Chapter 48: R Mode

1. What the Mode Actually Is (Simple & Honest Intuition)

The mode is:

the value (or values) that appears most frequently in the data set.

That’s it — no math, no summing, no sorting involved in the middle.

Real-life feeling Imagine you ask 20 friends in Hyderabad: “Which biryani do you prefer most?”

Answers:

• Hyderabadi: 9 people
• Ambur: 4 people
• Lucknowi: 3 people
• Kolkata: 2 people
• Vijayawada: 2 people

→ The mode = Hyderabadi (appears 9 times)

Even if someone says “I ate a ₹15,000 Hyderabadi family pack once”, it doesn’t change the mode — frequency is all that matters.

2. Important Facts About Mode (Many Beginners Miss These)

1. A data set can have no mode (every value appears exactly once)
2. It can have one mode → unimodal
3. It can have two modes → bimodal
4. It can have several modes → multimodal
5. Mode is the only measure of central tendency that works well with categorical / nominal data (colors, brands, cities, food types, gender, blood group, etc.)

3. Real Hyderabad Examples – When Mode Wins

Example A – Favorite food delivery cuisine

→ If Swiggy/Zomato asks “what cuisine do people order most in Hyderabad?”, they report the mode, not the mean price or median time.

Example B – Most common shoe size in a college class

→ Shoe companies care about mode when deciding which size to produce the most of.

Example C – Bimodal / multimodal data (very important real case)

→ This shows two clusters — college students (~22–24) and young working professionals (~35–38). Mode(s) reveal the bimodal / multimodal nature — mean and median would hide this completely.

4. How R Actually Computes Mode (Base R vs Modern Way)

Base R problem There is no built-in mode() function in base R that gives you the statistical mode — mode() does something completely different (it tells you the storage mode: numeric, character, etc.).

So people write workarounds:

Modern / recommended way 2026 (much cleaner)

5. When to Use Mode (Quick 2026 Decision Guide)

Use mode when:

• Data is categorical / nominal (colors, brands, cities, food types, gender, blood group, preferred language, most common complaint)
• You want the most frequent / most popular answer
• You suspect multiple clusters (bimodal / multimodal data)
• You are analyzing survey data where people choose from fixed options
• You want to know “what do most people do / prefer / buy”

Do NOT use mode when:

• Data is continuous numeric with few repeats (exam marks out of 100, temperatures, heights) → almost always no mode or meaningless
• All values appear roughly equally often → no mode
• You need a measure that uses every data point (mean does this)
• You want to do further math (variance, standard deviation need mean)

6. Your Mini Practice Right Now (Copy → Run & Play)

Now try these experiments:

1. Add ten more “Sev Puri” → see mode change
2. Make every item appear exactly twice → see “no mode” situation
3. Add a third strong contender → see bimodal / multimodal result

You just saw how mode behaves with your own data!

Clearer now?

Next logical questions?

• Want to see bimodal data visualized (histogram / density plot)?
• Learn quartiles, percentiles, IQR next?
• Compare mean / median / mode side-by-side on real datasets (iris, mtcars)?
• Or jump to first measure of spread (range, variance, standard deviation)?

Just tell me — whiteboard is ready! 📊🧮🚀