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))
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.
# Select points within any gate
mtcars_gated |>
filter(!is.na(gated))
## mpg cyl disp hp drat wt qsec vs am gear carb gated
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 2
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 1,2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1,2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1,2
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1,2
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 1
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1,2
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 1
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 1
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 1
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 1,2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1
# Select points within gate 2
mtcars_gated |>
filter(str_detect(gated, "2"))
## mpg cyl disp hp drat wt qsec vs am gear carb gated
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 2
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 1,2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1,2
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1,2
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1,2
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1,2
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 1,2
Details of the interactively drawn gates are saved to tidygate_env$gates. This variable is overwritten each time interactive gates are drawn, so save it right away if you would like to access it later.
# Inspect previously drawn gates
tidygate_env$gates |>
head()
## # A tibble: 6 × 3
## x y .gate
## <dbl> <dbl> <dbl>
## 1 20.4 3.60 1
## 2 20.3 3.86 1
## 3 18.7 4.26 1
## 4 16.0 4.34 1
## 5 12.1 4.34 1
## 6 11.7 4.26 1
# 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.
important_gates <-
read_rds("important_gates.rds")
mtcars |>
mutate(gated = gate(x = mpg, y = wt, programmatic_gates = important_gates)) |>
filter(!is.na(gated))
## mpg cyl disp hp drat wt qsec vs am gear carb gated
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 2
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 1,2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1,2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1,2
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1,2
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 1
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1,2
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 1
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 1
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 1
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 1,2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1
sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-apple-darwin20
## Running under: macOS Sonoma 14.6.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Helsinki
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] readr_2.1.5 stringr_1.5.1 ggplot2_3.5.1 dplyr_1.1.4
## [5] tidygate_1.0.14 knitr_1.48
##
## loaded via a namespace (and not attached):
## [1] viridis_0.6.5 plotly_4.10.4 utf8_1.2.4 generics_0.1.3
## [5] tidyr_1.3.1 stringi_1.8.4 hms_1.1.3 digest_0.6.37
## [9] magrittr_2.0.3 evaluate_0.24.0 grid_4.4.1 RColorBrewer_1.1-3
## [13] fastmap_1.2.0 jsonlite_1.8.8 gridExtra_2.3 promises_1.3.0
## [17] httr_1.4.7 purrr_1.0.2 fansi_1.0.6 viridisLite_0.4.2
## [21] scales_1.3.0 codetools_0.2-20 lazyeval_0.2.2 cli_3.6.3
## [25] shiny_1.9.1 rlang_1.1.4 munsell_0.5.1 withr_3.0.1
## [29] tools_4.4.1 tzdb_0.4.0 colorspace_2.1-1 httpuv_1.6.15
## [33] vctrs_0.6.5 R6_2.5.1 mime_0.12 lifecycle_1.0.4
## [37] htmlwidgets_1.6.4 pkgconfig_2.0.3 pillar_1.9.0 later_1.3.2
## [41] gtable_0.3.5 data.table_1.16.0 glue_1.7.0 Rcpp_1.0.13
## [45] xfun_0.47 tibble_3.2.1 tidyselect_1.2.1 rstudioapi_0.16.0
## [49] xtable_1.8-4 htmltools_0.5.8.1 compiler_4.4.1