I am plotting grouped barplots with error bars, but my error bars are very long as in this image
[![https://i.stack.imgur.com/VUByO.png][1]][1].
I would like shorter error bars as in this image
[![https://i.stack.imgur.com/JhaUJ.png][2]][2]
The code used
per$Leaf_Location <- factor(per$Leaf_Location, levels = unique(per$Leaf_Location))
per$Time <- factor(per$Time, levels = unique(per$Time))
ggplot(per, aes(x=Leaf_Location, y=Damage, fill=as.factor(Time))) +
stat_summary(fun.y=mean,
geom="bar",position=position_dodge(),colour="black",width=.7,size=.7) +
stat_summary(fun.ymin=min,fun.ymax=max,geom="errorbar",
color="black",position=position_dodge(.7), width=.2) +
stat_summary(geom = 'text', fun.y = max, position = position_dodge(.7),
label = c("a","b","c","d","d","a","b","c","d","d","a","b","c","d","d"), vjust = -0.5) +
scale_fill_manual("Legend", values = c("grey36","grey46","grey56","grey76","grey86","grey96")) +
xlab("Leaf Location") +
ylab("Damage ") +
theme_bw()
data:
per =
structure(list(Site = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Defathers",
"Kariithi", "Kimbimbi"), class = "factor"), Field = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L
), .Label = c("F1", "F2", "F3", "F4", "F5"), class = "factor"),
Leaf_Location = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("Lower", "Intermediate",
"Upper"), class = "factor"), Time = structure(c(1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L), .Label = c("20_days",
"40_days", "60_days", "80_days", "100_days"), class = "factor"),
Damage = c(25.25, 26.07, 24.43, 20.73, 17.8, 6.9, 45.05,
33.47, 24.43, 51.67, 41.72, 34.17, 81.67, 73.33, 55.83, 34.28,
26.08, 13.28, 26.27, 14.1, 6.93, 37.55, 29.33, 23.62, 49.17,
38.45, 31.38, 70.83, 60.83, 44.2, 31.03, 25.2, 14.97, 14.38,
6.5, 4.33, 52.2, 39.17, 30.97, 75, 62.5, 38.33, 87.5, 62.5,
57.5, 45.02, 31.02, 26.07, 46.72, 34.32, 21.5, 50.83, 34.23,
25.25, 45.83, 33.47, 27.7, 67.67, 57.5, 52.67, 30.98, 23.62,
9.1, 18.17, 18.57, 10.15, 46.67, 34.27, 23.62, 54.17, 40.05,
29.37, 70.83, 59.17, 47.53, 8.67, 5.63, 0.87, 9.87, 3.03,
0, 17.75, 6.88, 0, 62.5, 37.5, 27.7, 70.83, 57.5, 50.83,
6.5, 2.17, 1.3, 6.93, 3.03, 0.53, 14.82, 5.2, 0, 37.5, 28.52,
13, 75, 37.5, 37.5, 15.3, 9.53, 5.63, 9.43, 3.03, 0.43, 16.4,
6.07, 0, 57.5, 34.23, 21.98, 78.33, 62.5, 37.5, 12.08, 6.5,
1.3, 10.73, 3.03, 0, 15.2, 3.9, 0.43, 62.5, 37.5, 21.98,
64.17, 55.83, 41.73, 8.73, 3.57, 0, 8.57, 2.17, 0, 16.5,
7.7, 0.43, 42.58, 36.68, 13, 65.83, 47.5, 37.5, 8.03, 5.07,
0.43, 10.68, 7.27, 3.5, 48.38, 38.42, 24.83, 45.03, 38.4,
30.8, 73.33, 63.33, 50.83, 3.37, 2.17, 0.9, 9, 6.02, 5.2,
21.07, 12.37, 6.02, 45.02, 32.65, 21.67, 68.78, 56.68, 50,
0, 0, 0, 7.8, 4.33, 4.33, 25.17, 20.65, 13.15, 48.37, 39.23,
27.17, 75.83, 62.5, 49, 11.78, 12.72, 3.8, 20.18, 14.87,
8.95, 46.7, 39.32, 33.03, 49.18, 40.05, 24.43, 69.17, 60,
48.33, 0, 0, 0, 15.25, 9.82, 7.75, 45.9, 38.47, 35.52, 50.88,
37.61, 33.47, 79.17, 71.67, 58.33)), .Names = c("Site", "Field",
"Leaf_Location", "Time", "Damage"), row.names = c(NA, -225L), class = "data.frame")
Here's a simplified reproducible example to explain
first, some dummy data:
per = data.frame(x=rep(c('a','b'), each=100), y=c(2+rnorm(100), 3+rnorm(100,0,2)))
Now you are plotting the error bars, using fun.ymin=min, fun.ymax=max, which will cause them to extend the full range of the data, as in the following graph:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
geom_point(position = position_jitter(0.1)) +
stat_summary(fun.ymin=min, fun.ymax=max, geom="errorbar", width=0.4) +
theme_bw()
Whereas, it is more conventional to use error bars that extend either +/- one standard deviation, as in the following:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
stat_summary(
fun.ymin=function(y) {mean(y) - sd(y)},
fun.ymax=function(y) {mean(y) + sd(y)},
geom="errorbar", width=0.2) +
theme_bw()
Or one standard error, like this:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
stat_summary(
fun.ymin=function(y) {mean(y) - sqrt(var(y)/length(y))},
fun.ymax=function(y) {mean(y) + sqrt(var(y)/length(y))},
geom="errorbar", width=0.2) +
theme_bw()
EDIT - example data were added to question, after this answer was originally posted
We can applying exactly the same approach as above to your example data:
ggplot(per, aes(x=Leaf_Location, y=Damage, fill=as.factor(Time))) +
stat_summary(fun.y=mean, geom="bar",position=position_dodge(),colour="black",width=.7,size=.7) +
stat_summary(
fun.ymin=function(y) {mean(y) - sqrt(var(y)/length(y))},
fun.ymax=function(y) {mean(y) + sqrt(var(y)/length(y))},
geom="errorbar",
position=position_dodge(.7), width=.2)
Related
I would like to create a plot displaying two variables, with groupings (ellipses) for one and symbols coloured for the other. "RW1" on the x axis, "RW2" on the y axis, grouping ellipses according to "Morph.cluster" and colouring the geometric shapes according to "ALL", which is the genetic cluster. I'm using ade4 in the adegenet package for this. The issue is that I can only seem to get the plot working for one variable at a time.
My data looks like
ID ggpop AC70 AC50 ALL Morph.cluster RW1 RW2 RW3
LT-103 gg3 NA 2 2 1 0.00793 0.01137 -0.00379
LT-109 gg4 1 1 1 2 -0.0156 -0.01056 0.00869
LT-110 gg3 1 1 1 1 0.01585 0.00077 -0.00923
LT-12 gg5 4 4 4 1 0.01299 0.00383 0.00517
LT-122 gg4 NA 3 3 1 0.02727 0.01845 -0.0078
For some samples ggpop is NA. For these samples I hope that I I can just leave the colour empty. If not, I'll just make another gg category and label these ones as empty.
Using
library(ade4)
plot_All = plot(RWggclust2_eds$RW1,RWggclust2_eds$RW2, type="n", yaxt="n",
ylab="RW2 (17%)", xlab="RW1 (26%)")
axis(2, las=2)
myCol2=transp(c('#7F7F7FFF', '#D99694FF', '#00B0F0FF', '#00B050FF', '#000000FF'), 0.6)
abline(h=0,v=0,col="grey", lty=2)
s.class(cbind(RWggclust2_eds$RW1, RWggclust2_eds$RW2),
fac=as.factor(RWggclust2_eds$Morph.cluster),
add.plot=T,
col=myCol2,
cpoint=2,
clabel=0,
pch=c(15,16,17) [as.numeric(RWggclust2_eds$Morph.cluster)],
axesell=F,
addaxes=F,
cstar=0,
cellipse = 1.5)
I get the above with the ellipses drawn the way that I want, defining the morphological clusters, but the colours of the geometric shapes are not what I am looking for. I would like the colours to be according to the five groups defined in “ALL”, which are the genetic clusters.
However, using
RWggclust2_eds <- read.csv("RWggclust2_eds.csv")
str(RWggclust2_eds)
RWggclust2_eds$ALL = as.factor(RWggclust2_eds$ALL)
RWggclust2_eds$ALL = as.factor(RWggclust2_eds$ggpop)
RWggclust2_eds$Morph.cluster = as.factor(RWggclust2_eds$Morph.cluster)
plot_All = plot(RWggclust2_eds$RW1,RWggclust2_eds$RW2, type="n", yaxt="n",
ylab="RW2 (17%)", xlab="RW1 (26%)")
axis(2, las=2)
myCol2=transp(c('#7F7F7FFF', '#D99694FF', '#00B0F0FF', '#00B050FF', '#000000FF'), 0.6)
abline(h=0,v=0,col="grey", lty=2)
s.class(cbind(RWggclust2_eds$RW1, RWggclust2_eds$RW2),
fac=as.factor(RWggclust2_eds$ALL),
add.plot=T,
col=myCol2,
cpoint=2,
clabel=0,
pch=c(15,16,17) [as.numeric(RWggclust2_eds$Morph.cluster)],
axesell=F,
addaxes=F,
cstar=0,
cellipse = 1.5)
I get the above, with ellipses drawn on the genetic clusters (the “ALL” column in my dataset), which is not what I want (I want the shapes to be grouped according to my first plot), but the colours in the plot are correct.
I've tired all sorts of iterations of the "col" argument as well
#index=as.integer(RWggclust2_eds$ALL)
#sclasscolour=rainbow(6)
#sclasscolour=transp(c('#7F7F7FFF', '#D99694FF', '#00B0F0FF', '#00B050FF', '#000000FF'), 0.6)
#myCol2= transp(c(sclasscolour), 1) [index]
The issue is I think that the colour of symbols is being read from "fac", but is seems like the distrubtion of Morph.cluster is as well.
I would be glad to post my datafile if there is a way to do so.
You are almost there. I think in s.class, the color of the points will go together with the eclipse (at least as far as I have tried).
Since you had to layer the eclipse onto an empty plot, I think the easiest solution is to first plot the points according to what you like, then add on the eclipses without the points (setting pch=NA)
I used alpha from ggplot for the transparency but feel free to change it back
library(ade4)
library(ggplot2)
# i use alpha from ggplot2
# how i read and proceeded with your csv file
#CSV = read.csv("RWggclust2_eds.csv")
#RWggclust2_eds = CSV[,c("ggpop","ALL","Morph.cluster","RW1","RW2")]
# similar to what you did
# just providing names
myCol2=alpha(c('#7F7F7FFF', '#D99694FF', '#00B0F0FF', '#00B050FF', '#000000FF'), 0.6)
# set it according to popn
names(myCol2) = levels(RWggclust2_eds$ggpop)
# set the shapes
PCH = c(15,16,17)
# we can wrap it around using with
plot_All = with(RWggclust2_eds,
plot(RW1,RW2, yaxt="n",
ylab="RW2 (17%)", xlab="RW1 (26%)",
pch= PCH[Morph.cluster],
col=myCol2[as.character(ggpop)]
)
)
#provide a legend
legend("topleft",names(myCol2),fill=myCol2,horiz=T,cex=0.7)
axis(2, las=2)
abline(h=0,v=0,col="grey", lty=2)
s.class(cbind(RWggclust2_eds$RW1, RWggclust2_eds$RW2),
fac=as.factor(RWggclust2_eds$Morph.cluster),
add.plot=T,
cpoint=2,
clabel=0,
pch=NA,
axesell=F,
addaxes=F,
cstar=0,
cellipse = 1.5)
You get the plot:
Below is the data for making the plot
dput(RWggclust2_eds)
structure(list(ggpop = structure(c(3L, 4L, 3L, 5L, 4L, 1L, 3L,
3L, 1L, 1L, 4L, 3L, 5L, 1L, 3L, 1L, 1L, 1L, 1L, 4L, 3L, 1L, 1L,
3L, 1L, 3L, 3L, 5L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 4L, 3L, 1L,
4L, 5L, 3L, 4L, 1L, 2L, 2L, 5L, 5L, 4L, 4L, 2L, 5L, 2L, 2L, 3L,
3L, 1L, 1L, 3L, 4L, 1L, 3L, 1L, 1L, 1L, 4L, 1L, 2L, 4L, 1L, 5L,
1L, 4L, 3L, 5L, 1L, 3L, 5L, 1L, 3L, 4L, 4L, 5L, 5L, 3L, 3L, 1L,
1L, 3L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 5L,
3L, 1L, 3L, 4L, 4L, 1L, 1L, 3L, 4L, 3L, 1L, 1L, 4L, 1L, 1L, 2L,
4L, 1L, 4L, 5L, 5L, 1L, 1L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 1L, 1L,
2L, 3L, 1L, 1L, 5L, 4L, 4L, 3L, 2L, 4L, 3L, 3L, 3L, 2L, 3L, 2L,
3L, 3L, 2L, 4L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 5L, 1L, 1L,
1L, 4L, 1L, 1L, 4L, 4L, 1L, 3L, 3L, 1L, 4L, 4L, 1L, 4L, 4L, 3L,
3L, 5L, 2L, 3L, 1L, 1L, 4L, 3L, 3L, 4L, 2L, 3L, 1L, 3L, 3L, 3L,
3L, 2L, 3L, 5L, 5L, 3L, 1L, 5L, 4L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA), .Label = c("gg1", "gg2", "gg3", "gg4", "gg5"), class = "factor"),
ALL = c(2L, 1L, 1L, 4L, 3L, 3L, 1L, 2L, 2L, 3L, 5L, 1L, 4L,
3L, 1L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 1L, 3L, 1L, 1L, 1L,
2L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 2L, 2L, 3L, 1L, 4L, 1L, 1L,
2L, 5L, 5L, 4L, 4L, 1L, 3L, 1L, 4L, 5L, 5L, 5L, 2L, 3L, 3L,
1L, 5L, 3L, 5L, 2L, 5L, 3L, 1L, 3L, 5L, 2L, 3L, 4L, 5L, 1L,
1L, 4L, 2L, 1L, 4L, 2L, 2L, 1L, 1L, 4L, 4L, 3L, 1L, 5L, 3L,
3L, 5L, 2L, 4L, 5L, 2L, 5L, 2L, 5L, 3L, 2L, 3L, 1L, 2L, 4L,
1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 3L, 1L, 2L, 3L,
5L, 1L, 3L, 1L, 4L, 4L, 3L, 5L, 3L, 2L, 1L, 2L, 2L, 3L, 1L,
2L, 2L, 5L, 1L, 2L, 3L, 5L, 3L, 1L, 4L, 5L, 1L, 2L, 4L, 1L,
5L, 1L, 5L, 1L, 5L, 5L, 2L, 5L, 3L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 4L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 3L, 5L, 3L, 3L, 4L,
1L, 3L, 1L, 1L, 1L, 1L, 4L, 5L, 2L, 3L, 3L, 2L, 2L, 1L, 2L,
5L, 1L, 3L, 1L, 1L, 1L, 1L, 5L, 2L, 4L, 4L, 2L, 3L, 4L, 1L,
3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 2L,
1L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 2L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 3L, 1L, 4L, 1L, 2L,
3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L,
1L, 1L, 5L, 1L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 1L), Morph.cluster = c(1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
3L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 3L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 2L,
3L, 2L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L,
2L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 3L,
2L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L,
2L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 3L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 3L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
3L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L), RW1 = c(0.00793,
-0.0156, 0.01585, 0.01299, 0.02727, 0.00344, 0.031, 0.01568,
-0.00411, -0.00076, 0.01339, 0.02017, 0.02123, -0.0061, 0.01376,
-0.02301, 0.00501, 0.02418, -0.00102, -0.00813, 0.0032, -0.0122,
-0.00317, 0.00018, 0.00051, -0.00184, -0.00323, 0.01701,
-0.00027, 0.00288, -0.00689, -0.01864, 0.003, 0.00463, 0.0119,
-0.01515, 0.00364, 0.02048, -0.01233, -0.00774, -0.00348,
-0.00219, 0.00676, -0.00978, 0.00767, -0.00819, -0.01892,
-0.00584, 0.012, 0.00617, -0.01743, -0.02724, -0.03363, 0.01004,
0.02196, 0.01074, 0.0053, 0.00197, -0.01368, 0.00802, -0.00659,
-0.00521, -0.00938, 0.00901, 0.00639, 0.02151, -0.01637,
-0.02571, -0.00354, -0.00396, 0.03228, 0.01599, -0.00646,
0.00993, 0.02582, -0.01889, 0.01162, 0.00487, 0.02127, -0.00994,
-0.01862, 0.01338, 0.00229, 0.01818, 0.00687, 0.00545, 0.01723,
0.00438, 0.00832, 0.00095, -0.00751, -0.00645, -0.01469,
-0.01141, 0.01079, -0.00567, 0.00348, 0.00904, 0.00055, 0.02002,
-0.00382, -0.00244, 0.03351, -0.01225, 0.00865, -0.00105,
-0.01337, 0.00853, -0.00584, -0.001, -0.00068, -0.01427,
0.00742, 0.00352, -0.01016, -0.00713, -0.00129, -0.01557,
-0.00792, -0.00067, 0.00432, -0.01509, 0.03477, 0.01061,
-0.00097, 0.00797, -0.01066, -0.01534, 0.0192, 0.00266, -0.00549,
-0.02314, 0.00139, -0.02194, -0.00406, 0.00287, -0.01561,
-0.00329, 0.01625, 0.00822, 0.01719, -0.00545, 0.0019, 0.00278,
0.00436, 0.01424, -0.01102, -0.01331, 0.00928, 0.01181, 0.00416,
0.01673, 0.00715, -0.0159, -0.00208, -0.03367, 9e-04, -0.00011,
-0.02768, -0.01608, -0.00345, 0.00062, 0.00048, -0.01542,
0.02665, -0.0181, 0.0098, -0.00995, 0.00842, 0.00123, 0.00896,
0.02197, -0.00163, -0.01072, -0.01137, -0.01126, -0.01656,
0.00518, 0.03913, -0.02236, -0.02881, -0.0117, 0.01644, -0.00465,
-0.00811, 0.00107, 0.02483, 0.00197, -0.01018, 0.01023, 0.01279,
-0.01052, -0.0069, -0.01409, 0.01462, 0.00435, 0.01912, -0.01392,
0.02166, 0.00621, 0.0112, -0.01114, 0.00728, -0.0086, 5e-05,
0.00352, -0.00939, -0.01502, 0.00394, 0.01684, -0.00077,
-0.00037, 0.00563, -0.03636, 0.00443, 0.01819, 0.00603, -0.00363,
-0.02126, 0.01857, 0.00014, -0.03048, 0.00621, -0.00452,
0.02451, 0.00715, -0.00705, 0.01619, -0.016, -0.00362, -0.00949,
0.02046, -0.016, -0.01655, 0.00079, 0.00188, -0.01497, 0.01269,
0.02089, -0.01006, -0.01574, -0.00349, -0.01533, 0.01438,
0.0055, -0.00257, 0.00254, -0.00647, -0.00356, 0.02158, -0.03029,
0.01802, -0.00391, -0.01141, -0.0355, 0.00911, 0.00136, 0.00631,
-0.00551, -0.01403, 0.00929, 0.0059, -0.00238, 0.00028, -0.01683,
0.00347, -0.0213, 0.00411, 0.00953, 0.00029, -0.0028, 0.01801,
0.00405, -0.0079, -0.01496, -0.00638, -0.01092, -0.00773,
-0.00368, 0.00526, -0.00739), RW2 = c(0.01137, -0.01056,
0.00077, 0.00383, 0.01845, 0.00058, 0.02422, 0.00129, -0.01173,
-0.0161, 0.01019, 0.00135, 0.00579, 0.00576, 0.00751, 0.01384,
0.00408, -0.00634, -0.00044, -0.00168, -0.00021, 0.00019,
0.00428, -0.00924, 0.00098, 0.00518, -0.00184, -0.00469,
-0.00921, -3e-05, 0.00688, -0.00778, 0.00165, -0.00664, 0.00856,
-0.00157, 0.00322, -7e-05, -0.00405, 6e-04, -0.00464, -0.0019,
-0.00849, 0.00363, -0.01107, 0.00175, -0.03478, -0.00816,
-0.00338, 0.02075, -0.00039, -0.02465, -0.01461, 0.00529,
-0.00144, -0.00259, -0.00803, -0.00693, 0.00761, 0.01159,
0.01422, -0.00876, -0.00469, -0.00052, 0.00823, 0.01019,
0.00032, 0.00842, 0.02197, 4e-04, -0.02939, 0.00432, 0.00774,
0.00848, -0.00342, -0.00953, -0.01048, -0.0014, -0.01465,
0.0027, 0.00879, -4e-05, 0.00985, -0.03324, 0.01427, 0.00036,
-0.01587, -0.0012, 0.00331, 0.0089, -0.00684, -0.0348, 0.00497,
0.01468, -0.00218, -0.01662, 0.00608, -0.01143, 0.00312,
-0.01165, 0.00966, 0.00179, -0.01137, 0.00335, 0.0152, -0.00442,
0.01031, -0.01745, -0.01658, 0.003, 0.011, -0.00583, 0.0171,
0.00062, 0.00842, 0.00759, -0.00401, 0.01916, -0.00491, 0.01416,
0.00117, -0.00164, -0.02815, 0.00648, 0.00067, -0.006, -0.00454,
0.00368, 0.00666, 0.00352, 0.00368, -0.00999, 0.01213, -0.0022,
-0.0015, -0.02383, 0.00748, -4e-05, -0.02224, -0.01355, 0.00525,
0.00142, 0.01344, 0.00639, 0.00822, 0.01765, 0.00431, -0.0019,
0.00204, 0.03139, 0.00251, -0.00123, -0.00833, -0.01289,
0.00716, 0.00198, 0.00263, 0.00632, 0.00179, -5e-05, 0.0088,
-0.0017, -0.00574, 0.00356, -0.02567, 0.00984, 0.00209, 0.0072,
