Package {limpidR}

Type: Package
Title: Reproducible Analysis of Freshwater Microplastic Data
Version: 0.1.0
Author: Chanikya Naidu [aut, cre]
Maintainer: Chanikya Naidu <thefisherieschanikyaneeti@gmail.com>
Description: Provides validation, harmonization, descriptive analysis, compositional analysis, transparent risk components, grouped cross-validation, visualization, and predictive modelling tools for freshwater microplastic datasets. The package includes synthetic demonstration data conforming to the LIMPID-India data model and emphasizes explicit units, provenance, percentage closure, non-imputation of missing environmental covariates, and leakage-aware model evaluation.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (≥ 4.1.0)
Imports: ggplot2
Suggests: knitr, rmarkdown, readxl, testthat (≥ 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2026-09-22 08:50:13 UTC; chani
Repository: CRAN
Date/Publication: 2026-09-30 11:50:02 UTC

Reproducible Analysis of Freshwater Microplastic Data

Description

Tools for loading, validating, harmonizing, analysing, modelling and visualizing freshwater microplastic datasets, with bundled synthetic demonstration tables that follow the LIMPID-India schema.

Details

The package emphasizes explicit units, provenance, composition closure, grouped validation, and transparent assumptions. Missing environmental values are not silently imputed.

Author(s)

Chanikya Naidu <thefisherieschanikyaneeti@gmail.com>

Transparent risk components and reproducibility metadata

Description

Calculate assumption-explicit risk components and report reproducibility/citation metadata.

Usage

calculate_risk(data, abundance_reference, hazard_scores = NULL,
  weights = NULL, component_max = NULL, thresholds = NULL)

limpid_session_info()

limpid_citation(doi = NULL, creators = "Chanikya Naidu")

Arguments

data

A limpid_db or event-level data frame.

abundance_reference

Positive reference abundance in the same unit as MP_Mean.

hazard_scores

Optional named polymer hazard scores supplied by the researcher.

weights

Optional named composite-score weights.

component_max

Explicit positive scaling maxima required when weights are used.

thresholds

Optional ascending numeric composite-score cut points.

doi

Assigned LIMPID-India dataset DOI.

creators

Creator string for citation output.

Details

No universal polymer hazard scores, composite weights or risk classes are hard-coded. Assumptions must be supplied explicitly.

Value

A risk-component data frame, sessionInfo object, or citation string.

Load, clean and validate freshwater microplastic data

Description

Functions for reading LIMPID-India tables, unit harmonization and structural validation.

Usage

limpid_tables()

load_limpid(path = NULL, tables = limpid_tables(), validate = TRUE, quiet = FALSE)

check_database(data, strict = FALSE, tolerance = 0.2)

validate_mp_data(data, type = c("abundance", "morphology", "size", "polymer",
  "events", "lake"), tolerance = 0.2)

clean_mp_data(data, abundance_col = "MP_Mean", unit_col = "Unit",
  target_unit = "particles L^-1", invalid_action = c("error", "flag", "na"))

convert_mp_units(x, from, to = "particles L^-1")

Arguments

path

NULL, a compatible CSV directory, or an Excel workbook. Excel input requires readxl.

tables

Character vector of table names.

validate

Logical; validate after loading.

quiet

Logical; suppress load message.

data

A data frame or limpid_db, depending on function.

strict

Logical; stop when a failing database check is found.

tolerance

Allowed percentage deviation from 100 for composition closure.

type

Expected table schema.

abundance_col

Name of abundance column.

unit_col

Name of unit column.

target_unit

Target abundance unit.

invalid_action

Action for invalid abundance values.

x

Numeric abundance vector.

from

Source abundance unit.

to

Target abundance unit.

Value

A loaded database, validation table, cleaned object, or converted numeric vector.

Examples

db <- load_limpid()
check_database(db)
convert_mp_units(1000, "particles m^-3", "particles L^-1")

Model and validate freshwater microplastic abundance

Description

Lognormal or Gamma regression with original-scale prediction and grouped cross-validation.

