绘图练习来源：

https://www.datanovia.com/en/blog/beautiful-radar-chart-in-r-using-fmsb-and-ggplot-packages/

Demo data

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
##           Biology Physics Maths Sport English Geography Art Programming Music
## Student.1     7.9      10   3.7   8.7     7.9       6.4 2.4           0    20
## Student.2     3.9      20  11.5  20.0     7.2      10.5 0.2           0    20
## Student.3     9.4       0   2.5   4.0    12.4       6.5 9.8          20    20

注意： The data should be organized as follow:

The row 1 must contain the maximum values for each variable

The row 2 must contain the minimum values for each variable

Data for cases or individuals should be given starting from row 3

The number of columns or variables must be more than 2

library(fmsb)
# Define the variable ranges: maximum and minimum
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")

# Bind the variable ranges to the data
df <- rbind(max_min, exam_scores)
df
##           Biology Physics Maths Sport English Geography  Art Programming Music
## Max          20.0      20  20.0  20.0    20.0      20.0 20.0          20    20
## Min           0.0       0   0.0   0.0     0.0       0.0  0.0           0     0
## Student.1     7.9      10   3.7   8.7     7.9       6.4  2.4           0    20
## Student.2     3.9      20  11.5  20.0     7.2      10.5  0.2           0    20
## Student.3     9.4       0   2.5   4.0    12.4       6.5  9.8          20    20

Basic radar plot

# Plot the data for student 1

library(fmsb)
student1_data <- df[c("Max", "Min", "Student.1"), ]
radarchart(student1_data)

Customize the radar charts

# 通过定义函数
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, ...
  )
}

op <- par(mar = c(1, 2, 2, 1))
create_beautiful_radarchart(student1_data, caxislabels = c(0, 5, 10, 15, 20))

par(op)

Create radar charts for multiple individuals

Create the radar chart of the three students on the same plot:

# 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")
)
# 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 = 1.5
  )

par(op)

Create separated spider charts for each individual. This is recommended when you have more than 3 series.

# 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)