litteR is a user-friendly tool for analyzing litter data (e.g., beach litter data). The current version (0.9.1) contains routines for:
The focus of this version of litteR is to provide a a user-friendly, flexible, robust, transparent, and relatively simple tool for litter analysis. Although litteR is distributed as an R-package, experience with R is not required. If you need more information on how to install R, RStudio, and litteR, please consult our installation guide.
Litter data are count data. As has been illustrated in the histogram below (copied with permission from Hanke et al., 2019), litter data generally have skewed distributions. All procedures in litteR are based on robust statistical methods. They do not require distributional assumptions and are relatively robust for outliers.
This user guide consists of two parts. In the first part, the user interface is described, the second part provides details on the technicalities.
For applications with (a previous version of) litteR see Schulz et al. (2019). litteR is the successor of the Litter Analyst software (Schulz et al., 2017).
Before litteR can be used, it should be installed or updated in case you installed litteR before. See our installation guide fore details.
You need to install litteR only once, but you need to load this package each time you start RStudio.
The litteR-package should be loaded in RStudio before you can use it. This can be done by running the following code in the R-console or the RStudio-console:
A startup messsage appears that gives some essential instructions to start using litteR.
The easiest way to start working with litteR is to create an empty project directory. This directory can be filled with example and reference files by running:
The argument of function
create_litter_project (i.e., the quoted part in parentheses) is an existing work directory on your computer. This can be any valid directory name with sufficient user privileges. Note for MS-Windows users: R requires forward slashes!
It is also possible to run
create_litter_project() without an argument. In that case, a simple graphical user interface pops up for interactive directory selection.
litteR can be started typing
litter() in the RStudio console (see the figure below).
litter(), a simple graphical user interface pops up for file selection. An example of a file selection dialogue is given below.
litteR needs three input files:
These input files are described below.
The type file contains a list of all litter types that are allowed to use in the data file. It also indicates to which litter group each litter type belongs. Two example files, named ‘
types-ospar.csv’ and ‘
types-ospar-tc-sup-fish-plastic.csv’ are automatically generated when using the
create_litter_project-function, a described earlier in this tutorial. A type file assigns each litter type (
type_name) to one or more litter groups. The first 10 rows of ’
types-ospar-tc-sup-fish-plastic.csv are given in the table below.
|Plastic: Yokes ||x||x||x|
|Plastic: Bags ||x||x||x|
|Plastic: Small_bags ||x||x||x|
|Plastic: Bag_ends ||x||x||x|
|Plastic: Drinks ||x||x||x|
|Plastic: Cleaner ||x||x||x|
|Plastic: Food ||x||x||x|
|Plastic: Toiletries ||x||x||x|
|Plastic: Oil_small ||x||x|
|Plastic: Oil_large ||x||x|
The following columns are in this table:
type_name. This column is required and gives all litter types that are allowed in the data file. Litter types given in this column need to be unique;
included: This column indicates whether a type specified in column
type_namewill be used in the analysis or not. Only
type_namesthat are included in the analysis will contribute to the total litter count (TC).
PLASTIC, etc.: these columns give the definition of each litter group. In the example above three groups are given: ‘single use plastics’ (SUP), ‘fisheries related litter’ (FISH), and ‘plastics’ (PLASTIC). A cross (x) indicates that a litter type in
type_nameis a member of a litter group or not. A cross (x) means ‘a member’, an empty cell means ‘not a member’.
The user may use one of the provided type files as a template for his own type file. litteR will use the type file that has been specified in the settings-file.
litteR performs regional aggregation at the group level. In order to perform regional aggregation at the type level (the columns in the data file), a group with only one or a few litter types of interest can be constructed in the type file, and then regionally aggregated by running litteR.
litteR supports a simple and flexible data format. It is similar to the OSPAR-format. The data are stored in so called wide format: each row refers to a single survey, each column to a single litter type or metadata. The table below gives an example of a small part (i.e., the upper left corner) of a data file.
|location_code||date||Plastic: Yokes ||Plastic: Bags ||Plastic…|
date are always required and define unique records (rows) with litter survey data for a specific date and location (e.g., a specific beach, or a location along a river). litteR will use these data to estimate statistics (as the median and trend) for each
location_code may contain location codes (as in the example above), but also full names like ‘Bergen’, ‘Noordwijk’, and ‘La Grève des Courses’. Full names may be more clear when interpreting the results.
date column gives the monitoring date in ISO format, i.e., YYYY-mm-dd (for example 2021-09-21, to indicate 21 September 2021). For convenience, the OSPAR-format (dd/mm/YYYY) is currently also supported (for example 21/09/2021, to indicate 21 September 2021).
Plastic: Yokes ,
Plastic: Bags , … contain the counts for specific litter types. Each litter type (column name) should be listed in the litter type file. Only litter types in the litter type file are valid column names. All column names that are not valid litter types are considered as optional metadata. These columns are ignored by litteR and do not affect the results.
There is one exception: the column
region_code is optional and should be available when the locations (in column
location_code) also need to be spatially aggregated. Each
region_code is related to one or more
location_code(s) that are part of that region.
In the data file below, one
region_code (NL) is provided for all locations in
location_code. Therefore, litteR will spatially aggregate the results for all locations (NL001 … NL004) within the specified region (NL).
|region_code||location_code||date||Plastic: Yokes ||Plastic: Bags ||Plastic…|
A data file can be constructed easily from existing litter files. As an example consider the OSPAR-format below:
|Beach ID||Beach name||Country||Survey date||Plastic: Yokes ||Plastic: Bags ||Plastic…|
One can simply rename existing columns to the names required by litteR. This can be done with a spreadsheet program or a text editor. For instance, renaming
Survey date to respectively
date gives the following valid litteR format:
|location_code||Beach name||region_code||date||Plastic: Yokes ||Plastic: Bags ||Plastic…|
Beach name is not recognized by litteR, and is therefore ignored.
