Calculating quantiles by group is crucial for understanding data distribution within subgroups. Instead of a single quartile for your entire dataset, you get quartiles per group;revealing how distributions differ across categories. I use this constantly when analyzing business metrics by department, region, or customer segment.

When to Calculate Quantiles by Group

You’ll use grouped quantiles when:

  • Comparing distributions across categories
  • Identifying outliers within each group
  • Setting thresholds or percentiles per category
  • Analyzing percentile performance by segment
  • Quality control across production batches

Key Quantiles

  • Q1 (0.25): 25th percentile - first quartile
  • Q2 (0.50): 50th percentile - median
  • Q3 (0.75): 75th percentile - third quartile
  • Q4 (1.00): 100th percentile - maximum

Basic Approach

library(dplyr)

df %>%
  group_by(grouping_column) %>%
  summarize(
    q25 = quantile(value_column, 0.25),
    q50 = quantile(value_column, 0.50),
    q75 = quantile(value_column, 0.75)
  )

Examples with Explanations

Using quantile() Function

Let’s see how we can calculate quantile values of column of of data frame:

# Load library
library(dplyr)

# Create data frame
df <- data.frame(Machine_name=c("A","B","A","C","D","B","C","D","D","C","A","B","B","C","D","D"),
                 Pressure=c(12.39,11.25,12.15,13.48,13.78,12.89,12.21,12.58,11.25,11.69,78.96,14.52,14.56,11.23,12.36,12.85),
                 Temperature=c(78,89,85,84,81,79,77,85,77,78,75,74,71,79,76,78),
                 Humidity=c(5,7,1,2,7,8,9,4,5,1,3,4,7,8,9,5))

# Define quantiles of interest
q = c(.25, .5, .75, 1)

# Calculate quantiles by grouping Machine_name
df %>%
  group_by(Machine_name) %>%
  summarize(quant25 = quantile(Pressure, probs = q[1]), 
            quant50 = quantile(Pressure, probs = q[2]),
            quant75 = quantile(Pressure, probs = q[3]),
            quant100 = quantile(Pressure, probs = q[4]))

Output:

 Machine_name quant25 quant50 quant75 quant100
  <chr>          <dbl>   <dbl>   <dbl>    <dbl>
1 A               12.3    12.4    45.7     79.0
2 B               12.5    13.7    14.5     14.6
3 C               11.6    12.0    12.5     13.5
4 D               12.4    12.6    12.8     13.8

The output shows quantile values for the Pressure column, grouped by Machine_name. Notice how each machine (A, B, C, D) has different quantile values, revealing different pressure distributions per machine.

Common Mistakes to Avoid

Mistake 1: Forgetting group_by()

# ❌ WRONG - This calculates overall quantiles, not by group!
df %>%
  summarize(q50 = quantile(Pressure, 0.5))  # One value for all groups

# ✅ CORRECT - group_by() first
df %>%
  group_by(Machine_name) %>%
  summarize(q50 = quantile(Pressure, 0.5))  # One per group

Mistake 2: Quantile values as decimals vs percentages

# ❌ CONFUSION - Using percentage instead of decimal
quantile(Pressure, 75)  # WRONG - returns 75th percentile? No!

# ✅ CORRECT - Use decimals (0 to 1)
quantile(Pressure, 0.75)  # Correct: 75th percentile

Mistake 3: Not handling NA values

# ❌ PROBLEM - NA values cause issues
df %>%
  group_by(Machine) %>%
  summarize(q50 = quantile(Pressure, 0.5))  # May return NA!

# ✅ SOLUTION - Handle NAs explicitly
df %>%
  group_by(Machine) %>%
  summarize(q50 = quantile(Pressure, 0.5, na.rm = TRUE))

Mistake 4: Confusing type parameter

# Different quantile calculation methods exist (type 1-9)
# Most use type=7 (default) - uses linear interpolation
quantile(x, 0.5, type = 1)   # Older style
quantile(x, 0.5, type = 7)   # Modern (default)

Pro Tips

  1. Calculate multiple quantiles efficiently:

    df %>%
  group_by(group) %>%
  summarize(quants = list(quantile(value, c(0.25, 0.5, 0.75))))

  2. Use across() for multiple columns:

    df %>%
  group_by(group) %>%
  summarize(across(where(is.numeric), ~quantile(., 0.5)))

  3. Compare quantiles across groups visually:

    df %>%
  group_by(Machine) %>%
  summarize(across(Pressure, list(q25 = ~quantile(., 0.25),
                                  q50 = ~quantile(., 0.5),
                                  q75 = ~quantile(., 0.75))))

  4. IQR (Interquartile Range) per group:

    df %>%
  group_by(group) %>%
  summarize(IQR = IQR(value, na.rm = TRUE))  # Q3 - Q1

See Also