在晶格封装中的水平条形图上重新排序y轴-Java 学习之路

我对R比较陌生，我想知道是否有人可以帮助我制作一个我想用格子包创建的条形图 . 我已设法创建下面的图（不能发布，因为我是一个新用户） . 每个小组代表单独物种的丰度，而条形代表每个物种在特定深度的幼虫阶段的堆积丰度 . 问题是我想以更直观的方式呈现深度，每个面板顶部0米，底部90米 - 这意味着轴和条子一起“翻转” . 我使用以下代码创建了这个图：

# create a new column for Species and Depth as factors
    stn8_9$Depth_mF<-as.factor(stn8_9$Depth_m)
    stn8_9$SpeciesF<-as.factor(stn8_9$Species)

    # log root transform data
    stn8_9$logAbundance_per_m3<-(stn8_9$Abundance_per_m3)^(1/4) 

    # now create chart
    barchart(Depth_mF~logAbundance_per_m3 | SpeciesF,
    data=stn8_9[stn8_9$SpeciesF!="CYP" & stn8_9$Stn==9,],
    horiz=TRUE, ylab="depth (m)",xlab="Abundance (#/m3)", 
    main="Station 9", origin=0,
    col=c("red","orange","yellow","green","blue","purple"),
    stack=TRUE, groups=stn8_9$Stage,
    key=
    list(title="Stage", cex.title=1,text=list(c("1","2","3","4","5","6")),
    space="right", rectangles=list(size=2,border="white",
    col=c("red","orange","yellow","green","blue","purple"))))

数据集在底部提供（希望它的格式正确）

我知道条形图将我的“深度”值转换为因子，我尝试使用reorder（）和relevel（），并设法让轴标签翻转，但条形图保持在同一位置（不知道为什么） . 我想顶部的“0米”酒吧和底部的“90米”酒吧 - 有人可以帮忙吗？

