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PhotosynQ | R

Truly Collaborative Plant Research

PhotosynQ helps you to make your plant research more efficient. For an advanced analysis, this package allows to pull data from projects right into R. We recommend to use it with RStudio.

Installation

If you don’t already have, install RStudio and R first.

From Package Archive File

Download the latest release of the PhotosynQ R package. Select the file indicated as Source code (tar.gz). This is the format required by RStudio.

  1. Open RStudio
  2. Select Tools from the menu and click on Install Packages.
  3. Select Install from: Package Archive File (.tgz; .tar-gz)
  4. Package archive: Click on Browse… and select the downloaded file.
  5. Click on Install to finish the installation and close the dialog.

Development version with devtools

For users that already have a development environment, devtools provides an easy installation from the GitHub repository.

  1. Open RStudio
  2. Install the release version of devtools from CRAN with install.packages("devtools")
  3. Make sure you have a working development environment.
  4. Install the development version of PhotosynQ-R: devtools::install_github("PhotosynQ/PhotosynQ-R")

Getting started

Create a list of data frames in a single step from the data of a Project. Each frame in the list represents one measurement protocol. A user account for PhotosynQ is required to access the data. You will find the ID of your project on the project page.

PhotosynQ::login("john.doe@domain.com")
ID <- 1556
dfs <- PhotosynQ::getProject(ID)

Preparing the data for analysis

The flagged measurements are included in the dataset and most likely needs to be removed for further analysis. You can use the filter() function of the dplyr library to remove the flagged measurement from the data frame. You might want to use the same function to select a subset of measurement from your data frame.

# Select a Protocol from the List of Data Frames
df <- dfs$`Protocol Name`

# View the Protocol Output
View(df)

# Filter out flagged data
library(dplyr)
df_filtered <- filter(df, status == "submitted")

Separate Functions

Login

email <- "john.doe@domain.com"
login <- PhotosynQ::login(email)

Logout

PhotosynQ::logout()

Get Project Information

ID <- 1556
project_info <- PhotosynQ::getProjectInfo(ID)

Get Project Data

ID <- 1556
project_data <- PhotosynQ::getProjectData(ID)

# Use raw data
processed_data <- FALSE
project_data <- PhotosynQ::getProjectData(ID, processed_data)

Create a Data frame

dataframe <- PhotosynQ::createDataframe(project_info, project_data)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.