Installation

ggfortify can be installed from GitHub or CRAN:

# Github
if(!require(devtools)) install.packages("devtools")
devtools::install_github("sinhrks/ggfortify")

# CRAN
install.packages("ggfortify")

Loading ggfortify

library("ggfortify")

Plotting matrix

The function autoplot.matrix() is used:

autoplot(object, geom = "tile")

• object: an object of class matrix
• geom: allowed values are “tile” (for heatmap) or “point” (for scatter plot)

The *mtcars** data set is used in the example below.

df <- mtcars[, c("mpg", "disp", "hp", "drat", "wt")]
df <- as.matrix(df)

Plot a heatmap:

# Heatmap
autoplot(scale(df))

Plot a scatter plot: The data should be a matrix with 2 columns named V1 and V2. The R code below plots mpg by wt. We start by renaming column names.

# Extract the data
df2 <- df[, c("wt", "mpg")]
colnames(df2) <- c("V1", "V2")

# Scatter plot
autoplot(df2, geom = 'point') +
  labs(x = "mpg", y = "wt")

Plotting diagnostics for LM and GLM

The function autoplot.lm() is used to plot diagnostic plots for LM and GLM [in stats package].

autoplot(object, which = c(1:3, 5))

• object: stats::lm instance
• which: If a subset of the plots is required, specify a subset of the numbers 1:6.
• ncol and nrow allows you to specify the number of subplot columns and rows.

# Compute a linear model
m <- lm(Petal.Width ~ Petal.Length, data = iris)

# Create the plot
autoplot(m, which = 1:6, ncol = 2, label.size = 3)

# Change the color by groups (species)
autoplot(m, which = 1:6, label.size = 3, data = iris,
         colour = 'Species')

# Compute a generalized linear model
m <- glm(Murder ~ Assault + UrbanPop + Rape,
         family = gaussian, data = USArrests)

# Create the plot
# Change the theme and colour
autoplot(m, which = 1:6, ncol = 2, label.size = 3,
         colour = "steelblue") + theme_bw()

Plotting time series

autoplot(AirPassengers)

library(changepoint)
autoplot(cpt.meanvar(AirPassengers))

library(strucchange)
autoplot(breakpoints(Nile ~ 1))

Plotting PCA (Principal Component Analysis)

• Data set: iris
• Function: autoplot.prcomp()

# Prepare the data
df <- iris[, -5]

# Principal component analysis
pca <- prcomp(df, scale. = TRUE)

# Plot
autoplot(pca, loadings = TRUE, loadings.label = TRUE,
         data = iris, colour = 'Species')

Plotting K-means

• Data set: USArrests
• Function: autoplot.kmeans()

The original data is required as kmeans object doesn’t store original data. Samples will be colored by groups (clusters).

autoplot(kmeans(USArrests, 3), data = USArrests,
         label = TRUE, label.size = 3, frame = TRUE)

Plotting cluster package

ggfortify supports cluster::clara, cluster::fanny and cluster::pam classes. These functions return object containing original data, so there is no need to pass original data explicitly.

The R code below shows an example for pam() function:

library(cluster)
autoplot(pam(iris[-5], 3), frame = TRUE, frame.type = 'norm')

Plotting Local Fisher Discriminant Analysis

library(lfda)
# Local Fisher Discriminant Analysis (LFDA)
model <- lfda(iris[,-5], iris[, 5], 4, metric="plain")
autoplot(model, data = iris, frame = TRUE, frame.colour = 'Species')

Plotting survival curves

library(survival)
fit <- survfit(Surv(time, status) ~ sex, data = lung)
autoplot(fit)

Learn more

ggfortify

Infos

This analysis has been performed using R software (ver. 3.2.1) and ggplot2 (ver. 1.0.1)