如何以一种方式为每个变量勾勒选择具有正态分布的图形答案

【问题标题】：How to outline in one way for each variables selected a graph with normality distribution如何以一种方式为每个变量勾勒选择具有正态分布的图形
【发布时间】：2021-09-03 18:11:13
【问题描述】：

我正在尝试对我正在处理的数据集的一系列特定变量执行正态分布测试，并且我编写了以下命令

as.data.frame(d) %>% 
      dplyr::select(
        age, T1.rt, CR.rt) %>% 
      na.omit() %>% 
      apply(., 2, ad.test)

但是，我不知道如何以一种方式为每个变量输入轮廓命令，我选择了包含正态曲线的正态分布直方图。具体来说，我所研究的直方图代码应该具有以下特征：

取变量“年龄”

windows(width=7, height=7); par(lwd=1, las=1, family="sans", cex=1, 
  mgp=c(3.0,1,0))
hist2(d$age, freq=F, main="", xlab="age", ylab="", col="darkgray")
curve(dnorm(x, mean=mean(d$age[!is.na(d$age)]), 
  sd=sd(d$age[!is.na(d$age)])), add=T)
skewness.kurtosis(d$age)
ks.test(d$age, "pnorm", mean=mean(d$age[!is.na(d$age)]), 
  sd=sd(d$age[!is.na(d$age)]))

