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 by Wajid Jawaid.
For more tidy data analysis:
- tidyomics - A software ecosystem for tidy analysis of omic data.
- tidyHeatmap - Produce heatmaps with tidy principles.
Installation
# 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.
library(dplyr)
library(ggplot2)
library(stringr)
library(readr)
library(tidygate)
mtcars |>
head()
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
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.
mtcars_gated <-
mtcars |>
mutate(gated = gate(x = mpg, y = wt, colour = disp))