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README.Rmd
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---
title: "tidygate: add gate information to your tibble"
output: github_document
---
<!---
[](https://travis-ci.org/stemangiola/tidygate) [](https://coveralls.io/github/stemangiola/tidygate?branch=master)
-->
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html)
<!-- badges: end -->
```{r setup, echo=FALSE, include=FALSE}
library(knitr)
library(tidygate)
library(dplyr)
library(ggplot2)
library(stringr)
library(readr)
knitr::opts_chunk$set(cache = TRUE, warning = FALSE, message = FALSE, cache.lazy = FALSE)
```
## Introduction
tidygate allows you to interactively gate points on a scatter plot. Interactively drawn gates are recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is based on the package [gatepoints](https://github.com/wjawaid/gatepoints) by Wajid Jawaid.
For more tidy data analysis:
- [tidyomics](https://github.com/tidyomics) - A software ecosystem for tidy analysis of omic data.
- [tidyHeatmap](https://github.com/stemangiola/tidyHeatmap) - Produce heatmaps with tidy principles.
## Installation
```{r, eval=FALSE}
# From Github
devtools::install_github("stemangiola/tidygate")
# From CRAN
install.package("tidygate")
```
## Example usage
tidygate provides a single user-facing function: `gate`. The following examples make use of this function, four packages from the tidyverse and the inbuilt `mtcars` dataset.
```{r}
library(dplyr)
library(ggplot2)
library(stringr)
library(readr)
library(tidygate)
mtcars |>
head()
```
By default, `gate` creates an interactive scatter plot based on user-defined X and Y coordinates. Colour, shape, size and alpha can be defined as constant values, or can be controlled by values in a specified column.
Once the plot has been created, multiple gates can be drawn with the mouse. When you have finished, click continue. `gate` will then return a vector of strings, recording the gates each X and Y coordinate pair is within.
```{r eval=FALSE}
mtcars_gated <-
mtcars |>
mutate(gated = gate(x = mpg, y = wt, colour = disp))
```
```{r, echo=FALSE}
load("data/demo_gate_data.rda")
# Load pre-recorded brush path from data for example
tidygate_env <<- rlang::env()
tidygate_env$gates <- demo_gate_data
mtcars_gated <-
mtcars |>
mutate(gated = gate(x = mpg, y = wt, programmatic_gates = tidygate_env$gates))
```
To select points which appear within any gates, filter for non-NA values. To select points which appear within a specific gate, string pattern matching can be used.
```{r}
# Select points within any gate
mtcars_gated |>
filter(!is.na(gated))
# Select points within gate 2
mtcars_gated |>
filter(str_detect(gated, "2"))
```
Details of the interactively drawn gates are saved to `tidygate_env$gates`. This variable is overwritten each time interactive gates are drawn, so save it right away if you would like to access it later.
```{r}
# Inspect previously drawn gates
tidygate_env$gates |>
head()
```
```{r, eval=FALSE}
# Save if needed
tidygate_env$gates |>
write_rds("important_gates.rds")
```
If previously drawn gates are supplied to the `programmatic_gates` argument, points will be gated programmatically. This feature allows the reproduction of previously drawn interactive gates.
```{r, eval=FALSE}
important_gates <-
read_rds("important_gates.rds")
mtcars |>
mutate(gated = gate(x = mpg, y = wt, programmatic_gates = important_gates)) |>
filter(!is.na(gated))
```
```{r, echo=FALSE}
mtcars |>
mutate(gated = gate(x = mpg, y = wt, programmatic_gates = tidygate_env$gates)) |>
filter(!is.na(gated))
```