Ignore outliers in ggplot2 boxplot

by

Use geom_boxplot(outlier.shape = NA) to not display the outliers and scale_y_continuous(limits = c(lower, upper)) to change the axis limits.

An example.

n <- 1e4L
dfr <- data.frame(
  y = exp(rlnorm(n)),  #really right-skewed variable
  f = gl(2, n / 2)
)

p <- ggplot(dfr, aes(f, y)) + 
  geom_boxplot()
p   # big outlier causes quartiles to look too slim

p2 <- ggplot(dfr, aes(f, y)) + 
  geom_boxplot(outlier.shape = NA) +
  scale_y_continuous(limits = quantile(dfr$y, c(0.1, 0.9)))
p2  # no outliers plotted, range shifted

Actually, as Ramnath showed in his answer (and Andrie too in the comments), it makes more sense to crop the scales after you calculate the statistic, via coord_cartesian.

coord_cartesian(ylim = quantile(dfr$y, c(0.1, 0.9)))

(You’ll probably still need to use scale_y_continuous to fix the axis breaks.)

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