Radar/Spider Plot

Authors

[Contributors] ShiXiang Wang, Peng Luo, Hong Yang, KeXin Li, Ying Shi, Hu Zheng.

A radar chart, spider chart, or web chart is a two-dimensional chart type used to plot a series of values over one or more quantitative variables. The fmsb library is an excellent tool for building this type of chart in R.

Example

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming Language: R
Dependencies: fmsb

# Install packages
if (!requireNamespace("fmsb", quietly = TRUE)) {
  install.packages("fmsb")
}

# Load packages
library(fmsb)

Data Preparation

This paper primarily uses the built-in iris dataset in R, custom datasets, and the TCGA database.

The input data format must be very specific. Each row must contain a single entity. Each column is a quantitative variable. The first two rows provide the minimum and maximum values to be used for each variable.

# 1.R built-in data - iris
head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

# 2.Self-built dataset
## Here we create a data set about the performance of three students in different subjects
set.seed(99)                  
data <- as.data.frame(matrix( sample( 0:20 , 15 , replace=F) , ncol=5))
colnames(data) <- c("math" , "english" , "biology" , "music" , "R-coding" )
rownames(data) <- paste("mister" , letters[1:3] , sep="-")
data <- rbind(rep(20,5) , rep(0,5) , data) 

# 3.TCGA database (gene expression data of liver cancer)
tcga_group_radar <- readr::read_csv(
"https://bizard-1301043367.cos.ap-guangzhou.myqcloud.com/tcga_group_radar.csv")
tcga_simple_bar <- data.frame(tcga_group_radar[3,])

Visualization

1. Basic Plot

Take iris data as an example

# Data collation
iris_setosa <- iris[c(1:50),]
iris_setosa <- iris_setosa[,-5]
iris_setosa_radar <- rbind(rep(6,4),rep(0,4),iris_setosa)
# plot
par(mar = c(1, 1, 1, 1))
radarchart(iris_setosa_radar)

This radar chart depicts the distribution of four basic properties for a sample from the genus setosa.

Using the TCGA database as an example

par(mar = c(1, 1, 1, 1))
tcga_simple_bar <- rbind(rep(2.5,3),rep(0,3),tcga_simple_bar)
radarchart(tcga_simple_bar)

This radar chart describes the expression levels of TIGAR, RNF44, and RAB4B genes in a sample.

Tip

Key parameters:

Polygonal features: - pcol：Line color - pfcol：Fill color - plwd：Line width

Grid features: - cglcol：Grid color - cglty：Grid line type - axislabcol：Axis line color - caxislabels：Axis labels to display - cglwd：Grid width

Group Label: - vlcex：Group label size

radarchart(iris_setosa_radar,
           axistype=1 ,  
           pcol=rgb(0.2,0.5,0.5,0.9),
           pfcol=rgb(0.2,0.5,0.5,0.1),
           plwd=4,  
           cglcol="grey",cglty=1,axislabcol="grey",
           caxislabels=seq(0,20,5), cglwd=0.8, vlcex=0.8)

This radar chart describes the distribution of four basic properties of a sample from the setosa species.

2. Multi-group radar chart

Take self-built dataset as an example

radarchart(data)

This radar chart describes the distribution of scores of three students in various subjects.

Adjust parameters

# Set color parameters
colors_border=c(rgb(0.2,0.5,0.5,0.9),rgb(0.8,0.2,0.5,0.9),rgb(0.7,0.5,0.1,0.9) )
colors_in=c(rgb(0.2,0.5,0.5,0.4),rgb(0.8,0.2,0.5,0.4) ,rgb(0.7,0.5,0.1,0.4) )

# plot
radarchart(data,
           axistype=1 , 
           pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1,
           cglcol="grey", cglty=1, 
           axislabcol="grey", caxislabels=seq(0,20,5), 
           cglwd=0.8,
           vlcex=0.8
)

# Add a legend
legend(x=0.7, y=1, 
       legend = rownames(data[-c(1,2),]), bty = "n",
       pch=20,col=colors_in , text.col = "grey", 
       cex=1.2, pt.cex=3)

This radar chart describes the distribution of scores of three students in various subjects.

Taking the TCGA database as an example

par(mar = c(1, 1, 1, 1))
tcga_group_radar <- rbind(rep(3,3),rep(0,3),tcga_group_radar)
tcga_group_radar <- as.data.frame(tcga_group_radar) # It must be converted into a data frame before it can be drawn.
radarchart(tcga_group_radar)

This radar chart describes the expression levels of TIGAR, RNF44, and RAB4B genes in a sample.

Applications

1. Comprehensive comparison of multivariate data

RadarApp1 — Figure 7: Comprehensive comparison of multivariate data

This study used radar charts to analyze the role and prognostic value of the YIF1B gene in pan-cancer. [1]

2. Applicable to comprehensive evaluation of multiple indicators

RadarApp2 — Figure 8: Applicable to comprehensive evaluation of multiple indicators

The study used radar charts to present various areas of frailty assessment in older adults. [2]

Reference

[1] Liu J, Chen Z, Zhao P, Li W. Prognostic and immune regulating roles of YIF1B in Pan-Cancer: a potential target for both survival and therapy response evaluation. Biosci Rep. 2020 Jul 31;40(7):BSR20201384. doi: 10.1042/BSR20201384. PMID: 32648580; PMCID: PMC7378310.

[2] Jung HW. Visualizing Domains of Comprehensive Geriatric Assessments to Grasp Frailty Spectrum in Older Adults with a Radar Chart. Ann Geriatr Med Res. 2020 Mar;24(1):55-56. doi: 10.4235/agmr.20.0013. Epub 2020 Mar 27. PMID: 32743323; PMCID: PMC7370779.

[3] Nakazawa M (2024). fmsb: Functions for Medical Statistics Book with some Demographic Data. R package version 0.7.6, https://CRAN.R-project.org/package=fmsb.