GGally R package: Extension to ggplot2 for correlation matrix and survival plots - R software and data visualization


GGally extends ggplot2 by providing several functions including:

  • ggcor(): for pairwise correlation matrix plot
  • ggpairs(): for scatterplot plot matrix
  • ggsurv(): for survival plot

Installation

GGally can be installed from GitHub or CRAN:

# Github
if(!require(devtools)) install.packages("devtools")
devtools::install_github("ggobi/ggally")
# CRAN
install.packages("GGally")

Loading GGally package

library("GGally")

ggcorr(): Plot a correlation matrix

The function ggcorr() draws a correlation matrix plot using ggplot2.

The simplified format is:

ggcorr(data, palette = "RdYlGn", name = "rho", 
       label = FALSE, label_color = "black",  ...)

  • data: a numerical (continuous) data matrix
  • palette: a ColorBrewer palette to be used for correlation coefficients. Default value is “RdYlGn”.
  • name: a character string used for legend title.
  • label: logical value. If TRUE, the correlation coefficients are displayed on the plot.
  • label_color: color to be used for the correlation coefficient


The function ggcorr() can be used as follow:

# Prepare some data
df <- mtcars[, c(1,3,4,5,6,7)]
# Correlation plot
ggcorr(df, palette = "RdBu", label = TRUE)

ggplot2 and ggally - R software and data visualization

Read also: ggplot2 correlation matrix heatmap

ggpairs(): ggplot2 matrix of plots

The function ggpairs() produces a matrix of scatter plots for visualizing the correlation between variables.

The simplified format is:

ggpairs(data, columns = 1:ncol(data), title = "",  
  axisLabels = "show", columnLabels = colnames(data[, columns]))

  • data: data set. Can have both numerical and categorical data.
  • columns: columns to be used for the plots. Default is all columns.
  • title: title for the graph
  • axisLabels: Allowed values are either “show” to display axisLabels, “internal” for labels in the diagonal plots, or “none” for no axis labels
  • columnLabels: label names to be displayed. Defaults to names of columns being used.


ggpairs(df)

ggplot2 and ggally - R software and data visualization

ggsurv(): Plot survival curve using ggplot2

The function ggsurv() can be used to produces Kaplan-Meier plots using ggplot2 .

The simplified format is:

ggsurv(s, surv.col = "gg.def", plot.cens = TRUE, cens.col = "red",
       xlab = "Time", ylab = "Survival", main = "")

  • s: an object of class survfit
  • surv.col: color of the survival estimate. The default value is black for one stratum; default ggplot2 colors for multiple strata. It can be also a vector containing the color names for each stratum.
  • plot.cens: logical value. If TRUE, marks the censored observations.
  • cens.col: color of the points that mark censored observations.
  • xlab, ylab: label of x-axis and y-axis, respectively
  • main: the plot main title


Data

We’ll use lung data from the package survival:

require(survival)
data(lung, package = "survival")
head(lung[, 1:5])
##   inst time status age sex
## 1    3  306      2  74   1
## 2    3  455      2  68   1
## 3    3 1010      1  56   1
## 4    5  210      2  57   1
## 5    1  883      2  60   1
## 6   12 1022      1  74   1

The data above includes:

  • time: Survival time in days
  • status: censoring status 1 = censored, 2 = dead
  • sex: Male = 1; Female = 2

In the next section we’ll plot the survival curves of male and female.

Survival curves

require("survival")
# Fit survival functions
surv <- survfit(Surv(time, status) ~ sex, data = lung)
# Plot survival curves
surv.p <- ggsurv(surv)
surv.p

ggplot2 and ggally - R software and data visualization

It’s possible to change the legend of the plot as follow:

require(ggplot2)
surv.p + guides(linetype = FALSE) +
scale_colour_discrete(name   = 'Sex', breaks = c(1,2), 
                      labels = c('Male', 'Female'))

ggplot2 and ggally - R software and data visualization

Infos

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


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