I'm trying to create a facet wrapped ggplot boxplot with dataframe dataw and I'm trying to modify the labels of each subplot.
dataw <- structure(list(base = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L), .Label = c("A", "C", "G", "T"), class = "factor"), pos = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L), values = c(13, 22, 16, 21, 52, 1,
1.709, 2.121, 2.061, 2.233, 3.388, 1, 5, 6, 6, 2, 1, 0.856, 1.116,
1.207, 1.175, 0.95, 76, 45, 5, 1, 1, 15, 8.558, 5.44, 1.147,
0.857, 0.831, 10, 7, 40, 4, 10, 5, 1.547, 1.174, 4.777, 1.071,
1.356, 7, 0, 1, 6, 1, 8, 1.322, 0.728, 0.83, 1.178, 0.831, 4,
2, 0, 1, 3, 0, 1.098, 0.96, 0.63, 0.888, 1.013, 13, 22, 16, 21,
52, 1, 1.709, 2.121, 2.061, 2.233, 3.388, 3, 6, 7, 2, 9, 11,
0.952, 1.474, 1.45, 0.967, 1.306, 13, 22, 16, 21, 52, 1, 1.709,
2.121, 2.061, 2.233, 3.388, 3, 8, 15, 0, 5, 2, 1.014, 1.583,
2.289, 0.773, 1.135, 10, 3, 8, 1, 4, 2, 1.504, 1.03, 1.244, 0.884,
1.047, 4, 1, 0, 2, 5, 1, 1.066, 0.862, 0.689, 0.963, 1.125, 2,
0, 0, 2, 0, 1, 0.919, 0.723, 0.479, 0.922, 0.721, 7, 8, 0, 8,
7, 0, 1.299, 1.236, 0.779, 1.298, 1.224, 13, 22, 16, 21, 52,
1, 1.709, 2.121, 2.061, 2.233, 3.388, 45, 38, 41, 13, 34, 1,
2.817, 2.264, 2.398, 1.374, 3.848, 3, 0, 1, 1, 2, 14, 0.973,
0.641, 0.846, 0.866, 0.909, 13, 22, 16, 21, 52, 1, 1.709, 2.121,
2.061, 2.233, 3.388, 7, 0, 0, 1, 2, 1, 1.37, 0.436, 0.706, 0.685,
0.902, 0, 5, 5, 0, 7, 1, 0.597, 1.113, 1.079, 0.71, 1.222, 3,
1, 4, 0, 23, 8, 0.992, 0.84, 1.07, 0.762, 2.399, 17, 7, 18, 6,
10, 1, 2.4, 1.315, 1.948, 1.135, 1.306, 21, 8, 50, 4, 6, 12,
2.412, 1.254, 3.857, 1.075, 1.168, 13, 22, 16, 21, 52, 1, 1.709,
2.121, 2.061, 2.233, 3.388), type = structure(c(2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L), .Label = c("ipdRatio", "score"), class = "factor"),
labels = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L
), .Label = c("D<U+2192>", "G<U+2192>", "A<U+2192>", "K<U+2192>",
"C<U+2192>", "T<U+2192>"), class = "factor")), .Names = c("base",
"pos", "values", "type", "labels"), row.names = c("1", "2", "3",
"4", "5", "3942", "3943", "3944", "3945", "3946", "3947", "11",
"21", "31", "41", "51", "63", "64", "65", "66", "67", "68", "12",
"22", "32", "42", "52", "2953", "2954", "2955", "2956", "2957",
"2958", "13", "23", "33", "43", "53", "2461", "2462", "2463",
"2464", "2465", "2466", "14", "24", "34", "44", "54", "7493",
"7494", "7495", "7496", "7497", "7498", "111", "214", "311",
"411", "511", "4874", "4875", "4876", "4877", "4878", "4879",
"121", "221", "321", "421", "521", "9356", "9357", "9358", "9359",
"9360", "9361", "131", "231", "331", "431", "531", "9221", "9222",
"9223", "9224", "9225", "9226", "15", "25", "35", "45", "55",
"93561", "93571", "93581", "93591", "93601", "93611", "112",
"215", "312", "412", "512", "1579", "1580", "1581", "1582", "1583",
"1584", "122", "222", "322", "422", "522", "1782", "1783", "1784",
"1785", "1786", "1787", "132", "232", "332", "432", "532", "3398",
"3399", "3400", "3401", "3402", "3403", "16", "26", "36", "46",
"56", "2257", "2258", "2259", "2260", "2261", "2262", "113",
"216", "313", "413", "513", "1027", "1028", "1029", "1030", "1031",
"1032", "123", "223", "323", "423", "523", "8654", "8655", "8656",
"8657", "8658", "8659", "133", "233", "333", "433", "539", "702",
"703", "704", "705", "706", "707", "17", "27", "37", "47", "57",
"8123", "8124", "8125", "8126", "8127", "8128", "114", "217",
"314", "414", "514", "93562", "93572", "93582", "93592", "93602",
"93612", "124", "224", "324", "424", "524", "3700", "3701", "3702",
"3703", "3704", "3705", "134", "234", "334", "434", "5310", "8233",
"8234", "8235", "8236", "8237", "8238", "18", "28", "38", "48",
"58", "1542", "1543", "1544", "1545", "1546", "1547", "115",
"218", "315", "415", "515", "533", "534", "535", "536", "537",
"538", "125", "225", "325", "425", "525", "208", "209", "210",
"211", "212", "213", "135", "235", "335", "435", "5311", "93563",
"93573", "93583", "93593", "93603", "93613"), class = "data.frame")
These are the first few rows of dataw
head(dataw)
base pos values type labels
1 A 1 13 score D<U+2192>
2 A 1 22 score D<U+2192>
3 A 1 16 score D<U+2192>
4 A 1 21 score D<U+2192>
5 A 1 52 score D<U+2192>
3942 A 1 1 score D<U+2192>
I'm plotting it like so.
prettify <- theme(panel.background = element_rect(fill = NA,color="gray"),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(size=.1, color="black",linetype="dotted"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_line(size=.1, color="black"),
legend.position="bottom")
ggplot(dataw,aes(x = base, y = values, color = type, group = base)) +
geom_boxplot() +
facet_wrap(type ~ pos, scales="free_y", nrow = 2) +
theme_gray() %+replace% prettify
Currently the sublabels are the type value followed by a comma and the pos value. However I would like to get rid of the type value, and label it so that the labels of each subplot are in the format: "Position [pos value], [labels value]"
What would be the best way to go about this? Thank you.
