Some of these packages may be hard to install, i.e. depending on system resources (especially rggobi).

library(tourr)
library(rggobi)
library(factoextra)
library(tidyverse)
library(ggalt)
library(rgl)

(ggobi has been archived: use remotes::install_version("rggobi", "2.1.22") or remotes::install_github("ggobi/rggobi"); see the github repository https://github.com/ggobi/rggobi and the ggobi web site http://ggobi.org/)

• scatterplots (point locations)
• traces (functions: parallel coordinates, Andrews plots)
• glyphs (stars/radar charts/faces/etc.)

• finding gestalt: focusing views (projection)
• posing queries: brushing/linking
• making comparisons: arranging many views

• adds a dimension (2D to 3D)
• smoothness/object continuity
• real-time user control: allows user to search/tune

ggobi(laser)

three components:

• data matrix (\(n \times p\))
• tour path that produces a smooth sequence of projection matrices (\(p \times d\)) (apropos("_tour$"))
• display method that renders the projected data (apropos("animate_"))

optional extras (for “guided tour”):

• search strategy (apropos("^search_"))
• index (objective) function (cmass, holes, lda_pp)

Goal: allow automated, systematic search through entire space of projections

f <- flea[, 1:6]
animate(f,
        ## tour_path = guided_tour(holes()),
        tour_path = grand_tour(),
        display = display_xy(col = flea$species))

apropos("animate_")

##  [1] "animate_andrews"   "animate_density2d" "animate_depth"    
##  [4] "animate_dist"      "animate_faces"     "animate_groupxy"  
##  [7] "animate_image"     "animate_pca"       "animate_pcp"      
## [10] "animate_sage"      "animate_scatmat"   "animate_slice"    
## [13] "animate_stars"     "animate_stereo"    "animate_trails"   
## [16] "animate_xy"

apropos("_tour$")

##  [1] "dependence_tour"     "frozen_guided_tour"  "frozen_tour"        
##  [4] "grand_tour"          "guided_section_tour" "guided_tour"        
##  [7] "little_tour"         "local_tour"          "new_tour"           
## [10] "planned_tour"

Examples/data from Holmes and Huber (2019)

• prcomp (SVD): preferred (also princomp)
• same data; projects in a way that (hopefully) focuses on interesting angles in the data
• assumes linear features
• normality not necessary, but better-behaved with normal data

load("../data/wine.RData")      ## numeric matrix (wine)
load("../data/wineClass.RData") ## metadata (wine.class)

winePCAd <- prcomp(wine, scale = TRUE) ## compute PCA
f0 <- fviz_pca_biplot(winePCAd, geom = "point", habillage = wine.class,
                      col.var = "violet", addEllipses = TRUE, ellipse.level = 0.69)
tmp_plot <- function(p) { ## tweaks
  p + ggtitle("") + coord_fixed() +
    scale_colour_brewer(palette="Dark2") +
    scale_fill_brewer(palette="Dark2")
}

print(tmp_plot(f0))

aug_data <- (get_pca_ind(winePCAd)$coord
  %>% as_tibble()
  %>% mutate(class = wine.class, .before=1)
)

f1 <- fviz_pca_biplot(winePCAd, geom = "point", habillage = wine.class,
   col.var = "violet")
f2 <- tmp_plot(f1) + ggalt::geom_encircle(data = aug_data,
                                aes(x=Dim.1,y=Dim.2,group=class,
                                    colour=class,
                                    fill=class),alpha=0.2,
                                show.legend=FALSE)

print(f2)

adjust_pcaplot <- function(p, pca, dims=1:2) {
  sdvec <- pca$sdev[dims]
  p + coord_fixed(ratio = sdvec[2]/sdvec[1])
}
f3 <- adjust_pcaplot(f2, winePCAd)

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

print(f3)

cvec <- RColorBrewer::brewer.pal("Dark2", n=3)
with(aug_data,
     plot3d(Dim.1, Dim.2, Dim.3, col=cvec[wine.class], size=4))
with(aug_data,
     plot3d(Dim.1, Dim.2, Dim.3, col=cvec[wine.class], size=2, type = "s"))