Chapter 10 Radar Chart

Mohan Duan and Run Zhang

Here we introduce two kinds of plot that not covered in our class: radar chart.

10.1 Introduction of Radar Chart

A radar chart is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point.

  • Pros:

    1. great tool to compare different entities easily.
    2. easier for reader to understood than a column diagram.
    3. useful in drawing comparisons on the basis of different parameters.

  • Cons:

    1. if there are so many variables to compare, radar chart can be over-crowded.
    2. not ideal for making trade-off decisions or comparing vastly distinctive variables.
    3. radar charts can distort data to some extent.

Here we explain a tool for drawing radar chart in R

10.2 Basic Radar Chart

  • Using package ‘fmsb’
#install.packages('fmsb')
library(fmsb)
  • creating data of the nine enneagram type descriptions for person A
personA <- as.data.frame(matrix(sample(2:9, 9, replace = T), ncol = 9))
colnames(personA) <- c("Reformer", "Helper", "Achiever", "Individual", 
                       "Investigator", "Loyalist", "Enthusiast", "Challenger", "Peacemaker")
personA
##   Reformer Helper Achiever Individual Investigator Loyalist Enthusiast
## 1        5      2        8          9            3        3          3
##   Challenger Peacemaker
## 1          6          3
  • set the min and max of each personality to show on the plot
personA <- rbind(rep(10,9) , rep(0,9) , personA)
  • Using ‘radarchart()’ to plot radar graph
radarchart(personA)

10.3 Customize Radar Chart

The ‘radarchart()’ function offers several options to customize the chart:

  • Polygon features:

    • ‘pcol’ → line color

    • ‘pfcol’ → fill color

    • ‘plwd’ → line width

  • Grid features:

    • ‘cglcol’ → color of the net

    • ‘cglty’ → net line type (see possibilities)

    • ‘axislabcol’ → color of axis labels

    • ‘caxislabels’ → vector of axis labels to display

    • ‘cglwd’ → net width

  • Labels:

    • ‘vlcex’ → group labels size
radarchart( personA  , axistype=1 , 
 
    ##custom polygon
    pcol=rgb(0.2,0.5,0.5,0.9) , pfcol=rgb(0.2,0.5,0.5,0.5) , plwd=4 , 
 
    ##custom the grid
    cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,10,2.5), cglwd=0.8,
 
    ##custom labels
    vlcex=0.8 
    )

10.4 Radar Chart with several individuals

10.4.1 basic plot

For next application, we are going to measure data scientists based on the skills required for data scientists. * Required Skills for a Data Scientist:

* Programming: SQL,Python, R, JAVA,MATLAB
* Machine Learning(ML): Natural Language Processing, Classification, Clustering,Ensemble methods, Deep Learning
* Visualization: Tableau, SAS, D3.js, Python, Java, R libraries
* Big Data: MongoDB, Oracle, Microsoft Azure, Cloudera
* Communication

Reference: https://www.mastersindatascience.org/careers/data-scientist/

  • create data
ds_data <- as.data.frame(matrix( sample( 0:20 , 15 , replace=F) , ncol=5))
colnames(ds_data) <- c("Programming" , "ML" , "Visulization" 
                    , "Big Data","Communication"  )
rownames(ds_data) <- paste("data scientist" , letters[1:3] , sep="-")
ds_data
##                  Programming ML Visulization Big Data Communication
## data scientist-a           0  9           18       12            13
## data scientist-b           3 17            7       10             8
## data scientist-c           4  1           15       16            19
ds_bind <- rbind(rep(20,5) , rep(0,5) , ds_data)
 
## plot with default options:
radarchart(ds_bind)

10.4.2 customize

The ‘radarchart()’ function offers several options to customize the chart:

  • Polygon features:

    • ‘pcol’ → line color

    • ‘pfcol’ → fill color

    • ‘plwd’ → line width

  • Grid features:

    • ‘cglcol’ → color of the net

    • ‘cglty’ → net line type (see possibilities)

    • ‘axislabcol’ → color of axis labels

    • ‘caxislabels’ → vector of axis labels to display

    • ‘cglwd’ → net width

  • Labels:

    • ‘vlcex’ → group labels size
## Color vector
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 with default options:
b<- radarchart( ds_bind  , axistype=1 , 
    ##custom polygon
    pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1,
    ##custom the grid
    cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,20,5), cglwd=0.8,
    ##custom labels
    vlcex=0.8 
    )
b
## NULL
## Add a legend
legend(x=0.7, y=1, legend = rownames(ds_bind[-c(1,2),]), bty = "n", pch=20 , col=colors_in , text.col = "grey", cex=1.2, pt.cex=3)

10.4.3 Radar vs. Bar Chart

#devtools::install_github("ricardo-bion/ggradar", 
#                          dependencies = TRUE)
library(ggplot2)
library(dplyr)
library(ggradar)
library(tidyr)
library(scales)
library(tibble)
data(USArrests)
tidy_ds <- ds_data %>% 
  tibble::rownames_to_column( "scientist")
tidy_ds <- pivot_longer(tidy_ds,!c("scientist"  ), names_to = "skill", values_to = "score") 
ggplot(tidy_ds, aes(x = skill, y = score, fill = scientist)) +
  geom_bar(position = "dodge", stat = "identity")

10.4.4 Using package ‘ggradar’

new_data = USArrests%>%
  as_tibble(rownames = "group") %>%
  mutate_at(vars(-group), rescale)%>%
  filter(group %in% c("California","New York","New Jersey","Maryland"))

ggradar(new_data)