This app is a web-based tool that allows researchers to analyze net CO2 assimilation rate acrossed a light-dark transient (i.e., post illumination burst; PIB). The app allows you to upload a CSV file, check data quality, and estimate five unique photosynthetic parameters from the PIB data. The app is built on the R programming language and the Shiny web framework.
The app is hosted at https://l-gregory.shinyapps.io/pib_analyzer/ with 25 active hours per month.
To run the app locally, you need to have R (version 4.0.0 or higher) and RStudio installed on your computer. You also need to install the following packages:
shiny - The web framework used shinythemes - A package used for customizing the appearance of the app dplyr - A collection of R packages used for data wrangling ggplot2 - A package used for data visualization purrr - A package that contains tools for working wih vectors
You can install these packages by running the following command in R:
install.packages(c("shiny", "shinythemes", "dplyr", "ggplot2", "purrr"))
To install the app, you can download the code from the GitHub repository (do the following command in the terminal, after setting the desired directory):
git clone https://github.com/L-gregory/PIB_analyzer.git
If the script has run successfully, a new folder titled "PIB_analyzer" should be located in the desired directory
Open the folder and open the ShinyApp.R file in RStudio. Click the "Run App" button in the top right corner of the script editor window. This will launch the app in a new window.
In the "Upload Data" tab, you can upload your CSV file and select the columns containing the time and net assimilation rate data. You can also view a table of the selected data and a plot of the original data.
If you do not have your own data to upload, you can use the provided demo data by clicking on the "Load Demo Data" checkbox.
After checking for data quality, select the "Fit" button. If the fitting is successful, the plot will updated with the linear regression overtop of the steady state dark respiration, and a table of fit parameters will be displayed.
If yu are unhappy with the fitting, you can adjust the number of values used in the linear regession (default = 50).
The App was developed by Luke Gregory and Mauricio Tejera-Nieves under the supervision of Berkley Walker at Michigan State University.