Try replacing the entire ggplot statement with
ggplot(data=transform(dataw, plt_labels = paste("Position ", pos, ", ", labels, sep="")),aes(x = base, y = values, color = type, group = base)) +
geom_boxplot() +
facet_grid(type ~ plt_labels, scales="free_y") +
theme_gray() %+replace% prettify
which should give
Related
I would like to create a plot displaying two variables, with groupings (ellipses) for one and symbols coloured for the other. "RW1" on the x axis, "RW2" on the y axis, grouping ellipses according to "Morph.cluster" and colouring the geometric shapes according to "ALL", which is the genetic cluster. I'm using ade4 in the adegenet package for this. The issue is that I can only seem to get the plot working for one variable at a time.
My data looks like
ID ggpop AC70 AC50 ALL Morph.cluster RW1 RW2 RW3
LT-103 gg3 NA 2 2 1 0.00793 0.01137 -0.00379
LT-109 gg4 1 1 1 2 -0.0156 -0.01056 0.00869
LT-110 gg3 1 1 1 1 0.01585 0.00077 -0.00923
LT-12 gg5 4 4 4 1 0.01299 0.00383 0.00517
LT-122 gg4 NA 3 3 1 0.02727 0.01845 -0.0078
For some samples ggpop is NA. For these samples I hope that I I can just leave the colour empty. If not, I'll just make another gg category and label these ones as empty.
Using
library(ade4)
plot_All = plot(RWggclust2_eds$RW1,RWggclust2_eds$RW2, type="n", yaxt="n",
ylab="RW2 (17%)", xlab="RW1 (26%)")
axis(2, las=2)
myCol2=transp(c('#7F7F7FFF', '#D99694FF', '#00B0F0FF', '#00B050FF', '#000000FF'), 0.6)
abline(h=0,v=0,col="grey", lty=2)
s.class(cbind(RWggclust2_eds$RW1, RWggclust2_eds$RW2),
fac=as.factor(RWggclust2_eds$Morph.cluster),
add.plot=T,
col=myCol2,
cpoint=2,
clabel=0,
pch=c(15,16,17) [as.numeric(RWggclust2_eds$Morph.cluster)],
axesell=F,
addaxes=F,
cstar=0,
cellipse = 1.5)
I get the above with the ellipses drawn the way that I want, defining the morphological clusters, but the colours of the geometric shapes are not what I am looking for. I would like the colours to be according to the five groups defined in “ALL”, which are the genetic clusters.
However, using
RWggclust2_eds <- read.csv("RWggclust2_eds.csv")
str(RWggclust2_eds)
RWggclust2_eds$ALL = as.factor(RWggclust2_eds$ALL)
RWggclust2_eds$ALL = as.factor(RWggclust2_eds$ggpop)
RWggclust2_eds$Morph.cluster = as.factor(RWggclust2_eds$Morph.cluster)
plot_All = plot(RWggclust2_eds$RW1,RWggclust2_eds$RW2, type="n", yaxt="n",
ylab="RW2 (17%)", xlab="RW1 (26%)")
axis(2, las=2)
myCol2=transp(c('#7F7F7FFF', '#D99694FF', '#00B0F0FF', '#00B050FF', '#000000FF'), 0.6)
abline(h=0,v=0,col="grey", lty=2)
s.class(cbind(RWggclust2_eds$RW1, RWggclust2_eds$RW2),
fac=as.factor(RWggclust2_eds$ALL),
add.plot=T,
col=myCol2,
cpoint=2,
clabel=0,
pch=c(15,16,17) [as.numeric(RWggclust2_eds$Morph.cluster)],
axesell=F,
addaxes=F,
cstar=0,
cellipse = 1.5)
I get the above, with ellipses drawn on the genetic clusters (the “ALL” column in my dataset), which is not what I want (I want the shapes to be grouped according to my first plot), but the colours in the plot are correct.
I've tired all sorts of iterations of the "col" argument as well
#index=as.integer(RWggclust2_eds$ALL)
#sclasscolour=rainbow(6)
#sclasscolour=transp(c('#7F7F7FFF', '#D99694FF', '#00B0F0FF', '#00B050FF', '#000000FF'), 0.6)
#myCol2= transp(c(sclasscolour), 1) [index]
The issue is I think that the colour of symbols is being read from "fac", but is seems like the distrubtion of Morph.cluster is as well.
I would be glad to post my datafile if there is a way to do so.
You are almost there. I think in s.class, the color of the points will go together with the eclipse (at least as far as I have tried).
