README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

options(tibble.print_min = 5, tibble.print_max = 5)
```

# miplicorn <a href='https://bailey-lab.github.io/miplicorn/'><img src='man/figures/logo.png' align="right" height="139" /></a>

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miplicorn establishes a unified analysis framework in R for molecular inversion
probe (MIP) and amplicon-targeted sequencing analysis after micro haplotyping or
variant calling. It provides tools for parsing large variant files, filtering
and manipulating the data, and basic analyses and visualization.

## Installation
You may install the package from
[Github](https://github.com/bailey-lab/miplicorn) using `devtools`.

::: .pkgdown-release
```{r, eval = FALSE}
# Install most recent released version
devtools::install_github("bailey-lab/miplicorn@v0.2.1")
```
:::

::: .pkgdown-devel
```{r, eval = FALSE}
# Install development version
devtools::install_github("bailey-lab/miplicorn")
```
:::

## Usage
See `vignette("miplicorn")` for a more extensive introduction and a
demonstration of several features of the package.
```{r usage, warning = FALSE}
library(miplicorn)

ref_file <- miplicorn_example("reference_AA_table.csv")
alt_file <- miplicorn_example("alternate_AA_table.csv")
cov_file <- miplicorn_example("coverage_AA_table.csv")

data <- read_tbl_ref_alt_cov(ref_file, alt_file, cov_file, gene == "atp6" | gene == "crt")
data

plot_coverage(data, mutation_name)

prev <- mutation_prevalence(data, threshold = 5)
prev

plot(prev)
```