数据集：

dput(stn8_9)
structure(list(Stn = c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L), Depth_m = c(90L, 90L, 90L, 90L, 90L, 90L, 90L, 
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 
90L, 90L, 90L, 90L, 90L, 90L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 
60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 
60L, 60L, 60L, 60L, 60L, 60L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 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, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 
70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 70L, 
70L, 70L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 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), Species = structure(c(1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 
6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 
5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 
6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 
6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 
6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L), .Label = c("BB", 
"BC", "CH", "CYP", "SB", "VS"), class = "factor"), Stage = c(2L, 
3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 
3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 
4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 
4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 
5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 
5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 
5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 
6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 
6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 
7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 
2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 
2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 
3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 
3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 
3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 
4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 
4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 
5L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 2L, 3L, 4L, 5L, 6L), Abundance_per_m3 = c(0, 
0, 0, 1.267024758, 1.267024758, 0, 0, 0, 0, 0, 5.068099033, 0, 
0, 0, 0, 25.34049517, 0, 0, 3.801074275, 0, 0, 2.534049517, 7.60214855, 
12.67024758, 6.335123791, 0, 0, 0, 0, 3.044963144, 4.059950858, 
2.029975429, 1.014987715, 4.059950858, 5.074938573, 7.104914002, 
3.044963144, 0, 2.029975429, 0, 0, 30.44963144, 0, 0, 4.059950858, 
2.029975429, 0, 11.16486486, 4.059950858, 11.16486486, 2.029975429, 
1.014987715, 0, 0, 0, 0, 0, 9.899386594, 4.949693297, 15.83901855, 
16.82895721, 10.88932525, 3.959754638, 6.929570616, 0, 0, 0, 
24.74846649, 0, 0, 5.939631957, 0, 0.989938659, 1.979877319, 
0.989938659, 1.979877319, 0.989938659, 1.979877319, 0, 0, 0, 
0, 0, 17.89544764, 1.988383071, 9.941915354, 5.965149212, 7.953532283, 
0, 15.90706457, 3.976766141, 1.988383071, 0, 23.86059685, 1.988383071, 
9.941915354, 1.988383071, 0, 0, 0, 9.941915354, 61.63987519, 
51.69795984, 9.941915354, 0, 0, 0, 0, 0, 28.83473086, 48.05788476, 
33.64051933, 14.41736543, 0, 4.805788476, 38.44630781, 4.805788476, 
0, 0, 28.83473086, 19.2231539, 28.83473086, 43.25209628, 33.64051933, 
0, 72.08682714, 163.3968082, 692.0335406, 321.9878279, 86.50419257, 
0, 0, 0, 0, 0, 19.85102993, 9.925514965, 9.925514965, 29.7765449, 
0, 9.925514965, 39.70205986, 19.85102993, 0, 0, 29.7765449, 9.925514965, 
29.7765449, 9.925514965, 19.85102993, 0, 29.7765449, 178.6592694, 
744.4136224, 416.8716286, 49.62757483, 0, 0, 0, 0, 0, 11.25305392, 
3.215158262, 12.86063305, 16.07579131, 8.037895656, 19.29094957, 
6.430316525, 4.822737393, 0, 0, 51.4425322, 1.607579131, 3.215158262, 
1.607579131, 1.607579131, 1.607579131, 1.607579131, 8.037895656, 
6.430316525, 1.607579131, 1.607579131, 0, 0, 0, 0, 0, 30.07822022, 
12.03128809, 15.03911011, 15.03911011, 9.023466065, 13.5351991, 
6.015644043, 0, 1.503911011, 0, 27.07039819, 0, 6.015644043, 
3.007822022, 0, 1.503911011, 9.023466065, 25.56648718, 16.54302112, 
15.03911011, 6.015644043, 0, 0, 0, 4.939940207, 0, 39.51952166, 
14.81982062, 4.939940207, 4.939940207, 4.939940207, 0, 4.939940207, 
4.939940207, 0, 0, 29.63964124, 4.939940207, 4.939940207, 14.81982062, 
0, 0, 29.63964124, 197.5976083, 568.0931238, 261.816831, 54.33934228, 
0, 0, 0, 10.62671701, 0, 21.25343402, 10.62671701, 21.25343402, 
21.25343402, 31.88015104, 0, 0, 0, 0, 0, 10.62671701, 31.88015104, 
0, 31.88015104, 21.25343402, 0, 138.1473212, 244.4144913, 371.9350954, 
212.5343402, 31.88015104, 0, 0, 0, 69.50153499, 19.85758143, 
39.71516285, 29.78637214, 79.4303257, 49.64395357, 79.4303257, 
9.928790713, 39.71516285, 89.35911642, 9.928790713, 0, 29.78637214, 
79.4303257, 19.85758143, 178.7182328, 148.9318607, 99.28790713, 
317.7213028, 615.5850242, 1211.312467, 327.6500935, 69.50153499