我只是在这里报告我正在处理的数据集的一些观察结果：

dput(head(d,50))
structure(list(ID = c("P1323", "P1323", "P1323", "P1323", "P1323", 
"P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
"P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
"P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
"P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
"P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
"P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
"P1323", "P1323", "P1323"), gender = c("F", "F", "F", "F", "F", 
"F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", 
"F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", 
"F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", 
"F", "F", "F", "F", "F", "F"), age = c(19, 19, 19, 19, 19, 19, 
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19), fixation_time = c(60, 
60, 60, 60, 60, 70, 50, 50, 50, 70, 70, 60, 50, 60, 70, 70, 50, 
70, 70, 60, 70, 50, 50, 50, 60, 70, 60, 50, 60, 70, 60, 70, 50, 
60, 70, 50, 50, 70, 70, 70, 70, 50, 60, 50, 60, 60, 70, 50, 60, 
60), block = 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, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t1.key = c("None", "None", 
"None", "space", "None", "space", "None", "None", "None", "space", 
"None", "None", "space", "None", "None", "space", "None", "None", 
"space", "None", "space", "space", "space", "None", "None", "None", 
"space", "space", "None", "None", "space", "None", "None", "None", 
"None", "None", "None", "space", "space", "None", "None", "None", 
"None", "space", "None", "None", "space", "None", "space", "None"
), T1.response = c(0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 
0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 
0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0), COND = c("NR", 
"NR", "NR", "R", "NR", "R", "NR", "NR", "NR", "R", "NR", "NR", 
"R", "NR", "NR", "R", "NR", "NR", "R", "NR", "R", "R", "R", "NR", 
"NR", "NR", "R", "R", "NR", "NR", "R", "NR", "NR", "NR", "NR", 
"NR", "NR", "R", "R", "NR", "NR", "NR", "NR", "R", "NR", "NR", 
"R", "NR", "R", "NR"), T1.rt = c(NA, NA, NA, 0.812299799988978, 
NA, 0.72336569998879, NA, NA, NA, 0.772733500052709, NA, NA, 
0.606754800013732, NA, NA, 0.601030899968464, NA, NA, 0.838272600027267, 
NA, 0.305548300035298, 0.849945599969942, 0.748269900039304, 
NA, NA, NA, 0.859215400007088, 0.95704890001798, NA, NA, 0.874362500035204, 
NA, NA, NA, NA, NA, NA, 0.270455699996091, 0.75726039998699, 
NA, NA, NA, NA, 0.762694000033662, NA, NA, 0.789715700026136, 
NA, 0.90579859999707, NA), CR.key = c("p", "p", "p", "p", "p", 
"p", "p", "p", "p", "p", "p", "p", "p", "p", "o", "p", "i", "i", 
"h", "u", "i", "u", "o", "o", "p", "p", "p", "o", "p", "i", "o", 
"p", "p", "p", "o", "o", "o", "p", "i", "p", "p", "o", "o", "i", 
"i", "o", "o", "i", "i", "u"), CR.rt = c(0.651771800010465, 0.585048799985088, 
0.652350199990906, 0.69888829998672, 1.01917029998731, 0.550036200031173, 
0.0361186999944039, 0.568817299965303, 0.452191599993966, 0.514980700041633, 
0.619590600021184, 0.719264700019266, 0.466181399999186, 0.45217840000987, 
0.668881699966732, 0.914478300022893, 1.01910460001091, 1.40315000002738, 
1.69993370003067, 1.71914210001705, 1.29938790004235, 0.698139799991623, 
0.848338100011461, 0.651829700043891, 0.486136299965438, 0.703567499993369, 
0.76673849998042, 0.54929809999885, 0.718664799991529, 0.768383099988569, 
0.898415500007104, 0.819344500021543, 0.61898209998617, 0.737225699995179, 
1.03654629999073, 0.971092400024645, 1.4362695000018, 0.999490200018045, 
0.932840399967972, 0.586312200000975, 0.786785800009966, 1.01987839996582, 
0.93673920002766, 0.715710600023158, 0.819960499997251, 0.75370900001144, 
0.818668299994897, 0.903600800025742, 1.1176545000053, 1.10352450003847
), trial_num = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 
14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 
33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 
50, 51, 52, 53), ldots = c(48, 48, 52, 55, 51, 51, 52, 49, 45, 
55, 49, 49, 51, 49, 48, 52, 45, 49, 45, 55, 51, 48, 55, 51, 45, 
45, 52, 48, 48, 48, 55, 51, 49, 48, 49, 51, 51, 55, 51, 49, 45, 
55, 51, 55, 55, 52, 52, 48, 49, 52), rdots = c(52, 52, 48, 45, 
49, 49, 48, 51, 55, 45, 51, 51, 49, 51, 52, 48, 55, 51, 55, 45, 
49, 52, 45, 49, 55, 55, 48, 52, 52, 52, 45, 49, 51, 52, 51, 49, 
49, 45, 49, 51, 55, 45, 49, 45, 45, 48, 48, 52, 51, 48), TASK = c("left", 
"left", "left", "left", "left", "left", "left", "left", "left", 
"left", "left", "left", "left", "left", "left", "left", "left", 
"left", "left", "left", "left", "left", "left", "left", "left", 
"left", "left", "left", "left", "left", "left", "left", "left", 
"left", "left", "left", "left", "left", "left", "left", "left", 
"left", "left", "left", "left", "left", "left", "left", "left", 
"left"), T1.correct = c(0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 
0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 
0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1), Go.Nogo..whether.a.person.should.respond. = c("NR", 
"NR", "R", "R", "R", "R", "R", "NR", "NR", "R", "NR", "NR", "R", 
"NR", "NR", "R", "NR", "NR", "NR", "R", "R", "NR", "R", "R", 
"NR", "NR", "R", "NR", "NR", "NR", "R", "R", "NR", "NR", "NR", 
"R", "R", "R", "R", "NR", "NR", "R", "R", "R", "R", "R", "R", 
"NR", "NR", "R"), T1.ACC = c(1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 
1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0), CR = c(4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 2, 2, 9, 1, 2, 1, 
3, 3, 4, 4, 4, 3, 4, 2, 3, 4, 4, 4, 3, 3, 3, 4, 2, 4, 4, 3, 3, 
2, 2, 3, 3, 2, 2, 1), difficulty = c("medium", "medium", "medium", 
"easy", "hard", "hard", "medium", "hard", "easy", "easy", "hard", 
"hard", "hard", "hard", "medium", "medium", "easy", "hard", "easy", 
"easy", "hard", "medium", "easy", "hard", "easy", "easy", "medium", 
"medium", "medium", "medium", "easy", "hard", "hard", "medium", 
"hard", "hard", "hard", "easy", "hard", "hard", "easy", "easy", 
"hard", "easy", "easy", "medium", "medium", "medium", "hard", 
"medium")), row.names = c(NA, -50L), class = c("tbl_df", "tbl", 
"data.frame"))

