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))
Related
I am getting an error
Error in svd(X) : infinite or missing values in 'x'
while doing summary of ordinal regression model. This is my code ..
library(MASS)
a <- dget('dput.txt')
lep <- polr(bmicat2 ~ Leptin, data = a,Hess = TRUE)
summary(lep)
Error in svd(X) : infinite or missing values in 'x'
sample data is given to replicate the error. Can someone please help.
structure(list(bmicat2 = structure(c(1L, 3L, 2L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 3L,
2L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 1L,
1L, 2L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L,
3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 2L,
2L, 1L, 1L, 1L, 3L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 3L, 1L, 3L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 3L,
1L, 2L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 2L, 1L, 3L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 2L, 2L, 3L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 3L,
1L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 1L, 3L, 3L,
1L, 2L, 3L, 3L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 1L,
3L, 3L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 2L, 1L,
3L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
3L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 3L,
2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L,
1L, 1L, 1L, 3L, 1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 3L, 2L, 2L,
1L, 1L, 3L, 2L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 3L,
3L, 3L, 2L, 1L, 1L, 3L, 3L, 1L, 2L, 3L, 3L, 1L, 3L, 3L, 1L, 1L,
1L, 3L, 1L, 3L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 1L,
3L, 2L, 2L, 2L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 2L, 3L, 1L, 3L,
1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 3L,
1L, 1L, 3L), .Label = c("Normal", "Overweight", "Obesity"), class = "factor"),
Leptin = c(47710.88, 200022.04, 161067.35, 55561.76, 100728.06,
69783.61, 54981.01, 58801.34, 128313.05, 157378.46, 292366.57,
121608.04, 206046.88, 54981.01, 154969.33, 516791.75, 104766.73,
440134.48, 286576.47, 343513.87, 40020.2, 30077.63, 359266.48,
290381.41, 23005.7, 48080.73, 134741.37, 114631.03, 49644.25,
139956.69, 138242.54, 19862, 64541.08, 57119.32, 115382.48,
7238.99, 154969.33, 82321.93, 85406.54, 19170.47, 57208.08,
277488.74, 290791.3, 206703.97, 25333.82, 20134.62, 32823.3,
231036.03, 111986.18, 352190.59, 128041.35, 185025.96, 63451.72,
143404.56, 71163.46, 252067.35, 46223.39, 185077.75, 172339.07,
41381.36, 91498.49, 233969.82, 24245.94, 248133.29, 145890.48,
196431.01, 146690.84, 218617.65, 151333.68, 245695.08, 336242.88,
266936.45, 64105.63, 301181.31, 150192.02, 253863.48, 314169.03,
406059.04, 68228.37, 335171.04, 37547.56, 123713.8, 75034,
45708.91, 67449.43, 15920.57, 38444.37, 19170.47, 174853.97,
236689.18, 22879.68, 34599.46, 57562.83, 177486.58, 244481.84,
122637.48, 58094.2, 82921.65, 382788.21, 119733.17, 64192.75,
8787.33, 17146.98, 21986.45, 13077.71, 18320.19, 119777.74,
61615.67, 5708.97, 24307.05, 244118.62, 10780.13, 12158.23,
80265.64, 70215.06, 122189.66, 48219.23, 156702.5, 128313.05,
115072.95, 152956.29, 107776.73, 108914.21, 85835.09, 13608.28,
24853.42, 58359.56, 29967.69, 168944.14, 22435.55, 67709.18,
17444.89, 51058.87, 21072.27, 34702.75, 9711.01, 43870.71,
113571.74, 26863.91, 294914.16, 15920.57, 23381.51, 309409.6,
19587.14, 99905.3, 79494.37, 90768.21, 131129.25, 411527.63,
