename, logger = create_logger(level = "INFO"))
Arguments
filename |
name of litter file |
logger |
optional logger object (see |
Value
validated settings file
Sample From an ECDF
Description
Type stable implementation of an Empirical Cumulative Distribution Function (ECDF) sampler.
Usage
recdf(x, n)recdf(x, n)
Arguments
x |
numeric vector |
n |
number of draws |
Value
vector of n elements of the same type as x
See Also
Examples
recdf(1:5, 10)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, ...)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)
-
test_statistic(regional_kendall): Extracts Regional Kendall Z -
p_value(regional_kendall): Extract Regional Kendall p-value
References
Gilbert, R.O., 1987. Statistical methods for environmental pollution monitoring.
See Also
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)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)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, ...)sequenize(x, ...)
Arguments
x |
object to sequenize |
... |
further arguments passed to or from other methods. |
See Also
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, ...)## 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))sequenize(c(1:4, 8:9))
Slope
Description
Extract slope.
Usage
slope(x, ...)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()stat_adj_boxplot() stat_adj_boxplot_outlier()
Functions
-
stat_adj_boxplot_outlier(): add outliers to adjusted boxplot
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()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, ...)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, ...)theil_sen(x, y, ...) ## S3 method for class 'theil_sen' slope(x, ...) ## S3 method for class 'theil_sen' intercept(x, ...)
Arguments
x |
time vector ( |
y |
numeric value. |
... |
further arguments passed to or from other methods. |
Value
object of class Theil_Sen.
Methods (by generic)
-
slope(theil_sen): Extract slope. -
intercept(theil_sen): Extract intercept.
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)# 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: , where
and are the first, second and third
quartiles respectively.
Usage
trimean(x, ...) ## Default S3 method: trimean(x, ...)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)
-
trimean(default): Tukey's trimean
References
https://en.wikipedia.org/wiki/Trimean
Examples
stopifnot(trimean(0:100) == mean(0:100)) stopifnot(trimean(0:100) == median(0:100))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"), ...)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 |
Value
validated object of class wide
validated object of class litter_types
validated settings (list)
Methods (by class)
-
validate(litter): validate litter data. -
validate(litter_types): validate litter_types file -
validate(settings): validate settings file
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, ...)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)
-
test_statistic(wilcoxon): Extract test statistic V -
p_value(wilcoxon): Extract p-value
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)# 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)