0.00377, -0.0085, 0.01386, 0.00746, 0.00415, -0.01666, 0.00516,
0.00883, -0.01979, 0.01931, 0.00208, 0.00411, 0.01303, -0.00108,
0.00854, -0.00262, -0.02486, 0.0179, -0.00035, 0.01651, 0.00952,
0.00668, -0.00279, 0.00344, 0.00314, -0.00599, 0.00954, -0.02629,
-0.00226, 0.00282, 0.00765, 0.00624, -0.00497, -0.00269,
0.01129, -0.02122, 0.00841, -0.0011, -0.00747, 0.01913, -0.0024,
0.01528, -0.00184, -0.005, -0.01863, 0.02115, -0.00814, 0.01531,
0.00641, -0.00111, -0.01767, 0.01528, -0.00414, -0.01503,
-0.00082, 0.00429, 0.00032, 0.01733, -0.00656, 0.00176, -0.00597,
0.00423, -0.00541, 0.01085, -0.00327, -4e-04, 0.01826, -0.00852,
0.0098, -0.01052, 0.0188, 0.00234, -0.00113, -0.00335, -0.01121,
-0.00305, 0.0065, -0.0032, -0.00877, -0.01151, -0.01051,
-0.00295, 0.00751, -0.00828, -0.00164, -0.00109, 0.01665,
0.01285, -0.00875, -0.01182, -0.01427, 0.01631, -0.00241,
-0.0055, 0.00393, 0.00351, 0.00061, -0.00063, 0.01053, -0.00094,
0.00509, 0.00162, 0.00295, -0.00789, -0.03162, 0.00523, -0.00361,
-0.00171, -0.00459, 0.00237, -0.03454, -0.01103, 0.00944)), class = "data.frame", row.names = c(NA,
-281L))
I am working on a a model with lmer where I would like to get estimated marginal means with the emmeanslibrary. This is my dataframe:
df <- structure(list(treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), .Label = c("CCF", "UN"), class = "factor"), level = structure(c(2L,
3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L,
4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L,
2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L,
3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L,
4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L,
2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 2L,
3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L), .Label = c("A", "F", "H", "L"
), class = "factor"), random = structure(c(3L, 3L, 3L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L), .Label = c("1.6", "2", "3.2", "5", NA), class = "factor"),
continuous = c(72.7951770264767, 149.373765810534, 1.64153094886205,
54.6697408615215, 25.5801835808851, 1.45794117443253, 25.3660934894788,
91.2321704897132, 2.75353217433675, 44.1995276851725, 33.1854545470435,
5.36536076058866, 29.6807620242672, 80.6077496067764, 0.833434180091457,
13.6789475327185, 77.4930412025109, 3.65998714174906, 25.2848344605563,
136.632099849828, 2.56715261161435, 28.6733878840584, 66.800616194317,
1.37475468782539, 23.007491380183, 84.980285774607, 1.13569710795522,
33.8610875632139, 56.1234827517798, 1.32327007970416, 60.0843812879313,
43.4487832450889, 1.14942423621912, 53.6673704529947, 146.746167255051,
3.91593723271292, 27.0321687961004, 89.5925729244878, 1.47707078226047,
44.0523211310831, 115.087908243373, 1.94039630728038, 86.4074806697431,
43.3266206881612, 2.81456503996437, 66.868588961071, 229.797526052566,
1.07971524769264, 30.3390107111747, 116.680801084036, 1.67711446647817,
69.0961010697534, 78.5454363192614, 1.92137892126384, 53.5708546850303,
37.7175476710608, 1.96087397451467, 25.5166981770257, 37.3755071788757,
2.21602000526086, 10.3266195584378, 38.1458490762217, 2.7508022340832,
44.5864920143771, 8.45382647692274, 2.63204944520792, 87.5376946978593,
27.2354119098268, 3.38134648323956, 26.8815471706502, 14.5539972194568,
2.0556994322415, 27.4619977737491, 32.8546665896602, 2.66809379088059,
42.3815445857533, 21.3359802201685, 2.19167325121191, 53.3189825439001,
13.5708790223439, 2.22274607227071, 88.297423835906, 8.50554349658773,
3.5764241495006, 29.284865737912, 21.1213079519954, 2.3070166819956,
10.7659615128225, 33.4813413290485, 2.49896565066211, 59.0935696616465,
13.2863515051715, 4.36424795471221, 72.1627847396763, 9.09326343200557,
2.13701784901259, 27.5824079679471, 8.84486812842272, 1.98293342019671,
17.5321126287485, 19.1806349705231, 5.03952187899644, 58.3473975730234,
9.17287686145614, 2.99575072457674)), class = "data.frame", row.names = c(NA,
105L))
This is my model:
library(lme4)
model <- lmer((continuous) ~ treatment + level + (1|random), data= df, REML = TRUE)
The data as it is does not meet the model assumptions, but still I am wondering why I get a negative estimated marginal mean (response) on treatment "UN" level "L" (see lettering table) when I don't have any negative numbers in df$continuous?
library(multcompView)
library(emmeans)
lsm.mixed_C <- emmeans::emmeans(my_model,pairwise ~ treatment * level, type="response")
lettering <- CLD(lsm.mixed_C,alpha=0.05,Letters=letters,
adjust= "tukey")
The short answer is because you badly need to include the interaction in your model. Compare:
model2 <- lmer((continuous) ~ treatment * level + (1|random),
data= df, REML = TRUE)
emmip(model2, treatment ~ level)
with:
emmip(model, treatment ~ level)
In model2, both EMMs at level L are close to zero. If you remove the interaction from the model, you force those two profiles to be parallel, while maintaining a sizeable positive difference between treatments CCF and UN, forcing the estimate for UN to go negative. In actual fact, though, all six estimates for treatment x level combinations are seriously distorted.
I can't repeat it enough. emmeans() summarizes a model. If you give it a bad model, you get dumb results. Thanks for the great illustration of this point.
I'm trying to get ggplot to order my boxplots based on median value after splittin the data into several different facets.
