2. Data preprocessing

In quollr: Visualising How Nonlinear Dimension Reduction Warps Your Data

options(rmarkdown.html_vignette.check_title = FALSE)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE, 
  message = FALSE
)

Here, we'll walk through the process of preprocessing 2-D embedding data to obtain regular hexagons.

library(quollr)
library(dplyr)

First, you'll need 2-D embedding data generated for your high-dimensional data. For our example, we'll use a $3\text{-}D$ S-curve dataset with four additional noise dimensions (scurve). We've used UMAP as our non-linear dimension reduction method (NLDR) to generate embeddings for the scurve data.

scaled_umap <- gen_scaled_data(nldr_data = scurve_umap)

glimpse(scaled_umap)

The function gen_scaled_data() standardises the 2-D embedding and rescales it so that hexagons generated during visualisation or analysis will be regular.

quollr documentation built on Aug. 8, 2025, 6:08 p.m.