If several box plots are to be displayed on a graphic where the X-axis represents a unique level, but one which is continuous (like per-year over a very long study) is there a well known way to condense a large number of boxplots aside from the obvious of aggregating up by large subsets of the X?

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    $\begingroup$ I'm not sure I follow the situation motivating this question. What's wrong with having a figure with a lot of boxplots in it? I've seen that a number of times. Could you provide an example dataset for people to work with? $\endgroup$ Commented Mar 29, 2019 at 20:47
  • $\begingroup$ I don't see a way around a lot of boxplots, density plots, or similar. You can group things in panels, e.g. a grid of panels. Otherwise you can reduce the data and show e.g. a heatmap of average values, which would throw away any variability. $\endgroup$ Commented Mar 29, 2019 at 20:56
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    $\begingroup$ If appropriate you could consider shrinking each boxplot to a solid line with a small gap at the median, dashes for the whiskers and dots for the outliers. You can then fit a lot of boxplots onto the same panel $\endgroup$
    – Henry
    Commented Mar 29, 2019 at 21:16
  • $\begingroup$ Is the problem that you do not want a lot of box plots? For example, there is limited room and the high number of box plots would not fit? $\endgroup$
    – Jon
    Commented Mar 30, 2019 at 1:29
  • $\begingroup$ I think even a hundred boxplots looks fine (you can modify the boxplot a bit if needed). x <- sample(101:200,10000,replace=TRUE); y <- 50 + x/9.5 +5*sin(x/45)+ rnorm(10000); a <- boxplot(y~x) However, you could just join the medians, quartiles and whisker-ends (this is rough but conveys the idea - you'd want to make a prettier version than this): matplot(t(a$stats),type="l") $\endgroup$
    – Glen_b
    Commented Mar 30, 2019 at 2:58

1 Answer 1


Illustrating @gung's suggestion, here are boxplots (made in R)--one for each of 10 'years'.

Each boxplot shows 12 (simulated) 'monthly' values of a variable x, which is gamma distributed. Gamma shapes increase gradually from 2 to 8 during the 120 months, and rates from about .32 to about .45.

Red line segments connect the averages for the 10 years. Such a graph is still clearly readable with 30 years instead of 10 (second figure).

enter image description here

enter image description here

# R code for first figure
set.seed(329)                      # for reproducibility 
Month = 1:120
shape = seq(2, 8, len=120); rate = seq(.1,.2,len=120)^.5
x = rgamma(120, shape, rate)
Year = rep(1:10, each=12)
MAT = matrix(x, byrow=T, nrow=10)  # a row for each year
a = rowMeans(MAT)                  # 10 averages
boxplot(x ~ Year, col="skyblue2", pch=19)
 lines(1:10, a, type="l", lwd=2, col="red")

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