summarise with dplyr outputting only one value - r

I have a dataset like so:
> dput(data_melt)
structure(list(Compound = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Compd1", "Compound1"
), class = "factor"), Concentration = structure(c(5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c(".01uM",
".1uM", "1.0uM", "10uM", "DMSO"), class = "factor"), Co.Agonist = structure(c(1L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L), .Label = c("High/High",
"High/Low", "Low/High"), class = "factor"), variable = structure(c(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, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), class = "factor", .Label = c("Tau1",
"Tau2", "Peak.Mean", "Area.Mean", "SS1.Mean")), value = c(1.38196479885153,
1.14405508500302, 0.988961970528585, 1.44849126088629, 0.492462666110073,
NA, 2.17712741416582, 1.67028746892543, 1.96489211952819, 1.2460774224718,
NA, 0.485501088636007, 0.580120526488655, 0.530538989313623,
0.884536240505712, 0.322958355856638, 0.740882962734369, 1.18088839355135,
1.48745421674211, 1.16792544841743, 1.11656132754921, 1.14457816659658,
0.0675070264176897, 0.176054869732887, 0.174862277854592, 0.200470189214318,
0.187717771153427, 0.181176140081454, 0.117339926372974, 0.0941816692818621,
0.156408537242293, 0.171156092362873, 0.0642141717879837, 0.107013341555486,
0.0892122245482354, 0.151976744172333, 0.198474636073771, 0.188703600586299,
0.10970902239241, 0.117358989261514, 0.100312892958432, 0.118208485589655,
0.154895187369863, 0.101035151359696, -3926.26508451201, -696.475731092535,
-4384.77847338655, -718.718487256701, -3164.8941685203, -818.006663108841,
-4658.25223372398, -826.496302684798, -2416.89272653148, -2558.96929067338,
-672.257745869921, -3996.85447223941, -706.5215296652, -4190.52281192937,
-726.870892539311, -2852.22943401345, -831.88857277573, -4580.4780146496,
-816.712564805672, -6189.10619924791, -2540.95473989213, -713.422629648631,
7712.37025286162, 1906.38208801373, 7583.22998649368, 1879.68741296455,
7424.47445663593, 2236.40541039894, 6178.69685860507, 2064.41869983299,
5953.06397562968, 6373.90332689516, 1813.36551434687, 4585.08608292281,
1524.44544360278, 4343.20955707026, 1547.28354007935, 4772.3012092321,
1889.5819203618, 4251.33850498831, 1687.78145119834, 5978.28926211454,
3879.07376129486, 1533.61842684178, -622.178041494169, -301.118488704851,
-618.132026278872, -302.614140229218, -821.035687044046, -383.60893819189,
-683.302506820162, -331.474546574133, -764.139865695781, -546.931098421476,
-300.006976301825, -622.178041494169, -301.118488704851, -618.132026278872,
-302.614140229218, -821.035687044046, -383.60893819189, -683.302506820162,
-331.474546574133, -764.139865695781, -546.931098421476, -300.006976301825
)), .Names = c("Compound", "Concentration", "Co.Agonist", "variable",
"value"), row.names = c(NA, -110L), class = c("data.table", "data.frame"
), .internal.selfref = <pointer: 0x0000000000120788>)
>
My goal is to summarize the value column by the preceding columns (Compound name, Concentration, Co.Agonist, and variable.
I've tried this:
DatAgg = data_melt %>% group_by(Concentration,Co.Agonist,variable)%>%
summarise(mean=mean(data_melt$value,na.rm=TRUE),sd=sd(data_melt$value))
but the output doesn't collapse the data.frame to only the unique combinations and it only returns one value for all the variables.
I'm looking to use this dplyr solution to get mean and sd for each unique combination of the group_by variables.


