pca.Rmd

---
title: "Principle components analysis"
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(fig.align = "center")
knitr::opts_chunk$set(fig.height = 4)
knitr::opts_chunk$set(fig.width = 10)
```



?princomp



```{r}
data(iris)

# log transform 
log.ir <- log(iris[, 1:4])
ir.species <- iris[, 5]
 
# apply PCA - scale. = TRUE is highly 
# advisable, but default is FALSE. 
ir.pca <- prcomp(log.ir,
                 center = TRUE,
                 scale. = TRUE) 

```



```{r, fig.width=4}
# plot method
plot(ir.pca, type = "l")
```


```{r, fig.height=8}
biplot(ir.pca)

```


## Links

https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/    
http://rcharlie.com/2017-04-13-Coachella/