Usage

model_mp_abundance(data, formula = NULL,
  method = c("lognormal_lm", "gamma_glm"), na_action = c("omit", "fail"))

predict_mp(model, newdata, interval = c("none", "confidence", "prediction"), level = 0.95)

cross_validate_mp(data, formula = NULL,
  method = c("lognormal_lm", "gamma_glm"), group = "Lake_ID")

Arguments

data

A limpid_db or model-ready data frame.

formula

Model formula with untransformed abundance response.

method

Model family.

na_action

Missing-data handling for fitting.

model

A limpid_model.

newdata

Data frame for prediction.

interval

Prediction interval type.

level

Interval confidence level.

group

Grouping column for held-out folds.

Details

The default lognormal model uses a log1p response and Duan smearing for back-transformation. Grouped cross-validation defaults to Lake_ID to reduce spatial leakage.

Value

A limpid_model, prediction data frame, or cross-validation result list.

Examples

db <- load_limpid()
m <- model_mp_abundance(db, MP_Mean ~ Season_Global + Lake_Type)
cross_validate_mp(db, MP_Mean ~ Season_Global + Lake_Type)

Visualize freshwater microplastic patterns

Description

Seasonal, composition, depth and coordinate-based point visualizations.

Usage

plot_lake_map(data, value_col = "MP_Mean")

classify_hotspots(data, value_col = "MP_Mean", method = c("quantile", "robust_z"))

map_mp_hotspots(data, value_col = "MP_Mean", method = c("quantile", "robust_z"))

plot_seasonality(data, lake = NULL, value_col = "MP_Mean")

plot_morphology_profile(data, id_col = "Lake_Name")

plot_polymer_profile(data, id_col = "Lake_Name")

plot_depth_profile(data, depth_col = "Depth_m", value_col = "MP_Mean", group_col = NULL)

Arguments

data

A limpid_db or appropriate data frame.

value_col

Numeric indicator column.

method

Relative hotspot classification method.

lake

Optional lake name or identifier filter.

id_col

Identity/group column for profile bars.

depth_col

Depth column in metres.

group_col

Optional depth-profile grouping column.

Details

Hotspot functions provide relative point classification and do not imply spatial interpolation. Depth plotting requires genuinely depth-resolved data and will reject the bundled event-level database.

Value

A ggplot2 object, except classify_hotspots() which returns a data frame.

Descriptive and compositional microplastic analysis

Description

Summaries, event-level joins, composition closure, CLR transformation and Aitchison distance.

Usage

summarise_abundance(data, by = c("Lake_Name", "Season_Global"),
  value_col = "MP_Mean", conf_level = 0.95)

analyse_morphology(data, group_by = c("Lake_Name", "Season_Global"))

analyse_size_distribution(data, group_by = c("Lake_Name", "Season_Global"))

analyse_polymers(data, group_by = c("Lake_Name", "Season_Global"))

composition_closure(data, cols, tolerance = 0.2,
  action = c("flag", "renormalize", "error"))

clr_transform(data, cols, pseudocount = 1e-06)

aitchison_distance(data, cols, pseudocount = 1e-06)

build_model_data(data, include_environment = TRUE, primary_eligible_only = FALSE)

provenance_summary(data)

Arguments

data

A limpid_db or data frame, depending on function.

by

Grouping columns for abundance summaries.

value_col

Numeric abundance or indicator column.

conf_level

Confidence level for mean confidence intervals.

group_by

Optional composition grouping columns.

cols

Composition component columns.

tolerance

Allowed deviation from 100 percent.

action

Closure action.

pseudocount

Positive zero replacement on the proportion scale.

include_environment

Join environmental context into model data.

primary_eligible_only

Restrict to explicitly primary-model-eligible rows.

Value

A data frame, distance object, or named list, depending on function.

Examples

db <- load_limpid()
summarise_abundance(db)
analyse_polymers(db)