Ignore outliers in ggplot2 boxplot

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.)

I had the same problem and precomputed the values for Q1, Q2, median, ymin, ymax using boxplot.stats:

# Load package and generate data
library(ggplot2)
data <- rnorm(100)

# Compute boxplot statistics
stats <- boxplot.stats(data)$stats
df <- data.frame(x="label1", ymin=stats[1], lower=stats[2], middle=stats[3], 
                 upper=stats[4], ymax=stats[5])

# Create plot
p <- ggplot(df, aes(x=x, lower=lower, upper=upper, middle=middle, ymin=ymin, 
                    ymax=ymax)) + 
    geom_boxplot(stat="identity")
p

The result is a boxplot without outliers.

One idea would be to winsorize the data in a two-pass procedure:

1. run a first pass, learn what the bounds are, e.g. cut of at given percentile, or N standard deviation above the mean, or ...

2. in a second pass, set the values beyond the given bound to the value of that bound

I should stress that this is an old-fashioned method which ought to be dominated by more modern robust techniques but you still come across it a lot.

Here is a solution using boxplot.stats

# create a dummy data frame with outliers
df = data.frame(y = c(-100, rnorm(100), 100))

# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))


# compute lower and upper whiskers
ylim1 = boxplot.stats(df$y)$stats[c(1, 5)]

# scale y limits based on ylim1
p1 = p0 + coord_cartesian(ylim = ylim1*1.05)