R-ggplot盒形图，在图中打印标准偏差值？

我已尽力将此问题写得尽可能清楚完整，并感谢您的建设性批评：

我有一个名为my_tibble的tibble，如下所示：

# A tibble: 36 x 5
# Groups:   fruit [4]
   fruit length weight length_sd weight_sd
   <fct>  <dbl>  <dbl>     <dbl>     <dbl>
 1 Apple  0.531 0.0730     0.211    0.0292
 2 Apple  0.489 0.0461     0.211    0.0292
 3 Apple  0.503 0.0796     0.211    0.0292
 4 Apple  0.560 0.0733     0.211    0.0292
 5 Apple  0.533 0.0883     0.211    0.0292
 6 Apple  0.612 0.127      0.211    0.0292
 7 Apple  0.784 0.0671     0.211    0.0292
 8 Apple  0.363 0.0623     0.211    0.0292
 9 Apple  1.000 0.0291     0.211    0.0292
10 Apple  0.956 0.0284     0.211    0.0292
# ... with 26 more rows

然后，我运行ggplot2，使用facet_grid绘制方框图：

这给了我：

到目前为止还不错

然而，我还没有使用标准差数据。我想做的是：

打印长度或重量的标准偏差值，具体取决于主地块内每个水果的哪个面

轻推使其不接触长方体图形本身，以及

在指定的小数位数处，例如给定字体和字号的3

理想情况下，我也希望能够在其中使用标准偏差符号sigma，所以也许可以使用表达式

因此，例如，在苹果长度的方框图顶部，会有一个文本，读[sigma symbol]=0.211，其他水果也是如此

我如何通过编程实现这一点，并从我的数据库中获取数据，这样我就不必通过annotate手动复制/粘贴数字了

多谢各位

以下是我的文件的dput：

---

你可以试试这个有点粗俗的：

d %>% 
  # transform from wide to long similar as you did already
  gather(k, v, -fruit, -ends_with("sd")) %>% 
  # add corresponding sd values 
  mutate(label = ifelse(k == "length", length_sd, weight_sd)) %>% 
  # prepare the label as expression
  mutate(label = paste0("sigma==", round(label, 3))) %>%       
  # add factor for alpha by adding the second group 
  group_by(k, add = T) %>% 
  mutate(Alpha=c(1, rep(0, n()-1))) %>% 
  ggplot(aes(fruit, v)) + 
  geom_boxplot() + 
  geom_text(aes(y=max(v) + 0.1,  
            label=label,
            alpha=factor(Alpha)), 
            size=3,
            show.legend = F, 
            parse = T) +
  facet_grid(~k) +
  scale_alpha_manual(values=c(0, 1))

您必须转换sd值对应于列标签中的FROUT和k列的数据。然后，您必须添加一个二进制因子，以避免使用alpha参数进行过度绘制

d %>% 
  gather(k, v, -fruit, -ends_with("sd")) %>% 
  mutate(label=ifelse(k == "length",length_sd,weight_sd )) %>% 
  group_by(k, add=T) %>% 
  mutate(Alpha=c(1,rep(0,n()-1))) %>% 
  head(3)
# A tibble: 3 x 7
# Groups:   fruit, k [1]
  fruit length_sd weight_sd k          v label Alpha
  <fct>     <dbl>     <dbl> <chr>  <dbl> <dbl> <dbl>
1 Apple     0.211    0.0292 length 0.531 0.211     1
2 Apple     0.211    0.0292 length 0.489 0.211     0
3 Apple     0.211    0.0292 length 0.503 0.211     0

---

谢谢@Jimbou，代码对我来说有点复杂，但我正在一行一行地努力理解它。同时，有没有办法在绘图中使用实际的希腊西格玛符号，而不是拼写s-i-g-m-a？和2使用alpha参数的过度绘制指的是什么？alpha变量做什么？@hpy delete alpha=factorAlpha然后您将看到文本被绘制了5次。因此，要隐藏其中四个，必须减小大小或alpha值。因此，我在每个水果和钾的组合中添加了一个1和四个0。看到我的编辑了吗，看到她了吗？集合。。。用x:z选择x和z之间的所有变量，用-y排除y。我包括了-y，因为我不想收集sd列。噢，我终于得到了它。-是为了防止某些变量被收集，现在有意义了。非常感谢。

