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https://github.com/BIO-RSG/PhytoFit

Shiny app for viewing satellite chla and modelling phytoplankton blooms
https://github.com/BIO-RSG/PhytoFit

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Shiny app for viewing satellite chla and modelling phytoplankton blooms

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README 

        
# PhytoFit



[![DOI](https://zenodo.org/badge/277295931.svg)](https://zenodo.org/badge/latestdoi/277295931)



This app can be used to display satellite chlorophyll concentration, and calculate statistics and model phytoplankton blooms for regions within custom polygons. See below for example in screen capture.  





screencap




### How to cite



In publications, please include acknowledgements to [NASA OBPG](https://oceancolor.gsfc.nasa.gov) for the satellite data and the [BIO remote sensing group](https://github.com/BIO-RSG) for the application, and use this citation in the references:  



*Stephanie Clay, Chantelle Layton, & Emmanuel Devred. (2021). BIO-RSG/PhytoFit: First release (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.4770754*  



BibTeX format:  



    @misc{clay21,

      author       = {Clay, Stephanie and Layton, Chantelle and Devred, Emmanuel},

      title        = "PhytoFit",

      howpublished = "\url{https://github.com/BIO-RSG/PhytoFit}",

      year         = 2021

    }



## Prerequisites



1. Install the latest versions of R and RStudio.



2. Install the necessary packages:

```r

install.packages(c("fst", "shiny", "shinyWidgets", "shinyjs", "shinybusy", "leaflet", "stars", "leafem", "leafpm", "quantreg", "minpack.lm", "sp", "ggplot2", "ggpp", "dplyr", "tidyr", "raster", "RCurl", "sf", "fs"))

remotes::install_github("BIO-RSG/oceancolouR")

# if the line above doesn't work, try devtools::install_github("BIO-RSG/oceancolouR")

# if that doesn't work, try either install.packages("remotes") or install.packages("devtools") and then run the oceancolouR installation line again

```



3. Restart R after the packages have been installed.



## Getting started



1. Download this repository one of two ways:  



- Option 1: Code --> Download ZIP  



- Option 2: Using git (this will make it easier to download updates in the future, by simply using the `git pull` command): Open git bash terminal, navigate to the folder where you want to download the repository, and type: `git clone https://github.com/BIO-RSG/PhytoFit.git`  



2. Open the PhytoFit repository in RStudio:  



- File --> Open Project --> Navigate to the PhytoFit folder and open "PhytoFit.Rproj"  



3. Download the datasets of your choice:  



- Open `00_download_new_datasets.R` from the PhytoFit folder. Set *ask_user=FALSE* to download all available datasets, or *ask_user=TRUE* to ask before downloading each one. Alternatively, you can run the script from the command line like:  `Rscript [script directory]/00_download_new_datasets.R 'false'`, filling in the [script directory] with the location where you stored the script. *'false'* is the ask_user argument, set to *'true'* for prompts.    



4. To update existing datasets:  



- Similar to the download script in step 3, open `00_update_datasets.R` and set the *ask_user* argument, or run from the command line (e.g. `Rscript [script directory]/00_update_datasets.R 'false'`. This will update the datasets you have already downloaded with the most recent copies (and download any years of data missing from your local directory).  



**WARNINGS:**  

- Data files will be downloaded to `data/[region]/` subfolders of the PhytoFit repository - Do NOT move them from there or the app will not be able to read them.  

- If possible, please keep the data files if you intend to use them in the future, rather than re-downloading them later, to avoid excessive traffic on the ftp server.  

- Any data that is < 3 months old is "Near Real Time" (NRT) quality. NRT data is replaced with "Science quality" data after it becomes available, following the 3-month lag. More info here.  



## Running



Open app.R within RStudio, and click "Run app"



## Authors



* **Chantelle Layton** - *Initial concept, preliminary design, coding, and algorithm development/improvements*  

* **Stephanie Clay** - *Final app design and modifications, feature addition, new datasets, maintenance, and algorithm improvements*  

* **Emmanuel Devred** - *Scientific support, algorithm development/improvements, review and feature recommendations*  



## Acknowledgments



* **Andrea Hilborn** for many valuable suggestions



## Links



[User guide](https://github.com/BIO-RSG/PhytoFit/blob/master/USERGUIDE.md) (In progress)  

[Chl-a model performance evaluation](https://bio-rsg.github.io/chla_model_performance_summary.html)  

[References and data sources](https://github.com/BIO-RSG/PhytoFit/blob/master/USERGUIDE.md#references-and-data-sources)  

[Using the raw (binned) data](https://github.com/BIO-RSG/PhytoFit/blob/master/fst_tutorial.md) (This is a quick tutorial explaining how the raw satellite chlorophyll data used in PhytoFit can be read into R and manipulated for other purposes)  

[Code updates affecting the algorithms](https://github.com/BIO-RSG/PhytoFit/blob/master/updates.md) (Summary of updates that affected the way the bloom metrics are calculated)