Navigate
Home Compendium
Exercises
Tidyverse packages
dplyr ggplot2 tidyr stringr lubridate purrr forcats readr broom Tidyverse R for Data Science
Deep dives
dplyr Joins dplyr Window Functions dplyr group_by ggplot2 Themes ggplot2 Facets ggplot2 Color Scales ggplot2 Bar Charts ggplot2 Heatmaps tidyr Pivot tidyr Nest/Unnest Regex
Wrangling & EDA
Data Wrangling Data Cleaning EDA Data Visualization data.table dbplyr / SQL Web Scraping API Calls
Statistics
Hypothesis Testing Linear Regression Logistic Regression Correlation Probability Distributions
Machine Learning
Machine Learning tidymodels Random Forest XGBoost Clustering PCA Cross-Validation
Time Series
Time Series ARIMA
By Industry
Finance Marketing Analytics Healthcare Biostatistics Genomics Sports Analytics A/B Testing
Reporting & Apps
Shiny R Markdown
Levels
R for Beginners R Interview Questions
R r‑statistics.co

dplyr summarize vs summarise in R: Same Function

In dplyr, summarize() (American spelling) and summarise() (British spelling) are EXACT ALIASES of the same function. Pick one and stick with it; both produce identical results.

By Selva Prabhakaran  ·  Published May 12, 2026  ·  Last updated May 12, 2026
⚡ Quick Answer
df |> summarise(avg = mean(x))           # British
df |> summarize(avg = mean(x))           # American (same function)
identical(summarise, summarize)           # TRUE
df |> group_by(g) |> summarise(avg = mean(x))
df |> summarise(.by = g, avg = mean(x))   # dplyr 1.1+ scoped grouping

Need explanation? Read on for examples and pitfalls.

📊 Which spelling to use?
STARTtidyverse style guidesummarise (British)US-style codebasessummarize (American)consistency in your projectpick one and never mix

What summarize / summarise does in one sentence

summarise(.data, ...) and summarize(.data, ...) collapse rows into one (or more) per group, computing aggregations specified in .... They are EXACT aliases, both are defined to point at the same internal function.

Syntax

Both spellings: identical signatures, identical behavior.

Run live
Run live, no install needed. Every R block on this page runs in your browser. Click Run, edit the code, re-run instantly. No setup.
RMean MPG per cylinder, two spellings
library(dplyr) mtcars |> group_by(cyl) |> summarise(avg_mpg = mean(mpg)) mtcars |> group_by(cyl) |> summarize(avg_mpg = mean(mpg)) # Same result

  

  





Tip
The tidyverse style guide prefers summarise (British). dplyr was written by Hadley Wickham (NZ-based), so British spelling is the canonical choice. American developers may prefer summarize; the function literally doesn't care.



Five common patterns



1. Grouped aggregation




  

    
RPer-group statistics

    
mtcars |>
  group_by(cyl) |>
  summarise(
    n          = n(),
    mean_mpg   = mean(mpg),
    sd_mpg     = sd(mpg)
  )

    

      
      
    

    

  

  





2. Same with American spelling




  

    
Rsummarize works identically

    
mtcars |>
  group_by(cyl) |>
  summarize(
    n        = n(),
    mean_mpg = mean(mpg),
    sd_mpg   = sd(mpg)
  )

    

      
      
    

    

  

  





3. Modern .by syntax (dplyr 1.1+)




  

    
RNo group_by needed

    
mtcars |>
  summarise(.by = cyl, avg = mean(mpg))

    

      
      
    

    

  

  





summarize with .by works the same way.



4. Both spellings can coexist (but don't mix)




  

    
RMixed spellings work but are bad style

    
mtcars |>
  group_by(cyl) |>
  summarize(avg = mean(mpg)) |>      # American
  arrange(desc(avg))

# Same code, with summarise:
mtcars |>
  group_by(cyl) |>
  summarise(avg = mean(mpg)) |>      # British
  arrange(desc(avg))

    

      
      
    

    

  

  





5. Verify they're the same




  

    
RIdentical functions

    
identical(dplyr::summarise, dplyr::summarize)
#> [1] TRUE

    

      
      
    

    

  

  





Key Insight
Aliasing both spellings was a deliberate dplyr decision. It avoids forcing one camp's preference on the other. The function's behavior is 100% identical regardless of which spelling you call.



summarise() vs summarize() vs aggregate()



Three "summarise" approaches in R.







