Plotting distributions (ggplot2)

Problem

You want to plot a distribution of data.

Solution

This sample data will be used for the examples below:

  1. set.seed(1234)
  2. dat <- data.frame(cond = factor(rep(c("A","B"), each=200)), 
  3.                    rating = c(rnorm(200),rnorm(200, mean=.8)))
  4. # View first few rows
  5. head(dat)
  6. #>   cond     rating
  7. #> 1    A -1.2070657
  8. #> 2    A  0.2774292
  9. #> 3    A  1.0844412
  10. #> 4    A -2.3456977
  11. #> 5    A  0.4291247
  12. #> 6    A  0.5060559
  14. library(ggplot2)

Histogram and density plots

The qplot function is supposed make the same graphs as ggplot, but with a simpler syntax. However, in practice, it’s often easier to just use ggplot because the options for qplot can be more confusing to use.

  1. ## Basic histogram from the vector "rating". Each bin is .5 wide.
  2. ## These both result in the same output:
  3. ggplot(dat, aes(x=rating)) + geom_histogram(binwidth=.5)
  4. # qplot(dat$rating, binwidth=.5)
  6. # Draw with black outline, white fill
  7. ggplot(dat, aes(x=rating)) +
  8.     geom_histogram(binwidth=.5, colour="black", fill="white")
  10. # Density curve
  11. ggplot(dat, aes(x=rating)) + geom_density()
  13. # Histogram overlaid with kernel density curve
  14. ggplot(dat, aes(x=rating)) + 
  15.     geom_histogram(aes(y=..density..),      # Histogram with density instead of count on y-axis
  16.                    binwidth=.5,
  17.                    colour="black", fill="white") +
  18.     geom_density(alpha=.2, fill="#FF6666")  # Overlay with transparent density plot

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Add a line for the mean:

  1. ggplot(dat, aes(x=rating)) +
  2.     geom_histogram(binwidth=.5, colour="black", fill="white") +
  3.     geom_vline(aes(xintercept=mean(rating, na.rm=T)),   # Ignore NA values for mean
  4.                color="red", linetype="dashed", size=1)

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Histogram and density plots with multiple groups

  1. # Overlaid histograms
  2. ggplot(dat, aes(x=rating, fill=cond)) +
  3.     geom_histogram(binwidth=.5, alpha=.5, position="identity")
  5. # Interleaved histograms
  6. ggplot(dat, aes(x=rating, fill=cond)) +
  7.     geom_histogram(binwidth=.5, position="dodge")
  9. # Density plots
  10. ggplot(dat, aes(x=rating, colour=cond)) + geom_density()
  12. # Density plots with semi-transparent fill
  13. ggplot(dat, aes(x=rating, fill=cond)) + geom_density(alpha=.3)

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Add lines for each mean requires first creating a separate data frame with the means:

  1. # Find the mean of each group
  2. library(plyr)
  3. cdat <- ddply(dat, "cond", summarise, rating.mean=mean(rating))
  4. cdat
  5. #>   cond rating.mean
  6. #> 1    A -0.05775928
  7. #> 2    B  0.87324927
  9. # Overlaid histograms with means
  10. ggplot(dat, aes(x=rating, fill=cond)) +
  11.     geom_histogram(binwidth=.5, alpha=.5, position="identity") +
  12.     geom_vline(data=cdat, aes(xintercept=rating.mean,  colour=cond),
  13.                linetype="dashed", size=1)
  15. # Density plots with means
  16. ggplot(dat, aes(x=rating, colour=cond)) +
  17.     geom_density() +
  18.     geom_vline(data=cdat, aes(xintercept=rating.mean,  colour=cond),
  19.                linetype="dashed", size=1)

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Using facets:

  1. ggplot(dat, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white") + 
  2.     facet_grid(cond ~ .)
  4. # With mean lines, using cdat from above
  5. ggplot(dat, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white") + 
  6.     facet_grid(cond ~ .) +
  7.     geom_vline(data=cdat, aes(xintercept=rating.mean),
  8.                linetype="dashed", size=1, colour="red")

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See Facets (ggplot2)) for more details.

Box plots

  1. # A basic box plot
  2. ggplot(dat, aes(x=cond, y=rating)) + geom_boxplot()
  4. # A basic box with the conditions colored
  5. ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot()
  7. # The above adds a redundant legend. With the legend removed:
  8. ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() +
  9.     guides(fill=FALSE)
  11. # With flipped axes
  12. ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() + 
  13.     guides(fill=FALSE) + coord_flip()

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It’s also possible to add the mean by using stat_summary.

  1. # Add a diamond at the mean, and make it larger
  2. ggplot(dat, aes(x=cond, y=rating)) + geom_boxplot() +
  3.     stat_summary(fun.y=mean, geom="point", shape=5, size=4)

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