Exploring the functionality of radar plots with the FMSB library

#load our data; today, we'll be looking at multiple students and their performance across school subjects
exam_scores <- data.frame(
    row.names = c("Student.1", "Student.2", "Student.3"),
      Biology = c(7.9, 3.9, 9.4),
      Physics = c(10, 20, 0),
        Maths = c(3.7, 11.5, 2.5),
        Sport = c(8.7, 20, 4),
      English = c(7.9, 7.2, 12.4),
    Geography = c(6.4, 10.5, 6.5),
          Art = c(2.4, 0.2, 9.8),
  Programming = c(0, 0, 20),
        Music = c(20, 20, 20)
)
exam_scores

NA

#install and load the package we're using to make radar plots
install.packages("fmsb")

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.1/fmsb_0.7.5.tgz'
Content type 'application/x-gzip' length 366431 bytes (357 KB)
=================
downloaded 126 KB

Warning in install.packages :
  downloaded length 129424 != reported length 366431
Warning in install.packages :
  URL 'https://cran.rstudio.com/bin/macosx/contrib/4.1/fmsb_0.7.5.tgz': status was 'Failure when receiving data from the peer'

Error in download.file(url, destfile, method, mode = "wb", ...) : 
  download from 'https://cran.rstudio.com/bin/macosx/contrib/4.1/fmsb_0.7.5.tgz' failed

Warning in install.packages :
  download of package ‘fmsb’ failed

library(fmsb)

Prepare our data to follow the radar plot specifications

• the first row specifies the maximum values for every column (ex, the maximum grades in biology, physics, math, etc)
• the second row specifies the min values for ever column
• the third row is where we can start inputting data for each student

#make a dataframe with the max and min values for each school subject
max_min <- data.frame(
  Biology = c(20, 0), Physics = c(20, 0), Maths = c(20, 0),
  Sport = c(20, 0), English = c(20, 0), Geography = c(20, 0),
  Art = c(20, 0), Programming = c(20, 0), Music = c(20, 0)
)
rownames(max_min) <- c("Max", "Min")

# make sure all of our exam scores fit within the max and min
df <- rbind(max_min, exam_scores)
df

Make the radar plot for one student

#extract the column that has to do with Student 2 from our big df, above
student2_data <- df[c("Max", "Min", "Student.2"), ]
radarchart(student2_data)

Awesome! We’ve made our first radar plot, even though it looks a bit bland. Lets make it look a bit better:

#specify a function that takes data and parameters on how to customize it, then returns a beautiful chart
#the function has default values for some variables, like color, in case we didn't specify them
create_beautiful_radarchart <- function(data, color = "#00AFBB", 
                                        vlabels = colnames(data), vlcex = 0.7,
                                        caxislabels = NULL, title = NULL){radarchart(
    data, axistype = 1,
    # Customize the polygon
    pcol = color, pfcol = scales::alpha(color, 0.5), plwd = 2, plty = 1,
    # Customize the grid
    cglcol = "grey", cglty = 1, cglwd = 0.8,
    # Customize the axis
    axislabcol = "grey", 
    # Variable labels
    vlcex = vlcex, vlabels = vlabels,
    caxislabels = caxislabels, title = title,
                                        )}

# use the create_beautiful_radarchart function to pretty-ify Student 2's data
create_beautiful_radarchart(student2_data, color = "#00AFFF", title = "Student 2's Graph", caxislabels = c(0, 25, 50, 75, 100))

op <- par(mar = c(1, 2, 2, 1))

create_beautiful_radarchart(student2_data, color = "#00ABFF", title = "Student 2's Performance")
par(op)

From this, we can see that Student 2 is strong in Physics, Sports, and Music, but didn’t perform well on their Programming assignments. Let’s see how he compares to his peers:

Comparing multiple students

# Reduce plot margin using par()
op <- par(mar = c(1, 2, 2, 2))
# Create the radar charts
create_beautiful_radarchart(
  data = df, caxislabels = c(0, 5, 10, 15, 20),
  color = c("#00AFBB", "#E7B800", "#FC4E07"), title = "Our Student's Data"
)
# Add an horizontal legend
legend(
  x = "bottom", legend = rownames(df[-c(1,2),]), horiz = TRUE,
  bty = "n", pch = 20 , col = c("#00AFBB", "#E7B800", "#FC4E07"),
  text.col = "black", cex = 1, pt.cex = 1
  )

#cex = font size, pt.cex = the size of the little color circles
par(op)

oops, its kinda hard to see each person’s data through all this overlap. Let’s showcase each student’s performance side by side:

# Define colors and titles
colors <- c("#00AFBB", "#E7B800", "#FC4E07")
titles <- c("Student.1", "Student.2", "Student.3")

# Reduce plot margin using par()
# Split the screen in 3 parts
op <- par(mar = c(1, 1, 1, 1))
par(mfrow = c(1,3))

# Create the radar chart
for(i in 1:3){
  create_beautiful_radarchart(
    data = df[c(1, 2, i+2), ], caxislabels = c(0, 5, 10, 15, 20),
    color = colors[i], title = titles[i]
    )
}
par(op)

*finish up demo by demonstrating how to compare each student’s data to an average