R - ggplot箱图在图中印有标准偏差值吗？

Question

我试图尽可能清楚和完整地写这个问题，并感谢你的建设性批评：

我有一个名为tibble的my_tibble，看起来像这样：

# 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

length_sd和weight_sd变量是length和width的标准偏差（是的，我知道这些数字是无意义的）对于fruit因子变量中的每个分组的四个果实，即Apple，Banana，Orange和Strawberry。

我想制作一个长度和重量的盒子图，所以我先qazxs wpoied数据：

gather()

然后我跑

my_tibble_gathered <- my_tibble %>% 
    ungroup() %>% 
    gather("length", "weight", key = "measurement", value = "value")

与ggplot2制作盒子图：

facet_grid()

这给了我：

ggplot(data = my_tibble_gathered) +
    geom_boxplot(mapping = aes(x = fruit, y = value)) + 
    facet_grid(~measurement)

到现在为止还挺好。

但是，我还没有使用标准偏差数据。我想要的是：

1. 打印主图中每个水果的标准偏差值（长度或重量取决于它们所在的方位），
2. 推动不要触摸盒子本身，并且
3. 具有给定字体和字体大小的指定小数位数（例如3）。
4. 理想情况下，我也希望能够在其中使用标准偏差符号（sigma）（所以也许使用？）。

因此，例如，在expression() Apple的方框图上，会有文字显示“[sigma symbol] = 0.211”，其他lengths也是如此。

我如何以编程方式执行此操作并从fruit获取数据，以便我不必通过my_tibble手动复制/粘贴数字？

非常感谢你。

这是annotate()的dput()：

my_tibble

Answer 1

你可以试试这个有点hackish：

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))

您必须转换值对应于sd和fruit列的数据，如k列。然后，您必须添加二进制因子以避免使用alpha参数进行过度绘制。

label