This is part of a larger Shiny app I've written. Under default parameters, I can generate three faceted boxplots that order correctly:
boxData <- structure(list(Classification = structure(c(4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Pluripotent/ Undifferentiated",
"Endoderm", "Mesoderm", "Ectoderm"), class = c("ordered", "factor"
)), value = c(0.000255214868214152, 0.000108050996652777, 0.00751505823956855,
8.71801689770664, 5.71059263813113e-05, 4.90291746067526e-05,
0.000129388767504551, 2.52712436532327e-07, 5345.09546573398,
0.0020991194782334, 4.33360773005175e-06, 1.8200776481618, 3.44754305553851e-06,
4.38932775031697, 0.00720892572385782, 7.53517216121544e-05,
0.221288441144887, 0.00104230990042965, 0.00288742662358172,
4.20947546944294e-05, 9.62973878475845e-07, 0.00710967831313203,
26.9833955280036, 0.00265697432110539, 1.41814003567946, 0.261340025051291,
0.00159083508412152, 9.55044905589291e-06, 0.0122931632086495,
8.54789134364452e-06, 2.01899938950824e-05, 1.55354988683742e-06,
0.000441285511108929, 0.000353500530366103, 0.125347054487635,
109.440278770173, 2.03304264082645e-05, 2.01899938950824e-05,
0.000148628664387571, 2.89902659683517e-06, 207.073625180606,
3.52469070261441e-07, 3.15047327017105e-06, 0.639049681601525,
2.11937734339159e-05, 0.484309094613314, 0.0126387710681522,
0.000124981311087457, 0.010701820155981, 0.00520458916051572,
0.002548740132205, 6.70653961877279e-06, 1.1372650836283e-06,
0.0028674817110041, 6.38196191847228, 0.00104230990042965, 2.77791027153022,
0.385285554179204, 3.23552539344696, 0.00129215960928528, 3313.17800288969,
0.42454812322342, 0.427501088945987, 0.0252775421363044, 1.3790172222154e-05,
0.000499925244349826, 0.575943821174679, 3.66456124110476e-05,
0.000979273863184647, 1.71186456807568e-06, 0.000506903940694852,
3.95489796579998e-05, 7.60789146241221e-07, 5.53083255055159e-07,
0.000283178626588241, 5.68632541814152e-07, 89.5114292952616,
2.15183665744117e-06, 9.48447928546097e-06, 1.10616651011032e-06,
6.83831307491562e-05, 0.000231612381626088, 0.361984543094889,
5.91197625260395e-05, 0.000979273863184647, 2.83936549218472e-06,
0.000979273863184647, 5.11112358098405e-05, 1.714153924998e-07,
5.19634300333657e-07, 0.000285939985649123, 0.000340041865397713,
0.11809338012465, 60.884369685235, 2.29364239206782e-05, 1.59952159960469e-05,
0.000213718586351138, 2.65657707341963e-06, 3635.65603745587,
1.08786283557826e-07, 3.36257994807117e-06, 0.482299092292068,
1.40214978558205e-05, 0.506277403675245, 0.00847835446782661,
5.84677257215999e-05, 0.00674484030136259, 0.00483589957358377,
0.0017456741452281, 6.45120458509457e-06, 6.32689066217975e-07,
0.00245170310797391, 9.30496033238278, 0.000922604532223834,
1.94261499108326, 0.348202870167258, 0.000995700862302919, 9.18683915124066e-06,
0.00490340621594781, 9.51081233425213e-06, 1.64449027258861e-05,
1.32828853670982e-06, 0.000283964853893518, 0.000480891817820092,
0.103521332666818, 96.202334596196, 1.57750051307367e-05, 2.09600255345096e-05,
0.000200793473806753, 1.29196641682183e-06, 179.519904082227,
2.39744324779145e-07, 2.44454941589392e-06, 0.492433221447773,
1.07746460295468e-05, 0.437695664847132, 0.00947275639891981,
9.69768554804815e-05, 0.0056325346541415, 0.00470366164543522,
0.00172164093341244, 6.91422987569681e-06, 8.82439067876674e-07,
0.00253816223135828, 5.84822979360013, 0.000929021754230271,
2.31017156910716, 0.278934830581241, 2.84415482117455, 0.00100262650949219,
2661.45599990874, 0.357992185300285, 0.37579036951639, 0.0210213626331535,
1.87597483406766e-05, 4.9165300967331e-05, 0.353063601096188,
2.84344613435294e-05, 0.00277749494255326, 1.32828853670982e-06,
0.00108958918195797, 9.25073867082013e-06, 1.4059026149049e-07,
4.29154362580066e-07, 0.000537294242854559, 8.10925044524043e-06,
0.020165038913309, 9.91469621624329e-06, 1.63313094852695e-05,
8.58308725160133e-07, 2.34183669433728e-05, 0.000352033415883844,
0.28087497575791, 4.58728478413563e-05, 0.0007598488052299, 1.48407969771465e-06,
0.0223745115812679, 1.15479796826903e-05, 1.33006491938229e-07,
4.03200286568411e-07, 83.9815202938853, 211.131788444181, 1.73147313103931,
0.162893393670412, 6347.61978641754, 1.56049096034741, 0.532923368033971,
0.651573574681646, 22.0392007421302, 0.05154584678813, 85997.0767809387,
2.10234581817541, 1994.76074197656, 17462.8329237372, 1.76785506212734,
49735.9012814537, 1.57134503333516, 340.615434516655, 3.73730938753272,
2.07340220203944, 0.974004268543241, 53.8920290309386, 28.8800232787977,
0.0604547706008708, 6.41744933081988, 1.9615580079771, 0.384751805040216,
1.53900722016086, 1.68412590721683, 2.31658561238929, 1.62675839626425,
2.23767420207142, 1.67249279982813, 1.53900722016086, 1.51781925297405,
0.717972255311719, 1.08072540203935, 1.6958399292663, 1.74351647907412,
1.6958399292663, 0.98077900398855, 0.000159075579756261, 1.32133840565826,
1.57134503333516, 1.79253339913881, 2.00277451142267, 1.74351647907412,
2.66105808216138, 0.90250072746243, 2.059080166868, 1.50733490955838,
1.3966785324674, 1.61552155521922, 1.42602571736414, 1.90791910109511,
1.38703096913138, 1.38703096913138, 1.49692298679269, 1.69583992926629,
2.16145080407871, 2.67956720485568, 1.3966785324674, 1.53900722016086,
1.70763542878249, 0.921464186198703, 3.32188009636358, 10.5707072452661,
6.5522935828786, 1.68412590721683, 7.57896056479413, 1.43594451062343,
0.312515575646302, 34.1070955541741, 2339.52511354582, 11.0962477530511,
8.17942824487938, 1.68412590721683, 0.418123199957032, 804.528657067602,
0.679243142274472, 1.47631440568283, 1.75564359521904, 2.81278639982623,
4.14680440407889, 1.68412590721683, 2.33269873957693, 1.68412590721683,
1.70763542878249, 1.37745004638314, 1.68412590721683), listElement = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Endoderm",
"Mesoderm", "Ectoderm"), class = "factor")), .Names = c("Classification",
"value", "listElement"), row.names = c(NA, -270L), class = "data.frame")
To generate the boxplot:
boxData$temp <- paste(substr(boxData$Classification,1,6),
as.character(boxData$listElement))
ggplot(boxData, aes(reorder(boxData$temp, value, median),value, fill=Classification))+
geom_boxplot()+
scale_y_log10()+
ylab("Fold Expression Change")+
xlab("Gene Classification")+
theme(axis.text.x=element_text(angle=90, hjust=1, size=6))+
facet_wrap(~listElement, scales='free', ncol=1)+
scale_x_discrete(labels=setNames(as.character(boxData$Classification), boxData$temp))
But if a parameter is changed and we only have two samples rather than three (In this case, the same data, but with twice as many 'endoderm' samples and no 'mesoderm' samples), the boxplots look really weird:
