Question

我已经被困在一个特定问题上超过一天了，我希望你们可以帮助我。我想用ggplot2创建单独的箱图，用于每个特定位置（loc_nr）的一组环境变量（并且每个位置具有不同数量的数据点）。我只设法在x轴上为所有位置创建一个带有许多箱图（表示环境变量）的大图。我想用箱形图（每个位置一个）生成多个小数字。

我的数据集（小部分）：所有变量名都向左移，loc_nr应从01开始，so_temp在0.230等等。

     loc_nr so_temp so_spcond  so_ph so_turbid so_chl1  so_o2  depth current water_fluc substrate   silt org_matter wood_nr connect veg_shore veg_water0 veg_water1  shade water_colour bycatch
1        01   0.230    -0.670  1.096    -0.386  -0.585  1.428 -0.468  -0.492     -1.008    -1.010 -0.863      3.933  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291        0.639   3.318
2        01   0.178    -1.065  0.663    -0.315  -0.608  1.428 -0.406  -0.492     -1.008    -0.386 -0.863      3.933  -0.131  -0.706    -0.343      1.094     -0.157 -0.291       -1.481  -0.410
3        01   0.645    -0.670  0.969     0.185  -0.314  1.206 -0.220  -0.492     -1.008    -0.386  0.031      2.510  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291       -1.481  -0.410
4        01   0.075    -0.276  0.383     0.224  -0.314  1.157 -0.096  -0.492     -1.008    -1.010  0.031      2.510  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291       -1.481  -0.410
5        01   0.807    -0.276  0.332     1.779  -0.224  0.115 -0.468  -0.492     -1.008    -1.010  1.818      5.357  -0.033  -0.706    -0.343     -0.277     -0.157 -0.291       -0.774  -0.410
6        01   0.184    -0.276  0.816     0.363  -0.269  0.401 -0.406  -0.492     -1.008    -0.386  1.818      5.357  -0.033  -0.706    -0.343     -0.277     -0.157 -0.291       -0.774  -0.410
7        01   0.052    -1.065  1.452     0.839  -0.066 -0.117 -0.406  -0.492     -1.008    -0.386  0.031     -0.337  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291       -1.481  -0.410
8        01   0.553    -0.276  0.561     0.576  -0.201  0.963 -0.282  -0.492     -1.008    -0.386  0.925     -0.337  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
9        01   0.173    -0.407  0.791    -0.085  -0.269  0.634 -0.592  -0.492     -1.008     1.484  0.031     -0.337  -0.131  -0.706    -0.343     -0.277     -0.157  0.981       -0.774  -0.410
10       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.096  -0.492      0.990    -0.386 -0.863      2.510   0.261   0.471    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
11       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.220  -0.492      0.990    -0.386  0.031      3.933  -0.033   0.471    -0.343     -0.277     -0.157 -0.291        0.639   1.454
12       02   2.565    -0.144  1.223    -0.262  -0.698  2.039  0.028  -0.492      0.990    -0.386  0.031      1.086  -0.033   0.471    -0.343     -0.277     -0.157 -0.291        0.639   1.454
13       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.220  -0.492      0.990    -0.386 -0.863      1.086  -0.131   0.471    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
14       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.530  -0.492      0.990     1.484 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
15       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.406  -0.492      0.990     1.484 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157 -0.291       -0.068  -0.410
16       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.592  -0.492      0.990     0.237 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157  2.253       -0.774  -0.410
17       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.158  -0.492      0.990     0.237 -0.863     -0.337  -0.033   0.471    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
18       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.406  -0.492      0.990    -1.010 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157  0.981        0.639  -0.410
19       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.654  -0.492      0.990    -1.010 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157 -0.291       -0.774  -0.410
20       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.592  -0.492      0.990    -1.010 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157 -0.291       -0.774  -0.410
22       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.406  -0.492      0.990    -1.010 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157  0.981        0.639  -0.410
23       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.654  -0.492      0.990    -1.010 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157 -0.291       -0.774  -0.410
24       02   2.565    -0.144  1.223    -0.262  -0.698  2.039 -0.592  -0.492      0.990    -1.010 -0.863     -0.337  -0.131   0.471    -0.343     -0.277     -0.157 -0.291       -0.774  -0.410
25       03   0.818    -0.144 -0.966    -0.472  -0.641 -0.582 -0.220   2.026      0.990     0.237 -0.863     -0.337  -0.131   1.648    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
27       03   0.818    -0.144 -0.966    -0.472  -0.641 -0.582 -0.592   2.026      0.990     1.484 -0.863     -0.337  -0.131   1.648    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
28       03   0.818    -0.144 -0.966    -0.472  -0.641 -0.582 -0.530   2.026      0.990     1.484 -0.863     -0.337  -0.131   1.648    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
29       05   1.706    -0.013  0.154    -0.405  -0.134  0.159 -0.096  -0.492      0.990    -0.386  1.818     -0.337  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291        0.639   1.454
30       05   1.706    -0.013  0.154    -0.405  -0.134  0.159 -0.468  -0.492      0.990    -0.386  1.818     -0.337  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
31       05   1.706    -0.013  0.154    -0.405  -0.134  0.159 -0.096  -0.492      0.990     0.237 -0.863     -0.337  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291       -1.481  -0.410
32       05   1.706    -0.013  0.154    -0.405  -0.134  0.159 -0.530  -0.492      0.990     1.484 -0.863     -0.337  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291       -1.481  -0.410
33       05   1.706    -0.013  0.154    -0.405  -0.134  0.159 -0.530  -0.492      0.990     1.484 -0.863     -0.337  -0.131  -0.706    -0.343     -0.277     -0.157 -0.291       -1.481  -0.410
44       07  -0.202    -0.013  0.561     2.957   4.310 -0.432 -0.220  -0.492     -1.008    -1.010  0.925     -0.337   0.359  -1.884    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
45       07  -0.162     1.039  0.205    -0.104   0.047 -0.267  0.401  -0.492     -1.008    -1.010  1.818     -0.337  -0.131  -1.884    -0.343     -0.277     -0.157 -0.291        0.639  -0.410
46       07   0.132     1.039  0.154    -0.124   0.250 -0.325 -0.530  -0.492     -1.008    -1.010  2.712     -0.337  -0.131  -1.884    -0.343     -0.277     -0.157 -0.291        0.639  -0.410

我正在使用此代码生成图：

library(ggplot2)
dat.m <- reshape2::melt(env_alles,id.vars='loc_nr', measure.vars=c("so_temp", "so_spcond", "so_ph", "so_turbid", "so_chl1", "so_o2", "depth", "current",
                                                      "water_fluc", "substrate", "silt","org_matter", "wood_nr", "connect", "veg_shore",
                                                      "veg_water0", "veg_water1","shade", "water_colour", "bycatch")) 
p <- ggplot(dat.m) + geom_boxplot(aes(x=loc_nr, y=value, color=variable))

This figure is produced

我想在网格中显示每个位置（在这个较小的数据集的情况下：位置01,02,03,05,07）的（一组环境变量）箱图的单独数字。

希望你们能帮我解决这个问题。非常感谢提前！

Answer 1

当您继续学习和探索R包facet_wrap时，您会发现ggplot2功能非常有用！

这是你的想法吗？

ggplot(dat.m) + 
 geom_boxplot(aes(x = loc_nr, y=value, color=variable))+ 
 facet_wrap(~loc_nr, scales = 'free_x')

由于每variable有多个loc_nr s，我们使用position = 'dodge'来调整variable的水平位置。

包含X值范围的箱线图的多个图表（ggplot2）

1 个答案: