使用ggplot将直方图转换为r中的小提琴图

Question

我目前正试图在Hadley Wickham的大量资源（“r for data scientist”，“ggplot2 Elegant Graphics for Data Analysis”）的帮助下学习r。到目前为止，我能够在那里找到我所有问题的答案（非常感谢，哈德利！），但这次不是。

目前，我正在处理来自仪器的数据，该仪器通过粒子散射的光估计粒度（DLS，Zetasizer Nano，Malvern Instruments）。从该装置提取的数据是一些汇总统计（例如平均粒度）和直方图数据：x =大小（在箱中分开），y =强度[％]。这是我测量的一个测量：

   # A tibble: 70 x 3
   sample_name        intensities      bins
   <chr>                    <dbl>     <dbl>
 1 core formulation 1         0       0.4  
 2 core formulation 1         0       0.463
 3 core formulation 1         0       0.536
 4 core formulation 1         0       0.621
 5 core formulation 1         0       0.720
 6 core formulation 1         0       0.833
 7 core formulation 1         0       0.965
 8 core formulation 1         0       1.12 
 9 core formulation 1         0       1.29 
10 core formulation 1         0       1.50 
11 core formulation 1         0       1.74 
12 core formulation 1         0       2.01 
13 core formulation 1         0       2.33 
14 core formulation 1         0       2.70 
15 core formulation 1         0       3.12 
16 core formulation 1         0       3.62 
17 core formulation 1         0       4.19 
18 core formulation 1         0       4.85 
19 core formulation 1         0       5.62 
20 core formulation 1         0       6.50 
21 core formulation 1         0       7.53 
22 core formulation 1         0       8.72 
23 core formulation 1         0      10.1  
24 core formulation 1         0      11.7  
25 core formulation 1         0      13.5  
26 core formulation 1         0      15.7  
27 core formulation 1         0      18.2  
28 core formulation 1         0      21.0  
29 core formulation 1         0      24.4  
30 core formulation 1         0      28.2  
31 core formulation 1         0      32.7  
32 core formulation 1         0      37.8  
33 core formulation 1         0      43.8  
34 core formulation 1         0.2    50.8  
35 core formulation 1         1.4    58.8  
36 core formulation 1         3.7    68.1  
37 core formulation 1         6.9    78.8  
38 core formulation 1        10.2    91.3  
39 core formulation 1        12.9   106.   
40 core formulation 1        14.4   122.   
41 core formulation 1        14.4   142.   
42 core formulation 1        13     164.   
43 core formulation 1        10.3   190.   
44 core formulation 1         7.1   220.   
45 core formulation 1         3.9   255    
46 core formulation 1         1.5   295.   
47 core formulation 1         0.2   342    
48 core formulation 1         0     396.   
49 core formulation 1         0     459.   
50 core formulation 1         0     531.   
51 core formulation 1         0     615.   
52 core formulation 1         0     712.   
53 core formulation 1         0     825    
54 core formulation 1         0     955.   
55 core formulation 1         0    1106    
56 core formulation 1         0    1281    
57 core formulation 1         0    1484    
58 core formulation 1         0    1718    
59 core formulation 1         0    1990    
60 core formulation 1         0    2305    
61 core formulation 1         0    2669    
62 core formulation 1         0    3091    
63 core formulation 1         0    3580    
64 core formulation 1         0    4145    
65 core formulation 1         0    4801    
66 core formulation 1         0    5560    
67 core formulation 1         0    6439    
68 core formulation 1         0    7456    
69 core formulation 1         0    8635    
70 core formulation 1         0   10000    