有人知道吗？我应该如何继续使用上述代码？此外，我希望这些图表可以在同一个窗口中一起绘制，而不是重叠。

【问题讨论】：

标签： r graph normal-distribution

【解决方案1】：

好的，现在，有了你的数据，我们可以再做一次。

我们会加载您的数据

df=structure(list(ID = c("P1323", "P1323", "P1323", "P1323", "P1323", 
 "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
 "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
 "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
 "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
 "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
 "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", "P1323", 
 "P1323", "P1323", "P1323"), gender = c("F", "F", "F", "F", "F", 
 "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", 
 "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", 
 "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", 
 "F", "F", "F", "F", "F", "F"), age = c(19, 19, 19, 19, 19, 19, 
 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19), fixation_time = c(60, 
 60, 60, 60, 60, 70, 50, 50, 50, 70, 70, 60, 50, 60, 70, 70, 50, 
 70, 70, 60, 70, 50, 50, 50, 60, 70, 60, 50, 60, 70, 60, 70, 50, 
 60, 70, 50, 50, 70, 70, 70, 70, 50, 60, 50, 60, 60, 70, 50, 60, 
 60), block = 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, 0, 0, 0, 
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t1.key = c("None", "None", 
 "None", "space", "None", "space", "None", "None", "None", "space", 
 "None", "None", "space", "None", "None", "space", "None", "None", 
 "space", "None", "space", "space", "space", "None", "None", "None", 
 "space", "space", "None", "None", "space", "None", "None", "None", 
 "None", "None", "None", "space", "space", "None", "None", "None", 
 "None", "space", "None", "None", "space", "None", "space", "None"
 ), T1.response = c(0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 
 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 
 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0), COND = c("NR", 
 "NR", "NR", "R", "NR", "R", "NR", "NR", "NR", "R", "NR", "NR", 
 "R", "NR", "NR", "R", "NR", "NR", "R", "NR", "R", "R", "R", "NR", 
 "NR", "NR", "R", "R", "NR", "NR", "R", "NR", "NR", "NR", "NR", 
 "NR", "NR", "R", "R", "NR", "NR", "NR", "NR", "R", "NR", "NR", 
 "R", "NR", "R", "NR"), T1.rt = c(NA, NA, NA, 0.812299799988978, 
 NA, 0.72336569998879, NA, NA, NA, 0.772733500052709, NA, NA, 
 0.606754800013732, NA, NA, 0.601030899968464, NA, NA, 0.838272600027267, 
 NA, 0.305548300035298, 0.849945599969942, 0.748269900039304, 
 NA, NA, NA, 0.859215400007088, 0.95704890001798, NA, NA, 0.874362500035204, 
 NA, NA, NA, NA, NA, NA, 0.270455699996091, 0.75726039998699, 
 NA, NA, NA, NA, 0.762694000033662, NA, NA, 0.789715700026136, 
 NA, 0.90579859999707, NA), CR.key = c("p", "p", "p", "p", "p", 
 "p", "p", "p", "p", "p", "p", "p", "p", "p", "o", "p", "i", "i", 
 "h", "u", "i", "u", "o", "o", "p", "p", "p", "o", "p", "i", "o", 
 "p", "p", "p", "o", "o", "o", "p", "i", "p", "p", "o", "o", "i", 
 "i", "o", "o", "i", "i", "u"), CR.rt = c(0.651771800010465, 0.585048799985088, 
 0.652350199990906, 0.69888829998672, 1.01917029998731, 0.550036200031173, 
 0.0361186999944039, 0.568817299965303, 0.452191599993966, 0.514980700041633, 
 0.619590600021184, 0.719264700019266, 0.466181399999186, 0.45217840000987, 
 0.668881699966732, 0.914478300022893, 1.01910460001091, 1.40315000002738, 
