覆盖具有相同因子的框

Question

在下图中，每个因素（周）都有三个方框，。但是，我宁愿每周的盒子都放在一起。（相同的X位置），因此，他们会占用更少space.

这是一个带有代码的玩具数据集，

    dat <- read.table(text = '   animal_id condition week_number label variable        value
1         12     adlib           1  <NA> pc.value -0.110826762
2         13     adlib           1  <NA> pc.value -0.037862740
3         14     adlib           1  <NA> pc.value -0.252474983
4         15      pair           1  <NA> pc.value  0.097274297
5         16      pair           1  <NA> pc.value -0.015273153
6         17     adlib           1  <NA> pc.value -0.069534177
7         18      pair           1  <NA> pc.value -0.033537590
8          1       con           1  <NA> pc.value  0.167484278
9          2       con           1  <NA> pc.value  0.183647180
10        19       con           1  <NA> pc.value  0.229669153
11        20       con           1  <NA> pc.value  0.229764374
12         3       con           1     3 pc.value  0.357263429
13        21       con           1  <NA> pc.value  0.220650230
14        22       con           1    22 pc.value  0.126420908
15        23       con           1  <NA> pc.value  0.297326296
16        24       con           1  <NA> pc.value  0.227480415
17        25      pair           1  <NA> pc.value -0.379717347
18        26      pair           1  <NA> pc.value -0.262408892
19        27      pair           1  <NA> pc.value -0.044676089
20        28      pair           1  <NA> pc.value -0.270336604
21        29     adlib           1  <NA> pc.value -0.220693336
22        30     adlib           1  <NA> pc.value -0.206574036
23         4       con           1  <NA> pc.value  0.264751685
24        32     adlib           1  <NA> pc.value -0.005645539
25        33     adlib           1  <NA> pc.value -0.374329026
26        34      pair           1  <NA> pc.value -0.201130564
27        36     adlib           1  <NA> pc.value -0.227777601
28         5       con           1  <NA> pc.value  0.233753789
29         6       con           1  <NA> pc.value  0.247092487
30         7      pair           1  <NA> pc.value -0.237548606
31         8     adlib           1  <NA> pc.value -0.158702696
32         9     adlib           1  <NA> pc.value -0.049164200
33         1       con           2  <NA> pc.value -0.248305928
34         2       con           2  <NA> pc.value -0.313808423
35         3       con           2  <NA> pc.value -0.240626126
36         4       con           2  <NA> pc.value -0.235823821
37         5       con           2  <NA> pc.value -0.360493457
38         6       con           2  <NA> pc.value -0.137924685
39         7      pair           2  <NA> pc.value  0.133718228
40         8     adlib           2  <NA> pc.value  0.061174814
41         9     adlib           2  <NA> pc.value  0.246007965
42        10      pair           2  <NA> pc.value  0.255396196
43        11      pair           2    11 pc.value -0.138252815
44        12     adlib           2  <NA> pc.value  0.105495107
45        13     adlib           2  <NA> pc.value  0.192678628
46        14     adlib           2  <NA> pc.value -0.051937483
47        15      pair           2  <NA> pc.value  0.102928123
48        16      pair           2  <NA> pc.value  0.164727853
49        17     adlib           2  <NA> pc.value -0.052299544
50        18      pair           2  <NA> pc.value  0.267744360
51        19       con           2  <NA> pc.value -0.213689714
52        20       con           2  <NA> pc.value -0.308084378
53        21       con           2  <NA> pc.value -0.292024696
54        22       con           2  <NA> pc.value -0.100708378
55        23       con           2  <NA> pc.value -0.374211596
56        24       con           2  <NA> pc.value -0.375983300
57        25      pair           2  <NA> pc.value  0.210358577
58        26      pair           2  <NA> pc.value  0.207683682
59        27      pair           2  <NA> pc.value  0.298564351
60        28      pair           2  <NA> pc.value  0.186188623
61        29     adlib           2  <NA> pc.value  0.107437391
62        30     adlib           2  <NA> pc.value  0.161707111
63        32     adlib           2  <NA> pc.value  0.052134760
64        33     adlib           2  <NA> pc.value  0.200857395
65        34      pair           2  <NA> pc.value  0.189955861
66        35     adlib           2  <NA> pc.value  0.263397668
67        36     adlib           2  <NA> pc.value  0.036017649',head=T, fill=T)


dat$week_number = as.character(dat$week_number)
dat$condition = as.character(dat$condition)
dat$label = as.character(dat$label)
plt <- ggplot(dat, aes(x = week_number, y = value)) + 
  geom_boxplot(aes(fill=condition)) 
plt

Answer 1

我们需要 position = "identity" 和 alpha，看起来还是不太理想：

ggplot(dat, aes(x = week_number, y = value, fill = condition)) + 
  geom_boxplot(position = "identity", alpha = 0.3) +

覆盖具有相同因子的框

overlaying boxes with the same factor

r

ggplot2

boxplot