Since you had to layer the eclipse onto an empty plot, I think the easiest solution is to first plot the points according to what you like, then add on the eclipses without the points (setting pch=NA)
I used alpha from ggplot for the transparency but feel free to change it back
library(ade4)
library(ggplot2)
# i use alpha from ggplot2
# how i read and proceeded with your csv file
#CSV = read.csv("RWggclust2_eds.csv")
#RWggclust2_eds = CSV[,c("ggpop","ALL","Morph.cluster","RW1","RW2")]
# similar to what you did
# just providing names
myCol2=alpha(c('#7F7F7FFF', '#D99694FF', '#00B0F0FF', '#00B050FF', '#000000FF'), 0.6)
# set it according to popn
names(myCol2) = levels(RWggclust2_eds$ggpop)
# set the shapes
PCH = c(15,16,17)
# we can wrap it around using with
plot_All = with(RWggclust2_eds,
plot(RW1,RW2, yaxt="n",
ylab="RW2 (17%)", xlab="RW1 (26%)",
pch= PCH[Morph.cluster],
col=myCol2[as.character(ggpop)]
)
)
#provide a legend
legend("topleft",names(myCol2),fill=myCol2,horiz=T,cex=0.7)
axis(2, las=2)
abline(h=0,v=0,col="grey", lty=2)
s.class(cbind(RWggclust2_eds$RW1, RWggclust2_eds$RW2),
fac=as.factor(RWggclust2_eds$Morph.cluster),
add.plot=T,
cpoint=2,
clabel=0,
pch=NA,
axesell=F,
addaxes=F,
cstar=0,
cellipse = 1.5)
You get the plot:
Below is the data for making the plot
dput(RWggclust2_eds)
structure(list(ggpop = structure(c(3L, 4L, 3L, 5L, 4L, 1L, 3L,
3L, 1L, 1L, 4L, 3L, 5L, 1L, 3L, 1L, 1L, 1L, 1L, 4L, 3L, 1L, 1L,
3L, 1L, 3L, 3L, 5L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 4L, 3L, 1L,
4L, 5L, 3L, 4L, 1L, 2L, 2L, 5L, 5L, 4L, 4L, 2L, 5L, 2L, 2L, 3L,
3L, 1L, 1L, 3L, 4L, 1L, 3L, 1L, 1L, 1L, 4L, 1L, 2L, 4L, 1L, 5L,
1L, 4L, 3L, 5L, 1L, 3L, 5L, 1L, 3L, 4L, 4L, 5L, 5L, 3L, 3L, 1L,
1L, 3L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 5L,
3L, 1L, 3L, 4L, 4L, 1L, 1L, 3L, 4L, 3L, 1L, 1L, 4L, 1L, 1L, 2L,
4L, 1L, 4L, 5L, 5L, 1L, 1L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 1L, 1L,
2L, 3L, 1L, 1L, 5L, 4L, 4L, 3L, 2L, 4L, 3L, 3L, 3L, 2L, 3L, 2L,
3L, 3L, 2L, 4L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 5L, 1L, 1L,
1L, 4L, 1L, 1L, 4L, 4L, 1L, 3L, 3L, 1L, 4L, 4L, 1L, 4L, 4L, 3L,
3L, 5L, 2L, 3L, 1L, 1L, 4L, 3L, 3L, 4L, 2L, 3L, 1L, 3L, 3L, 3L,
3L, 2L, 3L, 5L, 5L, 3L, 1L, 5L, 4L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA), .Label = c("gg1", "gg2", "gg3", "gg4", "gg5"), class = "factor"),
ALL = c(2L, 1L, 1L, 4L, 3L, 3L, 1L, 2L, 2L, 3L, 5L, 1L, 4L,
3L, 1L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 1L, 3L, 1L, 1L, 1L,
2L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 2L, 2L, 3L, 1L, 4L, 1L, 1L,
2L, 5L, 5L, 4L, 4L, 1L, 3L, 1L, 4L, 5L, 5L, 5L, 2L, 3L, 3L,
1L, 5L, 3L, 5L, 2L, 5L, 3L, 1L, 3L, 5L, 2L, 3L, 4L, 5L, 1L,
1L, 4L, 2L, 1L, 4L, 2L, 2L, 1L, 1L, 4L, 4L, 3L, 1L, 5L, 3L,
3L, 5L, 2L, 4L, 5L, 2L, 5L, 2L, 5L, 3L, 2L, 3L, 1L, 2L, 4L,
1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 3L, 1L, 2L, 3L,
5L, 1L, 3L, 1L, 4L, 4L, 3L, 5L, 3L, 2L, 1L, 2L, 2L, 3L, 1L,
2L, 2L, 5L, 1L, 2L, 3L, 5L, 3L, 1L, 4L, 5L, 1L, 2L, 4L, 1L,
5L, 1L, 5L, 1L, 5L, 5L, 2L, 5L, 3L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 4L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 3L, 5L, 3L, 3L, 4L,
1L, 3L, 1L, 1L, 1L, 1L, 4L, 5L, 2L, 3L, 3L, 2L, 2L, 1L, 2L,
5L, 1L, 3L, 1L, 1L, 1L, 1L, 5L, 2L, 4L, 4L, 2L, 3L, 4L, 1L,
3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 2L,
1L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 2L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 3L, 1L, 4L, 1L, 2L,
3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L,
1L, 1L, 5L, 1L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 1L), Morph.cluster = c(1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
3L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 3L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 2L,
3L, 2L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L,
2L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 3L,
2L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L,
2L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 3L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 3L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
3L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L), RW1 = c(0.00793,
-0.0156, 0.01585, 0.01299, 0.02727, 0.00344, 0.031, 0.01568,
-0.00411, -0.00076, 0.01339, 0.02017, 0.02123, -0.0061, 0.01376,
-0.02301, 0.00501, 0.02418, -0.00102, -0.00813, 0.0032, -0.0122,
-0.00317, 0.00018, 0.00051, -0.00184, -0.00323, 0.01701,
-0.00027, 0.00288, -0.00689, -0.01864, 0.003, 0.00463, 0.0119,
-0.01515, 0.00364, 0.02048, -0.01233, -0.00774, -0.00348,
-0.00219, 0.00676, -0.00978, 0.00767, -0.00819, -0.01892,
-0.00584, 0.012, 0.00617, -0.01743, -0.02724, -0.03363, 0.01004,
0.02196, 0.01074, 0.0053, 0.00197, -0.01368, 0.00802, -0.00659,
-0.00521, -0.00938, 0.00901, 0.00639, 0.02151, -0.01637,