As an alternative, one may also add new columns with valid litteR names to the data file and fill them with the contents of existing columns. See the example below:
|region_code||location_code||date||Beach ID||Beach name||Country||Survey date||Plastic…|
This can be done quite easily with a spreadsheet program. The original columns of the OSPAR-format (
Survey date) are ignored by litteR.
It is advised to use
location_codes that are easily recognized by the user. For instance, in the example above,
location_code ‘Bergen’ is easier to interpret than
location_code ‘NL001’. Obviously, this choice does not affect the litteR-results.
The settings file contains all settings needed to run litteR. An example of the contents of a settings file is given in the figure below:
# litteR settings file # Period to analyse (YYYY-mm-dd) date_min: 2012-01-01 date_max: 2017-12-31 # Percentage of total count to analyse (0 < percentage_total_count <= 100) percentage_total_count: 80 # Data file. # Note: the datafile must be in the same path as the settings file # Note: the file extension should be .csv file_data: beach-litter-nl-2012-2017.csv # Type file. Defines the types and their groups file_types: types-ospar.csv # Select trend figures to plot in the report # Note: this can be zero, one, or more than one location_code, region_code, # group_code, and/or type_name location_code: ["NL001", "NL004"] region_code: ["NL"] group_code: ["TC", "SUP", "FISH"] type_name: ["Plastic: Bags "] # figure quality (high or low) figure_quality: high # cutoff value vertical axis with litter counts (percentage) cutoff_count_axis: 100
The settings-file contains the following entries:
date_max, the first and final date of the period to analyze. Dates should be given in ISO format, i.e., YYYY-mm-dd (for example 2021-09-21, to indicate 21 September 2021);
percentage_total_count: the percentage of the total count used to estimate statistics. See the section on descriptive statistics for more information;
file_data: name of the data file (including its path, e.g., c:/my-litter-directory/my-litter-data.csv);
file_types: name of the type file (including its path, e.g., c:/my-litter-directory/types-ospar.csv);
location_code: name(s) of the location(s) to plot. These should exist in column
location_codein the data file. As mentioned in the previous section,
location_codes should be readily interpretable for the user, as these codes are also used in the litteR-results (tables and plots);
region_code: name(s) of the region(s) to plot. These should exist in column
region_codein the data file;
group_code: name(s) of group(s) to plot. Litter groups should be available as column names in the type file;
type_name: name(s) of type(s) to plot; Type names should be available in the type file and data file;
figure_quality: quality of the plots in the report, either
cutoff_count_axis: optional cutoff value as a percentage of the vertical count axis in trend plots. A cutoff value is useful to improve the readability of a plot in case of a few very high litter counts.
All input files are validated by litteR. The following validation rules apply:
litteR produces three output files:
For convenience, all input and output files are stored as a snapshot in a directory with names like
litteR-results-20210904T221809, where the final part of the name is a timestamp.
litteR produces an HTML-report that can best be viewed with modern web browsers like Mozilla FireFox, Google Chrome, or Safari. These browsers are freely available from the internet.
The filename of each report starts with ‘litter-results’, followed by a timestamp: YYYYmmddTHHMMSS and the extension html. For example:
This section briefly describes each section in the HTML-report
This section gives a summary of the settings in the settings file.
In this section (potential) problems in the input files are reported. These problems are also stored in the log file.
location_code in the data file, adjusted boxplots are given of the total count for the detection of outliers. Outliers are given as dots (if any) in adjusted box-and-whisker plots. Adjusted boxplots are more suitable for outlier detection in case of skewed distributions than traditional box plots. An example of these box-and-whisker plots are given below.
location_code and group/type name, the following statistics are estimated:
These statistics will be estimated for all litter types with the greatest counts making up a percentage of the total count and for all litter groups. This percentage is given as
percentage_total_count in the settings file.
The descriptive statistics for the litter types and groups are stored in a CSV-file with a name starting with
litteR-results and ending with a timestamp. The statistics for litter groups are also printed as a table and shown as bar plots in the report: one plot for each
location_code column of the data file. An example is given in the figure below. If you want other groups, or only a subset of groups, you should modify the type file.
When the data file contains column
region_code, the data for the
location_codes in that region are spatially aggregated in a stepwise fashion:
location_code) within that region (
Note that these statistics are so called intra-block statistics, i.e., data from individual
location_codes are not merged.
The summary statistics are:
mean: the mean of the means of the individual locations (
location_code) within a region (
region_code) for each litter group;
median: the median of the medians of the individual locations (
location_code) within a region (
region_code) for each litter group;
slope: the median of the Theil-Sen slopes of the individual locations (
location_code) within a region (
region_code) for each litter group. Data from different locations have not been mixed in the computation of the Theil-Sen slopes. This method is similar to the one in Gilbert (1987) except that in our procedure all locations within a region contribute equally to the regional trend.
location_code, and the type names and group codes specified in the settings file, trends are estimated by means of the Theil-Sen slope estimator: a robust non-parametric estimator of slope (counts / year). The significance of the estimated slopes is tested by means of the Mann-Kendall test. The Mann-Kendall test is a non-parametric test and as such does not make distributional assumptions on the data.
The figure below gives examples of trend plots for total count (TC), single use plastics (SUP), and plastic bags at the beach of Terschelling (The Netherlands). In each plot, the black dots are the observations, the thin gray line segments connect the dots and guide the eye, and the red line is the Theil-Sen slope.