132269.4, 83735.58, 92014.19, 57030.54, 62578.38, 131676.19,
238711.8, 48080.73, 429691.15, 88750.76, 32770.45, 163022.26,
98045.93, 8421.27, 113659.95, 27210.74, 421265.4, 225005.8,
39431.91, 18748.27, 224660.83, 13695.33, 11186.36, 42727.62,
863581.67, 18031.36, 250895.16, 326547.74, 69351.93, 34288.82,
64932.66, 23381.51, 392858.01, 32399.47, 176370.64, 31651.9,
17592.65, 93304.24, 71938.68, 297130.98, 58624.7, 311565.88,
143685.29, 204518.07, 28689.43, 612308, 119688.61, 116888.21,
60738.48, 274462.8, 307122.48, 202886.86, 119777.74, 194409.2,
259555.37, 479766.12, 97527.66, 177029.67, 17146.98, 70560.06,
50922.4, 213003.8, 142470.09, 26747.8, 235563.59, 49460.99,
185181.36, 27210.74, 156220.34, 284284.42, 254982.61, 67059.61,
46736.22, 97657.21, 399497.2, 13433.03, 385197.12, 143685.29,
46503.31, 333111.93, 228589.14, 40117.95, 86563.73, 459114.82,
334559.84, 39824.45, 90295.84, 19309.96, 25333.82, 15604.37,
548683.37, 13954.22, 63974.92, 283344.56, 47061.73, 14794.69,
56852.91, 63320.83, 21335.74, 18176.13, 44060.31, 31436.88,
779337.47, 177842.32, 15683.81, 1804.18, 18031.36, 150572.24,
69956.22, 353788.11, 42823.25, 25927.76, 23131.34, 351871.83,
130355.47, 48034.54, 9711.01, 296020.99, 77994.24, 106641.08,
NA, 68876.79, 64323.41, 37947.12, 166071.05, 239189.29, 376318.03,
132041.15, 48818.15, 209454.17, 25572.25, 232324.09, 142750.23,
34185.02, 21204.24, 6040.1, 5708.97, 252190.95, 129219.75,
50922.4, 97786.76, 117464.95, 84978.04, 15122, 6356.95, NA,
299220.74, 28801.64, 535105.27, 421922.32, 9486.52, 159801.65,
139492.77, 40410.71, 28011.68, 67189.58, 10780.13, 173997.11,
15283.91, 192081.41, 169590.94, 29747.24, 57961.45, 55606.38,
351712.55, 157233.52, 117553.73, 201046.58, NA, 204409.11,
468179.87, 201640.99, 374968.45, 330911.94, 153817.84, 45098.7,
83050.16, NA, 20134.62, 20606.55, 328498.24, 22115.29, 40313.21,
196751.22, 6356.95, 8042.41, 252005.56, 124522.47, 9711.01,
168745.34, 110580.08, 78722.96, 104897.35, 43728.34, 20270.11,
339321.01, 170488.25, 55829.38, 173292.85, 29967.69, 5708.97,
30132.53, 12715.53, 180648.71, 146219.86, 33716.28, 85835.09,
110404.53, 327146.88, 259303.38, 164396.3, 206156.31, 21204.24,
105158.65, 152478.41, 327897.07, 29025.44, 36237.65, 426358.52,
265525.58, 52464.02, 287117.75, 658217.42, 67709.18, 107645.6,
46829.29, 186790.78, 68920, 363915.23, 269385.74, 126324.01,
146361.1, 153243.28, 101161.4, 318839.47, 132223.74, 96190,
143966.19, 162141.4, 63495.34, 35371.14, 261070.81, 197071.7,
240146.08, 73100.38, 63713.39, 248622.82, 92616.07, 163120.25,
95026.25, 266807.98, 153434.72, 145937.52, 127950.82, 376487.04,
81208.15, 311998.55, 41767.47, 59595.08, 13256.19, 35011.88,
99083.27, 47571.98, 174450.51, 8296.48, 35524.66, 68747.15,
214064.04, 127272.35, 70603.17, 99256.27, 19862, 145373.38,
184560.15, 57828.63, 115426.71)), row.names = c(NA, -425L
), class = c("tbl_df", "tbl", "data.frame"))
It is an issue of lep$Hessian having NA values. If you do this:
lep <- polr(bmicat2 ~ Leptin, data = a,Hess = TRUE)
lep$Hessian[1,1]<-0
lep$Hessian[1,2]<-0
lep$Hessian[1,3]<-0
lep$Hessian[2,1]<-0
lep$Hessian[3,1]<-0
summary(lep)
You do not have any problems but the output from lep and summary(lep) are pretty much the same, I think. Is it the imputation of 0 to missing values in the Hessian that disturbing?
As mention here https://cran.r-project.org/web/packages/energy/energy.pdf
the e.dist returns Returns the E-distances (energy statistics) between clusters.
