# gcplyr

## 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, maximum density (carrying capacity), lag time, area under the curve, diauxic shifts, extinction, and more without fitting an equation for growth to your data.

**Please send all questions, requests, comments, and bugs to mikeblazanin@gmail.com**

## Installation

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

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

## Getting Started

The best way to get started is to check out the online documentation, which includes examples of all of the most common `gcplyr`

functions and walks through how to import, reshape, and analyze growth curve data using `gcplyr`

from start to finish.

This documentation is also available as a series of pdf vignette files:

- Introduction
- Importing and transforming data
- Incorporating experimental designs
- Pre-processing and plotting data
- Processing data
- Analyzing data
- Dealing with noise
- Best practices and other tips
- Working with multiple plates
- Using make_design to generate experimental designs

## Citation

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.