The syntax should be
data_melt %>%
group_by(Concentration,Co.Agonist,variable) %>%
summarise(Mean = mean(value, na.rm=TRUE),
Sd = sd(value, na.rm=TRUE))
In the OP's code, it was taking the mean of the whole 'value' column (mean(data_melt$value, na.rm=TRUE)) after grouping by the variables. So, there will be a single unique value.
As the 'data_melt' is a data.table object, data.table methods can be used as well.
data_melt[, .(Mean = mean(value, na.rm=TRUE), Sd = sd(value, na.rm=TRUE)),
by = .(Concentration, Co.Agonist, variable)]

Related

Calculating kappa statistics using irr R library

This was the question where it was shown how to use the concept.
I have naive question about this function which was give here which is this where it assigns predicted levels the cluster.
pred2labels = function(pred,actual){
pred = as.character(pred)
actual = as.character(actual)
tab = as.matrix(table(pred,actual))
assignment = colnames(tab)[max.col(tab)]
names(assignment) = rownames(tab)
assignment[pred]
}
I tried to do the same my question is do i need to generate a predicted labels for my data of clusters?
here is my data frame
dput(bb)
structure(list(FAB = structure(c(4L, 2L, 5L, 3L, 4L, 5L, 4L,
4L, 5L, 3L, 4L, 2L, 4L, 3L, 2L, 3L, 5L, 5L, 4L, 3L, 2L, 5L, 3L,
5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 2L, 3L, 5L, 3L, 5L, 3L,
2L, 1L, 6L, 6L, 5L, 5L, 5L, 6L, 6L, 3L, 7L, 3L, 5L, 6L, 2L, 5L,
2L, 3L, 3L, 2L, 6L, 2L, 2L, 2L, 2L, 1L, 6L, 2L, 5L, 2L, 2L, 9L,
5L, 1L, 5L, 2L, 5L, 5L, 6L, 2L, 3L, 6L, 5L, 2L, 1L, 8L, 3L, 5L,
3L, 6L, 1L, 2L, 2L, 5L, 3L, 5L, 6L, 5L, 5L, 3L, 5L, 3L, 2L, 3L,
3L, 2L, 6L, 1L, 2L, 3L, 6L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L,
2L, 5L, 2L, 3L, 2L, 3L, 5L, 1L, 3L, 1L, 6L, 5L, 5L, 3L, 5L, 3L,
2L, 1L, 2L, 5L, 7L, 8L, 6L, 2L, 8L, 3L, 3L, 1L, 2L, 2L, 2L, 1L,
3L, 6L, 5L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 5L, 2L, 9L, 2L, 1L, 1L,
2L, 6L, 6L), .Label = c("M0", "M1", "M2", "M3", "M4", "M5", "M6",
"M7", "nc"), class = "factor"), RISK_CYTO = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 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, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 2L,
4L, 2L, 4L, 2L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 2L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L), .Label = c("Good", "Intermediate",
"N.D.", "Poor"), class = "factor"), Class = c(1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 4L, 1L, 1L, 4L, 1L, 1L, 4L, 1L, 2L, 4L, 1L, 4L,
1L, 4L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 4L, 5L,
3L, 2L, 2L, 4L, 3L, 6L, 2L, 2L, 6L, 2L, 2L, 2L, 3L, 6L, 5L, 2L,
2L, 3L, 6L, 2L, 4L, 5L, 6L, 2L, 3L, 3L, 4L, 5L, 3L, 5L, 4L, 2L,
4L, 3L, 4L, 2L, 3L, 4L, 4L, 5L, 2L, 2L, 5L, 5L, 2L, 4L, 4L, 6L,
6L, 4L, 2L, 3L, 5L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 4L, 2L, 3L, 2L,
4L, 3L, 5L, 4L, 6L, 2L, 5L, 4L, 3L, 4L, 5L, 4L, 2L, 4L, 6L, 4L,
1L, 4L, 5L, 6L, 1L, 4L, 4L, 5L, 4L, 2L, 3L, 5L, 3L, 5L, 2L, 2L,
4L, 2L, 1L, 4L, 3L, 5L, 5L, 6L, 2L, 2L, 3L, 6L, 1L, 5L, 5L, 5L,
5L, 3L, 3L, 6L, 5L, 4L, 6L, 3L, 5L, 5L, 5L, 5L, 5L, 2L, 1L, 5L,
6L, 5L, 5L, 6L, 2L, 2L)), row.names = c(NA, -170L), class = "data.frame")
My steps were this
library(irr)
clus_arrange = bb %>% dplyr::select(Class,FAB)
names(clus_arrange)[1] = "clus"
clus_arrange$predicted_label = pred2labels(clus_arrange$clus,clus_arrange$FAB)
kappam.light(cluster_r)
My output is this
Light's Kappa for m Raters
Subjects = 170
Raters = 2
Kappa = 0.266
z = 6.62
p-value = 3.58e-11
My question is the approach right way of doing it what i had followed from that answer?
UPDATED ANSWER BASED ON THIS TUTORIAL
table <- table(clus_arrange$FAB, clus_arrange$clus)
table
kappam.fleiss(table, detail=TRUE)
My question is which one is methodically and logically correct