And as inputs it accepts data matrix of pooled sample or Euclidean distances.
I want to compute the e-distance between 100 observations of my data set.
Look what I did:
> disteuc<-dist(DATABASE,method = "euclidean")
> edist(disteuc,sizes=100)
dist(0)
Why I get null value ??
This is the first 100 rows of my data set:
> dput(DATABASE[1:100,])
structure(list(TYPE_PEAU = c(2L, 2L, 3L, 2L, 2L, 2L, 2L, 4L,
3L, 2L, 2L, 2L, 2L, 1L, 4L, 2L, 2L, 2L, 4L, 2L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 4L, 3L, 4L, 2L, 2L, 2L, 2L, 4L, 2L, 1L, 2L, 2L, 2L,
2L, 4L, 3L, 2L, 4L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 2L, 4L, 2L, 2L,
3L, 4L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 4L, 2L, 4L, 2L, 4L,
3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 4L, 2L, 2L, 2L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L), SENSIBILITE = c(3L,
2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
1L, 3L, 3L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 3L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 2L,
1L, 3L, 3L), IMPERFECTIONS = c(2L, 2L, 3L, 3L, 1L, 2L, 2L, 3L,
2L, 2L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 2L, 2L, 3L, 2L, 2L, 1L,
2L, 2L, 2L, 3L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L,
3L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 3L, 3L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 3L,
2L, 2L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 1L, 2L, 1L), BRILLANCE = c(3L,
3L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 2L,
3L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L), GRAIN_PEAU = c(3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L,
2L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 3L,
3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 2L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 2L, 3L, 2L, 2L, 1L,
3L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 1L), RIDES_VISAGE = c(3L,
1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L,
3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L,
3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 3L,
3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L,
3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 3L,
3L, 1L, 3L), ALLERGIES = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), MAINS = c(2L, 2L,
3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L,
2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L,
3L, 2L), PEAU_CORPS = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 3L,
1L, 3L, 2L, 3L, 1L, 2L, 2L, 3L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L,
1L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L), INTERET_ALIM_NATURELLE = c(1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 1L,
3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 3L), INTERET_ORIGINE_GEO = c(1L, 1L, 2L, 3L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L,
1L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L,
1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 3L,
1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 2L), INTERET_VACANCES = c(1L,
2L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
3L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
3L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L), INTERET_ENVIRONNEMENT = c(1L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L), INTERET_COMPOSITION = 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, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), AGE_INTERVAL = c(3L, 3L, 4L, 2L, 2L, 3L, 3L, 4L,
