README.md
In gg1d: Exploratory Data Analysis using Tiled One-Dimensional Graphics

gg1d

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Effortlessly visualize all columns in a data frame with vertically aligned plots and automatic plot selection based on variable type. Plots are fully interactive, and custom tooltips can be added.

Why 1 dimensional plots?

To understand trends in your data, especially correlative relationships between 2 or more features, it can be useful to densely stack visual representations of each feature vertically, regardless of data type. By unifying the $x$-axis across each plot, gg1d turns a series of 1D plots into an $n\text{-dimensional}$ visualization where $n = \text{number of columns in dataset}$. Note the key idea of gg1d is to ‘preserve the individual.’ gg1d does NOT plot distributions of properties, but rather each value of a feature for each subject/observation in the dataset.

gg1d can be used for exploratory data analysis (EDA) or to produce publication quality graphics summarizing a dataset.

install.packages("gg1d")

You can install the development version of gg1d from GitHub with:

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

remotes::install_github("selkamand/gg1d")

For examples of interactive gg1d plots see the gg1d gallery

# Load library
library(gg1d)

# Read data
path_gg1d <- system.file("example.csv", package = "gg1d")
df <- read.csv(path_gg1d, header = TRUE, na.strings = "")

# Plot data, sort by Glasses
gg1d(
  df,
  col_id = "ID",
  col_sort = "Glasses",
  interactive = FALSE,
  verbose = FALSE,
  options = gg1d_options(legend_nrow = 2)
)

Customise colours by supplying a named list to the palettes argument

gg1d(
  df,
  col_id = "ID",
  col_sort = "Glasses",
  palettes = list("EyeColour" = c(
    Brown = "rosybrown4",
    Blue = "steelblue",
    Green = "seagreen"
  )),
  interactive = FALSE,
  verbose = FALSE,
  options = gg1d_options(legend_nrow = 2)
)