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What this package can do

gcplyr was created to make it easier to import, wrangle, and do model-free analyses of microbial growth curve data, as commonly output by plate readers.

  • gcplyr can flexibly import all the common data formats output by plate readers and reshape them into ‘tidy’ formats for analyses.
  • gcplyr can import experimental designs from files or directly in R, then merge this design information with density data.
  • This merged tidy-shaped data is then easy to work with and plot using functions from gcplyr and popular packages dplyr and ggplot2.
  • gcplyr can calculate plain and per-capita derivatives of density data.
  • gcplyr has several methods to deal with noise in density or derivatives data.
  • gcplyr can extract parameters like growth rate/doubling time, carrying capacity, diauxic shifts, extinction, and more without fitting an equation for growth to your data.

Please send all questions, requests, comments, and bugs to mikeblazanin [at] gmail [dot] com


You can install the most recently-released version from GitHub by running the following lines in R:


You can install the version most-recently released on CRAN by running the following line in R:

Getting Started

The best way to get started is to read through the articles series, which breaks down a typical workflow using gcplyr from start to finish, starting with the introduction:

  1. Introduction: vignette("gc01_gcplyr")
  2. Importing and transforming data: vignette("gc02_import_reshape")
  3. Incorporating experimental designs: vignette("gc03_incorporate_designs")
  4. Pre-processing and plotting your data: vignette("gc04_preprocess_plot")
  5. Processing your data: vignette("gc05_process")
  6. Analyzing your data: vignette("gc06_analyze")
  7. Dealing with noise: vignette("gc07_noise")
  8. Statistics, merging other data, and other resources: vignette("gc08_conclusion")
  9. Working with multiple plates: vignette("gc09_multiple_plates")


Please cite software as:

Blazanin, Michael. 2023. gcplyr: an R package for microbial growth curve data analysis. bioRxiv doi: 10.1101/2023.04.30.538883.