CODEWITHSUNDEEP
rggplot2
Faheem Mitha
Faheem Mitha

Reputation: 6326

Positioning two legends independently in a faceted ggplot2 plot

I have a plot generated by ggplot2, which contains two legends. The placing of the legends is not ideal, so I would like to adjust them. I've been trying to imitate the method shown in the answer to "How do I position two legends independently in ggplot". The example shown in that answer works. However, I can't get the method described to work for my situation.

I'm using R 2.15.3 (2013-03-01), ggplot2_0.9.3.1, lattice_0.20-13, gtable_0.1.2, gridExtra_0.9.1 on Debian squeeze.

Consider the plot generated by minimal.R. This is similar to my actual plot.

########################
minimal.R
########################

get_stat <- function()
  {
    n = 20
    q1 = qnorm(seq(3, 17)/20, 14, 5)
    q2 = qnorm(seq(1, 19)/20, 65, 10)
    Stat = data.frame(value = c(q1, q2),
      pvalue = c(dnorm(q1, 14, 5)/max(dnorm(q1, 14, 5)), d = dnorm(q2, 65, 10)/max(dnorm(q2, 65, 10))),
      variable = c(rep('true', length(q1)), rep('data', length(q2))))
    return(Stat)
  }

stat_all<- function()
{
  library(ggplot2)
  library(gridExtra)
  stathuman = get_stat()
  stathuman$dataset = "human"
  statmouse = get_stat()
  statmouse$dataset = "mouse"
  stat = merge(stathuman, statmouse, all=TRUE)
  return(stat)
}

simplot <- function()
  {
    Stat = stat_all()
    Pvalue = subset(Stat, variable=="true")
    pdf(file = "CDF.pdf", width = 5.5, height = 2.7)
    stat = ggplot() + stat_ecdf(data=Stat, n=1000, aes(x=value, colour = variable)) +
      theme(legend.key = element_blank(), legend.background = element_blank(), legend.position=c(.9, .25), legend.title = element_text(face = "bold")) +
        scale_x_continuous("Negative log likelihood") +
          scale_y_continuous("Proportion $<$ x") +
            facet_grid(~ dataset, scales='free') +
              scale_colour_manual(values = c("blue", "red"), name="Data type",
                                  labels=c("Gene segments", "Model"), guide=guide_legend(override.aes = list(size = 2))) +
                geom_area(data=Pvalue, aes(x=value, y=pvalue, fill=variable), position="identity", alpha=0.5) +
                  scale_fill_manual(values = c("gray"), name="Pvalue", labels=c(""))
    print(stat)
    dev.off()
  }

simplot()

This results in the following plot. As can be seen, the Data type and Pvalue legends are not well positioned. I modified this code to minimal2.R.

enter image description here

With Version 1, which should put the legend on the top, the code runs without error, but no legend is shown.

EDIT: There are two boxes displayed, one on top of the other. The top one is blank. If I do not set the heights in grid.arrange(), as suggeted by @baptiste, then the legend and the plot are both placed in the bottom box. If I set the height as shown, then I don't see the legend.

EDIT2: It seems the extra blank box was called by grid.newpage, which I copied from the earlier question. I'm not sure why it was there. If I don't use that line, then I just get one box/page.

With Version 2, I get this error.

Error in UseMethod("grid.draw") :
  no applicable method for 'grid.draw' applied to an object of class "c('gg', 'ggplot')"
Calls: simplot -> grid.draw

EDIT: If I use print(plotNew) as suggested by @baptiste, then I get the following error

Error in if (empty(data)) { : missing value where TRUE/FALSE needed 
Calls: simplot ... facet_map_layout -> facet_map_layout.grid -> locate_grid.

I tried to figure out what is going on here, but I could not find much relevant information.

NOTES:

  1. I'm not sure why I'm getting the staircase effect for the empirical CDF. I'm sure there is an obvious explanation. Please enlighten me if you know.

  2. I'm willing to consider alternatives to this code and even ggplot2 for producing this graph, if anyone can suggest alternatives, e.g. matplotlib, which I have never seriously experimented with.

  3. Adding

    print(ggplot_gtable(ggplot_build(stat2)))

    to minimal2.R gives me

    TableGrob (7 x 7) "layout": 12 grobs
    z     cells       name                                 grob
1   0 (1-7,1-7) background       rect[plot.background.rect.186]
2   1 (3-3,4-4)  strip-top absoluteGrob[strip.absoluteGrob.135]
3   2 (3-3,6-6)  strip-top absoluteGrob[strip.absoluteGrob.141]
4   5 (4-4,3-3)     axis-l  absoluteGrob[GRID.absoluteGrob.129]
5   3 (4-4,4-4)      panel                gTree[GRID.gTree.155]
6   4 (4-4,6-6)      panel                gTree[GRID.gTree.169]
7   6 (5-5,4-4)     axis-b  absoluteGrob[GRID.absoluteGrob.117]
8   7 (5-5,6-6)     axis-b  absoluteGrob[GRID.absoluteGrob.123]
9   8 (6-6,4-6)       xlab          text[axis.title.x.text.171]
10  9 (4-4,2-2)       ylab          text[axis.title.y.text.173]
11 10 (4-4,4-6)  guide-box                    gtable[guide-box]
12 11 (2-2,4-6)      title            text[plot.title.text.184]

    I don't understand this breakdown. Can anyone explain? Does guide-box correspond to the legend, and how does one know this?

Here is the modified version of my code, minimal2.R.