4L, 3L, 4L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L,
2L, 4L, 2L, 3L, 2L, 4L, 3L, 2L, 4L, 4L, 3L, 3L, 4L, 4L, 3L, 3L,
2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 4L, 3L, 2L,
2L, 4L, 2L, 2L, 4L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 4L, 3L, 2L, 2L,
3L, 2L, 4L, 2L, 4L, 3L, 2L, 4L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L,
2L, 3L, 3L, 4L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L), ATTENTE_BEAUTE_1 = c(1L,
6L, 4L, 4L, 6L, 6L, 3L, 1L, 1L, 4L, 3L, 6L, 2L, 5L, 5L, 6L, 7L,
4L, 6L, 3L, 4L, 6L, 1L, 1L, 1L, 3L, 6L, 2L, 6L, 3L, 4L, 4L, 6L,
3L, 6L, 6L, 1L, 2L, 1L, 3L, 3L, 6L, 2L, 1L, 4L, 6L, 1L, 6L, 6L,
1L, 6L, 6L, 5L, 1L, 3L, 2L, 4L, 3L, 4L, 6L, 7L, 1L, 2L, 6L, 2L,
6L, 6L, 6L, 3L, 6L, 4L, 1L, 5L, 6L, 1L, 1L, 3L, 3L, 6L, 1L, 6L,
6L, 1L, 6L, 4L, 4L, 4L, 2L, 6L, 1L, 6L, 1L, 1L, 1L, 3L, 2L, 4L,
6L, 6L, 6L), ATTENTE_BEAUTE_2 = c(2L, 2L, 3L, 6L, 4L, 1L, 4L,
7L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 6L, 2L, 2L, 2L, 2L, 2L, 2L,
6L, 4L, 1L, 2L, 1L, 4L, 2L, 3L, 1L, 4L, 7L, 4L, 1L, 6L, 3L, 2L,
1L, 4L, 2L, 7L, 7L, 1L, 5L, 5L, 7L, 4L, 7L, 1L, 2L, 1L, 5L, 7L,
4L, 6L, 1L, 2L, 4L, 3L, 6L, 4L, 4L, 4L, 4L, 4L, 5L, 7L, 1L, 2L,
4L, 3L, 7L, 2L, 6L, 4L, 7L, 5L, 7L, 1L, 1L, 5L, 4L, 6L, 6L, 2L,
1L, 1L, 4L, 3L, 4L, 3L, 3L, 1L, 1L, 6L, 2L, 2L, 2L), MILIEU_VIE = c(1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), PROFIL_SELECTIONNE = c(1L, 32L, 21L, 23L, 34L, 31L,
15L, 6L, 1L, 20L, 14L, 34L, 9L, 28L, 28L, 32L, 42L, 20L, 32L,
14L, 20L, 32L, 1L, 5L, 3L, 13L, 32L, 7L, 34L, 14L, 21L, 19L,
34L, 18L, 34L, 31L, 5L, 8L, 1L, 13L, 15L, 32L, 12L, 6L, 19L,
35L, 4L, 36L, 34L, 6L, 31L, 32L, 25L, 4L, 18L, 9L, 23L, 13L,
20L, 34L, 39L, 5L, 9L, 34L, 9L, 34L, 34L, 35L, 18L, 31L, 20L,
3L, 27L, 36L, 1L, 5L, 15L, 18L, 35L, 6L, 31L, 31L, 4L, 34L, 23L,
23L, 20L, 7L, 31L, 3L, 33L, 3L, 2L, 2L, 13L, 7L, 23L, 32L, 32L,
32L), NOMBRE_ACHAT = c(14L, 6L, 3L, 9L, 8L, 13L, 10L, 14L, 4L,
3L, 10L, 8L, 12L, 3L, 7L, 6L, 4L, 13L, 3L, 3L, 6L, 13L, 3L, 4L,
6L, 7L, 4L, 12L, 5L, 6L, 16L, 3L, 14L, 4L, 4L, 6L, 9L, 13L, 3L,
5L, 12L, 4L, 3L, 6L, 3L, 6L, 6L, 3L, 6L, 4L, 3L, 3L, 7L, 3L,
12L, 12L, 10L, 3L, 6L, 7L, 14L, 3L, 18L, 7L, 5L, 4L, 7L, 17L,
6L, 6L, 3L, 6L, 17L, 10L, 12L, 5L, 13L, 15L, 6L, 3L, 11L, 6L,
7L, 7L, 16L, 3L, 3L, 3L, 3L, 6L, 3L, 4L, 3L, 10L, 3L, 4L, 6L,
5L, 14L, 3L), NOMBRE_CADEAU = c(2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L)), .Names = c("TYPE_PEAU",
"SENSIBILITE", "IMPERFECTIONS", "BRILLANCE", "GRAIN_PEAU", "RIDES_VISAGE",
"ALLERGIES", "MAINS", "PEAU_CORPS", "INTERET_ALIM_NATURELLE",
"INTERET_ORIGINE_GEO", "INTERET_VACANCES", "INTERET_ENVIRONNEMENT",
"INTERET_COMPOSITION", "AGE_INTERVAL", "ATTENTE_BEAUTE_1", "ATTENTE_BEAUTE_2",
"MILIEU_VIE", "PROFIL_SELECTIONNE", "NOMBRE_ACHAT", "NOMBRE_CADEAU"
), row.names = c(NA, 100L), class = "data.frame")
>
You are only specifying a cluster size, if you have 100 data and they are all in the same cluster there are no distances to other clusters.
> edist(disteuc,sizes=100)
dist(0)
> edist(disteuc,sizes=c(10, 90))
1
2 42.51959
> edist(disteuc,sizes=c(10, 40, 50))
1 2
2 44.32714
3 39.80484 35.26888
I have plenty of measurements (N ~ 500.000) of two laboratory tests that I wanted to plot using geom_boxplot. These measurements were created using three different analyzers.
However, many of these measurements are extreme outliers which leads to extremely wide scales, which again results in boxes that are too small to really read or compare.
