ggplot2-手动将boxplotfacet标签指定为符号/数学表达式

我有一个tibble名为的数据框my_data,如下所示:

> my_data
# A tibble: 60 x 4
   SPECIES       simulation_id psi_hat p_hat
                        
 1 Grey squirrel            74   0.527 0.306
 2 Grey squirrel           102   0.526 0.316
 3 Grey squirrel           142   0.527 0.309
 4 Grey squirrel           121   0.527 0.309
 5 Grey squirrel            25   0.526 0.317
 6 Grey squirrel            50   0.527 0.309
 7 Grey squirrel            67   0.491 0.326
 8 Grey squirrel            19   0.527 0.306
 9 Grey squirrel           174   0.527 0.302
10 Grey squirrel            46   0.527 0.309
# ... with 50 more rows

有三种不同SPECIES的my_data.

我想为每个变量psi_hat和p_hat变量制作一个箱形图SPECIES.我开始准备我的tibble喜欢:

my_data_plot <- my_data %>% 
    gather("psi_hat", "p_hat", key = "parameter", value = "estimate")

然后将其绘制成如下:

ggplot(data = my_data_plot, mapping = aes(x = SPECIES)) +
    geom_boxplot(mapping = aes(y = estimate)) + 
    facet_grid(. ~ parameter)

这给了我:

我现在要做的是将facet标记为实际的希腊符号而不是例如"psi_hat",以便"psi_hat"将得到expression(hat(psi))与facet标签等效的符号.

我知道facet_grid我可以使用labeller = label_parse,但这意味着我的变量名必须是实际表达式,但是当变量名必须是函数调用(即hat(psi))时,我不知道如何做到这一点.例如,我试过my_data <- my_data %>% rename(hat(psi) = "psi_hat")这给了我这个错误:

Error: unexpected '=' in:
"my_data <- my_data %>% 
    rename(hat(psi) ="

我也考虑过labeller = bquote,但是当我在这个图中只有列时,这似乎要求我指定行和列.

所以,我想知道是否有需要或方式为我手动定义这些方面标签的表达式？或者是否有更多的算法实现这一目标？

谢谢!

这里是dput()的my_data:

