tidygate: high-level data analysis and manipulation in tidyverse style

2024-09-17

Lifecycle:maturing

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:

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))