), Depth_mF = structure(c(7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 
2L, 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, 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 2L, 2L, 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), .Label = c("5", "10", "20", "40", "60", 
"70", "90"), class = "factor"), SpeciesF = structure(c(1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 
6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 
5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 
6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 
6L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L), .Label = c("BB", 
"BC", "CH", "CYP", "SB", "VS"), class = "factor"), logAbundance_per_m3 = c(0, 
0, 0, 1.06095331789381, 1.06095331789381, 0, 0, 0, 0, 0, 1.50041457128417, 
0, 0, 0, 0, 2.24364310060701, 0, 0, 1.39629309072760, 0, 0, 1.26169323444953, 
1.6604816781224, 1.88667144022303, 1.58649525090772, 0, 0, 0, 
0, 1.32097777276068, 1.41948299515758, 1.19363816214162, 1.00372605177853, 
1.41948299515758, 1.50092052805914, 1.63263727001607, 1.32097777276068, 
0, 1.19363816214162, 0, 0, 2.34906757441938, 0, 0, 1.41948299515758, 
1.19363816214162, 0, 1.82794602415898, 1.41948299515758, 1.82794602415898, 
1.19363816214162, 1.00372605177853, 0, 0, 0, 0, 0, 1.77378946506859, 
1.49157320259098, 1.99495023677811, 2.02541630374003, 1.81656207258872, 
1.41064284060004, 1.62246964847031, 0, 0, 0, 2.23042217070931, 
0, 0, 1.56113292684677, 0, 0.99747511829459, 1.18620450786389, 
0.99747511829459, 1.18620450786389, 0.99747511829459, 1.18620450786389, 
0, 0, 0, 0, 0, 2.05676958572452, 1.18747647396250, 1.77569149802404, 
1.562806928309, 1.67934533442396, 0, 1.99708942036914, 1.41215547164577, 
1.18747647396250, 0, 2.21014275343154, 1.18747647396250, 1.77569149802404, 
1.18747647396250, 0, 0, 0, 1.77569149802404, 2.80198262342921, 
2.68144165217603, 1.77569149802404, 0, 0, 0, 0, 0, 2.31728246613553, 
2.63294121550744, 2.40832821053419, 1.94859451890365, 0, 1.48061165227674, 
2.49008206159717, 1.48061165227674, 0, 0, 2.31728246613553, 2.09390107914848, 
2.31728246613553, 2.56449460796253, 2.40832821053419, 0, 2.91382843883587, 
3.57528685520031, 5.12898921614741, 4.23603815839925, 3.04971523426329, 
0, 0, 0, 0, 0, 2.11079356271994, 1.77495874023182, 1.77495874023182, 
2.33597707218005, 0, 1.77495874023182, 2.5101707230885, 2.11079356271994, 
0, 0, 2.33597707218005, 1.77495874023182, 2.33597707218005, 1.77495874023182, 
2.11079356271994, 0, 2.33597707218005, 3.65600169507374, 5.2234035271001, 
4.51856552762811, 2.65418238899045, 0, 0, 0, 0, 0, 1.83154502725781, 
1.33906170112536, 1.89371921865949, 2.00236428275782, 1.68378094745549, 
2.09574482014368, 1.59242170246783, 1.48191537399859, 0, 0, 2.67812340235484, 
1.12601218427985, 1.33906170112536, 1.12601218427985, 1.12601218427985, 
1.12601218427985, 1.12601218427985, 1.68378094745549, 1.59242170246783, 
1.12601218427985, 1.12601218427985, 0, 0, 0, 0, 0, 2.34187135068818, 
1.86242173581786, 1.96927122377906, 1.96927122377906, 1.73317871692943, 
1.91807754972377, 1.56610376120967, 0, 1.10740258963914, 0, 2.28099146904783, 
0, 1.56610376120967, 1.31693103877130, 0, 1.10740258963914, 1.73317871692943, 
2.24862878114404, 2.01675761776174, 1.96927122377906, 1.56610376120967, 
0, 0, 0, 1.49083789392989, 0, 2.50728048152353, 1.96205300969286, 
1.49083789392989, 1.49083789392989, 1.49083789392989, 0, 1.49083789392989, 
1.49083789392989, 0, 0, 2.33328739913925, 1.49083789392989, 1.49083789392989, 
1.96205300969286, 0, 0, 2.33328739913925, 3.74925881222597, 4.88207990404496, 
4.02253091444491, 2.71505476657560, 0, 0, 0, 1.80550950406603, 
0, 2.14712474844036, 1.80550950406603, 2.14712474844036, 2.14712474844036, 
2.37618413862639, 0, 0, 0, 0, 0, 1.80550950406603, 2.37618413862639, 
0, 2.37618413862639, 2.14712474844036, 0, 3.42835366591009, 3.95395514204289, 
4.39153946529161, 3.8181877309365, 2.37618413862639, 0, 0, 0, 
2.88734446548044, 2.11096769918679, 2.51037780725583, 2.33616978566337, 
2.98535914973356, 2.65440135387150, 2.98535914973356, 1.77510517087316, 
2.51037780725583, 3.07457234475636, 1.77510517087316, 0, 2.33616978566337, 
2.98535914973356, 2.11096769918679, 3.65630330777027, 3.49338864171756, 
3.15663297601737, 4.22193539810782, 4.98106273377854, 5.899484260135, 
4.25453963683082, 2.88734446548044)), .Names = c("Stn", "Depth_m", 
"Species", "Stage", "Abundance_per_m3", "Depth_mF", "SpeciesF", 
"logAbundance_per_m3"), row.names = c(NA, -286L), class = "data.frame")

1 回答

0
我希望我正确地读你，因为我只看到从5到90，没有0的米 . 但是如果你只是让那个变量成为一个有序的因子，并且“反向”排序的值，你会得到我认为你的描述：
```
d$Depth_mF <- factor(d$Depth_m,
                     levels = rev(sort(unique(d$Depth_m))),
                     ordered = TRUE)
```
回复于 2024-05-14T04:44:57+08:00

在晶格封装中的水平条形图上重新排序y轴

1 回答

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