structure(list(SPECIES = c("Grey squirrel", "Grey squirrel", 
"Grey squirrel", "Grey squirrel", "Grey squirrel", "Grey squirrel", 
"Grey squirrel", "Grey squirrel", "Grey squirrel", "Grey squirrel", 
"Grey squirrel", "Grey squirrel", "Grey squirrel", "Grey squirrel", 
"Grey squirrel", "Grey squirrel", "Grey squirrel", "Grey squirrel", 
"Grey squirrel", "Grey squirrel", "Red fox", "Red fox", "Red fox", 
"Red fox", "Red fox", "Red fox", "Red fox", "Red fox", "Red fox", 
"Red fox", "Red fox", "Red fox", "Red fox", "Red fox", "Red fox", 
"Red fox", "Red fox", "Red fox", "Red fox", "Red fox", "Roe Deer", 
"Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", 
"Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", 
"Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", "Roe Deer", 
"Roe Deer"), simulation_id = c(74L, 102L, 142L, 121L, 25L, 50L, 
67L, 19L, 174L, 46L, 162L, 31L, 199L, 147L, 163L, 18L, 170L, 
172L, 106L, 157L, 158L, 200L, 187L, 103L, 144L, 77L, 78L, 139L, 
75L, 57L, 171L, 195L, 90L, 136L, 109L, 30L, 152L, 97L, 74L, 8L, 
161L, 73L, 130L, 151L, 150L, 184L, 198L, 177L, 97L, 88L, 123L, 
190L, 83L, 75L, 143L, 159L, 103L, 16L, 200L, 2L), psi_hat = c(0.526863413764263, 
0.525835491514182, 0.526515540307052, 0.526515540305866, 0.525668605170743, 
0.526515540309525, 0.491205525300549, 0.526863413760793, 0.527216807861263, 
0.526515540305866, 0.526515540306427, 0.526003604543373, 0.526003604547465, 
0.527039406294591, 0.491512271725946, 0.526343604414775, 0.526515540311415, 
0.490421555053814, 0.526003604541449, 0.526688801109048, 0.79766843868635, 
0.801911295111649, 0.797668438685209, 0.79766843868356, 0.736091175845588, 
0.801911294988272, 0.797668438684876, 0.797668438686537, 0.764665429923081, 
0.806326437262252, 0.769630414385322, 0.806326437300003, 0.733141462334417, 
0.801911295128621, 0.777085591061084, 0.801911295171974, 0.703578316906639, 
0.777085591642251, 0.793615265014951, 0.806326437459794, 0.662677377511043, 
0.701654360968483, 0.704629679152389, 0.70622149033349, 0.663754148939585, 
0.704629679094459, 0.705416487515374, 0.677464341430293, 0.707045218951227, 
0.662338556587023, 0.674456696561803, 0.701654360967044, 0.639650521202062, 
0.625380503760432, 0.704629679094605, 0.664561357366103, 0.704629679143759, 
0.636859548660175, 0.66375414894085, 0.707045219029558), p_hat = c(0.305763297214646, 
0.315819064091298, 0.309115591855344, 0.309115591855175, 0.317494717397372, 
0.309115591855145, 0.326303719905328, 0.305763297214592, 0.302410595448716, 
0.309115591855175, 0.309115591855139, 0.314143327866378, 0.314143327866402, 
0.304086998925611, 0.322825888575185, 0.310791594807592, 0.309115591855151, 
0.335654773573103, 0.314143327866529, 0.307439493743849, 0.0690109229482788, 
0.0677020110780009, 0.0690109229483345, 0.0690109229474338, 0.0744703724155286, 
0.0677020110840996, 0.0690109229257095, 0.0690109229256593, 0.0704604468308165, 
0.0663903378649822, 0.0691283355131073, 0.0663903378091044, 0.0737444081467713, 
0.0677020110873479, 0.0665479524846632, 0.0677020110750217, 0.073966658329169, 
0.0665479525245426, 0.0703192747621257, 0.0663903375835434, 0.166196509352831, 
0.157146399287494, 0.152026850762839, 0.14946227707946, 0.163675003941755, 
0.152026850344473, 0.150744974403108, 0.15740696035152, 0.148178744896067, 
0.167783160277052, 0.162569260757996, 0.157146399287514, 0.162501076719809, 
0.177160578693552, 0.152026850344548, 0.162149934290892, 0.152026850679299, 
0.168221557280052, 0.163675003941561, 0.148178745461308)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -60L))

AndS... 5

你可以试试这个.因为您的数据是长格式的,所以我使用case_when一个表达式来重命名变量,以便由贴标机读取

library(tidyverse)

df %>% 
    gather(key = "parameter", value = "estimate", -SPECIES, -simulation_id) %>%
  mutate(parameter = case_when(
    parameter == "psi_hat" ~ "hat(psi)",
    parameter == "p_hat" ~ "hat(p)"
  )) %>%
  ggplot(aes(x = SPECIES)) +
    geom_boxplot(mapping = aes(y = estimate)) + 
    facet_grid(. ~ parameter, labeller = label_parsed)

由reprex包(v0.2.0)于2018-08-18创建.

你可以试试这个.因为您的数据是长格式的,所以我使用case_when一个表达式来重命名变量,以便由贴标机读取

library(tidyverse)

df %>% 
    gather(key = "parameter", value = "estimate", -SPECIES, -simulation_id) %>%
  mutate(parameter = case_when(
    parameter == "psi_hat" ~ "hat(psi)",
    parameter == "p_hat" ~ "hat(p)"
  )) %>%
  ggplot(aes(x = SPECIES)) +
    geom_boxplot(mapping = aes(y = estimate)) + 
    facet_grid(. ~ parameter, labeller = label_parsed)

由reprex包(v0.2.0)于2018-08-18创建.