 1.69993370003067, 1.71914210001705, 1.29938790004235, 0.698139799991623, 
 0.848338100011461, 0.651829700043891, 0.486136299965438, 0.703567499993369, 
 0.76673849998042, 0.54929809999885, 0.718664799991529, 0.768383099988569, 
 0.898415500007104, 0.819344500021543, 0.61898209998617, 0.737225699995179, 
 1.03654629999073, 0.971092400024645, 1.4362695000018, 0.999490200018045, 
 0.932840399967972, 0.586312200000975, 0.786785800009966, 1.01987839996582, 
 0.93673920002766, 0.715710600023158, 0.819960499997251, 0.75370900001144, 
 0.818668299994897, 0.903600800025742, 1.1176545000053, 1.10352450003847
 ), trial_num = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 
 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 
 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 
 50, 51, 52, 53), ldots = c(48, 48, 52, 55, 51, 51, 52, 49, 45, 
 55, 49, 49, 51, 49, 48, 52, 45, 49, 45, 55, 51, 48, 55, 51, 45, 
 45, 52, 48, 48, 48, 55, 51, 49, 48, 49, 51, 51, 55, 51, 49, 45, 
 55, 51, 55, 55, 52, 52, 48, 49, 52), rdots = c(52, 52, 48, 45, 
 49, 49, 48, 51, 55, 45, 51, 51, 49, 51, 52, 48, 55, 51, 55, 45, 
 49, 52, 45, 49, 55, 55, 48, 52, 52, 52, 45, 49, 51, 52, 51, 49, 
 49, 45, 49, 51, 55, 45, 49, 45, 45, 48, 48, 52, 51, 48), TASK = c("left", 
 "left", "left", "left", "left", "left", "left", "left", "left", 
 "left", "left", "left", "left", "left", "left", "left", "left", 
 "left", "left", "left", "left", "left", "left", "left", "left", 
 "left", "left", "left", "left", "left", "left", "left", "left", 
 "left", "left", "left", "left", "left", "left", "left", "left", 
 "left", "left", "left", "left", "left", "left", "left", "left", 
 "left"), T1.correct = c(0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 
 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 
 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1), Go.Nogo..whether.a.person.should.respond. = c("NR", 
 "NR", "R", "R", "R", "R", "R", "NR", "NR", "R", "NR", "NR", "R", 
 "NR", "NR", "R", "NR", "NR", "NR", "R", "R", "NR", "R", "R", 
 "NR", "NR", "R", "NR", "NR", "NR", "R", "R", "NR", "NR", "NR", 
 "R", "R", "R", "R", "NR", "NR", "R", "R", "R", "R", "R", "R", 
 "NR", "NR", "R"), T1.ACC = c(1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 
 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 
 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0), CR = c(4, 
 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 2, 2, 9, 1, 2, 1, 
 3, 3, 4, 4, 4, 3, 4, 2, 3, 4, 4, 4, 3, 3, 3, 4, 2, 4, 4, 3, 3, 
 2, 2, 3, 3, 2, 2, 1), difficulty = c("medium", "medium", "medium", 
 "easy", "hard", "hard", "medium", "hard", "easy", "easy", "hard", 
 "hard", "hard", "hard", "medium", "medium", "easy", "hard", "easy", 
 "easy", "hard", "medium", "easy", "hard", "easy", "easy", "medium", 
 "medium", "medium", "medium", "easy", "hard", "hard", "medium", 
 "hard", "hard", "hard", "easy", "hard", "hard", "easy", "easy", 
 "hard", "easy", "easy", "medium", "medium", "medium", "hard", 
 "medium")), row.names = c(NA, -50L), class = c("tbl_df", "tbl", 
 "data.frame"))

现在我们将更改它们的表示，以便这些行包含单独的变量名称和数据（tibble 中的tibble）

library(tidyverse)
df1 = df %>% 
  pivot_longer(where(is.numeric), names_to = "variable", values_to = "value") %>% 
  group_by(variable) %>% 
  nest()

输出df1

# A tibble: 12 x 2
# Groups:   variable [12]
   variable      data             
   <chr>         <list>           
 1 age           <tibble [50 x 9]>
 2 fixation_time <tibble [50 x 9]>
 3 block         <tibble [50 x 9]>
 4 T1.response   <tibble [50 x 9]>
 5 T1.rt         <tibble [50 x 9]>
 6 CR.rt         <tibble [50 x 9]>
 7 trial_num     <tibble [50 x 9]>
 8 ldots         <tibble [50 x 9]>
 9 rdots         <tibble [50 x 9]>
10 T1.correct    <tibble [50 x 9]>
11 T1.ACC        <tibble [50 x 9]>
12 CR            <tibble [50 x 9]>

tibble 中的 tibble 是什么？让我们检查一下。

df1$data[[6]] %>% select(ID, value)

输出

# A tibble: 50 x 2
   ID     value
   <chr>  <dbl>
 1 P1323 0.652 
 2 P1323 0.585 
 3 P1323 0.652 
 4 P1323 0.699 
 5 P1323 1.02  
 6 P1323 0.550 
 7 P1323 0.0361
 8 P1323 0.569 
 9 P1323 0.452 
10 P1323 0.515 
# ... with 40 more rows

好吧，既然我们在单独的tibble 中有数据，让我们创建一个函数来返回我们感兴趣的统计信息。

add.statistic = function(data) {
  x = data$value[!is.na(data$value)] 
  suppressWarnings(tibble(
    n = length(x),
    nunique = length(unique(x)),
    mean = mean(x),
    sd = sd(x),
    skew = ifelse(nunique>1,moments::skewness(x), NA),
    kur = ifelse(nunique>1,moments::kurtosis(x), NA),
    ks.p = ifelse(nunique>1,stats::ks.test(x, "pnorm", mean, sd)$p.value, NA),
    ad.stat = ifelse(nunique>1,nortest::ad.test(x)$statistic, NA),
    ad.p = ifelse(nunique>1,nortest::ad.test(x)$p.value, NA),
    sw.stat = ifelse(nunique>1,stats::shapiro.test(x)$statistic, NA),
    sw.p = ifelse(nunique>1,stats::shapiro.test(x)$p.value, NA)
  ))
}

这里需要几个 cmets。变量可以包含所有相同的值。那么像skewness、 kurtosis、shapiro 之类的统计数据是没有意义的。为此，我在那里添加了ifelse 函数和nunique 变量。

此外，测试ks.test 可能会生成警告。为此，我使用了suppressWarnings 使其静音。

现在让我们看看add.statistic 函数是如何工作的

add.statistic(df1$data[[6]])

输出

# A tibble: 1 x 11
      n nunique  mean    sd  skew   kur  ks.p ad.stat    ad.p sw.stat    sw.p
  <int>   <int> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1    50      50 0.818 0.313 0.849  4.53 0.456    1.17 0.00421   0.928 0.00465

宾果！我们有统计数据！现在让我们把它放在一起。

df1 = df %>% 
  pivot_longer(where(is.numeric), names_to = "variable", values_to = "value") %>% 
  group_by(variable) %>% 
  nest() %>% 
  mutate(stat = map(data, add.statistic)) 

df1 %>%  unnest(stat)