########################
minimal2.R
########################

get_stat <- function()
  {
    n = 20
    q1 = qnorm(seq(3, 17)/20, 14, 5)
    q2 = qnorm(seq(1, 19)/20, 65, 10)
    Stat = data.frame(value = c(q1, q2),
      pvalue = c(dnorm(q1, 14, 5)/max(dnorm(q1, 14, 5)), d = dnorm(q2, 65, 10)/max(dnorm(q2, 65, 10))),
      variable = c(rep('true', length(q1)), rep('data', length(q2))))
    return(Stat)
  }

stat_all<- function()
{
  library(ggplot2)
  library(gridExtra)
  library(gtable)
  stathuman = get_stat()
  stathuman$dataset = "human"
  statmouse = get_stat()
  statmouse$dataset = "mouse"
  stat = merge(stathuman, statmouse, all=TRUE)
  return(stat)
}

simplot <- function()
  {
    Stat = stat_all()
    Pvalue = subset(Stat, variable=="true")
    pdf(file = "CDF.pdf", width = 5.5, height = 2.7)

    ## only include data type legend
    stat1 = ggplot() + stat_ecdf(data=Stat, n=1000, aes(x=value, colour = variable)) +
      theme(legend.key = element_blank(), legend.background = element_blank(), legend.position=c(.9, .25), legend.title = element_text(face = "bold")) +
        scale_x_continuous("Negative log likelihood") +
          scale_y_continuous("Proportion $<$ x") +
            facet_grid(~ dataset, scales='free') +
              scale_colour_manual(values = c("blue", "red"), name="Data type", labels=c("Gene segments", "Model"), guide=guide_legend(override.aes = list(size = 2))) +
                geom_area(data=Pvalue, aes(x=value, y=pvalue, fill=variable), position="identity", alpha=0.5) +
                  scale_fill_manual(values = c("gray"), name="Pvalue", labels=c(""), guide=FALSE)

    ## Extract data type legend
    dataleg <- gtable_filter(ggplot_gtable(ggplot_build(stat1)), "guide-box")

    ## only include pvalue legend
    stat2 = ggplot() + stat_ecdf(data=Stat, n=1000, aes(x=value, colour = variable)) +
      theme(legend.key = element_blank(), legend.background = element_blank(), legend.position=c(.9, .25), legend.title = element_text(face = "bold")) +
        scale_x_continuous("Negative log likelihood") +
          scale_y_continuous("Proportion $<$ x") +
            facet_grid(~ dataset, scales='free') +
              scale_colour_manual(values = c("blue", "red"), name="Data type", labels=c("Gene segments", "Model"), guide=FALSE) +
                geom_area(data=Pvalue, aes(x=value, y=pvalue, fill=variable), position="identity", alpha=0.5) +
                  scale_fill_manual(values = c("gray"), name="Pvalue", labels=c(""))

    ## Extract pvalue legend
    pvalleg <- gtable_filter(ggplot_gtable(ggplot_build(stat2)), "guide-box")

    ## no legends
    stat = ggplot() + stat_ecdf(data=Stat, n=1000, aes(x=value, colour = variable)) +
      theme(legend.key = element_blank(), legend.background = element_blank(), legend.position=c(.9, .25), legend.title = element_text(face = "bold")) +
        scale_x_continuous("Negative log likelihood") +
          scale_y_continuous("Proportion $<$ x") +
            facet_grid(~ dataset, scales='free') +
              scale_colour_manual(values = c("blue", "red"), name="Data type", labels=c("Gene segments", "Model"), guide=FALSE) +
                geom_area(data=Pvalue, aes(x=value, y=pvalue, fill=variable), position="identity", alpha=0.5) +
                  scale_fill_manual(values = c("gray"), name="Pvalue", labels=c(""), guide=FALSE)

    ## Add data type legend: version 1 (data type legend should be on top)
    ## plotNew <- arrangeGrob(dataleg, stat, heights = unit.c(dataleg$height, unit(1, "npc") - dataleg$height), ncol = 1)

    ## Add data type legend: version 2 (data type legend should be somewhere in the interior)
    ## plotNew <- stat + annotation_custom(grob = dataleg, xmin = 7, xmax = 10, ymin = 0, ymax = 4)

    grid.newpage()
    grid.draw(plotNew)
    dev.off()
  }

simplot()

Upvotes: 3

Views: 2079

Answers (1)

user2550477
user2550477

Reputation: 46

It can be done with grid.arrange and arrangeGrob, but it's a pain to adjust the heights and widths correctly.

grid.arrange(arrangeGrob(dataleg, pvalleg, nrow=1, ncol=2, widths=c(unit(1, "npc"), unit(5, "cm"))), stat, nrow=2, heights=c(unit(.2, "npc"), unit(.8, "npc")))

I usually prefer to make a new plot with an appropriate legend and use this new legend: 

 h <- ggplot(data.frame(a=rnorm(10), b=rnorm(10), c=factor(rbinom(10, 1,.5), labels=c("Gene segments", "Model")), d=factor("")), 
        aes(x=a, y=b)) +
   geom_line(aes(color=c), size=1.3) + geom_polygon(aes(fill=d)) +
   scale_color_manual(values=c("blue", "red"), name="Data type") + 
   scale_fill_manual(values="gray", name="P-value") 
 g_legend<-function(a.gplot){
   tmp <- ggplot_gtable(ggplot_build(a.gplot))
   leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
   legend <- tmp$grobs[[leg]]
   return(legend)
 }
 legend <- g_legend(h)

 grid.arrange(stat, legend, nrow=1, ncol=2, widths=c(unit(.8, "npc"), unit(.2, "npc")))
 grid.arrange(legend, stat, nrow=2, ncol=1, heights=c(unit(.2, "npc"), unit(.8, "npc")))

Upvotes: 2

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