以下是使用dput()命令生成的数据：

structure(list(sample_name = c("core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1", "core formulation 1", 
"core formulation 1", "core formulation 1"), intensities = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1.4, 3.7, 6.9, 10.2, 12.9, 
14.4, 14.4, 13, 10.3, 7.1, 3.9, 1.5, 0.2, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), bins = c(0.4, 
0.4632, 0.5365, 0.6213, 0.7195, 0.8332, 0.9649, 1.117, 1.294, 
1.499, 1.736, 2.01, 2.328, 2.696, 3.122, 3.615, 4.187, 4.849, 
5.615, 6.503, 7.531, 8.721, 10.1, 11.7, 13.54, 15.69, 18.17, 
21.04, 24.36, 28.21, 32.67, 37.84, 43.82, 50.75, 58.77, 68.06, 
78.82, 91.28, 105.7, 122.4, 141.8, 164.2, 190.1, 220.2, 255, 
295.3, 342, 396.1, 458.7, 531.2, 615.1, 712.4, 825, 955.4, 1106, 
1281, 1484, 1718, 1990, 2305, 2669, 3091, 3580, 4145, 4801, 5560, 
6439, 7456, 8635, 10000)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -70L))

我可以从这些数据中生成一个没有问题的直方图：

library(tidyverse)
ggplot (DLS_intensities_core, aes(bins,intensities) ) + 
  geom_line() + 
  scale_x_continuous(trans = 'log10')

为了显示我的粒度的整体分布，我想将这些数据转换为小提琴图并使用设备在我的图的第二层提供的汇总统计。

因此，我想转换这些数据，以便能够从中创建一个小提琴情节。

我已经尝试将它提供给小提琴情节的stat_density（）参数，但到目前为止还没有成功。

你知道怎么用这些数据创建小提琴情节吗？

非常感谢你！

最好，

多米尼克

Answer 1

在您回复第二条评论后，我会更新此信息（如果需要）。你可以得到bins和intensities的小提琴情节：

library(hrbrthemes)

gather(DLS_intensities_core, measure, value, -sample_name) %>% 
  ggplot(aes(measure, value)) +
  geom_violin(scale = "count") +
  scale_y_comma() +
  facet_wrap(~measure, scales="free") +
  labs(
    x = NULL, y = "A better label than this",
    title = "A better title than this",
    caption = "NOTE: Free Y scales"
  ) +
  theme_ipsum_rc(grid="Y") +
  theme(axis.text.x = element_blank())

我通常也喜欢在点上分层：

gather(DLS_intensities_core, measure, value, -sample_name) %>% 
  ggplot(aes(measure, value)) +
  geom_violin(scale = "count") +
  ggbeeswarm::geom_quasirandom() +
  scale_y_comma() +
  facet_wrap(~measure, scales="free") +
  labs(
    x = NULL, y = "A better label than this",
    title = "A better title than this",
    caption = "NOTE: Free Y scales"
  ) +
  theme_ipsum_rc(grid="Y") +
  theme(axis.text.x = element_blank())

根据你的评论，也许这可能是一个更好的方式来显示bins分布以及与intensities的关系：

library(hrbrthemes)
library(tidyverse)

ggplot(DLS_intensities_core, aes(x="", bins)) +
  geom_violin(scale = "count") +
  ggbeeswarm::geom_quasirandom(
    aes(size = intensities, fill = intensities), shape = 21
  ) +
  scale_y_comma(trans="log10") +
  viridis::scale_fill_viridis(direction = -1, trans = "log1p") +
  scale_size_continuous(trans = "log1p", range = c(2, 10)) +
  guides(fill = guide_legend()) +
  labs(
    x = NULL, y = "A better label than this",
    title = "A better title than this"
  ) +
  theme_ipsum_rc(grid="Y")

你必须做一些其他的自定义转换，试图让小提琴的形状随强度变化（并且它不会真正反映出那时的分布）。

Answer 2

我找到了解决问题的方法，它可能不是很优雅：

library (tidyverse)

DLS_intensities_core <- DLS_intensities_core %>% 
  mutate(counts = intensities * 10 )

vectors <- DLS_intensities_core %>%
  filter(counts > 0) 

bins_v <- vectors$bins
count_v <- vectors$counts

violin_DLSdata <- as.tibble(rep.int(bins_v, count_v))
violin_DLSdata$sample_name <- "core formulation 1"

ggplot (violin_DLSdata, aes(sample_name, value)) + 
  geom_violin() + 
  labs(
    x = NULL, y = "size"
  ) +
  scale_y_continuous(trans = 'log10', limits = c(1, 1000))

我的整个数据集看起来像这样：我添加了：summary statistic as red dot with errorbars。

你怎么看？