boxData <- structure(list(Classification = structure(c(4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Pluripotent/ Undifferentiated",
"Endoderm", "Mesoderm", "Ectoderm"), class = c("ordered", "factor"
)), value = c(0.000255214868214152, 0.000108050996652777, 0.00751505823956855,
8.71801689770664, 5.71059263813113e-05, 4.90291746067526e-05,
0.000129388767504551, 2.52712436532327e-07, 5345.09546573398,
0.0020991194782334, 4.33360773005175e-06, 1.8200776481618, 3.44754305553851e-06,
4.38932775031697, 0.00720892572385782, 7.53517216121544e-05,
0.221288441144887, 0.00104230990042965, 0.00288742662358172,
4.20947546944294e-05, 9.62973878475845e-07, 0.00710967831313203,
26.9833955280036, 0.00265697432110539, 1.41814003567946, 0.261340025051291,
0.00159083508412152, 9.55044905589291e-06, 0.0122931632086495,
8.54789134364452e-06, 2.01899938950824e-05, 1.55354988683742e-06,
0.000441285511108929, 0.000353500530366103, 0.125347054487635,
109.440278770173, 2.03304264082645e-05, 2.01899938950824e-05,
0.000148628664387571, 2.89902659683517e-06, 207.073625180606,
3.52469070261441e-07, 3.15047327017105e-06, 0.639049681601525,
2.11937734339159e-05, 0.484309094613314, 0.0126387710681522,
0.000124981311087457, 0.010701820155981, 0.00520458916051572,
0.002548740132205, 6.70653961877279e-06, 1.1372650836283e-06,
0.0028674817110041, 6.38196191847228, 0.00104230990042965, 2.77791027153022,
0.385285554179204, 3.23552539344696, 0.00129215960928528, 3313.17800288969,
0.42454812322342, 0.427501088945987, 0.0252775421363044, 1.3790172222154e-05,
0.000499925244349826, 0.575943821174679, 3.66456124110476e-05,
0.000979273863184647, 1.71186456807568e-06, 0.000506903940694852,
3.95489796579998e-05, 7.60789146241221e-07, 5.53083255055159e-07,
0.000283178626588241, 5.68632541814152e-07, 89.5114292952616,
2.15183665744117e-06, 9.48447928546097e-06, 1.10616651011032e-06,
6.83831307491562e-05, 0.000231612381626088, 0.361984543094889,
5.91197625260395e-05, 0.000979273863184647, 2.83936549218472e-06,
0.000979273863184647, 5.11112358098405e-05, 1.714153924998e-07,
5.19634300333657e-07, 3.36257994807117e-06, 0.482299092292068,
1.40214978558205e-05, 0.00847835446782661, 5.84677257215999e-05,
0.00674484030136259, 0.00483589957358377, 0.0017456741452281,
6.45120458509457e-06, 6.32689066217975e-07, 0.00245170310797391,
9.30496033238278, 0.000922604532223834, 1.94261499108326, 0.348202870167258,
0.506277403675245, 0.000285939985649123, 0.000340041865397713,
0.11809338012465, 60.884369685235, 2.29364239206782e-05, 1.59952159960469e-05,
0.000213718586351138, 2.65657707341963e-06, 3635.65603745587,
1.08786283557826e-07, 83.9815202938853, 211.131788444181, 1.73147313103931,
0.162893393670412, 6347.61978641754, 1.56049096034741, 0.532923368033971,
0.651573574681646, 22.0392007421302, 0.05154584678813, 85997.0767809387,
2.10234581817541, 1994.76074197656, 17462.8329237372, 1.76785506212734,
49735.9012814537, 1.57134503333516, 340.615434516655, 3.73730938753272,
2.07340220203944, 0.974004268543241, 53.8920290309386, 28.8800232787977,
0.0604547706008708, 6.41744933081988, 1.9615580079771, 1.57750051307367e-05,
2.09600255345096e-05, 0.000200793473806753, 1.29196641682183e-06,
179.519904082227, 2.39744324779145e-07, 2.44454941589392e-06,
0.492433221447773, 1.07746460295468e-05, 0.437695664847132, 0.00947275639891981,
9.69768554804815e-05, 0.0056325346541415, 0.00470366164543522,
0.00172164093341244, 6.91422987569681e-06, 8.82439067876674e-07,
1.57134503333516, 1.79253339913881, 2.00277451142267, 1.74351647907412,
2.66105808216138, 0.90250072746243, 2.059080166868, 1.50733490955838,
1.3966785324674, 1.61552155521922, 0.384751805040216, 1.53900722016086,
1.68412590721683, 0.000995700862302919, 9.18683915124066e-06,
0.00490340621594781, 9.51081233425213e-06, 1.64449027258861e-05,
1.32828853670982e-06, 0.000283964853893518, 0.000480891817820092,
0.103521332666818, 96.202334596196, 1.6958399292663, 0.98077900398855,
0.000159075579756261, 2.31658561238929, 1.62675839626425, 2.23767420207142,
1.67249279982813, 1.53900722016086, 1.51781925297405, 0.717972255311719,
1.08072540203935, 1.6958399292663, 1.74351647907412, 1.32133840565826,
0.0210213626331535, 1.87597483406766e-05, 4.9165300967331e-05,
0.00253816223135828, 5.84822979360013, 0.000929021754230271,
2.31017156910716, 0.278934830581241, 2.84415482117455, 0.00100262650949219,
2661.45599990874, 0.357992185300285, 0.37579036951639, 1.42602571736414,
1.90791910109511, 1.38703096913138, 0.353063601096188, 2.84344613435294e-05,
0.00277749494255326, 1.32828853670982e-06, 0.00108958918195797,
9.25073867082013e-06, 1.4059026149049e-07, 4.29154362580066e-07,
0.000537294242854559, 8.10925044524043e-06, 0.020165038913309,
9.91469621624329e-06, 1.63313094852695e-05, 8.58308725160133e-07,
1.43594451062343, 0.312515575646302, 34.1070955541741, 2339.52511354582,
11.0962477530511, 8.17942824487938, 1.68412590721683, 0.418123199957032,
804.528657067602, 0.679243142274472, 10.5707072452661, 6.5522935828786,
1.68412590721683, 1.38703096913138, 1.49692298679269, 1.69583992926629,
2.16145080407871, 2.67956720485568, 1.3966785324674, 1.53900722016086,
1.70763542878249, 0.921464186198703, 3.32188009636358, 7.57896056479413,
2.34183669433728e-05, 0.000352033415883844, 0.28087497575791,
4.58728478413563e-05, 0.0007598488052299, 1.48407969771465e-06,
0.0223745115812679, 1.15479796826903e-05, 1.33006491938229e-07,
4.03200286568411e-07, 1.47631440568283, 1.75564359521904, 2.81278639982623,
4.14680440407889, 1.68412590721683, 2.33269873957693, 1.68412590721683,
1.70763542878249, 1.37745004638314, 1.68412590721683), listElement = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Endoderm",
"Ectoderm"), class = "factor")), .Names = c("Classification",
"value", "listElement"), row.names = c(NA, -270L), class = "data.frame")
Running the same code as above:
boxData$temp <- paste(substr(boxData$Classification,1,6),
as.character(boxData$listElement))
ggplot(boxData, aes(reorder(boxData$temp, value, median),value, fill=Classification))+
geom_boxplot()+
scale_y_log10()+
ylab("Fold Expression Change")+
xlab("Gene Classification")+
theme(axis.text.x=element_text(angle=90, hjust=1, size=6))+
facet_wrap(~listElement, scales='free', ncol=1)+
scale_x_discrete(labels=setNames(as.character(boxData$Classification), boxData$temp))
gives a strange-looking graph:
This graph should look the same as the first graph, just with two facets rather than three. If I don't try to reorder the values by median, this graph plots fine. I've fiddled with a number of things, but can't seem to fix this issue. I'm sure I've made a stupid mistake somewhere, but can't seem to find it.
