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.
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gcplyrcan flexibly import all the common data formats output by plate readers and reshape them into ‘tidy’ formats for analyses. -
gcplyrcan import experimental designs from files or directly inR, then merge this design information with density data. - This merged tidy-shaped data is then easy to work with and plot using functions from
gcplyrand popular packagesdplyrandggplot2. -
gcplyrcan calculate plain and per-capita derivatives of density data. -
gcplyrhas several methods to deal with noise in density or derivatives data. -
gcplyrcan 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
Installation
You can install the most recently-released version from GitHub by running the following lines in R:
install.packages("devtools")
devtools::install_github("mikeblazanin/gcplyr")You can install the version most-recently released on CRAN by running the following line in R:
install.packages("gcplyr")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:
- Introduction:
vignette("gcplyr") - Importing and transforming data:
vignette("import_transform") - Incorporating design information:
vignette("incorporate_designs") - Pre-processing and plotting your data:
vignette("preprocess_plot") - Processing your data:
vignette("process") - Analyzing your data:
vignette("analyze") - Dealing with noise:
vignette("noise") - Statistics, merging other data, and other resources:
vignette("conclusion")