# 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, 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@gmail.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 check out the online
documentation, which includes examples of all of the most common
`gcplyr`

functions and walks through how to import,
manipulate, 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
design information
- Pre-processing
and plotting data
- Processing
data
- Analyzing
data
- Dealing
with noise
- Statistics,
merging other data, and other resources

## 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.