Any help would be greatly appreciated!
It looks to me that you are reordering the factor "temp" without releveling the dataset. What about bringing the ordering operation outside the ggplot call?
boxData$temp <- paste(substr(boxData$Classification,1,6),
as.character(boxData$listElement))
fac <- with(boxData, reorder(temp, value, median, order = TRUE))
boxData$temp <- factor(boxData$temp, levels = levels(fac))
ggplot(boxData, aes(temp,value, fill=Classification))+
geom_boxplot()+
scale_y_log10()+
ylab("Fold Expression Change")+
xlab("Gene Classification")+
theme(axis.text.x=element_text(angle=90, hjust=1, size=6))+
facet_wrap(~listElement, scales='free', ncol=1)+
scale_x_discrete(labels=setNames(as.character(boxData$Classification), boxData$temp))
This is what you would expect, right?
I'm trying to create a facet wrapped ggplot boxplot with dataframe dataw and I'm trying to modify the labels of each subplot.
dataw <- structure(list(base = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L), .Label = c("A", "C", "G", "T"), class = "factor"), pos = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L), values = c(13, 22, 16, 21, 52, 1,
1.709, 2.121, 2.061, 2.233, 3.388, 1, 5, 6, 6, 2, 1, 0.856, 1.116,
1.207, 1.175, 0.95, 76, 45, 5, 1, 1, 15, 8.558, 5.44, 1.147,
0.857, 0.831, 10, 7, 40, 4, 10, 5, 1.547, 1.174, 4.777, 1.071,
1.356, 7, 0, 1, 6, 1, 8, 1.322, 0.728, 0.83, 1.178, 0.831, 4,
2, 0, 1, 3, 0, 1.098, 0.96, 0.63, 0.888, 1.013, 13, 22, 16, 21,
52, 1, 1.709, 2.121, 2.061, 2.233, 3.388, 3, 6, 7, 2, 9, 11,
0.952, 1.474, 1.45, 0.967, 1.306, 13, 22, 16, 21, 52, 1, 1.709,
2.121, 2.061, 2.233, 3.388, 3, 8, 15, 0, 5, 2, 1.014, 1.583,
2.289, 0.773, 1.135, 10, 3, 8, 1, 4, 2, 1.504, 1.03, 1.244, 0.884,
1.047, 4, 1, 0, 2, 5, 1, 1.066, 0.862, 0.689, 0.963, 1.125, 2,
0, 0, 2, 0, 1, 0.919, 0.723, 0.479, 0.922, 0.721, 7, 8, 0, 8,
7, 0, 1.299, 1.236, 0.779, 1.298, 1.224, 13, 22, 16, 21, 52,
1, 1.709, 2.121, 2.061, 2.233, 3.388, 45, 38, 41, 13, 34, 1,
2.817, 2.264, 2.398, 1.374, 3.848, 3, 0, 1, 1, 2, 14, 0.973,
0.641, 0.846, 0.866, 0.909, 13, 22, 16, 21, 52, 1, 1.709, 2.121,
2.061, 2.233, 3.388, 7, 0, 0, 1, 2, 1, 1.37, 0.436, 0.706, 0.685,
0.902, 0, 5, 5, 0, 7, 1, 0.597, 1.113, 1.079, 0.71, 1.222, 3,
1, 4, 0, 23, 8, 0.992, 0.84, 1.07, 0.762, 2.399, 17, 7, 18, 6,
10, 1, 2.4, 1.315, 1.948, 1.135, 1.306, 21, 8, 50, 4, 6, 12,
2.412, 1.254, 3.857, 1.075, 1.168, 13, 22, 16, 21, 52, 1, 1.709,
2.121, 2.061, 2.233, 3.388), type = structure(c(2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L), .Label = c("ipdRatio", "score"), class = "factor"),
labels = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L
), .Label = c("D<U+2192>", "G<U+2192>", "A<U+2192>", "K<U+2192>",
"C<U+2192>", "T<U+2192>"), class = "factor")), .Names = c("base",
"pos", "values", "type", "labels"), row.names = c("1", "2", "3",
"4", "5", "3942", "3943", "3944", "3945", "3946", "3947", "11",
"21", "31", "41", "51", "63", "64", "65", "66", "67", "68", "12",
"22", "32", "42", "52", "2953", "2954", "2955", "2956", "2957",
"2958", "13", "23", "33", "43", "53", "2461", "2462", "2463",
"2464", "2465", "2466", "14", "24", "34", "44", "54", "7493",
"7494", "7495", "7496", "7497", "7498", "111", "214", "311",
"411", "511", "4874", "4875", "4876", "4877", "4878", "4879",
"121", "221", "321", "421", "521", "9356", "9357", "9358", "9359",
"9360", "9361", "131", "231", "331", "431", "531", "9221", "9222",
"9223", "9224", "9225", "9226", "15", "25", "35", "45", "55",
"93561", "93571", "93581", "93591", "93601", "93611", "112",
"215", "312", "412", "512", "1579", "1580", "1581", "1582", "1583",
"1584", "122", "222", "322", "422", "522", "1782", "1783", "1784",
"1785", "1786", "1787", "132", "232", "332", "432", "532", "3398",
"3399", "3400", "3401", "3402", "3403", "16", "26", "36", "46",
"56", "2257", "2258", "2259", "2260", "2261", "2262", "113",
"216", "313", "413", "513", "1027", "1028", "1029", "1030", "1031",
"1032", "123", "223", "323", "423", "523", "8654", "8655", "8656",
"8657", "8658", "8659", "133", "233", "333", "433", "539", "702",
"703", "704", "705", "706", "707", "17", "27", "37", "47", "57",
"8123", "8124", "8125", "8126", "8127", "8128", "114", "217",
"314", "414", "514", "93562", "93572", "93582", "93592", "93602",
"93612", "124", "224", "324", "424", "524", "3700", "3701", "3702",
"3703", "3704", "3705", "134", "234", "334", "434", "5310", "8233",
"8234", "8235", "8236", "8237", "8238", "18", "28", "38", "48",
"58", "1542", "1543", "1544", "1545", "1546", "1547", "115",
"218", "315", "415", "515", "533", "534", "535", "536", "537",
"538", "125", "225", "325", "425", "525", "208", "209", "210",
"211", "212", "213", "135", "235", "335", "435", "5311", "93563",
"93573", "93583", "93593", "93603", "93613"), class = "data.frame")
These are the first few rows of dataw
head(dataw)
base pos values type labels
1 A 1 13 score D<U+2192>
2 A 1 22 score D<U+2192>
3 A 1 16 score D<U+2192>
4 A 1 21 score D<U+2192>
5 A 1 52 score D<U+2192>
3942 A 1 1 score D<U+2192>
I'm plotting it like so.
