Jitter if multiple outliers in ggplot2 boxplot

Question

I am trying to find a suitable show to illustrate various properties within and between school classes. For each class, there are only 15-30 data points (students).

Right now I am leaning towards a flatboard box showing only 1., 2. and 3. quartile + data, more than, for example, 1 population of SD +/- exemplary median.

I can do it.

However - I need to show this chart to some teachers in order to evaluate what they like best. I would like to compare my schedule with a regular box. But the normal boxplot looks the same if there is only one outlier or, for example, 5 outliers with the same value. In this case, it will unlock the deal.

eg.

test <-structure(list(value = c(3, 5, 3, 3, 6, 4, 5, 4, 6, 4, 6, 4, 4, 6, 5, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 5, 6, 6, 4, 3, 5, 4, 6, 5, 6, 4, 5, 5, 3, 4, 4, 6, 4, 4, 5, 5, 3, 4, 5, 8, 8, 8, 8, 9, 6, 6, 7, 6, 9), places = structure(c(1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L), .Label = c("a", "b"), class = "factor")), .Names = c("value", "places"), row.names = c(NA, -60L), class = "data.frame") ggplot(test, aes(x=places,y=value))+geom_boxplot() 

There are two outliers (“a”, 9), but only one “point” is displayed.

So my question is: how to shake emissions. And - what display would you suggest for this kind of data?

Answer 1

you can rephrase the function

 GeomBoxplot$draw<-function (., data, ..., outlier.colour = "black", outlier.shape = 16, outlier.size = 2, outlier.jitter=0) { defaults <- with(data, data.frame(x = x, xmin = xmin, xmax = xmax, colour = colour, size = size, linetype = 1, group = 1, alpha = 1, fill = alpha(fill, alpha), stringsAsFactors = FALSE)) defaults2 <- defaults[c(1, 1), ] if (!is.null(data$outliers) && length(data$outliers[[1]] >= 1)) { pp<-position_jitter(width=outlier.jitter,height=0) p<-pp$adjust(data.frame(x=data$x[rep(1, length(data$outliers[[1]]))], y=data$outliers[[1]]),.scale) outliers_grob <- GeomPoint$draw(data.frame(x=p$x, y = p$y, colour = I(outlier.colour), shape = outlier.shape, alpha = 1, size = outlier.size, fill = NA), ...) } else { outliers_grob <- NULL } with(data, ggname(.$my_name(), grobTree(outliers_grob, GeomPath$draw(data.frame(y = c(upper, ymax), defaults2), ...), GeomPath$draw(data.frame(y = c(lower, ymin), defaults2), ...), GeomRect$draw(data.frame(ymax = upper, ymin = lower, defaults), ...), GeomRect$draw(data.frame(ymax = middle, ymin = middle, defaults), ...)))) } ggplot(test, aes(x=places,y=value))+geom_boxplot(outlier.jitter=0.05) 

This is an ad-hoc solution. Of course, in the sense of OOP, you must subclass the GeomBoxplot class and override the function. This is easy because ggplot2 is enjoyable.

=== added, for example, subclass definition ===

 GeomBoxplotJitterOutlier <- proto(GeomBoxplot, { draw <- function (., data, ..., outlier.colour = "black", outlier.shape = 16, outlier.size = 2, outlier.jitter=0) { # copy the body of function 'draw' above and paste here. } objname <- "boxplot_jitter_outlier" desc <- "Box and whiskers plot with jittered outlier" guide_geom <- function(.) "boxplot_jitter_outlier" }) geom_boxplot_jitter_outlier <- GeomBoxplotJitterOutlier$build_accessor() 

then you can do with your subclass:

 ggplot(test, aes(x=places,y=value))+geom_boxplot_jitter_outlier(outlier.jitter=0.05) 

Answer 2

It seems that the accepted answer no longer works, since ggplot2 has been updated. After a long search on the net, I found the following: http://comments.gmane.org/gmane.comp.lang.r.ggplot2/3616 - Look at Winston Chang's answer -

It calculates outliers separately using ddply, and then outputs them using

 geom_dotplot() 

disabled outlier output on geom_boxplot ():

  geom_boxplot(outlier.colour = NA) 

Here is the complete code from the above URL:

 # This returns a data frame with the outliers only find_outliers <- function(y, coef = 1.5) { qs <- c(0, 0.25, 0.5, 0.75, 1) stats <- as.numeric(quantile(y, qs)) iqr <- diff(stats[c(2, 4)]) outliers <- y < (stats[2] - coef * iqr) | y > (stats[4] + coef * iqr) return(y[outliers]) } library(MASS) # Use the birthwt data set from MASS # Find the outliers for each level of 'smoke' library(plyr) outlier_data <- ddply(birthwt, .(smoke), summarise, lwt = find_outliers(lwt)) # This draws an ordinary box plot ggplot(birthwt, aes(x = factor(smoke), y = lwt)) + geom_boxplot() # This draws the outliers using geom_dotplot ggplot(birthwt, aes(x = factor(smoke), y = lwt)) + geom_boxplot(outlier.colour = NA) + #also consider: # geom_jitter(alpha = 0.5, size = 2)+ geom_dotplot(data = outlier_data, binaxis = "y", stackdir = "center", binwidth = 4) 

Answer 3

Does this get what you are looking for? The limit to which trembling begins is not automatic, but it is the beginning.

 g = ggplot(test, aes(x = places,y = value)) g + geom_boxplot(outlier.colour = rgb(0,0,0,0)) + geom_point(data = test[test$value > 8,], position = position_jitter(width = .4)) 

Answer 4

Given the small number of data points, you would like to build all the points not only due to outliers. This will help to find out the distribution of points inside your boxplot.

