---
title: "2. Data preprocessing"
output:
  rmarkdown::html_vignette:
    keep_md: true
vignette: >
  %\VignetteIndexEntry{2. Data preprocessing}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
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.

```{r setup}
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.

```{r}
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.