-0.02571, -0.00354, -0.00396, 0.03228, 0.01599, -0.00646,
0.00993, 0.02582, -0.01889, 0.01162, 0.00487, 0.02127, -0.00994,
-0.01862, 0.01338, 0.00229, 0.01818, 0.00687, 0.00545, 0.01723,
0.00438, 0.00832, 0.00095, -0.00751, -0.00645, -0.01469,
-0.01141, 0.01079, -0.00567, 0.00348, 0.00904, 0.00055, 0.02002,
-0.00382, -0.00244, 0.03351, -0.01225, 0.00865, -0.00105,
-0.01337, 0.00853, -0.00584, -0.001, -0.00068, -0.01427,
0.00742, 0.00352, -0.01016, -0.00713, -0.00129, -0.01557,
-0.00792, -0.00067, 0.00432, -0.01509, 0.03477, 0.01061,
-0.00097, 0.00797, -0.01066, -0.01534, 0.0192, 0.00266, -0.00549,
-0.02314, 0.00139, -0.02194, -0.00406, 0.00287, -0.01561,
-0.00329, 0.01625, 0.00822, 0.01719, -0.00545, 0.0019, 0.00278,
0.00436, 0.01424, -0.01102, -0.01331, 0.00928, 0.01181, 0.00416,
0.01673, 0.00715, -0.0159, -0.00208, -0.03367, 9e-04, -0.00011,
-0.02768, -0.01608, -0.00345, 0.00062, 0.00048, -0.01542,
0.02665, -0.0181, 0.0098, -0.00995, 0.00842, 0.00123, 0.00896,
0.02197, -0.00163, -0.01072, -0.01137, -0.01126, -0.01656,
0.00518, 0.03913, -0.02236, -0.02881, -0.0117, 0.01644, -0.00465,
-0.00811, 0.00107, 0.02483, 0.00197, -0.01018, 0.01023, 0.01279,
-0.01052, -0.0069, -0.01409, 0.01462, 0.00435, 0.01912, -0.01392,
0.02166, 0.00621, 0.0112, -0.01114, 0.00728, -0.0086, 5e-05,
0.00352, -0.00939, -0.01502, 0.00394, 0.01684, -0.00077,
-0.00037, 0.00563, -0.03636, 0.00443, 0.01819, 0.00603, -0.00363,
-0.02126, 0.01857, 0.00014, -0.03048, 0.00621, -0.00452,
0.02451, 0.00715, -0.00705, 0.01619, -0.016, -0.00362, -0.00949,
0.02046, -0.016, -0.01655, 0.00079, 0.00188, -0.01497, 0.01269,
0.02089, -0.01006, -0.01574, -0.00349, -0.01533, 0.01438,
0.0055, -0.00257, 0.00254, -0.00647, -0.00356, 0.02158, -0.03029,
0.01802, -0.00391, -0.01141, -0.0355, 0.00911, 0.00136, 0.00631,
-0.00551, -0.01403, 0.00929, 0.0059, -0.00238, 0.00028, -0.01683,
0.00347, -0.0213, 0.00411, 0.00953, 0.00029, -0.0028, 0.01801,
0.00405, -0.0079, -0.01496, -0.00638, -0.01092, -0.00773,
-0.00368, 0.00526, -0.00739), RW2 = c(0.01137, -0.01056,
0.00077, 0.00383, 0.01845, 0.00058, 0.02422, 0.00129, -0.01173,
-0.0161, 0.01019, 0.00135, 0.00579, 0.00576, 0.00751, 0.01384,
0.00408, -0.00634, -0.00044, -0.00168, -0.00021, 0.00019,
0.00428, -0.00924, 0.00098, 0.00518, -0.00184, -0.00469,
-0.00921, -3e-05, 0.00688, -0.00778, 0.00165, -0.00664, 0.00856,
-0.00157, 0.00322, -7e-05, -0.00405, 6e-04, -0.00464, -0.0019,
-0.00849, 0.00363, -0.01107, 0.00175, -0.03478, -0.00816,
-0.00338, 0.02075, -0.00039, -0.02465, -0.01461, 0.00529,
-0.00144, -0.00259, -0.00803, -0.00693, 0.00761, 0.01159,
0.01422, -0.00876, -0.00469, -0.00052, 0.00823, 0.01019,
0.00032, 0.00842, 0.02197, 4e-04, -0.02939, 0.00432, 0.00774,
0.00848, -0.00342, -0.00953, -0.01048, -0.0014, -0.01465,
0.0027, 0.00879, -4e-05, 0.00985, -0.03324, 0.01427, 0.00036,
-0.01587, -0.0012, 0.00331, 0.0089, -0.00684, -0.0348, 0.00497,
0.01468, -0.00218, -0.01662, 0.00608, -0.01143, 0.00312,
-0.01165, 0.00966, 0.00179, -0.01137, 0.00335, 0.0152, -0.00442,
0.01031, -0.01745, -0.01658, 0.003, 0.011, -0.00583, 0.0171,
0.00062, 0.00842, 0.00759, -0.00401, 0.01916, -0.00491, 0.01416,
0.00117, -0.00164, -0.02815, 0.00648, 0.00067, -0.006, -0.00454,
0.00368, 0.00666, 0.00352, 0.00368, -0.00999, 0.01213, -0.0022,
-0.0015, -0.02383, 0.00748, -4e-05, -0.02224, -0.01355, 0.00525,
0.00142, 0.01344, 0.00639, 0.00822, 0.01765, 0.00431, -0.0019,
0.00204, 0.03139, 0.00251, -0.00123, -0.00833, -0.01289,
0.00716, 0.00198, 0.00263, 0.00632, 0.00179, -5e-05, 0.0088,
-0.0017, -0.00574, 0.00356, -0.02567, 0.00984, 0.00209, 0.0072,
0.00377, -0.0085, 0.01386, 0.00746, 0.00415, -0.01666, 0.00516,
0.00883, -0.01979, 0.01931, 0.00208, 0.00411, 0.01303, -0.00108,
0.00854, -0.00262, -0.02486, 0.0179, -0.00035, 0.01651, 0.00952,
0.00668, -0.00279, 0.00344, 0.00314, -0.00599, 0.00954, -0.02629,
-0.00226, 0.00282, 0.00765, 0.00624, -0.00497, -0.00269,
0.01129, -0.02122, 0.00841, -0.0011, -0.00747, 0.01913, -0.0024,
0.01528, -0.00184, -0.005, -0.01863, 0.02115, -0.00814, 0.01531,
0.00641, -0.00111, -0.01767, 0.01528, -0.00414, -0.01503,
-0.00082, 0.00429, 0.00032, 0.01733, -0.00656, 0.00176, -0.00597,
0.00423, -0.00541, 0.01085, -0.00327, -4e-04, 0.01826, -0.00852,
0.0098, -0.01052, 0.0188, 0.00234, -0.00113, -0.00335, -0.01121,
-0.00305, 0.0065, -0.0032, -0.00877, -0.01151, -0.01051,
-0.00295, 0.00751, -0.00828, -0.00164, -0.00109, 0.01665,
0.01285, -0.00875, -0.01182, -0.01427, 0.01631, -0.00241,
-0.0055, 0.00393, 0.00351, 0.00061, -0.00063, 0.01053, -0.00094,
0.00509, 0.00162, 0.00295, -0.00789, -0.03162, 0.00523, -0.00361,
-0.00171, -0.00459, 0.00237, -0.03454, -0.01103, 0.00944)), class = "data.frame", row.names = c(NA,
-281L))
I am plotting grouped barplots with error bars, but my error bars are very long as in this image
[![https://i.stack.imgur.com/VUByO.png][1]][1].
