Principal component analysis (PCA) reduces the dimensionality ofmultivariate data, to two or three that can be visualized graphically with minimal loss of information. fviz_pca() provides ggplot2-based elegant visualization of PCA outputs from: i) prcomp and princomp [in built-in R stats], ii) PCA [in FactoMineR], iii) dudi.pca [in ade4] and epPCA [ExPosition]. Read more: PrincipalComponent Analysis
Note that, fviz_pca_xxx() functions are wrapper arround the corefunction fviz(), whih is also a wrapper arround thefunction ggscatter() [in ggpubr]. Therfore, further arguments, to bepassed to the function fviz() and ggscatter(), can be specified infviz_pca_ind() and fviz_pca_var().
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fviz_pca(X, ...)fviz_pca_ind( X, axes = c(1, 2), geom = c("point", "text"), geom.ind = geom, repel = FALSE, habillage = "none", palette = NULL, addEllipses = FALSE, col.ind = "black", fill.ind = "white", col.ind.sup = "blue", alpha.ind = 1, select.ind = list(name = NULL, cos2 = NULL, contrib = NULL), ...)fviz_pca_var( X, axes = c(1, 2), geom = c("arrow", "text"), geom.var = geom, repel = FALSE, col.var = "black", fill.var = "white", alpha.var = 1, col.quanti.sup = "blue", col.circle = "grey70", select.var = list(name = NULL, cos2 = NULL, contrib = NULL), ...)fviz_pca_biplot( X, axes = c(1, 2), geom = c("point", "text"), geom.ind = geom, geom.var = c("arrow", "text"), col.ind = "black", fill.ind = "white", col.var = "steelblue", fill.var = "white", gradient.cols = NULL, label = "all", invisible = "none", repel = FALSE, habillage = "none", palette = NULL, addEllipses = FALSE, title = "PCA - Biplot", ...)
Xan object of class PCA [FactoMineR]; prcomp and princomp [stats]; dudi and pca [ade4]; expOutput/epPCA [ExPosition].
...Additional arguments.
in fviz_pca_ind() and fviz_pca_var(): Additional arguments are passed to the functions fviz() andggpubr::ggpar().
in fviz_pca_biplot() and fviz_pca(): Additional arguments are passed to fviz_pca_ind() and fviz_pca_var().
axesa numeric vector of length 2 specifying the dimensions to be plotted.
geoma text specifying the geometry to be used for the graph. Allowed values are the combination of c("point", "arrow", "text"). Use "point" (to show only points); "text" to show only labels; c("point", "text") or c("arrow", "text") to show arrows and texts. Using c("arrow", "text") is sensible only for the graph of variables.
geom.ind, geom.varas geom but for individuals and variables, respectively. Default is geom.ind = c("point", "text), geom.var = c("arrow", "text").
repela boolean, whether to use ggrepel to avoid overplotting text labels or not.
habillagean optional factor variable for coloring the observations bygroups. Default value is "none". If X is a PCA object from FactoMineR package, habillage can also specify the supplementary qualitative variable (by its index or name) to be used for coloring individuals by groups (see ?PCA in FactoMineR).
palettethe color palette to be used for coloring or filling bygroups. Allowed values include "grey" for grey color palettes; brewerpalettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue","red"); and scientific journal palettes from ggsci R package, e.g.: "npg","aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and"rickandmorty". Can be also a numeric vector of length(groups); in thiscase a basic color palette is created using the functionpalette.
addEllipseslogical value. If TRUE, draws ellipses around the individuals when habillage != "none".
col.ind, col.varcolor for individuals and variables, respectively. Canbe a continuous variable or a factor variable. Possible values include also: "cos2", "contrib", "coord", "x" or "y". In this case, the colors for individuals/variables are automatically controlled by their qualities of representation ("cos2"), contributions ("contrib"), coordinates (x^2+y^2, "coord"), x values ("x") or y values ("y"). To use automatic coloring (by cos2, contrib, ....), make sure that habillage ="none".
fill.ind, fill.varsame as col.ind and col.var but for the fill color.
