r-statistics.co by Selva Prabhakaran

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data.table vs dplyr in R: Head-to-Head Performance Benchmark

dplyr wins on readability. data.table wins on speed and memory. This guide puts them side by side with syntax examples and benchmarks so you can choose the right tool for each job.

Syntax Side by Side

Every common operation in both dialects.

Operation dplyr data.table
Filter rows filter(df, x > 5) dt[x > 5]
Select columns select(df, x, y) dt[, .(x, y)]
Add column mutate(df, z = x+y) dt[, z := x+y]
Group summary `group_by(df, g) \ > summarise(m=mean(x))` dt[, .(m=mean(x)), by=g]
Sort arrange(df, desc(x)) dt[order(-x)]
Count count(df, g) dt[, .N, by=g]
Join left_join(a, b, by="id") b[a, on="id"]
Unique rows distinct(df, x) unique(dt, by="x")
Rename rename(df, new=old) setnames(dt, "old", "new")

Live Comparison

library(dplyr) # dplyr: readable pipeline mtcars |> filter(cyl == 6) |> select(mpg, hp, wt) |> arrange(desc(mpg))


  








  


    
library(data.table)

# data.table: concise bracket syntax
dt <- as.data.table(mtcars)
dt[cyl == 6, .(mpg, hp, wt)][order(-mpg)]


    


      

      

    


    


  








  


    
library(dplyr)
library(data.table)

# Grouped summary comparison
cat("dplyr:\n")
mtcars |>
  group_by(cyl) |>
  summarise(avg = round(mean(mpg), 1), n = n(), .groups = "drop")

cat("\ndata.table:\n")
dt <- as.data.table(mtcars)
dt[, .(avg = round(mean(mpg), 1), n = .N), by = cyl][order(cyl)]


    


      

      

    


    


  






Speed Benchmark





  


    
library(dplyr)
library(data.table)

# Create 100K row test data
set.seed(42)
n <- 100000
df <- data.frame(
  group = sample(letters[1:10], n, replace = TRUE),
  value = rnorm(n),
  cat   = sample(LETTERS[1:5], n, replace = TRUE)
)
dt <- as.data.table(df)

# Grouped mean: 10 iterations
t_dplyr <- system.time(
  for(i in 1:10) df |> group_by(group) |> summarise(m = mean(value), .groups = "drop")
)
t_dt <- system.time(
  for(i in 1:10) dt[, .(m = mean(value)), by = group]
)

cat("dplyr      (10x):", round(t_dplyr["elapsed"], 3), "sec\n")
cat("data.table (10x):", round(t_dt["elapsed"], 3), "sec\n")


    


      

      

    


    


  






On 10M+ row datasets, data.table is typically 3–10x faster than dplyr for grouped operations. The gap narrows for simple operations and widens for complex aggregations with many groups.



When to Choose Each







Scenario
Best choice
Why






Exploratory analysis
dplyr
Readable, discoverable API




Teaching/learning
dplyr
Verb-based syntax is intuitive




Large data (1M+ rows)
data.table
Speed and memory




Production pipelines
data.table
Faster, fewer allocations




Tidyverse integration
dplyr
Native ggplot2, tidyr compat




Minimal dependencies
data.table
Single package, no imports




Want both
dtplyr
dplyr syntax, data.table speed







The Best of Both: dtplyr





  


    
library(dplyr)
library(data.table)

# dtplyr translates dplyr code to data.table
# library(dtplyr)
# lazy_dt(df) |> filter(x > 5) |> group_by(g) |> summarise(m = mean(x)) |> as_tibble()

# Manual conversion works too
dt <- as.data.table(mtcars)
result <- dt[cyl == 4, .(avg_mpg = mean(mpg))]
as_tibble(result)  # Convert to tibble for tidyverse compat


    


      

      

    


    


  






data.table Unique Features





  


    
library(data.table)

dt <- as.data.table(mtcars, keep.rownames = "car")

# := modifies in place (no copy)
dt[, efficiency := round(mpg / wt, 2)]

# .SD: Subset of Data (apply to multiple columns)
dt[, lapply(.SD, mean), by = cyl, .SDcols = c("mpg", "hp", "wt")][
  , lapply(.SD, round, 1)]


    


      

      

    


    


  






Practice Exercises



Exercise 1: Translate dplyr to data.table



Convert this dplyr pipeline to data.table syntax.





  


    
library(dplyr)

# dplyr version:
mtcars |>
  filter(cyl %in% c(4, 6)) |>
  group_by(cyl) |>
  summarise(avg_mpg = mean(mpg), max_hp = max(hp), .groups = "drop") |>
  arrange(desc(avg_mpg))

# Write the data.table equivalent:
library(data.table)
dt <- as.data.table(mtcars)



    


      

      

    


    


  






Click to reveal solution

```r


  

    
library(data.table)
dt <- as.data.table(mtcars)
dt[cyl %in% c(4, 6), .(avg_mpg = mean(mpg), max_hp = max(hp)), by = cyl][order(-avg_mpg)]

    

      
      
    

    

  




**Explanation:** `DT[i, j, by]` = filter in `i`, compute in `j`, group in `by`. Chained `[order(-avg_mpg)]` sorts the result.





FAQ



Can I use dplyr functions on data.tables?



Yes — data.table inherits from data.frame. But you lose data.table's speed optimizations. For best of both, use dtplyr or convert explicitly.



Which should a beginner learn first?



dplyr. Its verb-based syntax (filter, select, mutate, summarise) is more intuitive. Learn data.table when you hit performance limits with large datasets.



Does data.table modify data in place?



Yes — := modifies the data.table without copying. This is faster and uses less memory, but can cause unexpected side effects if you're used to R's copy-on-modify behavior. Use copy(dt) when you need an independent copy.



What's Next?




        


        
      


      

        

        
© 2016- Selva Prabhakaran.
          This work is licensed under the Creative Commons License.