I would like shorter error bars as in this image
[![https://i.stack.imgur.com/JhaUJ.png][2]][2]
The code used
per$Leaf_Location <- factor(per$Leaf_Location, levels = unique(per$Leaf_Location))
per$Time <- factor(per$Time, levels = unique(per$Time))
ggplot(per, aes(x=Leaf_Location, y=Damage, fill=as.factor(Time))) +
stat_summary(fun.y=mean,
geom="bar",position=position_dodge(),colour="black",width=.7,size=.7) +
stat_summary(fun.ymin=min,fun.ymax=max,geom="errorbar",
color="black",position=position_dodge(.7), width=.2) +
stat_summary(geom = 'text', fun.y = max, position = position_dodge(.7),
label = c("a","b","c","d","d","a","b","c","d","d","a","b","c","d","d"), vjust = -0.5) +
scale_fill_manual("Legend", values = c("grey36","grey46","grey56","grey76","grey86","grey96")) +
xlab("Leaf Location") +
ylab("Damage ") +
theme_bw()
data:
per =
structure(list(Site = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Defathers",
"Kariithi", "Kimbimbi"), class = "factor"), Field = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L
), .Label = c("F1", "F2", "F3", "F4", "F5"), class = "factor"),
Leaf_Location = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("Lower", "Intermediate",
"Upper"), class = "factor"), Time = structure(c(1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L), .Label = c("20_days",
"40_days", "60_days", "80_days", "100_days"), class = "factor"),
Damage = c(25.25, 26.07, 24.43, 20.73, 17.8, 6.9, 45.05,
33.47, 24.43, 51.67, 41.72, 34.17, 81.67, 73.33, 55.83, 34.28,
26.08, 13.28, 26.27, 14.1, 6.93, 37.55, 29.33, 23.62, 49.17,
38.45, 31.38, 70.83, 60.83, 44.2, 31.03, 25.2, 14.97, 14.38,
6.5, 4.33, 52.2, 39.17, 30.97, 75, 62.5, 38.33, 87.5, 62.5,
57.5, 45.02, 31.02, 26.07, 46.72, 34.32, 21.5, 50.83, 34.23,
25.25, 45.83, 33.47, 27.7, 67.67, 57.5, 52.67, 30.98, 23.62,
9.1, 18.17, 18.57, 10.15, 46.67, 34.27, 23.62, 54.17, 40.05,
29.37, 70.83, 59.17, 47.53, 8.67, 5.63, 0.87, 9.87, 3.03,
0, 17.75, 6.88, 0, 62.5, 37.5, 27.7, 70.83, 57.5, 50.83,
6.5, 2.17, 1.3, 6.93, 3.03, 0.53, 14.82, 5.2, 0, 37.5, 28.52,
13, 75, 37.5, 37.5, 15.3, 9.53, 5.63, 9.43, 3.03, 0.43, 16.4,
6.07, 0, 57.5, 34.23, 21.98, 78.33, 62.5, 37.5, 12.08, 6.5,
1.3, 10.73, 3.03, 0, 15.2, 3.9, 0.43, 62.5, 37.5, 21.98,
64.17, 55.83, 41.73, 8.73, 3.57, 0, 8.57, 2.17, 0, 16.5,
7.7, 0.43, 42.58, 36.68, 13, 65.83, 47.5, 37.5, 8.03, 5.07,
0.43, 10.68, 7.27, 3.5, 48.38, 38.42, 24.83, 45.03, 38.4,
30.8, 73.33, 63.33, 50.83, 3.37, 2.17, 0.9, 9, 6.02, 5.2,
21.07, 12.37, 6.02, 45.02, 32.65, 21.67, 68.78, 56.68, 50,
0, 0, 0, 7.8, 4.33, 4.33, 25.17, 20.65, 13.15, 48.37, 39.23,
27.17, 75.83, 62.5, 49, 11.78, 12.72, 3.8, 20.18, 14.87,
8.95, 46.7, 39.32, 33.03, 49.18, 40.05, 24.43, 69.17, 60,
48.33, 0, 0, 0, 15.25, 9.82, 7.75, 45.9, 38.47, 35.52, 50.88,
37.61, 33.47, 79.17, 71.67, 58.33)), .Names = c("Site", "Field",
"Leaf_Location", "Time", "Damage"), row.names = c(NA, -225L), class = "data.frame")
Here's a simplified reproducible example to explain
first, some dummy data:
per = data.frame(x=rep(c('a','b'), each=100), y=c(2+rnorm(100), 3+rnorm(100,0,2)))
Now you are plotting the error bars, using fun.ymin=min, fun.ymax=max, which will cause them to extend the full range of the data, as in the following graph:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
geom_point(position = position_jitter(0.1)) +
stat_summary(fun.ymin=min, fun.ymax=max, geom="errorbar", width=0.4) +
theme_bw()
Whereas, it is more conventional to use error bars that extend either +/- one standard deviation, as in the following:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
stat_summary(
fun.ymin=function(y) {mean(y) - sd(y)},
fun.ymax=function(y) {mean(y) + sd(y)},
geom="errorbar", width=0.2) +
theme_bw()
Or one standard error, like this:
ggplot(per, aes(x, y)) +
