widget_scatterplots.Rmd

---
title: "3D scatterplots for PLNPCA"
author: "State of the R"
date: "24-28/08/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  screenshot.force = FALSE, 
  echo = TRUE,
  rows.print = 5,
  fig.width = 10, 
  fig.height = 10,
  message = FALSE, 
  warning = FALSE)
```

## Preliminaries

We will illustrate some widgets for 3D scatterplots  to enhance output of PCA in **PLNmodels**. The packages required for the analysis are **PLNmodels** plus some others for data manipulation and representation:

```{r dependencies}
library(PLNmodels)
library(tidyverse)
library(ggplot2)
```

Here are a collection of packages implementing various JS widgets:

```{r widgets}
library(DT)
library(scatterD3)
library(plotly)
library(edgebundleR)
library(threejs)
library(networkD3)
```

The _oaks amplicon data set_ at play gives the abundance of 114 taxa (66 bacterial OTU, 48 fungal OTUs) in 116 samples. For each sample, 11 additional covariates are known

```{r loading}
data(oaks)
datatable(oaks$Abundance[, 1:5] )
```

## PLNmodels Analyses 

### PCA analysis

We only fit two PCA models, which we know are in the right range for ICL

```{r principal component analysis, results='hide'}
my_PCAs <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = oaks, ranks = 27:28)
```

## Outputs and Vizualisation

### Principal Components Map 

We explore various scatterplot solutions to represent the individual factor map of the PLN PCA.

```{r data for scatterplot}
coord <- my_PCAs$getBestModel()$scores[, 1:3] %>% 
  as.data.frame() %>%   
  setNames(c("PC1", "PC2", "PC3")) %>% 
  add_column(tree = oaks$tree, names = rownames(oaks$Abundance))
```

#### ScatterD3 (Another fancy scatterplot)

```{r scatterD3}
scatterD3(data = coord, x = PC1, y = PC2, lab = names,
          col_var = tree, symbol_var = tree,
          xlab = "PC1", ylab = "PC2", col_lab = "tree",
          symbol_lab = "tree", lasso = TRUE)
```

#### Native plotly (3D scatterplot)

```{r plotly}
fig <- plot_ly(
  coord, x = ~PC1, y = ~PC2, z = ~PC3, color = ~tree, size = .35,
  text = ~paste('status:', tree), type = "scatter3d") %>% 
  layout(title = "Individual Factor Map of the Oaks powdery Mildew data set",
         scene = list(xaxis = list(title = 'PC1'),
                      yaxis = list(title = 'PC2'),
                      zaxis = list(title = 'PC3'))
  )
fig
```

#### threejs (3D scatterplot)

```{r threejs}
group <- rainbow(3)[as.numeric(oaks$tree)]
coord %>% select(1:3) %>% as.matrix() %>% 
scatterplot3js(col = group, size = 0.25, pch = ".", grid = FALSE, bg = "black")
```