I created a dummy data set sampling only 250 of these measurements to showcase the issue. The data as follows:
dummy <- structure(list(result = c(3.93, 2.708, 2.08, 1.8422, 0.897, 1.68,
3.56, 2.8954, 0.972, 2.99, 0.567, 2.01, 2.5629, 0.7958, 4.81,
3.539, 2.24, 4.79, 2.07, 5.56, 3.4761, 1.74, 1.5691, 0.964, 1.8171,
0.005, 0.663, 2.16, 2.37, 0.0164, 1.25, 3.086, 0.769, 1.9573,
1.05, 2.17, 1.6331, 8.2358, 0.58, 1.43, 0.9328, 1.94, 2.59, 5.06,
0.0574, 1.61, 1.01, 8.21, 2.77, 0.9938, 20.38, 3.71, 1.6731,
0.0259, 0.9701, 0.0114, 0.499, 38.8, 4.8689, 3.02, 1.176, 2.86,
1.96, 5.03, 0.7564, 0.903, 2.0017, 0.4928, 1.3993, 4.02, 1.97,
10.6109, 1.18, 1.68, 230, 1.9764, 3.81, 3.3518, 0.985, 3.4, 16.1,
1.5889, 3.13, 2.0168, 1.82, 4.75, 2.61, 2.0133, 1.1971, 0.4736,
74.1, 0.737, 5.21, 1.6495, 1.4, 3.7408, 0.68, 2.26, 2.5, 2.16,
0.459, 0.0281, 5.34, 1.3, 4.11, 1.9344, 3.9611, 2.79, 1.72, 8.9041,
1.47, 2.61, 3.02, 1.91, 3.49, 1.0161, 1.9, 1.63, 1.31, 1.81,
2.556, 0.972, 4.9, 8.313, 1.55, 0.875, 1.4379, 3.68, 0.716, 2.76,
2.1897, 0.3121, 1.4376, 2.56, 0.89, 3.0298, 0.6003, 1.2542, 1.61,
0.491, 3.08, 1.45, 1.94, 2.1503, 2.6605, 23.5, 1.54, 3.54, 4.22,
2.31, 1.082, 1.45, 1.77, 0.423, 11.9, 2.77, 4.8894, 0.8142, 0.158,
9.2012, 1.96, 0.467, 0.4081, 1.06, 2, 3.05, 2.81, 0.2151, 2.21,
0.95, 2.3647, 0.357, 1.7284, 1.31, 0.9586, 1.4548, 4.51, 0.022,
2.2629, 39.9014, 1.3403, 6.64, 4.62, 1.27, 1.18, 1.1, 0.565,
0.939, 1.9141, 3.855, 0.455, 3.14, 1.09, 1.0475, 0.971, 2.33,
1.16, 4.6919, 100, 0.2412, 2.53, 1.84, 1.04, 2.35, 4.89, 1.6384,
3.33, 1.82, 0.8973, 0.6061, 0.98, 6.18, 0.4258, 2.5555, 1.67,
5.37, 2.29, 2.93, 5.0596, 2.2328, 2.84, 7.73, 1.8, 2.3978, 3.02,
2.71, 0.618, 0.0035, 3.97, 0.9827, 2.3385, 5.07, 0.306, 3.13,
2.62, 2.81, 4.4749, 1.0362, 1.4896, 2.3907, 1.45, 2.2823, 2.3726,
2.1746, 4.08, 2.98, 2.57, 0.947, 2.16, 1.46), testName = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Test A", "Test B"
), class = "factor"), accountName = structure(c(3L, 2L, 3L, 2L,
2L, 5L, 6L, 2L, 6L, 3L, 2L, 3L, 1L, 1L, 5L, 2L, 3L, 5L, 3L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 6L, 1L, 3L, 4L, 2L, 2L, 1L, 2L, 5L, 6L,
2L, 4L, 3L, 4L, 4L, 3L, 3L, 3L, 4L, 5L, 4L, 6L, 3L, 2L, 5L, 3L,
2L, 5L, 1L, 4L, 5L, 1L, 2L, 3L, 1L, 5L, 3L, 3L, 2L, 3L, 2L, 2L,
2L, 3L, 1L, 1L, 1L, 5L, 6L, 4L, 3L, 1L, 3L, 5L, 3L, 1L, 3L, 2L,
5L, 3L, 2L, 2L, 2L, 4L, 5L, 1L, 3L, 1L, 3L, 4L, 2L, 5L, 6L, 6L,
1L, 3L, 6L, 2L, 6L, 2L, 2L, 1L, 3L, 4L, 2L, 3L, 5L, 2L, 1L, 4L,
6L, 2L, 1L, 3L, 1L, 3L, 3L, 4L, 3L, 2L, 4L, 5L, 4L, 1L, 1L, 2L,
1L, 1L, 6L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 5L, 4L, 2L, 4L, 3L, 5L,
3L, 1L, 2L, 6L, 3L, 3L, 5L, 3L, 1L, 4L, 3L, 4L, 6L, 3L, 1L, 3L,
3L, 6L, 1L, 4L, 3L, 3L, 1L, 4L, 2L, 1L, 1L, 1L, 3L, 6L, 2L, 1L,
2L, 3L, 1L, 3L, 1L, 6L, 3L, 1L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 6L,
3L, 1L, 4L, 1L, 4L, 2L, 1L, 6L, 3L, 2L, 3L, 3L, 4L, 4L, 6L, 3L,
4L, 1L, 5L, 1L, 3L, 3L, 2L, 2L, 3L, 2L, 1L, 1L, 5L, 3L, 6L, 1L,
1L, 2L, 1L, 3L, 6L, 5L, 3L, 3L, 4L, 2L, 4L, 2L, 6L, 1L, 4L, 1L,
3L, 1L, 3L, 2L, 2L, 1L), .Label = c("Lab 1", "Lab 2", "Lab 3",
"Lab 4", "Lab 5", "Lab 6"), class = "factor"), moduleCode = structure(c(2L,