stat_summary(fun.y = mean, geom="bar") +
stat_summary(
fun.ymin=function(y) {mean(y) - sqrt(var(y)/length(y))},
fun.ymax=function(y) {mean(y) + sqrt(var(y)/length(y))},
geom="errorbar", width=0.2) +
theme_bw()
EDIT - example data were added to question, after this answer was originally posted
We can applying exactly the same approach as above to your example data:
ggplot(per, aes(x=Leaf_Location, y=Damage, fill=as.factor(Time))) +
stat_summary(fun.y=mean, geom="bar",position=position_dodge(),colour="black",width=.7,size=.7) +
stat_summary(
fun.ymin=function(y) {mean(y) - sqrt(var(y)/length(y))},
fun.ymax=function(y) {mean(y) + sqrt(var(y)/length(y))},
geom="errorbar",
position=position_dodge(.7), width=.2)
I have a CSV with a Detect column where the result is Y or N. I've got my script to change shape dependent on that column, but I need to specify that Y is a filled in circle, while N is hollow circle.
library("ggplot2")
Report213 <- read.csv("FILE_NAME")
ggplot(data = Report213, aes(x = factor(Station_ID, level = c("NEB","NWB","LBC","WB","HR","FDP","FS","NR","PB")), y = Result, Group = Detect, colour = Station_ID,shape = Detect
)) + geom_point(aes(shape=Detect,size = 2)) +
facet_grid( . ~ Chemical ) +facet_wrap( ~ Chemical, scales= "free_y",ncol = 1) + theme(
panel.background = element_rect(fill = "white",
colour = "white",
size = 0.5, linetype = "solid"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "gray"),
panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
colour = "white"),
strip.background =element_rect(fill="#454545"),
strip.text = element_text(colour = 'white')
)
Appreciate any pointers.
dput output off Report213:
structure(list(Station_ID = structure(c(4L, 4L, 4L, 4L, 4L, 9L,
3L, 9L, 3L, 3L, 9L, 3L, 3L, 5L, 7L, 2L, 6L, 7L, 5L, 7L, 8L, 1L,
5L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
6L, 7L, 7L, 7L, 8L, 9L, 9L, 9L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 7L, 7L, 7L, 8L, 9L, 9L, 9L, 1L,
2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 7L,
7L, 7L, 8L, 9L, 9L, 9L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 6L, 7L, 7L, 7L, 8L, 9L, 9L, 9L), .Label = c("FDP",
"FS", "HR", "LBC", "NEB", "NR", "NWB", "PB", "WB"), class = "factor"),
Chemical = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 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,
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), .Label = c("4,4'-DDT", "CHLORDANE", "Total Aroclors",
"Total PAHs", "Total PCB Congeners"), class = "factor"),
Result = c(78.4176, 66.8307, 59.7295, 50.4102, 40.9341, 36.6868,
34.6394, 26.7728, 23.192, 18.091, 15.47568, 14.539, 13.8006,
4.489, 2.0159, 1.99509, 1.71768, 1.69251, 1.5165, 1.39725,
1.27822, 1.22813, 0.89586, 507.7, 135, 684, 8911, 4946, 780,
4920, 137.9, 559.5, 239.51, 902, 376, 655.4, 8299, 6500,
889, 502.8, 361.1, 17440, 555.8, 953, 5691, 1790, 0.3, 1,
14, 12, 20, 20, 21, 10, 14, 7.6, 7.3, 23, 7.7, 11, 1.5, 0.28,
8.1, 5.4, 11, 0.31, 0.62, 20, 22, 4.2, 6.8, 3.9, 6.7, 4.6,
6.4, 13, 51, 4.2, 50.8, 43.1, 41.9, 4.1, 4.4, 3.9, 4, 4.2,
4.5, 2.3, 4.3, 13, 6.8, 35, 1.1, 0.62, 0.053, 1, 7.4, 23,
3.7, 0.056, 2, 0.055, 0.054, 0.12, 0.053, 0.057, 0.13, 0.088,
0.11, 0.058, 1.1, 21, 1.5, 4.7, 1.6), Detect = 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,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L), .Label = c("N", "Y"), class = "factor")), class = "data.frame", row.names = c(NA,
-115L))
You can specify the shape by using scale_shape_manual
P.S: Use either facet_grid or facet_wrap not both at the same time
Edit: with ggplot2 v3.0.0 released in July 2018, you can use text/string to specify the shape. E.g. scale_shape_manual(values = c("circle", "circle open")). See more here
library(tidyverse)
Report213 <- Report213 %>%
mutate(Station_ID = factor(Station_ID,
