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quollr1dataprocessing.Rmd
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quollr1dataprocessing.Rmd
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---
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.