PCA Visualization in ggplot2

How to do PCA Visualization in ggplot2 with Plotly.

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ggfortify lets ggplot2 know how to interpret PCA objects. After loading ggfortify, you can use ggplot2::autoplot function for stats::prcomp and stats::princomp objects.

Default plot

library(plotly)
library(ggfortify)

df <- iris[1:4]
pca_res <- prcomp(df, scale. = TRUE)

p <- autoplot(pca_res)

ggplotly(p)

PCA result should only contains numeric values. If you want to colorize by non-numeric values which original data has, pass original data using data keyword and then specify column name by colour keyword. Use help(autoplot.prcomp) (or help(autoplot.*) for any other objects) to check available options.

library(plotly)
library(ggfortify)

df <- iris[1:4]
pca_res <- prcomp(df, scale. = TRUE)

p <- autoplot(pca_res, data = iris, colour = 'Species')

ggplotly(p)

Adding data labels

Passing label = TRUEdraws each data label using rownames

library(plotly)
library(ggfortify)

df <- iris[1:4]
pca_res <- prcomp(df, scale. = TRUE)

p <- autoplot(pca_res, data = iris, colour = 'Species', label = TRUE, label.size = 3)

ggplotly(p)

Passing shape = FALSE makes plot without points. In this case, label is turned on unless otherwise specified.

library(plotly)
library(ggfortify)

df <- iris[1:4]
pca_res <- prcomp(df, scale. = TRUE)

p <- autoplot(pca_res, data = iris, colour = 'Species', shape = FALSE, label.size = 3)

ggplotly(p)

Displaying eigenvectors.

Passing loadings = TRUE draws eigenvectors.

library(plotly)
library(ggfortify)

df <- iris[1:4]
pca_res <- prcomp(df, scale. = TRUE)

p <- autoplot(pca_res, data = iris, colour = 'Species', loadings = TRUE)

ggplotly(p)

You can attach eigenvector labels and change some options.

library(plotly)

df <- iris[1:4]
pca_res <- prcomp(df, scale. = TRUE)

p <- autoplot(pca_res, data = iris, colour = 'Species',
              loadings = TRUE, loadings.colour = 'blue',
              loadings.label = TRUE, loadings.label.size = 3)

ggplotly(p)

What About Dash?

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash for R at https://dashr.plot.ly/installation.

Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)