Function
Package
Spelling






summarise(...)
dplyr
British




summarize(...)
dplyr
American




aggregate(...)
base R
n/a







summarise and summarize are aliases. aggregate is the base R alternative; it has different syntax and is less pipe-friendly.



When to use which:



    


  • summarise for tidyverse style.
    • 


  • summarize for American teams.
    • 


  • aggregate only when avoiding tidyverse dependency.
    • 





A practical workflow



Pick one spelling per project and stick with it. Mixing the two within one codebase doesn't break anything but makes code review harder. Most tidyverse codebases use summarise; many enterprise R codebases use summarize.




  

    
RInteractive R

    
# Tidyverse style guide example:
df |>
  group_by(category) |>
  summarise(
    n        = n(),
    revenue  = sum(amount),
    avg      = mean(amount),
    .groups  = "drop"
  )

    

      
      
    

    

  

  





The lintr / styler tools generally don't enforce one spelling over the other.



Common pitfalls



Pitfall 1: searching code with the wrong spelling. If you search for summarise and the codebase uses summarize, you miss results. Use a regex search: summari[sz]e.



Pitfall 2: editor autocompletion lock-in. Some IDEs autocomplete one spelling but not the other depending on which you type first. Be deliberate.



Warning
summarise and summarize BOTH have a .by argument in dplyr 1.1+. Don't assume only one supports the modern features. They are exact aliases.



Try it yourself





Try it: Compute mean mpg per cyl using BOTH summarise and summarize, then verify the results are identical. Save to ex_check.



  

    
RYour turn: verify aliasing

    
ex_a <- mtcars |> group_by(cyl) |> summarise(avg = mean(mpg))
ex_b <- mtcars |> group_by(cyl) |> summarize(avg = mean(mpg))

ex_check <- # your code here

ex_check
#> Expected: TRUE

    

      
      
    

    

  

  





Click to reveal solution



  

    
RSolution

    
ex_check <- identical(ex_a, ex_b)

ex_check
#> [1] TRUE

    

      
      
    

    

  

  





Explanation: Both spellings produce byte-identical results.










After mastering both spellings, look at:



    


  • reframe(): variable-row-output cousin (1.1+)
    • 


  • mutate(): per-row transformation
    • 


  • group_by(): grouping context
    • 


  • .by: scoped grouping (1.1+)
    • 


  • count(): shortcut for group_by + summarise(n = n())
    • 





For most workflows, summarise/summarize is the workhorse, pick one and use it consistently.



Why dplyr supports both spellings



Hadley Wickham (dplyr's author) is from New Zealand and uses British spelling. US users repeatedly asked for the American spelling. Adding both as aliases satisfies both camps with zero behavioral cost. This is a small but representative example of the tidyverse's openness to multiple regional preferences (the same pattern applies to colour/color in ggplot2).



FAQ



Is summarize the same as summarise in dplyr?



Yes. They are exact aliases. identical(dplyr::summarise, dplyr::summarize) returns TRUE.



Which spelling should I use?



Personal preference. The tidyverse style guide uses summarise (British). American developers often prefer summarize. Pick one and be consistent within a project.



Does the dplyr documentation use summarise or summarize?



Both. The official dplyr help pages document summarise first with summarize listed as an alias.



Can I mix both spellings in the same script?



Yes, technically, but it's bad style. Mixing harms readability without any benefit. Pick one.



Do other dplyr verbs have American spellings?



Mostly no. summarise/summarize is the main aliased pair. Other functions like select, mutate, filter have only one spelling each.



          

        


        
      


      

        

        
© 2016-2026 Selva Prabhakaran.
          This work is licensed under the Creative Commons License.
        

        

          About ·
          RSS