The goal of vprr is to process Video Plankton Recorder (VPR) data in R. This package allows for manual classification of plankton images, calculation of important ecological metrics such as concentration of plankton, data visualization, and data output with self-contained metadata.

Detailed information about vprr and its use can be found in the VPR_processing vignette.

## Installation

You can install the released version of vprr from CRAN with:

install.packages("vprr")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Echisholm21/vprr")

## Details

vprr is designed to be used after processing VPR data with Visual Plankton (VP), a Matlab image classification software. Although not dependent on any specific elements of VP, the data processing is designed around VP file formats and directory structures.

Visual Plankton code can be found: On GC code (for internal DFO users), within the dfo-mar-odis group, and ‘visual plankton’ project. For permission to view the project on GC code, contact .

Figure 1. VPR data processing flow chart. Blue boxes represent software, green and yellow boxes represent data sets, where yellow is visual data and green is text format data. This package represents ‘Processing and Visualization (R)’.

The first element of processing VPR data is to classify the images output. This can be done in VP, using machine learning techniques and then checked or manually edited in vprr. vprr uses a GUI function vpr_manual_classification to allow a user to review and change image classifications.

Figure 2. A screenshot from vprr manual reclassification. VPR images are displayed in the RStudio Viewer, prompts are displayed in the RStudio console. Users are asked in VP classifications are correct, if not, they are asked to select the proper classification from a pre set list of categories.

Once images have been properly classified, all data sources are combined in order to analyze data and calculate relevant environmental metrics such as plankton concentration. Data from CTD files (vpr_ctd_read) and image classification (vpr_autoid_read) can be read in and combined into easy-to-use data frames. vprr combines VPR CTD, and VPR image classifications into depth bins before calculating concentration (vpr_roi_concentration).

After data is processed, it can be visualized for easy interpretation. Although this package does not focus on plotting, it does provide some basic plotting structures for tow-yo VPR data (vpr_plot_contour).

Figure 3. An example of visualization of VPR data showing calculated concentration of Calanus along the VPR tow-yo path, over density contours.