1L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 3L,
2L, 3L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 3L,
1L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L,
1L, 2L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 1L,
3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 3L, 3L,
1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 1L,
3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 2L,
3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 2L, 1L,
3L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 1L, 1L,
3L, 2L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 2L,
3L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 3L,
2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L,
1L, 3L, 2L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 2L, 1L,
1L, 3L, 3L, 1L, 1L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 3L, 3L, 1L, 2L,
3L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("X", "Y", "Z"
), class = "factor")), .Names = c("result", "testName", "accountName",
"moduleCode"), class = "data.frame", row.names = c(NA, -250L))
Using this code
library(ggplot2)
boxplot <- ggplot(data=dummy, aes(x=accountName, y=result, fill=moduleCode)) +
geom_boxplot(position=position_dodge(width=0.85), outlier.alpha=0.2) +
facet_wrap(~testName, scales="free_y", nrow=2) +
labs(fill="Module") +
theme_bw() +
theme(axis.ticks.x=element_line(color="black")) +
theme(axis.ticks.y=element_line(color="black")) +
theme(axis.text.x=element_text(size=10, angle=55, hjust=1, vjust=0.975, color="black")) +
theme(axis.text.y=element_text(color="black")) +
theme(panel.background=element_blank()) +
theme(axis.title.x=element_blank()) +
theme(axis.title.y=element_blank()) +
theme(axis.line=element_line(colour="black", size=0.5, linetype ="solid")) +
theme(panel.grid.major.y=element_line(color="#bdbdbd", linetype="dotted")) +
theme(panel.grid.minor.y=element_line(color="#bdbdbd", linetype="dotted")) +
theme(plot.margin=unit(c(0.5, 0.5, 0.5, 0.7), "cm"))
print(boxplot)
I created this plot
NOW, the question is:
How can I scale the y-axis of the facets independently, and manually - best case, so that it works generically, since I wanted to write a script that can handle comparison of two random tests?
In general, I want the range of the scales to be much smaller than the complete data's range, i.e., I want to focus on the range of the lower and upper extremes of the boxplot whiskers only for each of the facets independently.
Those can be obtained using
boxplot.stats(dummy$result)$stats[c(1, 5)]
I'm not looking for the scales='free_y' argument. Also, scale_y_continuous and coord_cartesian won't do the job because, from what I understand, they set scales globally for all facets representing the single tests, which can vary greatly. I tried adding empty layers using geom_blank with dummy data that is limited in range to the lower and upper whiskers extremes. The problem is, depending on the tests, there always might be extreme outliers in the real data still causing the scales to get really wide and thus ignoring the range of the dummy data plugged into geom_blank.
