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ggEDA

ggEDA streamlines exploratory data analysis by providing turnkey approaches to visualising n-dimensional data which can graphically reveal correlative or associative relationships between two or more features:

  • ggstack: tiled one-dimensional visualisations that more effectively show missingness and complex categorical relationships in smaller datasets.
  • ggparallel: parallel coordinate plots (PCPs) for examining large datasets with mostly quantitative features.

Installation

Development Version

You can install the development version of ggEDA from GitHub with:

if (!require("remotes"))
    install.packages("remotes")

remotes::install_github("CCICB/ggEDA")

Or from R-universe with:

install.packages("ggEDA", repos = "https://ropensci.r-universe.dev")

Quick Start

For examples of interactive EDA plots see the ggEDA gallery

# Load library
library(ggEDA)

# Plot data, sort by Glasses
ggstack(
  baseballfans,
  col_id = "ID",
  col_sort = "Glasses",
  interactive = FALSE,
  verbose = FALSE,
  options = ggstack_options(legend_nrow = 2)
)

Customise Colours

Customise colours by supplying a named list to the palettes argument

ggstack(
  baseballfans,
  col_id = "ID",
  col_sort = "Glasses",
  palettes = list("EyeColour" = c(
    Brown = "rosybrown4",
    Blue = "steelblue",
    Green = "seagreen"
  )),
  interactive = FALSE,
  verbose = FALSE,
  options = ggstack_options(legend_nrow = 2)
)

Parallel Coordinate Plots

For datasets with many observations and mostly numeric features, parallel coordinate plots may be more appropriate.

ggparallel(
 data = minibeans,
 col_colour = "Class",
 order_columns_by = "auto",
 interactive = FALSE
)
#> ℹ Ordering columns based on mutual information with [Class]

 ggparallel(
   data = minibeans,
   col_colour = "Class",
   highlight = "DERMASON",
   order_columns_by = "auto",
   interactive = FALSE
 )
#> ℹ Ordering columns based on how well they differentiate 1 group from the rest [DERMASON] (based on mutual information)

 ggparallel(
   data = minibeans,
   order_columns_by = "auto",
   interactive = FALSE
 )
#> ℹ To add colour to plot set `col_colour` to one of: Class
#> ℹ Ordering columns to minimise crossings
#> ℹ Choosing axis order via repetitive nearest neighbour with two-opt refinement

Community Contributions

All types of contributions are encouraged and valued. See our guide to community contributions for different ways to help.