my_tibble <- structure(list(fruit = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("Apple", 
"Banana", "Orange", "Strawberry"), class = "factor"), length = c(0.530543135476024, 
0.488977737310336, 0.503193533328075, 0.560337485188931, 0.533439933009971, 
0.611517111445543, 0.784118643975375, 0.362563771715571, 0.999994359802019, 
0.956308812233702, 0.332481969543643, 0.562729609348448, 0.635908731579197, 
0.565161511593215, 0.526448727581439, 0.429069715902935, 0.460919459557728, 
0.444385050459595, 0.503366669668819, 0.618141816193079, 0.516525710744663, 
0.481938965057342, 0.505085048888451, 0.457048653556098, 0.536921608675353, 
0.511397571854412, 0.442487815464855, 0.50103115023886, 0.305442471161553, 
0.424241364519466, 2.45596087585689e-09, 0.122698840602406, 0.131431902209926, 
0.205210819820745, 0.154445620769804, 0.161286627937974), weight = c(0.0729778030869548, 
0.0460942475327506, 0.0796304213241703, 0.0732813711244074, 0.0882995825748408, 
0.127183436952234, 0.0670534170610057, 0.0622813564507915, 0.0290840877242033, 
0.0283807418126428, 0.107361724942771, 0.119133737366527, 0.185844270761176, 
0.108155205104857, 0.189750275168087, 0.0845939609954818, 0.146490609941214, 
0.14150784543994, 0.122840037806175, 0.143552891056291, 0.16798564927051, 
0.241024152676673, 0.237508762873311, 0.20455939607561, 0.316350856257808, 
0.30730862083812, 0.184386251393058, 0.181923008217247, 0.332024894278287, 
0.194530111145869, 0.0166977795512452, 0.0569762924658561, 0.0739793228272142, 
0.0433330479654348, 0.099781312832018, 0.0396375225550451), length_sd = c(0.21053610140121, 
0.21053610140121, 0.21053610140121, 0.21053610140121, 0.21053610140121, 
0.21053610140121, 0.21053610140121, 0.21053610140121, 0.21053610140121, 
0.21053610140121, 0.0933430177635132, 0.0933430177635132, 0.0933430177635132, 
0.0933430177635132, 0.0933430177635132, 0.0933430177635132, 0.0933430177635132, 
0.0933430177635132, 0.0933430177635132, 0.0933430177635132, 0.067296241260161, 
0.067296241260161, 0.067296241260161, 0.067296241260161, 0.067296241260161, 
0.067296241260161, 0.067296241260161, 0.067296241260161, 0.067296241260161, 
0.067296241260161, 0.0695477116271205, 0.0695477116271205, 0.0695477116271205, 
0.0695477116271205, 0.0695477116271205, 0.0695477116271205), 
    weight_sd = c(0.0292441784658992, 0.0292441784658992, 0.0292441784658992, 
    0.0292441784658992, 0.0292441784658992, 0.0292441784658992, 
    0.0292441784658992, 0.0292441784658992, 0.0292441784658992, 
    0.0292441784658992, 0.033755823218546, 0.033755823218546, 
    0.033755823218546, 0.033755823218546, 0.033755823218546, 
    0.033755823218546, 0.033755823218546, 0.033755823218546, 
    0.033755823218546, 0.033755823218546, 0.0611975080850528, 
    0.0611975080850528, 0.0611975080850528, 0.0611975080850528, 
    0.0611975080850528, 0.0611975080850528, 0.0611975080850528, 
    0.0611975080850528, 0.0611975080850528, 0.0611975080850528, 
    0.0290125579882519, 0.0290125579882519, 0.0290125579882519, 
    0.0290125579882519, 0.0290125579882519, 0.0290125579882519
    )), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -36L), vars = "fruit", labels = structure(list(
    fruit = structure(1:4, .Label = c("Apple", "Banana", "Orange", 
    "Strawberry"), class = "factor")), class = "data.frame", row.names = c(NA, 
-4L), vars = "fruit", drop = TRUE), indices = list(0:9, 20:29, 
    10:19, 30:35), drop = TRUE, group_sizes = c(10L, 10L, 10L, 
6L), biggest_group_size = 10L)

d %>% 
  # transform from wide to long similar as you did already
  gather(k, v, -fruit, -ends_with("sd")) %>% 
  # add corresponding sd values 
  mutate(label = ifelse(k == "length", length_sd, weight_sd)) %>% 
  # prepare the label as expression
  mutate(label = paste0("sigma==", round(label, 3))) %>%       
  # add factor for alpha by adding the second group 
  group_by(k, add = T) %>% 
  mutate(Alpha=c(1, rep(0, n()-1))) %>% 
  ggplot(aes(fruit, v)) + 
  geom_boxplot() + 
  geom_text(aes(y=max(v) + 0.1,  
            label=label,
            alpha=factor(Alpha)), 
            size=3,
            show.legend = F, 
            parse = T) +
  facet_grid(~k) +
  scale_alpha_manual(values=c(0, 1))

d %>% 
  gather(k, v, -fruit, -ends_with("sd")) %>% 
  mutate(label=ifelse(k == "length",length_sd,weight_sd )) %>% 
  group_by(k, add=T) %>% 
  mutate(Alpha=c(1,rep(0,n()-1))) %>% 
  head(3)
# A tibble: 3 x 7
# Groups:   fruit, k [1]
  fruit length_sd weight_sd k          v label Alpha
  <fct>     <dbl>     <dbl> <chr>  <dbl> <dbl> <dbl>
1 Apple     0.211    0.0292 length 0.531 0.211     1
2 Apple     0.211    0.0292 length 0.489 0.211     0
3 Apple     0.211    0.0292 length 0.503 0.211     0