Compute rowwise mean and standard deviation

dplyr
dplyr rowwise()
Learn compute rowwise mean and standard deviation with this comprehensive R tutorial. Includes practical examples and code snippets.
Published

September 16, 2023

Introduction

Computing rowwise statistics allows you to calculate summary measures across columns for each row in your dataset. This is particularly useful when you have multiple measurements per observation and need to summarize them, such as calculating average scores across different tests or finding variability in repeated measurements.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

We need to calculate the mean and standard deviation of penguin body measurements (bill length, bill depth, and flipper length) for each individual penguin. This will help us understand the overall size and measurement variability for each penguin.

Step 1: Prepare the Data

Let’s start by selecting the relevant measurement columns and removing any missing values.

penguin_measurements <- penguins |>
  select(species, bill_length_mm, bill_depth_mm, flipper_length_mm) |>
  filter(complete.cases(.))

head(penguin_measurements)

This creates a clean dataset with only the species identifier and three body measurement columns.

Step 2: Calculate Rowwise Mean

We use rowwise() to tell dplyr to perform operations across columns within each row.

penguin_with_mean <- penguin_measurements |>
  rowwise() |>
  mutate(
    mean_measurement = mean(c(bill_length_mm, bill_depth_mm, flipper_length_mm))
  )

head(penguin_with_mean)

Each row now has a mean_measurement column showing the average of the three body measurements for that penguin.

Step 3: Add Rowwise Standard Deviation

Now we’ll add the standard deviation to measure how variable each penguin’s measurements are.

penguin_with_stats <- penguin_measurements |>
  rowwise() |>
  mutate(
    mean_measurement = mean(c(bill_length_mm, bill_depth_mm, flipper_length_mm)),
    sd_measurement = sd(c(bill_length_mm, bill_depth_mm, flipper_length_mm))
  ) |>
  ungroup()

head(penguin_with_stats)

The ungroup() removes the rowwise grouping, and each penguin now has both mean and standard deviation of their measurements.

Example 2: Practical Application

The Problem

Imagine we’re analyzing student test scores across multiple subjects and want to identify students with consistently high performance (high mean) versus those with variable performance (high standard deviation). We’ll simulate this scenario using car performance metrics from the mtcars dataset.

Step 1: Select Performance Metrics

We’ll treat horsepower, displacement, and quarter-mile time as our “test scores” to analyze.

car_performance <- mtcars |>
  select(mpg, hp, disp, qsec) |>
  rownames_to_column("car_model")

head(car_performance, 4)

This gives us a dataset where each car has multiple performance measurements we can summarize.

Step 2: Calculate Standardized Scores

First, let’s standardize the metrics so they’re on the same scale before computing rowwise statistics.

car_standardized <- car_performance |>
  mutate(
    across(c(hp, disp, qsec), ~ scale(.x)[,1])
  )

head(car_standardized, 4)

Now all performance metrics are standardized (mean 0, sd 1), making rowwise comparisons meaningful.

Step 3: Compute Rowwise Statistics

Let’s calculate mean performance and performance consistency for each car.

car_summary <- car_standardized |>
  rowwise() |>
  mutate(
    avg_performance = mean(c(hp, disp, qsec)),
    performance_variability = sd(c(hp, disp, qsec))
  ) |>
  ungroup()

head(car_summary, 4)

Each car now has an average performance score and a variability measure showing how consistent its performance is across metrics.

Step 4: Identify Top Performers

Finally, let’s find cars with high average performance and low variability (consistent performers).

top_performers <- car_summary |>
  filter(avg_performance > 0.5, performance_variability < 0.8) |>
  arrange(desc(avg_performance)) |>
  select(car_model, mpg, avg_performance, performance_variability)

print(top_performers)

This identifies vehicles that perform consistently well across all measured metrics.

Summary

  • Use rowwise() before mutate() to perform calculations across columns within each row
  • Combine multiple columns using c() within summary functions like mean() and sd()
  • Always use ungroup() after rowwise operations to remove the special grouping
  • Consider standardizing variables before computing rowwise statistics when columns have different scales

  • Rowwise operations are perfect for creating composite scores or identifying patterns across multiple measurements per observation