data.table vs dplyr Speed Test

data.table vs dplyr Speed Test Explained

This article walks you through the science of benchmarking R’s two most popular data-manipulation frameworks—data.table and dplyr—so you can write faster, more reliable code.

Understanding data.table and dplyr

data.table in a nutshell

data.table is an enhanced version of data.frame that stores column vectors by reference and performs most operations in C for maximum speed. Its terse syntax combines selection, assignment, and aggregation in a single square-bracket expression, avoiding expensive copies.

dplyr in a nutshell

dplyr is part of the tidyverse and offers a readable, verb-based grammar (filter(), mutate(), summarise(), group_by(), etc.). Operations are executed primarily in R with C++ back-ends for some key functions; piping with |> or %>% emphasizes clarity over raw throughput.

Why speed testing matters

Performance bottlenecks multiply when you scale from 10⁵ to 10⁸ rows. A task that feels instantaneous on your laptop can paralyze production pipelines or interactive dashboards. Understanding which package excels for a given workload helps you:

• Meet SLA-driven refresh windows
• Reduce cloud compute costs
• Provide responsive exploratory analysis

How to design a fair benchmark

1. Control your environment

Use the same R version, single CPU core, and disable other intensive processes. For reproducibility, set a random seed and record package versions.

2. Use representative data

Benchmarks should mirror your real-world workload. For example, 1 GB of mixed numeric and character columns with 5–10 groups is more revealing than the classic 1 000 × 5 toy data frame.

3. Measure correctly

Prefer the bench or microbenchmark package over base system.time(). They run multiple iterations, drop warm-up noise, and report memory allocations alongside wall-clock time.

Benchmark walk-through

The snippet below compares a common operation—grouped means—on a 10 million-row data set:

library(data.table)
library(dplyr)
library(bench)

set.seed(42)
rows <- 1e7
DT <- data.table(
id = sample(letters, rows, TRUE),
val = runif(rows)
)
DF <- as.data.frame(DT) # for dplyr

result <- bench::mark(
data_table = DT[, .(avg_val = mean(val)), by = id],
dplyr = DF %>% group_by(id) %>% summarise(avg_val = mean(val), .groups = "drop"),
iterations = 25,
check = FALSE
)
print(result)

You will typically see data.table outperform dplyr by 3-10×—but beware of blanket conclusions. Verb choice, column types, and group cardinality heavily influence outcomes.

Interpreting results

Focus on:

• Median and p95 times, not minimums (which may be cache-heated flukes).
• Memory: data.table reuses columns in place; dplyr often allocates new data frames. If garbage collection spikes in gc(), memory, not CPU, may be the culprit.
• Readability vs speed: A 20 ms difference on a 100 ms task may not justify sacrificing team readability.

Best practices for high-performance data manipulation

• Use setkey() or setindex() in data.table for fast joins.
• Leverage .data pronoun or .keep = "used" in dplyr to prevent accidental copies.
• Where dplyr is preferred, consider dtplyr, which converts pipelines to data.table under the hood.
• Chunk extremely wide tables to fit CPU cache.
• Profile with profvis or Rprof() to locate hotspots before rewriting everything.

Common mistakes and how to fix them

Jump to the next section for an opinionated list of red flags.

Common mistake #1: judging by a single run

One timing can be skewed by disk I/O, garbage collection, or OS scheduling. Always sample dozens of iterations.

Common mistake #2: benching filtered subsets

If you slice the first 100 k rows for speed, results won’t generalize. Benchmark on the full, production-sized data.

Common mistake #3: forgetting factor vs character

data.table internally coerces some character columns to factors during joins when keys are set, inflating time on first run. Pre-define column types to avoid this one-off cost.

Conclusion

data.table often wins raw speed contests, but dplyr’s clarity, extensive ecosystem, and dbplyr translation layer make it indispensable for many workflows. A thoughtful benchmark surfaces the true performance–maintainability trade-off—saving both CPU cycles and developer sanity.