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ggplot2 geom_step() in R: Step Function Lines

The geom_step() function in ggplot2 draws connected lines that step rather than smoothly slope between points. It is the right choice for piecewise-constant data like cumulative event plots or empirical CDFs.

By Selva Prabhakaran  ·  Published May 11, 2026  ·  Last updated May 11, 2026
⚡ Quick Answer
ggplot(df, aes(x, y)) + geom_step()
ggplot(df, aes(x, y)) + geom_step(direction = "vh")  # vertical-then-horizontal
ggplot(df, aes(x, y)) + geom_step(direction = "hv")  # horizontal-then-vertical (default)
geom_line()                                          # smooth/diagonal connections
stat_ecdf()                                          # cumulative distribution

Need explanation? Read on for examples and pitfalls.

📊 Is geom_step() the right tool?
STARTpiecewise-constant time-seriesgeom_step()empirical CDFstat_ecdf() (built on geom_step)smooth line connectiongeom_line()bar chartgeom_col()cumulative countgeom_step on cumsum()

What geom_step() does in one sentence

geom_step() connects points with HORIZONTAL and VERTICAL segments instead of diagonal ones, producing a stair-step appearance. Useful for piecewise-constant data.

Syntax

geom_step(direction = "hv", ...). direction is "hv" (default) or "vh".

Run live
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RStep function
library(ggplot2) library(dplyr) df <- tibble(x = c(1, 3, 5, 7), y = c(10, 25, 15, 30)) ggplot(df, aes(x, y)) + geom_step()

  

  





Tip
Use geom_step for cumulative counts, event timelines, or any "value stays constant until next event" data. Smooth-line interpolation would mislead.



Five common patterns



1. Cumulative event count




  

    
RRunning count over time

    
events |>
  arrange(timestamp) |>
  mutate(count = row_number()) |>
  ggplot(aes(timestamp, count)) +
  geom_step()

    

      
      
    

    

  

  





2. Direction "vh"




  

    
RVertical first, then horizontal

    
ggplot(df, aes(x, y)) +
  geom_step(direction = "vh")

    

      
      
    

    

  

  





3. Empirical CDF




  

    
RECDF via stat_ecdf

    
ggplot(mtcars, aes(mpg)) +
  stat_ecdf()

    

      
      
    

    

  

  





4. Compare hv vs vh




  

    
RTwo directions on same data

    
ggplot(df, aes(x, y)) +
  geom_step(direction = "hv", color = "blue") +
  geom_step(direction = "vh", color = "red")

    

      
      
    

    

  

  





5. Multiple groups




  

    
RPer-group steps

    
ggplot(df, aes(x, y, color = group)) +
  geom_step()

    

      
      
    

    

  

  





Key Insight
Direction "hv" (default) draws horizontal first, then vertical at each step. "vh" reverses. Pick based on whether the value applies BEFORE or AFTER the x-coordinate.



geom_step() vs geom_line() vs geom_path()







Function
Connection
Best for






geom_step()
Step (hv or vh)
Piecewise-constant




geom_line()
Diagonal lines
Smooth trends




geom_path()
Same as geom_line but follows row order
Trajectories







A practical workflow



Use geom_step for "value changes at discrete events" plots.




  

    
RInteractive R

    
prices |>
  arrange(timestamp) |>
  ggplot(aes(timestamp, price)) +
  geom_step() +
  scale_x_datetime() +
  labs(title = "Price changes over time")

    

      
      
    

    

  

  





Each price persists until the next change. Steps faithfully show this.



Common pitfalls



Pitfall 1: confusing direction. Default "hv" extends the previous y until the next x; "vh" jumps to new y first then extends. Pick based on data semantics.



Pitfall 2: using on smooth data. Step shows discrete changes. For continuous trends, use geom_line.



Warning
geom_step connects points in DATA ROW ORDER. If your data isn't sorted by x, the steps will be jumbled. Always arrange(x) first.



Try it yourself





Try it: Plot a cumulative count of mtcars rows ordered by mpg using geom_step. Save to ex_plot.



  

    
RYour turn: cumulative count

    
ex_plot <- mtcars |>
  arrange(mpg) |>
  mutate(rank = row_number()) |>
  # your code here

    

      
      
    

    

  

  





Click to reveal solution



  

    
RSolution

    
ex_plot <- mtcars |>
  arrange(mpg) |>
  mutate(rank = row_number()) |>
  ggplot(aes(mpg, rank)) +
  geom_step()

    

      
      
    

    

  

  





Explanation: Arrange by mpg, compute cumulative rank, plot as steps.










After mastering geom_step, look at:



    


  • geom_line(): smooth connections
    • 


  • geom_path(): row-order line
    • 


  • stat_ecdf(): empirical CDF (built on step)
    • 


  • geom_segment(): explicit segment drawing
    • 


  • scale_x_datetime(): time-axis scaling
    • 





FAQ



What does geom_step do in ggplot2?



geom_step() connects points with horizontal and vertical line segments, producing a stair-step appearance. Used for piecewise-constant data.



What is the difference between geom_step and geom_line?



geom_step uses right-angled segments (hv or vh). geom_line uses diagonal segments. Pick based on whether your data is piecewise-constant (step) or continuous (line).



What does direction "hv" mean?



Horizontal-first then vertical: extend previous y until next x, then jump to new y. Default. "vh" reverses.



Can I use geom_step for ECDF?



Yes, indirectly: stat_ecdf() is built on geom_step and is the standard ECDF function.



Does geom_step require sorted data?



Yes. It connects points in row order; unsorted data produces jumbled steps. arrange() first.



          

        


        
      


      

        

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

        

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