Abstract Characterization of microbial growth is of both fundamental and applied interest. Modern platforms can automate collection of high-throughput microbial growth curves, necessitating the development of computational tools to handle and analyze these data to produce insights. However, existing tools are limited. Many use parametric analyses that require mathematical assumptions about the microbial growth characteristics. Those that use non-parametric or model-free analyses often can only quantify a few traits of interest, and none are capable of importing and reshaping all known growth curve data formats. To address this gap, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in every known format, and reshape it under a flexible and extendable framework so that users can design custom analyses or plot data with popular visualization packages. gcplyr can also incorporate metadata and generate or import experimental designs to merge with data. Finally, gcplyr carries out model-free and non-parametric analyses, extracting a broad range of clinically and ecologically important traits, including initial density, lag time, growth rate and doubling time, carrying capacity, diauxie, area under the curve, extinction time, and more. In sum, gcplyr makes scripted analysis of growth curve data in R straightforward, streamlines common data wrangling and analysis steps, and easily integrates with common visualization and statistical analyses.

gcplyr: an R package for microbial growth curve data analysis