11.PCoA.Rmd

---
title: "PCoA"
output: 
  pdf_document: 
    keep_tex: yes
---


```{r, setup, include=FALSE}
knitr::opts_chunk$set(message=FALSE, warning=FALSE, eval=FALSE, tidy.opts=list(width.cutoff=20), tidy=TRUE, wrap=TRUE)

```
In this section, we build beta diversity PCoA plots using the core metrics calculated in Qiime2.


Make any character vectors in your metadata into factors for plotting purposes.
```{r}
metadata$<variable1> <-factor(metadata$<variable1>)
metadata$<variable2> <-factor(metadata$<variable2>)
metadata$<variable3> <-factor(metadata$<variable3>)
```


Here we plot the Weighted UniFrac metric as an example in the PC-plots. 
 - Choose metadata variables for size and color differentiation. 
 - Choose the beta diversity metric and the principal components you want to plot.
 - Facet wrap by selected variable to combine plots.
```{r}
<xx_wunifrac>$data$Vectors %>%
  select(SampleID, PC1, PC2, PC3) %>% 
  left_join(metadata, by=c("SampleID"="<metadata sample ID>")) %>% 
  ggplot(aes(x=PC1, y=PC2, color=<variable1>, size=<variable2>)) +
  geom_point(alpha=0.5) +
  theme_q2r() +
  scale_size_discrete(name="<variable2>") +
  scale_color_viridis_d(name="<variable1>") +
  facet_wrap(.~<variable3>)
#ggsave("PCoA_xx_wu.jpg", height=4.5, width=5.5, device="jpg")
```