Usage
The following shows some examples of creating biplots using the methods available with ggord. These methods were developed independently from the ggbiplot and factoextra packages, though the biplots are practically identical. Most methods are for results from principal components analysis, although methods are available for nonmetric multidimensional scaling, multiple correspondence analysis, correspondence analysis, and linear discriminant analysis. Available methods are as follows:
#> [1] ggord.acm* ggord.ca* ggord.capscale* ggord.cca*
#> [5] ggord.coa* ggord.dbrda* ggord.default* ggord.dpcoa*
#> [9] ggord.lda* ggord.mca* ggord.MCA* ggord.metaMDS*
#> [13] ggord.pca* ggord.PCA* ggord.ppca* ggord.prcomp*
#> [17] ggord.princomp* ggord.rda*
#> see '?methods' for accessing help and source code# principal components analysis with the iris data set # prcomp ord <- prcomp(iris[, 1:4]) p <- ggord(ord, iris$Species) p
library(ggplot2) p + scale_shape_manual('Groups', values = c(1, 2, 3))
p + theme_classic()
p + theme(legend.position = 'top')
# transparent ellipses p <- ggord(ord, iris$Species, poly = FALSE) p
# change linetype for transparent ellipses p <- ggord(ord, iris$Species, poly = FALSE, polylntyp = iris$Species) p
# convex hulls p <- ggord(ord, iris$Species, ellipse = FALSE, hull = TRUE) p
# change the vector labels with vec_lab new_lab <- list(Sepal.Length = 'SL', Sepal.Width = 'SW', Petal.Width = 'PW', Petal.Length = 'PL') p <- ggord(ord, iris$Species, vec_lab = new_lab) p
# observations as labels from row names p <- ggord(ord, iris$Species, obslab = TRUE) p
# map a variable to point sizes p <- ggord(ord, grp_in = iris$Species, size = iris$Sepal.Length, sizelab = 'Sepal\nlength') p
# change vector scaling, arrow length, line color, size, and type p <- ggord(ord, grp_in = iris$Species, arrow = 1, vec_ext = 3, veccol = 'red', veclsz = 1, vectyp = 'dotted') p
# change color of text labels on vectors, use ggrepel to prevent text overlap p <- ggord(ord, grp_in = iris$Species, labcol = 'purple', repel = TRUE) p
# faceted by group p <- ggord(ord, iris$Species, facet = TRUE, nfac = 1) p
# principal components analysis with the iris dataset # princomp ord <- princomp(iris[, 1:4]) ggord(ord, iris$Species)
# principal components analysis with the iris dataset # PCA library(FactoMineR) ord <- PCA(iris[, 1:4], graph = FALSE) ggord(ord, iris$Species)
# principal components analysis with the iris dataset # dudi.pca library(ade4) ord <- dudi.pca(iris[, 1:4], scannf = FALSE, nf = 4) ggord(ord, iris$Species)
# multiple correspondence analysis with the tea dataset # MCA data(tea, package = 'FactoMineR') tea <- tea[, c('Tea', 'sugar', 'price', 'age_Q', 'sex')] ord <- MCA(tea[, -1], graph = FALSE) ggord(ord, tea$Tea, parse = FALSE) # use parse = FALSE for labels with non alphanumeric characters
# multiple correspondence analysis with the tea dataset # mca library(MASS) ord <- mca(tea[, -1]) ggord(ord, tea$Tea, parse = FALSE) # use parse = FALSE for labels with non alphanumeric characters
# multiple correspondence analysis with the tea dataset # acm ord <- dudi.acm(tea[, -1], scannf = FALSE) ggord(ord, tea$Tea, parse = FALSE) # use parse = FALSE for labels with non alphanumeric characters
# nonmetric multidimensional scaling with the iris dataset # metaMDS library(vegan) ord <- metaMDS(iris[, 1:4]) ggord(ord, iris$Species)
# linear discriminant analysis # example from lda in MASS package ord <- lda(Species ~ ., iris, prior = rep(1, 3)/3) ggord(ord, iris$Species)
# correspondence analysis # dudi.coa ord <- dudi.coa(iris[, 1:4], scannf = FALSE, nf = 4) ggord(ord, iris$Species)
# double principle coordinate analysis (DPCoA) # dpcoa library(ade4) data(ecomor) grp <- rep(c("Bu", "Ca", "Ch", "Pr"), each = 4) # sample groups dtaxo <- dist.taxo(ecomor$taxo) # taxonomic distance between species ord <- dpcoa(data.frame(t(ecomor$habitat)), dtaxo, scan = FALSE, nf = 2) ggord(ord, grp_in = grp, ellipse = FALSE, arrow = 0.2, txt = 3)
# phylogenetic PCA # ppca library(adephylo) library(phylobase) library(ape) data(lizards) # example from help file, adephylo::ppca # original example from JOMBART ET AL 2010 # build a tree and phylo4d object liz.tre <- read.tree(tex=lizards$hprA) liz.4d <- phylobase::phylo4d(liz.tre, lizards$traits) # remove duplicated populations liz.4d <- phylobase::prune(liz.4d, c(7,14)) # correct labels lab <- c("Pa", "Ph", "Ll", "Lmca", "Lmcy", "Phha", "Pha", "Pb", "Pm", "Ae", "Tt", "Ts", "Lviv", "La", "Ls", "Lvir") tipLabels(liz.4d) <- lab # remove size effect dat <- tdata(liz.4d, type="tip") dat <- log(dat) newdat <- data.frame(lapply(dat, function(v) residuals(lm(v~dat$mean.L)))) rownames(newdat) <- rownames(dat) tdata(liz.4d, type="tip") <- newdat[,-1] # replace data in the phylo4d object # create ppca liz.ppca <- ppca(liz.4d,scale=FALSE,scannf=FALSE,nfposi=1,nfnega=1, method="Abouheif") # plot ggord(liz.ppca)
# distance-based redundancy analysis # dbrda from vegan data(varespec) data(varechem) ord <- dbrda(varespec ~ N + P + K + Condition(Al), varechem, dist = "bray") ggord(ord)
# distance-based redundancy analysis # capscale from vegan ord <- capscale(varespec ~ N + P + K + Condition(Al), varechem, dist = "bray") ggord(ord)
# species points as text # suppress site points ggord(ord, ptslab = TRUE, size = NA, addsize = 5, parse = TRUE)
