---
title: "1. Data preprocessing"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{1. 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 2D embedding data to obtain regular hexagons.

```{r setup}
library(quollr)
library(dplyr)
```

First, you'll need 2D embedding data generated for your training data. For our example, we'll use a 3-$d$ S-curve dataset with four additional noise dimensions. We've used UMAP as our non-linear dimension reduction technique to generate embeddings for the S-curve data.

```{r}
scaled_umap <- gen_scaled_data(data = s_curve_noise_umap)

glimpse(scaled_umap)
```

`gen_scaled_data` function preprocesses the 2D embedding data to obtain regular hexagons.