prettify <- theme(panel.background = element_rect(fill = NA,color="gray"),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(size=.1, color="black",linetype="dotted"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_line(size=.1, color="black"),
legend.position="bottom")
ggplot(dataw,aes(x = base, y = values, color = type, group = base)) +
geom_boxplot() +
facet_wrap(type ~ pos, scales="free_y", nrow = 2) +
theme_gray() %+replace% prettify
Currently the sublabels are the type value followed by a comma and the pos value. However I would like to get rid of the type value, and label it so that the labels of each subplot are in the format: "Position [pos value], [labels value]"
What would be the best way to go about this? Thank you.
Try replacing the entire ggplot statement with
ggplot(data=transform(dataw, plt_labels = paste("Position ", pos, ", ", labels, sep="")),aes(x = base, y = values, color = type, group = base)) +
geom_boxplot() +
facet_grid(type ~ plt_labels, scales="free_y") +
theme_gray() %+replace% prettify
which should give
The following code is a minimal (for some value of minimal....) example that uses lattice to produce boxplots. But the median line on those boxplot is a) coloured and b) very thin. How to get them to be black and tick?
a71<-structure(list(n = structure(c(1L, 2L, 2L, 4L, 4L, 1L, 1L, 4L,
2L, 1L, 1L, 2L, 2L, 4L, 2L, 2L, 3L, 4L, 1L, 2L, 2L, 3L, 2L, 2L,
2L, 4L, 3L, 3L, 4L, 2L, 4L, 2L, 1L, 3L, 2L, 3L, 4L, 1L, 4L, 1L,
3L, 3L, 2L, 1L, 1L, 3L, 3L, 1L, 2L, 4L, 3L, 2L, 3L, 1L, 4L, 1L,
4L, 2L, 3L, 4L, 4L, 4L, 1L, 3L, 3L, 3L, 4L, 2L, 2L, 2L, 4L, 4L,
4L, 1L, 4L, 3L, 2L, 2L, 4L, 4L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L,
3L, 1L, 3L, 3L, 4L, 1L, 3L, 2L, 1L, 3L, 1L, 2L), .Label = c("100",
"200", "400", "800"), class = "factor"), g = structure(c(3L,
3L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 1L,
3L, 1L, 2L, 3L, 2L, 2L, 2L, 3L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 3L,
1L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 1L,
1L, 1L, 2L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, 3L,
3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 1L,
3L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 3L,
3L, 1L, 3L), .Label = c("0", "0.5", "1"), class = "factor"),
cr = structure(c(1L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L,
3L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 2L, 3L, 1L,
3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 3L, 1L,
1L, 1L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 1L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 2L, 2L
), .Label = c("-0.4", "0", "0.4"), class = "factor"), bias = c(0.0162558992812201,
0.138354243932496, 0.0205686041691062, 0.269714433604472,
0.381044037439145, 0.0869422119950729, 0.331379037601084,
0.686894150152472, 0.0140922903231885, 0.225078933454863,
0.554444988164574, 0.076032683077827, 0.335284040888653,
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0.141353291599136, 0.0220267076189838, 0.242149484071382,
0.278319984858078, 0.193105829691662, 0.0259815643559331,
0.318504899459259, 0.00277002060524357, 0.212681621053374,
0.418358846098857, 0.358916156777489, 0.438248724241505,
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0.268111328952192, 0.123265441389974, 0.0376832357983068,
0.0353605481767078, 0.021227873083535, 0.0385614926552725,
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0.292085797406362, 0.198391364913347, 0.0779507859721407,
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0.260472754754191, 0.0151116521560003, 0.0422855023583402,
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0.111205748449328, 0.413083543393392, 0.0138839674143682,
0.22407421093074, 0.72309883706409, 0.423231501875638, 0.141932050342199,
0.133808548118004, 0.331500621801688, 0.127652280721512,
0.132083126730013, 0.261864564503826, 0.208243130464985,
0.18657049493156, 0.333701537602998, 0.404884075502013, 0.470789398932934,
0.115008599462104, 0.177984001517338, 0.331717679106776,
0.0862418839846533), group = structure(c(3L, 2L, 2L, 3L,
1L, 3L, 3L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 1L,
3L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 1L,
3L, 2L, 3L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 2L,
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3L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 3L,
1L, 1L, 3L, 2L, 1L, 3L), .Label = c("1", "2", "3"), class = "factor")), .Names = c("n",
"g", "cr", "bias", "group"), row.names = c(8721L, 6970L, 6686L,
9624L, 352L, 10545L, 7505L, 4216L, 6170L, 3309L, 10429L, 4302L,
5602L, 5680L, 1530L, 9234L, 5007L, 8004L, 721L, 10038L, 502L,
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8689L, 439L, 2590L, 8649L, 5447L, 1957L, 10570L), class = "data.frame")
library(lattice)
cl<-c('red','green','blue')
mypanel<-function(...){
panel.bwplot(...,pch="|",col="black",cex=4,fill=cl)
}
o1<-bwplot(a71$bias~a71$group|a71$cr*a71$g,type=c("l","g"),ylim=c(0,1),panel=mypanel)
plot(o1)
By changing some of the parameters of box.rectangle (a lattice-specific graphical parameter), you can manipulate the lines (including the median line) surrounding each of the box plots. This will change all the lines around the boxes, however, not just the median line.
myPars <- list(box.rectangle = list(lwd = 2, col = "black"))
lwd changes the line width (thickness). colchanges the color of the lines. Then pass this list to the par.settings argument in bwplot.
o1 <- bwplot(a71$bias ~ a71$group | a71$cr * a71$g,
type = c("l", "g"), ylim = c(0, 1), panel = mypanel,
par.settings = myPars)
plot(o1)
To see all of the parameters associated with box.rectangle, use
trellis.par.get("box.rectangle")
OP is happy with all lines thicker by #BenBarnes, but for completeness, it is possible to just thicken the median line. Using the fact that box.width defaults to 1/2:
bwplot(a71$bias~a71$group|a71$cr*a71$g,type=c("l","g"),ylim=c(0,1),
panel=function(x,y,...){
panel.bwplot(x,y,...,pch="|",col="black",cex=4,fill=cl)
medy <- by(y,list(x),median)
xx <- sort(unique(as.numeric(x)))
panel.segments(xx-.25,medy,xx+.25,medy,lwd=2)
}
)