transferice
The goal of transferice is to reconstruct past oceanographic conditions using fossils. All steps of data selection, model construction, and final predictions are implemented in a shiny (Chang et al. 2021) interface to provide a visual representation of the machine learning pipeline.
Installation
You can install the development version of transferice from GitHub with devtools:
# Install tranferice from GitHub:
# install.packages("devtools")
devtools::install_github("UtrechtUniversity/transferice")Shiny app
Run the app as follows:
# load package
library(transferice)
# run app
transferice_app()Funding
This project was funded by ERC Starting grant number 802835, OceaNice, awarded to Peter Bijl.
Credits
The construction of the R (R Core Team 2022) package transferice and associated documentation was aided by the packages; devtools (Wickham et al. 2021), roxygen2 (Wickham, Danenberg, et al. 2022), testthat (Wickham 2022), knitr (Xie 2014 ; Xie 2015), rmarkdown (Xie, Allaire, and Grolemund 2018; Xie, Dervieux, and Riederer 2020), and the superb guidance in the book: R packages: organize, test, document, and share your code, by Wickham (2015).
Data transformation, cleaning and visualization is performed with: dplyr (Wickham, François, et al. 2022), tibble (Müller and Wickham 2022), stringr (Wickham 2019), and rlang (Henry and Wickham 2022).
The app is build with shiny (Chang et al. 2021) and the guidance in the book: Mastering Shiny: Build Interactive Apps, Reports & Dashboards (Wickham 2020) was a great help in learning how to develop such applications. Furthermore, the packages shinyjs (Attali 2021), shinyWidgets (Perrier, Meyer, and Granjon 2022), shinycssloaders (Sali and Attali 2020), bslib (Sievert and Cheng 2021) and thematic (Sievert, Schloerke, and Cheng 2021) ensure user friendliness and visually pleasing graphics.
References
Attali, Dean. 2021. Shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds. https://deanattali.com/shinyjs/.
Chang, Winston, Joe Cheng, JJ Allaire, Carson Sievert, Barret Schloerke, Yihui Xie, Jeff Allen, Jonathan McPherson, Alan Dipert, and Barbara Borges. 2021. Shiny: Web Application Framework for r. https://shiny.rstudio.com/.
Henry, Lionel, and Hadley Wickham. 2022. Rlang: Functions for Base Types and Core r and Tidyverse Features. https://CRAN.R-project.org/package=rlang.
Müller, Kirill, and Hadley Wickham. 2022. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.
Perrier, Victor, Fanny Meyer, and David Granjon. 2022. shinyWidgets: Custom Inputs Widgets for Shiny. https://CRAN.R-project.org/package=shinyWidgets.
R Core Team. 2022. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Sali, Andras, and Dean Attali. 2020. Shinycssloaders: Add Loading Animations to a Shiny Output While It’s Recalculating. https://github.com/daattali/shinycssloaders.
Sievert, Carson, and Joe Cheng. 2021. Bslib: Custom Bootstrap ’Sass’ Themes for Shiny and Rmarkdown. https://CRAN.R-project.org/package=bslib.
Sievert, Carson, Barret Schloerke, and Joe Cheng. 2021. Thematic: Unified and Automatic Theming of Ggplot2, Lattice, and Base r Graphics. https://CRAN.R-project.org/package=thematic.
Wickham, Hadley. 2015. R Packages: Organize, Test, Document, and Share Your Code. O’Reilly Media, Inc. https://r-pkgs.org/.
———. 2019. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.
———. 2020. Mastering Shiny: Build Interactive Apps, Reports & Dashboards. O’Reilly Media, Inc. https://mastering-shiny.org/.
———. 2022. Testthat: Unit Testing for r. https://CRAN.R-project.org/package=testthat.
Wickham, Hadley, Peter Danenberg, Gábor Csárdi, and Manuel Eugster. 2022. Roxygen2: In-Line Documentation for r. https://CRAN.R-project.org/package=roxygen2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2022. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, Jim Hester, Winston Chang, and Jennifer Bryan. 2021. Devtools: Tools to Make Developing r Packages Easier. https://CRAN.R-project.org/package=devtools.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595.
———. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.
Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown.
Xie, Yihui, Christophe Dervieux, and Emily Riederer. 2020. R Markdown Cookbook. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown-cookbook.