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waves

R package for Vis-NIR spectral analysis

https://github.com/gorelab/waves

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 6 DOI reference(s) in README
  • ○
    Academic publication links
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    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (17.1%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

R package for Vis-NIR spectral analysis

Basic Info
Statistics
  • Stars: 6
  • Watchers: 3
  • Forks: 6
  • Open Issues: 10
  • Releases: 8
Created over 6 years ago · Last pushed over 2 years ago
Metadata Files
Readme Contributing License

README.md

waves

language: R CRAN/METACRAN R-CMD-check

Originally designed application in the context of resource-limited plant research and breeding programs, waves provides an open-source solution to spectral data processing and model development by bringing useful packages together into a streamlined pipeline. This package is wrapper for functions related to the analysis of point visible and near-infrared reflectance measurements. It includes visualization, filtering, aggregation, pretreatment, cross-validation set formation, model training, and prediction functions to enable open-source association of spectral and reference data.

Please note: function names were updated as of version 0.2.0. Old function names still work in this version but will be retired in upcoming package versions.

Cite

This package is documented in a peer-reviewed manuscript in the Plant Phenome Journal. Please cite the manuscript if you have found this package to be useful!

Hershberger, J, Morales, N, Simoes, CC, Ellerbrock, B, Bauchet, G, Mueller, LA, Gore MA. Making waves in Breedbase: An integrated spectral data storage and analysis pipeline for plant breeding programs. Plant Phenome J. 2021; 4:e20012. https://doi.org/10.1002/ppj2.20012

Use

Follow the installation instructions below, and then go wild! Use waves to analyze your own data. Please report any bugs or feature requests by opening issues in this repository.

More detailed examples can be found in the package vignette. The vignette can also be found by running the following: r vignette("waves")

Installation

Install the latest waves release directly from CRAN: r install.packages("waves") Alternatively, install the development version to get the most up-to-date (but not necessarily thoroughly tested) version: ``` r

install.packages("devtools")

devtools::install_github("GoreLab/waves") ```

Overview

  1. Format your data. Match spectra with reference values so that you have a dataframe with unique identifiers, reference values, and other metadata as columns to the left of spectral values. Spectral column names should start with "X".

  2. Visualize and filter spectra using plot_spectra() and filter_spectra().

  3. If you have more than one scan per unique identifier, aggregate the scans by mean or median with aggregate_spectra().

  4. Use test_spectra() to perform spectral pretreatment, cross-validation set formation, and model training functions over multiple iterations.

    • Applies any of 12 combinations of spectral pretreatment methods using pretreat_spectra().
    • Determines cross-validation scheme with format_cv(). Choose from random, stratified random, or a plant breeding-specific scheme from Jarquín et al., 2017. The Plant Genome.
    • Trains spectral prediction models using train_spectra().
      • Choose from partial least squares regression, random forest, and support vector machine algorithms
      • Uses k-fold cross validation within the training set to tune model hyperparameters
      • Outputs model performance statistics (RMSE, R2, Bias, etc.) as assessed with test set

  5. Save trained prediction models with save_model().

    • Intended for a production environment
    • Can evaluate spectral pretreatment methods using the input dataset
    • Selects best model using the metric provided (RMSE or R2)
    • Returns trained model with option to save as .Rds object

  6. Predict phenotypic values with new spectra and a saved model using predict_spectra().

Examples

The package comes with an example dataset (ikeogu.2017) from Ikeogu et al. (2017) PLoS ONE that can be used to try out package capabilities. This dataset includes vis-NIR spectra from cassava roots as well as two reference phenotypes:

  • Root dry matter content as measured by the oven method for the four studies included in the example dataset
  • Total carotenoid content as measured by HPLC

Owner

  • Name: The Gore Lab
  • Login: GoreLab
  • Kind: organization
  • Location: Cornell University

GitHub Events

Total
  • Issues event: 1
Last Year
  • Issues event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 128
  • Total Committers: 3
  • Avg Commits per committer: 42.667
  • Development Distribution Score (DDS): 0.125
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Jenna Hershberger j****9@c****u 112
Jenna Hershberger 3****9 15
Leonardo Ramirez-Lopez r****o@g****m 1
Committer Domains (Top 20 + Academic)
cornell.edu: 1

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 21
  • Total pull requests: 7
  • Average time to close issues: 4 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 2
  • Total pull request authors: 3
  • Average comments per issue: 0.38
  • Average comments per pull request: 0.29
  • Merged pull requests: 7
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • jmh579 (20)
  • Abiodun-Olayinka (1)
Pull Request Authors
  • jmh579 (5)
  • l-ramirez-lopez (1)
  • olivroy (1)
Top Labels
Issue Labels
enhancement (11) bug (5)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 129 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 3
  • Total versions: 8
  • Total maintainers: 1
cran.r-project.org: waves

Vis-NIR Spectral Analysis Wrapper

  • Versions: 8
  • Dependent Packages: 0
  • Dependent Repositories: 3
  • Downloads: 129 Last month
Rankings
Forks count: 9.6%
Dependent repos count: 16.4%
Stargazers count: 21.9%
Average: 27.7%
Dependent packages count: 28.6%
Downloads: 61.8%
Maintainers (1)
jmh579@cornell.edu
Last synced: about 1 year ago

Dependencies

DESCRIPTION cran
  • R >= 3.5 depends
  • caret * imports
  • dplyr * imports
  • ggplot2 * imports
  • lifecycle * imports
  • magrittr * imports
  • pls * imports
  • prospectr * imports
  • randomForest * imports
  • readr * imports
  • rlang * imports
  • scales * imports
  • spectacles * imports
  • stringr * imports
  • tibble * imports
  • tidyr >= 1.0 imports
  • tidyselect * imports
  • knitr * suggests
  • rmarkdown * suggests
  • testthat >= 2.1.0 suggests