interpret estimated marginal means (emmans aka lsmeans): negative response values

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.

Sort ggplot boxplots by median with facets

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?

How to add significance letters (anova) to ggplot geom_bar with facet wrap

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")

How to get the median line in bwplot thicker?

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,
0.0630810396519646, 0.358402154233125, 0.260940142571834,
0.141353291599136, 0.0220267076189838, 0.242149484071382,
0.278319984858078, 0.193105829691662, 0.0259815643559331,
0.318504899459259, 0.00277002060524357, 0.212681621053374,
0.418358846098857, 0.358916156777489, 0.438248724241505,
0.194398889511096, 0.2266870834128, 0.144338808446284, 0.149227951210927,
0.268111328952192, 0.123265441389974, 0.0376832357983068,
0.0353605481767078, 0.021227873083535, 0.0385614926552725,
0.130640111978654, 0.161865326447675, 0.174151298764213,
0.292085797406362, 0.198391364913347, 0.0779507859721407,
0.0045571464157577, 0.114734038438965, 0.0469613758623325,
0.64238405800387, 0.74508519247034, 0.0251182457091362, 0.217835062247358,
0.131159910126724, 0.130034859007596, 0.222418419987533,
0.0861715693619894, 0.185660520258661, 0.0940670543815277,
0.105680179626893, 0.215966730684923, 0.109008340760604,
0.0474735195202623, 0.192326789813641, 0.022147195644035,
0.277372858009381, 0.237574293593955, 0.123383946121193,
0.46406480500022, 0.123698482002945, 0.671442441453945, 0.0406004813894845,
0.260472754754191, 0.0151116521560003, 0.0422855023583402,
0.0405517218780402, 0.0441583998205882, 0.0958995639409343,
0.37588506579263, 0.098494760958735, 0.0928763466294421,
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,
3L, 3L, 3L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 2L, 1L, 3L,
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,
4891L, 2946L, 8502L, 622L, 1972L, 2403L, 3383L, 5880L, 1038L,
4756L, 9506L, 2169L, 1023L, 8506L, 6239L, 7768L, 3221L, 9536L,
5981L, 1507L, 4883L, 414L, 3117L, 3993L, 1923L, 9143L, 2673L,
4430L, 9520L, 9363L, 10602L, 95L, 1141L, 9660L, 4285L, 10704L,
154L, 531L, 6440L, 4876L, 7052L, 4397L, 3375L, 5075L, 1295L,
2620L, 334L, 9510L, 4690L, 4288L, 3576L, 2248L, 7693L, 8820L,
8135L, 4026L, 1906L, 10164L, 8616L, 423L, 5290L, 418L, 6486L,
4485L, 7042L, 955L, 2215L, 9031L, 8049L, 2323L, 1627L, 4212L,
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)
}
)

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