I also tried extracting and manipulating the scales after creating the graph object using function ggplot_build:
graph_object <- ggplot_build(boxplot)
graph_object$layout$panel_scales$y[[1]]$range$range <- boxplot.stats(dummy[with(dummy, testName=="Test A"),]$result)$stats[c(1, 5)]
graph_object$layout$panel_scales$y[[2]]$range$range <- boxplot.stats(dummy[with(dummy, testName=="Test B"),]$result)$stats[c(1, 5)]
I then tried plotting the manipulated object using print(graph_object) and graph_object$plot, however, the scales still are the 'original' wider ones including the extreme values. Am I missing anyhting?
Sorry for the very long post, I might have gotten lost somewhere on the way trying to solve this and overlooked the solution. Therefore, I would be really happy to see some ideas or even a proper solution to this.
Thanks a lot!
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$temp))
This is what you would expect, right?
When I make a barplot with significance letters from anova above the bars, I use following code:
anova_NDW_geel<-aov(nodule_dry_weight~treatment,inoculatieproef_geel_variety2)
HSD_NDW_geel <- HSD.test(anova_NDW_geel,"treatment",alpha=0.05,group=TRUE)$groups
HSD_NDW_means_geel <- HSD.test(anova_NDW_geel,"treatment",alpha=0.05,group=TRUE)$means
HSD_NDW_means_geel <- HSD_NDW_means_geel[order(-HSD_NDW_means_geel$nodule_dry_weight),]
p_HSD_NDW_geel <- ggplot(aes(x=treatment, y=NDW_mean_geel, width=0.6), data=inoculatieproef_mean_geel)+
geom_bar(stat="identity", data=HSD_NDW_geel, aes(x=trt, y=means), fill="gray40")+
geom_text(data=HSD_NDW_geel, aes(x=trt, y=means, label=M), size=5, vjust=-1, hjust=1)+
ggtitle("Zand")+
ylab("Droog gewicht wortelknolletjes (g)")+
xlab("Behandeling")+
geom_errorbar(aes(ymin=NDW_mean_geel-NDW_sd_geel,ymax=NDW_mean_geel+NDW_sd_geel),
position=position_dodge(width=0.5),width=0.1,size=0.3)+
theme_bw() +
theme(axis.line = element_line(colour="black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+
scale_y_continuous(expand = c(0, 0))+
theme(axis.text.x = element_text(angle = 0, hjust = 1, vjust = 0.5))+
theme(text = element_text(size=12))
which results in following graph: http://i.stack.imgur.com/bZidZ.png
This is probably not the best way to do this and when I want to add the letters to the barplots with facet wrap.
Here is a sample of the data I want to make a facet wrap with significance letters with:
structure(list(treatment = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L), .Label = c("1", "2",
"3", "4", "5", "6", "7", "8"), class = "factor"), block = structure(c(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, 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, 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, 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, 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, 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, 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, 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), .Label = c("I",
"II", "III", "IV"), class = "factor"), position = structure(c(2L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 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("b",
"gem(ab)"), class = "factor"), variety = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), location = 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, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 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("Geel",