col.ind.supcolor for supplementary individuals
alpha.ind, alpha.varcontrols the transparency of individual and variable colors, respectively. The value can variate from 0 (total transparency) to 1 (no transparency). Default value is 1. Possible values include also : "cos2", "contrib", "coord", "x" or "y". In this case, the transparency for the individual/variable colors are automatically controlled by their qualities ("cos2"), contributions ("contrib"), coordinates (x^2+y^2, "coord"), x values("x") or y values("y"). To use this, make sure that habillage ="none".
select.ind, select.vara selection of individuals/variables to be drawn. Allowed values are NULL or a list containing the arguments name, cos2 or contrib:
name: is a character vector containing individuals/variables to be drawn
cos2: if cos2 is in [0, 1], ex: 0.6, then individuals/variables with a cos2 > 0.6 are drawn. if cos2 > 1, ex: 5, then the top 5 individuals/variables with the highest cos2 are drawn.
contrib: if contrib > 1, ex: 5, then the top 5 individuals/variables with the highest contrib are drawn
col.quanti.supa color for the quantitative supplementary variables.
col.circlea color for the correlation circle. Used only when X is a PCA output.
gradient.colsvector of colors to use for n-colour gradient. Allowedvalues include brewer and ggsci color palettes.
labela text specifying the elements to be labelled. Default value is "all". Allowed values are "none" or the combination of c("ind", "ind.sup", "quali", "var", "quanti.sup"). "ind" can be used to label only active individuals. "ind.sup" is for supplementary individuals. "quali" is for supplementary qualitative variables. "var" is for active variables. "quanti.sup" is for quantitative supplementary variables.
invisiblea text specifying the elements to be hidden on the plot. Default value is "none". Allowed values are the combination of c("ind", "ind.sup", "quali", "var", "quanti.sup").
titlethe title of the graph
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# Principal component analysis# ++++++++++++++++++++++++++++++data(iris)res.pca <- prcomp(iris[, -5], scale = TRUE)# Graph of individuals# +++++++++++++++++++++# Default plot# Use repel = TRUE to avoid overplotting (slow if many points)fviz_pca_ind(res.pca, col.ind = "#00AFBB", repel = TRUE) # 1. Control automatically the color of individuals # using the "cos2" or the contributions "contrib" # cos2 = the quality of the individuals on the factor map# 2. To keep only point or text use geom = "point" or geom = "text".# 3. Change themes using ggtheme: http://www.sthda.com/english/wiki/ggplot2-themesfviz_pca_ind(res.pca, col.ind="cos2", geom = "point", gradient.cols = c("white", "#2E9FDF", "#FC4E07" ))# Color individuals by groups, add concentration ellipses# Change group colors using RColorBrewer color palettes# Read more: http://www.sthda.com/english/wiki/ggplot2-colors# Remove labels: label = "none".fviz_pca_ind(res.pca, label="none", habillage=iris$Species, addEllipses=TRUE, ellipse.level=0.95, palette = "Dark2") # Change group colors manually# Read more: http://www.sthda.com/english/wiki/ggplot2-colorsfviz_pca_ind(res.pca, label="none", habillage=iris$Species, addEllipses=TRUE, ellipse.level=0.95, palette = c("#999999", "#E69F00", "#56B4E9")) # Select and visualize some individuals (ind) with select.ind argument. # - ind with cos2 >= 0.96: select.ind = list(cos2 = 0.96) # - Top 20 ind according to the cos2: select.ind = list(cos2 = 20) # - Top 20 contributing individuals: select.ind = list(contrib = 20) # - Select ind by names: select.ind = list(name = c("23", "42", "119") ) # Example: Select the top 40 according to the cos2fviz_pca_ind(res.pca, select.ind = list(cos2 = 40)) # Graph of variables# ++++++++++++++++++++++++++++ # Default plotfviz_pca_var(res.pca, col.var = "steelblue") # Control variable colors using their contributionsfviz_pca_var(res.pca, col.var = "contrib", gradient.cols = c("white", "blue", "red"), ggtheme = theme_minimal()) # Biplot of individuals and variables# ++++++++++++++++++++++++++# Keep only the labels for variables# Change the color by groups, add ellipsesfviz_pca_biplot(res.pca, label = "var", habillage=iris$Species, addEllipses=TRUE, ellipse.level=0.95, ggtheme = theme_minimal())
