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ggplot2 geom_errorbar() in R: Add Error Bars to Plots

The geom_errorbar() function in ggplot2 draws vertical error bars from ymin to ymax at each x position. It is the standard tool for showing uncertainty on bar charts, point plots, and line plots.

By Selva Prabhakaran  ·  Published May 12, 2026  ·  Last updated May 12, 2026
⚡ Quick Answer
ggplot(df, aes(x, y, ymin = lo, ymax = hi)) + geom_errorbar()
geom_errorbar(width = 0.2)               # narrower bars
ggplot(df, aes(x, y)) + geom_col() + geom_errorbar(aes(ymin = y - sd, ymax = y + sd))
geom_pointrange(aes(ymin, ymax))         # alternative: point + range
geom_ribbon(aes(ymin, ymax))             # for continuous bands

Need explanation? Read on for examples and pitfalls.

📊 Is geom_errorbar() the right tool?
STARTerror bars on bar / point plotgeom_errorbar()point with rangegeom_pointrange()continuous band over xgeom_ribbon()horizontal error barsgeom_errorbarh() or aes(y, x)mean +/- SD per groupstat_summary()

What geom_errorbar() does in one sentence

geom_errorbar() draws vertical bars from ymin to ymax at each x, with horizontal whiskers at the top and bottom. Used for confidence intervals, standard errors, or any uncertainty interval per point.

Syntax

geom_errorbar(mapping = NULL, data = NULL, width = 0.5, ...). Requires aes(x, ymin, ymax).

Run live
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RError bars on bar chart
library(ggplot2) library(dplyr) mtcars |> group_by(cyl) |> summarise(mean_mpg = mean(mpg), sd_mpg = sd(mpg)) |> ggplot(aes(factor(cyl), mean_mpg)) + geom_col(fill = "steelblue") + geom_errorbar(aes(ymin = mean_mpg - sd_mpg, ymax = mean_mpg + sd_mpg), width = 0.2)

  

  





Tip
Default width is 0.5 (full category width); narrow with width = 0.2 for cleaner look. For continuous x, set width to a small value like 0.1.



Five common patterns



1. On bar chart




  

    
RMean +/- SD

    
df |>
  ggplot(aes(group, mean)) +
  geom_col() +
  geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd), width = 0.2)

    

      
      
    

    

  

  





2. On point plot




  

    
RCleaner alternative to bars

    
df |>
  ggplot(aes(group, mean)) +
  geom_point(size = 3) +
  geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd), width = 0.2)

    

      
      
    

    

  

  





3. Stat_summary auto-error




  

    
RCompute error from raw data

    
mtcars |>
  ggplot(aes(factor(cyl), mpg)) +
  stat_summary(fun = mean, geom = "point") +
  stat_summary(fun.data = mean_sdl, geom = "errorbar", width = 0.2)

    

      
      
    

    

  

  





4. Color and dodge




  

    
RSide-by-side error bars

    
df |>
  ggplot(aes(group, mean, color = subgroup)) +
  geom_point(position = position_dodge(0.5)) +
  geom_errorbar(aes(ymin = lo, ymax = hi), 
                position = position_dodge(0.5), width = 0.2)

    

      
      
    

    

  

  





5. Confidence intervals




  

    
R95% CI from a model

    
predictions |>
  ggplot(aes(x, fit)) +
  geom_point() +
  geom_errorbar(aes(ymin = lwr, ymax = upr), width = 0.1)

    

      
      
    

    

  

  





Key Insight
geom_errorbar requires ymin and ymax aesthetics. Default width is too wide for most plots; set 0.2 or smaller for cleaner appearance.



geom_errorbar() vs geom_pointrange() vs geom_ribbon()







Function
Visual
Best for






geom_errorbar()
Bar with whiskers
Discrete x with error




geom_pointrange()
Point + range bar
Cleaner combination




geom_ribbon()
Continuous band
Time-series uncertainty




geom_linerange()
Line only (no point)
Sparse error display







A practical workflow



Use stat_summary for "compute mean and error from raw data" in one step.




  

    
RInteractive R

    
ggplot(mtcars, aes(factor(cyl), mpg)) +
  geom_jitter(width = 0.1, alpha = 0.5) +
  stat_summary(fun = mean, geom = "point", size = 3, color = "red") +
  stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.2, color = "red")

    

      
      
    

    

  

  





Raw points + mean + standard error in one plot.



Common pitfalls



Pitfall 1: forgetting width. Default 0.5 looks ugly on most plots. Set width = 0.2.



Pitfall 2: wrong error type. SD is per-observation spread. SE is mean's uncertainty. CI is interval. Pick what your data shows.



Warning
geom_errorbar is for VERTICAL bars only; use geom_errorbarh() for HORIZONTAL. They have different aesthetics (xmin/xmax for h).



Try it yourself





Try it: Make a bar chart of mean mpg per cyl with error bars showing +/- standard error. Save to ex_plot.



  

    
RYour turn: bar + error

    
ex_plot <- mtcars |>
  group_by(cyl) |>
  summarise(mean_mpg = mean(mpg), se = sd(mpg) / sqrt(n())) |>
  # your code here

    

      
      
    

    

  

  





Click to reveal solution



  

    
RSolution

    
ex_plot <- mtcars |>
  group_by(cyl) |>
  summarise(mean_mpg = mean(mpg), se = sd(mpg) / sqrt(n())) |>
  ggplot(aes(factor(cyl), mean_mpg)) +
  geom_col(fill = "steelblue") +
  geom_errorbar(aes(ymin = mean_mpg - se, ymax = mean_mpg + se), width = 0.2)

    

      
      
    

    

  

  





Explanation: Compute mean and SE per cyl; plot as bar with error bars.










After mastering geom_errorbar, look at:



    


  • geom_pointrange(): point + range
    • 


  • geom_linerange(): line only
    • 


  • geom_ribbon(): continuous bands
    • 


  • geom_errorbarh(): horizontal bars
    • 


  • stat_summary(): compute error from raw data
    • 


  • mean_sdl(), mean_se(), mean_cl_normal(): stat helpers
    • 





FAQ



What does geom_errorbar do in ggplot2?



geom_errorbar() draws vertical bars from ymin to ymax at each x position with horizontal whiskers at the ends.



How do I make error bars narrower?



Set width: geom_errorbar(width = 0.2). Default is 0.5 (often too wide).



What is the difference between geom_errorbar and geom_pointrange?



errorbar has horizontal whiskers; pointrange combines a central point with a range bar (no whiskers).



How do I make horizontal error bars?



Use geom_errorbarh() with xmin and xmax. Or swap the aesthetic mapping: aes(y, x, ...).



Should I show SD, SE, or CI as error bars?



SD shows individual variation. SE shows mean uncertainty. CI is interval estimate. Pick by what you want the reader to interpret.



          

        


        
      


      

        

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

        

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