level = c("NEB","NWB","LBC","WB","HR","FDP","FS","NR","PB")))
ggplot(data = Report213,
aes(x = Station_ID,
y = Result)) +
geom_point(aes(color = Station_ID, shape = Detect), size = 2) +
scale_shape_manual(values = c(19, 1)) +
facet_wrap( ~ Chemical, scales = "free_y", ncol = 1) +
theme(
panel.background = element_rect(fill = "white",
colour = "white",
size = 0.5, linetype = "solid"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "gray"),
panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
colour = "white"),
strip.background =element_rect(fill = "#454545"),
strip.text = element_text(colour = 'white')
)
Edit 2: Add string ~ integer shape table for future references
pch_table <- c(
"square open" = 0,
"circle open" = 1,
"triangle open" = 2,
"plus" = 3,
"cross" = 4,
"diamond open" = 5,
"triangle down open" = 6,
"square cross" = 7,
"asterisk" = 8,
"diamond plus" = 9,
"circle plus" = 10,
"star" = 11,
"square plus" = 12,
"circle cross" = 13,
"square triangle" = 14,
"triangle square" = 14,
"square" = 15,
"circle small" = 16,
"triangle" = 17,
"diamond" = 18,
"circle" = 19,
"bullet" = 20,
"circle filled" = 21,
"square filled" = 22,
"diamond filled" = 23,
"triangle filled" = 24,
"triangle down filled" = 25
)
Data used
Report213 <- structure(list(Station_ID = structure(c(4L, 4L, 4L, 4L, 4L, 9L,
3L, 9L, 3L, 3L, 9L, 3L, 3L, 5L, 7L, 2L, 6L, 7L, 5L, 7L, 8L, 1L,
5L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
6L, 7L, 7L, 7L, 8L, 9L, 9L, 9L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 7L, 7L, 7L, 8L, 9L, 9L, 9L, 1L,
2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 7L,
7L, 7L, 8L, 9L, 9L, 9L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 6L, 7L, 7L, 7L, 8L, 9L, 9L, 9L), .Label = c("FDP",
"FS", "HR", "LBC", "NEB", "NR", "NWB", "PB", "WB"), class = "factor"),
Chemical = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 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,
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), .Label = c("4,4'-DDT", "CHLORDANE", "Total Aroclors",
"Total PAHs", "Total PCB Congeners"), class = "factor"),
Result = c(78.4176, 66.8307, 59.7295, 50.4102, 40.9341, 36.6868,
34.6394, 26.7728, 23.192, 18.091, 15.47568, 14.539, 13.8006,
4.489, 2.0159, 1.99509, 1.71768, 1.69251, 1.5165, 1.39725,
1.27822, 1.22813, 0.89586, 507.7, 135, 684, 8911, 4946, 780,
4920, 137.9, 559.5, 239.51, 902, 376, 655.4, 8299, 6500,
889, 502.8, 361.1, 17440, 555.8, 953, 5691, 1790, 0.3, 1,
14, 12, 20, 20, 21, 10, 14, 7.6, 7.3, 23, 7.7, 11, 1.5, 0.28,
8.1, 5.4, 11, 0.31, 0.62, 20, 22, 4.2, 6.8, 3.9, 6.7, 4.6,
6.4, 13, 51, 4.2, 50.8, 43.1, 41.9, 4.1, 4.4, 3.9, 4, 4.2,
4.5, 2.3, 4.3, 13, 6.8, 35, 1.1, 0.62, 0.053, 1, 7.4, 23,
3.7, 0.056, 2, 0.055, 0.054, 0.12, 0.053, 0.057, 0.13, 0.088,
0.11, 0.058, 1.1, 21, 1.5, 4.7, 1.6), Detect = 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,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L),
.Label = c("N", "Y"), class = "factor")),
class = "data.frame", row.names = c(NA,
-115L))
Created on 2018-06-09 by the reprex package (v0.2.0).
I am trying to figure out a way of introducing negative values of factors in a stacked barplot in ggplot2. The data is level of support for basic income among Finnish MPs. It is at the bottom of the post.
I can get a plot that is like the one I want (minus the negatively valued factors) with the following code:
library(forcats)
library(ggplot2)
support.plot <- ggplot(mpsupport.df, aes(fct_infreq(Party))) +
geom_bar (aes(fill=Support)) +
coord_flip() +
theme(legend.position = "bottom")+
ylab("Party") +
xlab("Number of MPs")
This gives the following:
What I would like is for the graph to be centred on the green-turquoise border, so that support for basic income was to the right, while opposition was to the left. Does this make sense?