"Merelbeke"), class = "factor"), year = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 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("2014",
"2015"), class = "factor"), nodule_dry_weight = c(0, 0.0467,
0.0328, 0.0885, 0.0081, 0.1086, 0.0788, 0.0267, 0, 0.0128, 0.0143,
0.0333, 0.006, 0.098, 0.0286, 0.011, 0, 0.0627, 0.0769, 0.0784,
0.023, 0.1504, 0.1026, 0.0254, 0, 0.0597, 0.0158, 0.0354, 0.0226,
0.3261, 0.0436, 0, 0, 0.0203, 0.0469, 0.0904, 0.1593, 0.0836,
0.056, 0.0037, 0, 0.0534, 0.0901, 0.0435, 0.0248, 0.0435, 0.0279,
0.0029, 0, 0.0545, 0.038, 0.0991, 0.0099, 0.1453, 0.1096, 0.0272,
0, 0.0319, 0.0624, 0.0508, 0.0415, 0.11, 0.0079, 0, 0, 0.1257,
0.1242, 0.2899, 0.024, 0.2175, 0.2979, 0.0396, 0, 0.1583, 0.2935,
0.2541, 0.1027, 0.4196, 0.2059, 0.0396, 0, 0.0891, 0.167, 0.0907,
0.2153, 0.3063, 0.2921, 0.0528, 0, 0.0928, 0.2109, 0.1514, 0.0821,
0.3607, 0.0996, 0.0069, 0, 0.0685, 0.3109, 0.1862, 0.0393, 0.286,
0.3418, 0.0459, 0, 0.0765, 0.3486, 0.3988, 0.1155, 0.6341, 0.3653,
0.039, 0, 0.0766, 0.3112, 0.1988, 0.05, 0.2856, 0.34, 0.0862,
0, 0.2621, 0.1146, 0.393, 0.1644, 0.3415, 0.1343, 0.019, 0, 0.0976,
0.1853, 0.0691, 0.0248, 0.1764, 0.1244, 0.1525, 0, 0.1529, 0.1069,
0.2833, 0.0204, 0.2966, 0.2371, 0.1464, 0, 0.0691, 0.2094, 0.1633,
0.0264, 0.1344, 0.0694, 0.1175, 0, 0.1783, 0.1434, 0.2136, 0.0873,
0.19, 0.1683, 0.1927, 0, 0.0571, 0.0599, 0.1061, 0.0244, 0.1256,
0.0894, 0.0123, 0, 0.1696, 0.1046, 0.2164, 0.0939, 0.1552, 0.2942,
0.1652, 0, 0.0844, 0.102, 0.0227, 0.025, 0.0654, 0.1234, 0.0702,
0, 0.0979, 0.1246, 0.0958, 0.0867, 0.1104, 0.1969, 0.227, 0,
0.3704, 0.4727, 0.2527, 0.2078, 0.3377, 0.308, 0.1293, 0, 0.2417,
0.3744, 0.2916, 0.1773, 0.433, 0.2446, 0.1382, 0, 0.4718, 0.4271,
0.4882, 0.1799, 0.4178, 0.518, 0.3915, 0, 0.3421, 0.3804, 0.2112,
0.4292, 0.3829, 0.1315, 0.2719, 0, 0.3197, 0.6867, 0.414, 0.3112,
0.2914, 0.4994, 0.369, 0.0256, 0.1494, 0.5577, 0.2538, 0.3854,
0.4151, 0.544, 0.4009, 0, 0.5208, 0.2962, 0.4175, 0.2689, 0.3374,
0.5075, 0.3601, 0, 0.704, 0.4631, 0.4573, 0.154, 0.5087, 0.4319,
0.4155)), .Names = c("treatment", "block", "position", "variety",
"location", "year", "nodule_dry_weight"), row.names = c(NA, -256L
), class = "data.frame")
I use following code for my graph with facet wrap:
inoculatieproef <- inoculatieproef %>%
group_by(treatment, location, variety, year) %>%
mutate(NDW_mean = mean(nodule_dry_weight),
NDW_sd = sd(nodule_dry_weight))
ggplot(data=inoculatieproef,aes(x=treatment, y=NDW_mean))+
facet_wrap(~location*variety*year,ncol=2)+
geom_bar(position="dodge", stat="identity")+
geom_errorbar(aes(ymin = NDW_mean - NDW_sd,
ymax = NDW_mean + NDW_sd),
width=0.1,size=0.3,
color = "darkgrey")+
theme_bw() +
theme(axis.line = element_line(colour="black"),
panel.grid.minor = element_blank(),
panel.background = element_blank())
How do I add on each barplot the significance letters (anova) in de the facet wrap graph?
No idea if the test fits your data distribution, but you can start with that:
library(tidyverse)
stat_pvalue <- dd %>%
group_by(location, variety, year) %>%
rstatix::t_test(nodule_dry_weight~treatment) %>%
filter(p < 0.05) %>%
group_by(location, variety, year) %>%
rstatix::add_significance("p") %>%
rstatix::add_y_position() %>%
mutate(y.position = seq(min(y.position), max(y.position),length.out = n())*1.1) %>%
ungroup()
ggplot(data=dd,aes(x=treatment, y=nodule_dry_weight))+
geom_boxplot() +
facet_wrap(~location + variety + year,ncol=2, scales = "free_y") +
ggpubr::stat_pvalue_manual(stat_pvalue, label = "p")