输出

# A tibble: 12 x 13
# Groups:   variable [12]
   variable      data                  n nunique   mean     sd    skew   kur          ks.p ad.stat      ad.p sw.stat      sw.p
   <chr>         <list>            <int>   <int>  <dbl>  <dbl>   <dbl> <dbl>         <dbl>   <dbl>     <dbl>   <dbl>     <dbl>
 1 age           <tibble [50 x 9]>    50       1 19      0     NA      NA    NA             NA     NA         NA     NA       
 2 fixation_time <tibble [50 x 9]>    50       3 60.4    8.07  -0.0719  1.57  0.0139         3.90   7.17e-10   0.799  8.36e- 7
 3 block         <tibble [50 x 9]>    50       1  0      0     NA      NA    NA             NA     NA         NA     NA       
 4 T1.response   <tibble [50 x 9]>    50       2  0.34   0.479  0.676   1.46  0.0000000391  10.1    3.7 e-24   0.599  1.85e-10
 5 T1.rt         <tibble [50 x 9]>    17      17  0.731  0.191 -1.42    4.15  0.209          1.20   2.74e- 3   0.819  3.78e- 3
 6 CR.rt         <tibble [50 x 9]>    50      50  0.818  0.313  0.849   4.53  0.456          1.17   4.21e- 3   0.928  4.65e- 3
 7 trial_num     <tibble [50 x 9]>    50      50 26.5   16.0   -0.0204  1.78  0.854          0.582  1.23e- 1   0.952  4.28e- 2
 8 ldots         <tibble [50 x 9]>    50       6 50.2    3.05   0.0230  2.28  0.297          1.27   2.35e- 3   0.918  2.06e- 3
 9 rdots         <tibble [50 x 9]>    50       6 49.8    3.05  -0.0230  2.28  0.297          1.27   2.35e- 3   0.918  2.06e- 3
10 T1.correct    <tibble [50 x 9]>    50       2  0.52   0.505 -0.0801  1.01  0.0000101      8.84   8.98e-22   0.636  6.99e-10
11 T1.ACC        <tibble [50 x 9]>    50       2  0.66   0.479 -0.676   1.46  0.0000000391  10.1    3.7 e-24   0.599  1.85e-10
12 CR            <tibble [50 x 9]>    50       5  3.32   1.25   1.39    9.82  0.00112        3.66   2.77e- 9   0.755  9.19e- 8

一切都很好。因此，让我们创建一个生成图形的函数。但是，仅适用于 nunique> 1!

的变量

create.plot = function(df, group){
  stat = df$stat[[1]]
  data = df$data[[1]]
  hist.val = hist(data$value, 30)
  if(stat$nunique ==1) return(NULL)
  statlab = stat %>%
    pivot_longer(everything(), names_to = "stat", values_to = "val") %>%
    mutate(x=hist.val$breaks[2],
           y=seq(max(hist.val$density)*0.1,
                 max(hist.val$density)*0.7,
                 length.out=length(stat)),
           lab = paste(stat,":",signif(val,3)))
  data %>% ggplot(aes(value))+
    geom_histogram(aes(y=..density..), colour="black", fill="white")+
    geom_density(alpha=.2, fill="red", col="red")+
    stat_function(fun = dnorm, args = list(mean = stat$mean, sd = stat$sd),
                  col="blue")+
    geom_label(data=statlab, aes(x=x, y=y, label = lab))+
    xlab(group)
}

让我们检查一下我们的create.plot 函数如何作用于选定的变量

create.plot(df1[6,], df1[6,1])

我不知道你是否真的预料到了，但我想你可能会喜欢这样的情节。

现在我们要做的就是将所有内容组合到一个简洁的命令中

df1 %>% group_by(variable) %>% 
  group_map(create.plot)

以下是部分精选图表

【讨论】：

我想我已经删除了它。如果只是让我通知它，请。
亲爱的 Marek Fiołka，您的代码看起来不错，但我会尝试运行它以检查它是否是我一直在寻找的代码。实际上，我出现以下错误： UseMethod("pivot_longer") 中的错误：当我在展示 add.statistic 函数的工作原理后尝试运行代码框架时，没有适用于“函数”类对象的“pivot_longer”方法。
检查您拥有的tidyverse 软件包的版本。我有 1.3.1。我再次检查了我的程序，一切正常。请记住，在 Stack Overflow 上，您感谢您接受正确的解决方案。看这里stackoverflow.com/help/someone-answers。
我已经尝试卸载并再次安装它，但我仍然遇到同样的问题。
如果你只是运行这样的命令df%>% pivot_longer (where (is.numeric), names_to = "variable", values_to = "value") 呢？