You can do this with geom_jitter, but note that box_plot already displays points for outliers, so to not display them twice, you need to disable the display of boxpot cells with geom_boxplot(outlier.shape = NA) .

 library("ggplot2") test <-structure(list(value = c(3, 5, 3, 3, 6, 4, 5, 4, 6, 4, 6, 4, 4, 6, 5, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 5, 6, 6, 4, 3, 5\ , 4, 6, 5, 6, 4, 5, 5, 3, 4, 4, 6, 4, 4, 5, 5, 3, 4, 5, 8, 8, 8, 8, 9, 6, 6, 7, 6, 9), places = structure(c(1L, 2L, 1L, 1L, 1L\ , 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, \ 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L), .Label = c("a", "b"), class =\ "factor")), .Names = c("value", "places"), row.names = c(NA, -60L), class = "data.frame") # adding a level that you will use latter for giving colors l <- rep(c(10,20,30,40,50,60), 10) test$levels<-l # [1] # original plot ggplot(test, aes(x=places,y=value))+geom_boxplot() # [2] # plot with outlier from boxplot and the points jittered to see # distribution (outliers and the same point from position jitter would be # counted twice for each different height) dev.new() ggplot(data=test, aes(x=places, y=value)) + geom_boxplot() + geom_jitter(position=position_jitter(width=0.1, height=0)) # [3] # make wider the jitter to avoid overplotting because there are a lot # of points with the same value, also remove the outliers from boxplot # (they are plotted with the geom_jitter anyway) dev.new() ggplot(data=test, aes(x=places, y=value)) + geom_boxplot(outlier.shape = NA) + geom_jitter(position=position_jitter(width=0.3, height=0)) # [4] # adding colors to the points to see if there is a sub-pattern in the distribution dev.new() ggplot(data=test, aes(x=places, y=value)) + geom_boxplot(outlier.shape = NA) + geom_jitter(position=position_jitter(width=0.3, height=0), aes(colour=levels)) # [5] # adding a bit of vertical jittering # jittering (a good option for a less discrete datasets) dev.new() ggplot(data=test, aes(x=places, y=value)) + geom_boxplot(outlier.shape = NA) + geom_jitter(position=position_jitter(width=0.3, height=0.05), aes(colour=levels)) # [6] # finally remember that position_jitter makes a jittering of a 40% of # the resolution of the data, so if you forget the height=0 you will # have a total different picture dev.new() ggplot(data=test, aes(x=places, y=value)) + geom_boxplot(outlier.shape = NA) + geom_jitter(position=position_jitter(width=0.2)) 

Answer 5

The abode code no longer works. For the current version of ggplot2, I used the following class:

 DrawGeomBoxplotJitterOutlier <- function(data, panel_params, coord, ..., outlier.jitter.width=NULL, outlier.jitter.height=0, outlier.colour = NULL, outlier.fill = NULL, outlier.shape = 19, outlier.size = 1.5, outlier.stroke = 0.5, outlier.alpha = NULL) { boxplot_grob <- ggplot2::GeomBoxplot$draw_group(data, panel_params, coord, ...) point_grob <- grep("geom_point.*", names(boxplot_grob$children)) if (length(point_grob) == 0) return(boxplot_grob) ifnotnull <- function(x, y) ifelse(is.null(x), y, x) if (is.null(outlier.jitter.width)) { outlier.jitter.width <- (data$xmax - data$xmin) / 2 } x <- data$x[1] y <- data$outliers[[1]] if (outlier.jitter.width > 0 & length(y) > 1) { x <- jitter(rep(x, length(y)), amount=outlier.jitter.width) } if (outlier.jitter.height > 0 & length(y) > 1) { y <- jitter(y, amount=outlier.jitter.height) } outliers <- data.frame( x = x, y = y, colour = ifnotnull(outlier.colour, data$colour[1]), fill = ifnotnull(outlier.fill, data$fill[1]), shape = ifnotnull(outlier.shape, data$shape[1]), size = ifnotnull(outlier.size, data$size[1]), stroke = ifnotnull(outlier.stroke, data$stroke[1]), fill = NA, alpha = ifnotnull(outlier.alpha, data$alpha[1]), stringsAsFactors = FALSE ) boxplot_grob$children[[point_grob]] <- ggplot2::GeomPoint$draw_panel(outliers, panel_params, coord) return(boxplot_grob) } GeomBoxplotJitterOutlier <- ggplot2::ggproto("GeomBoxplotJitterOutlier", ggplot2::GeomBoxplot, draw_group = DrawGeomBoxplotJitterOutlier) geom_boxplot_jitter_outlier <- function(mapping = NULL, data = NULL, stat = "boxplot", position = "dodge", ..., outlier.jitter.width=0, outlier.jitter.height=NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) { ggplot2::layer( geom = GeomBoxplotJitterOutlier, mapping = mapping, data = data, stat = stat, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, outlier.jitter.width=outlier.jitter.width, outlier.jitter.height=outlier.jitter.height, ...)) }