Data:
> dput(mpsupport.df)
structure(list(Party = structure(c(1L, 2L, 2L, 2L, 2L, 2L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 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, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 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, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 5L, 6L, 7L, 7L, 7L), .Label = c("National Coalition",
"Centre Party", "Social Democratic Party", "Left Alliance", "Christian Democrats",
"True Finns", "Swedish People's Party", "Greens"), class = "factor"),
Support = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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), .Label = c("fully.agree", "partially.agree",
"partially.disagree", "fully.disagree"), class = "factor")), .Names = c("Party",
"Support"), row.names = c("1", "2", "2.1", "2.2", "2.3", "2.4",
"4", "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "4.7", "6", "8",
"8.1", "8.2", "8.3", "8.4", "8.5", "8.6", "8.7", "8.8", "8.9",
"8.10", "8.11", "8.12", "8.13", "8.14", "9", "9.1", "9.2", "9.3",
"9.4", "9.5", "9.6", "9.7", "10", "10.1", "10.2", "10.3", "10.4",
"10.5", "10.6", "10.7", "10.8", "10.9", "10.10", "10.11", "10.12",
"10.13", "10.14", "10.15", "10.16", "10.17", "10.18", "10.19",
"10.20", "10.21", "10.22", "10.23", "10.24", "10.25", "10.26",
"10.27", "10.28", "10.29", "10.30", "10.31", "10.32", "10.33",
"11", "11.1", "11.2", "11.3", "12", "12.1", "12.2", "12.3", "13",
"14", "14.1", "14.2", "14.3", "14.4", "14.5", "14.6", "14.7",
"14.8", "14.9", "14.10", "14.11", "14.12", "14.13", "14.14",
"14.15", "14.16", "14.17", "14.18", "14.19", "14.20", "15", "15.1",
"17", "17.1", "17.2", "17.3", "17.4", "17.5", "17.6", "17.7",
"17.8", "17.9", "17.10", "17.11", "17.12", "17.13", "17.14",
"17.15", "17.16", "17.17", "17.18", "17.19", "18", "18.1", "18.2",
"18.3", "18.4", "18.5", "18.6", "18.7", "19", "19.1", "19.2",
"19.3", "19.4", "19.5", "19.6", "19.7", "19.8", "19.9", "19.10",
"19.11", "19.12", "19.13", "19.14", "19.15", "19.16", "19.17",
"19.18", "19.19", "19.20", "19.21", "19.22", "19.23", "21", "21.1",
"22", "22.1", "22.2", "22.3", "22.4", "22.5", "22.6", "22.7",
"22.8", "22.9", "22.10", "22.11", "22.12", "23", "23.1", "23.2",
"23.3", "25", "25.1", "25.2", "25.3", "25.4", "25.5", "25.6",
"27", "27.1", "27.2", "27.3", "27.4", "27.5", "29", "30", "31",
"31.1", "31.2"), class = "data.frame")
Try something along these lines:
library(ggplot)
library(forcats)
mpsupport.df$dummy = ifelse(mpsupport.df$Support %in% c("fully.agree", "partially.agree"), 1, -1)
agg = aggregate(dummy ~ Support + Party, data = mpsupport.df, FUN = sum)
ggplot(data = agg)+
geom_bar (aes(y = dummy, x= fct_infreq(Party), fill = factor(Support, levels = c("fully.agree", "partially.agree", "fully.disagree" ,"partially.disagree"))), stat= "identity") +
coord_flip()+
theme(legend.position = "bottom", legend.title = element_blank())
I have a dataframe called dataw that I'm trying to plot into dual facet wrapped boxplots.
dataw <- structure(list(base = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L), .Label = c("A", "C", "G", "T"), class = "factor"), pos = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L), values = c(13, 22, 16, 1, 1.709,
2.121, 2.061, 1, 5, 6, 1, 0.856, 1.116, 1.207, 76, 45, 5, 15,
8.558, 5.44, 1.147, 10, 7, 40, 5, 1.547, 1.174, 4.777, 7, 0,
1, 8, 1.322, 0.728, 0.83, 4, 2, 0, 0, 1.098, 0.96, 0.63, 13,
22, 16, 1, 1.709, 2.121, 2.061, 3, 6, 7, 11, 0.952, 1.474, 1.45,
13, 22, 16, 1, 1.709, 2.121, 2.061, 3, 8, 15, 2, 1.014, 1.583,
2.289, 10, 3, 8, 2, 1.504, 1.03, 1.244, 4, 1, 0, 1, 1.066, 0.862,
0.689, 2, 0, 0, 1, 0.919, 0.723, 0.479, 7, 8, 0, 0, 1.299, 1.236,
0.779, 13, 22, 16, 1, 1.709, 2.121, 2.061, 45, 38, 41, 1, 2.817,
2.264, 2.398, 3, 0, 1, 14, 0.973, 0.641, 0.846, 13, 22, 16, 1,
1.709, 2.121, 2.061, 7, 0, 0, 1, 1.37, 0.436, 0.706, 0, 5, 5,
1, 0.597, 1.113, 1.079, 3, 1, 4, 8, 0.992, 0.84, 1.07, 17, 7,
18, 1, 2.4, 1.315, 1.948, 21, 8, 50, 12, 2.412, 1.254, 3.857,
13, 22, 16, 1, 1.709, 2.121, 2.061), type = structure(c(2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L), .Label = c("ipdRatio", "score"), class = "factor")), .Names = c("base",
"pos", "values", "type"), row.names = c("1", "2", "3", "3942",
"3943", "3944", "3945", "11", "21", "31", "63", "64", "65", "66",
"12", "22", "32", "2953", "2954", "2955", "2956", "13", "23",
"33", "2461", "2462", "2463", "2464", "14", "24", "34", "7493",
"7494", "7495", "7496", "111", "212", "311", "4874", "4875",
"4876", "4877", "121", "221", "321", "9356", "9357", "9358",
"9359", "131", "231", "331", "9221", "9222", "9223", "9224",
"15", "25", "35", "93561", "93571", "93581", "93591", "112",
"213", "312", "1579", "1580", "1581", "1582", "122", "222", "322",
"1782", "1783", "1784", "1785", "132", "232", "332", "3398",
"3399", "3400", "3401", "16", "26", "36", "2257", "2258", "2259",
"2260", "113", "214", "313", "1027", "1028", "1029", "1030",
"123", "223", "323", "8654", "8655", "8656", "8657", "133", "233",
"333", "702", "703", "704", "705", "17", "27", "37", "8123",
"8124", "8125", "8126", "114", "215", "314", "93562", "93572",
"93582", "93592", "124", "224", "324", "3700", "3701", "3702",
"3703", "134", "234", "334", "8233", "8234", "8235", "8236",
"18", "28", "38", "1542", "1543", "1544", "1545", "115", "216",
"315", "533", "534", "535", "536", "125", "225", "325", "208",
"209", "210", "211", "135", "235", "335", "93563", "93573", "93583",
"93593"), class = "data.frame")
I'm plotting it like this:
prettify <- theme(panel.background = element_rect(fill = NA,color="gray"),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(size=.1, color="black",linetype="dotted"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_line(size=.1, color="black"),
legend.position="bottom")
ggplot(dataw,aes(x = base, y = values, color = type, group = type)) +
geom_boxplot() +
facet_wrap(type ~ pos, scales="free", nrow = 2) +
theme_gray() %+replace% prettify
But I keep getting only one boxplot in each plot square like so, when in fact I want 4 boxplots for each square:
Does anyone see what I am doing wrong here? Thanks!