What is a Radar Plot (Spider Chart)?

A radar plot (also called spider chart or star chart) is a multivariate visualization used to compare several quantitative variables across one or more entities.

Each variable is represented as an axis radiating from a central point. Values are plotted along these axes and connected to form a polygon.

At a glance, radar plots answer the question:
“Where are the strengths, weaknesses, and trade-offs across multiple dimensions?”

How to Read a Radar Plot

Key interpretation rules:

Each axis is one dimension
Examples:

  • Cost
  • Service level
  • Forecast accuracy
  • Flexibility
  • Reliability

Distance from the center = magnitude
Further out means “more” of that metric.

The shape matters more than the absolute values

  • Balanced shape → well-rounded solution
  • Sharp spikes → specialization
  • Indentations → weaknesses

Comparisons work best with 2–3 entities
Overlaying many polygons quickly becomes unreadable.

Typical Use Cases

Radar plots are especially useful when:

1. Comparing Alternatives

  • Inventory policies (EOQ vs. dynamic planning)
  • Forecasting models
  • Suppliers or logistics partners
  • ERP configuration options

2. Communicating Trade-offs

Radar plots shine where no single metric dominates, e.g.:

  • Lower cost vs. higher service level
  • Speed vs. stability
  • Flexibility vs. efficiency

3. Executive & Stakeholder Reporting

They are:

  • Intuitive
  • Compact
  • Easy to discuss in workshops

Where Radar Plots Work Well (and Where They Don’t)

Strengths

  • Multidimensional comparison
  • Pattern recognition
  • Storytelling with data

⚠️ Limitations

  • Poor for large numbers of variables (>8–10)
  • Sensitive to axis scaling
  • Not ideal for precise numeric comparison

Rule of thumb:

Radar plots are for insight, not for exact measurement.

Example: Inventory Strategy Comparison

Imagine two inventory strategies evaluated along five dimensions:

  • Cost efficiency
  • Service quality
  • Replenishment speed
  • Operational flexibility
  • Reliability

The comparison radar plot (like the third chart above) immediately shows:

  • Strategy A is stronger in reliability and quality
  • Strategy B excels in cost and flexibility
  • Neither dominates across all dimensions

This makes radar plots very effective in inventory optimization discussions, especially when arguing for a more balanced, dynamic policy.

Creating Radar Plots in R

Option 1: Using fmsb (classic and simple)

library(ggradar)
library(dplyr)

data <- data.frame(
  group = c("Option A", "Option B"),
  Cost = c(70, 85),
  Quality = c(85, 70),
  Speed = c(65, 75),
  Flexibility = c(60, 80),
  Reliability = c(80, 65)
)

ggradar(
  data,
  grid.min = 0,
  grid.mid = 50,
  grid.max = 100,
  values.radar = c("0%", "50%", "100%"),
  legend.position = "right"
)

Option 2: Using ggradar (ggplot-style)

library(ggradar)
library(dplyr)

data <- data.frame(
  group = c("Option A", "Option B"),
  Cost = c(70, 85),
  Quality = c(85, 70),
  Speed = c(65, 75),
  Flexibility = c(60, 80),
  Reliability = c(80, 65)
)

ggradar(
  data,
  grid.min = 0,
  grid.mid = 50,
  grid.max = 100,
  values.radar = c("0%", "50%", "100%"),
  legend.position = "right"
)

Conclusion:
Use radar plots when you want to compare multiple attributes across alternatives in a visually intuitive way — such as comparing model performance or risk profiles.