在 for 循环中组合多个 ggplot 几何图形

Question

我有一个来自调查的定量数据集。我想为我拥有的值（最小 lb、最大 ub 和模式 ml）绘制拟合三角分布。请注意，我正在使用 rtriang() 因为我的数据不包含可以拟合密度函数的分位数。至少这是我的理解。

这段代码返回了很多单独的图表。我想将每个i（或受访者）的geom_density 对象显示到一个图表中。我将如何实现这一目标？

scenarios <- c("s1", "s2")
questions <- c("q1", "q2")
respondents <- c("1","2","3")

data_long <- data.frame(id=c("1","2","3", "1","2","3", "1","2","3",
                               "1","2","3", "1","2","3", "1","2","3",
                               "1","2","3", "1","2","3", "1","2","3",
                               "1","2","3", "1","2","3", "1","2","3"),
                         variable=c("s1_q1_ml", "s1_q1_ml", "s1_q1_ml",
                                      "s1_q1_lb", "s1_q1_lb", "s1_q1_lb",
                                      "s1_q1_ub", "s1_q1_ub", "s1_q1_ub",
                                      "s1_q2_ml", "s1_q2_ml", "s1_q2_ml",
                                      "s1_q2_lb", "s1_q2_lb", "s1_q2_lb",
                                      "s1_q2_ub", "s1_q2_ub", "s1_q2_ub",
                                      "s2_q1_ml", "s2_q1_ml", "s2_q1_ml",
                                      "s2_q1_lb", "s2_q1_lb", "s2_q1_lb",
                                      "s2_q1_ub", "s2_q1_ub", "s2_q1_ub",
                                      "s2_q2_ml", "s2_q2_ml", "s2_q1_ml",
                                      "s2_q2_lb", "s2_q2_lb", "s2_q1_lb",
                                      "s2_q2_ub", "s2_q2_ub", "s2_q1_ub"),
                         value=c(70, 70, 70, 60, 60, 60, 80, 80, 80,
                                   70, 70, 70, 60, 60, 60, 80, 80, 80,
                                   70, 70, 70, 60, 60, 60, 80, 80, 80,
                                   70, 70, 70, 60, 60, 60, 80, 80, 80))

data_long <- setDT(data_long)

for (i in respondents) {
  for (j in scenarios) {
    for (k in questions) {
      t <- rtriang(n =100000, min=as.numeric(data_long[id==i & variable == paste(j, k, "lb", sep = "_")]$value), 
                   mode=as.numeric(data_long[id==i & variable == paste(j,k, "ml", sep = "_")]$value),
                   max=as.numeric(data_long[id==i & variable == paste(j,k, "ub", sep = "_")]$value))

      # Displaying the samples in a density plot
      plot <- ggplot() + geom_density(aes(t)) + xlim(0,100) + xlab("Probability in %")
      ggsave(plot,filename=paste(i,j,k,".png",sep="_"))
    }
  }
}

Answer 1

我不确定我是否完全理解。你在找这个吗？

library(tidyverse)
library(mc2d)

temp <- data_long %>%
          separate(variable, c("scenarios", "questions", "temp"),sep = "_") %>%
          group_split(id) %>%
          map(~{
              temp <- rtriang(
                        n =100000, 
                        min = .x %>% filter(temp == 'lb') %>% pull(value),
                        mode = .x %>% filter(temp == 'ml') %>% pull(value),
                        max = .x %>% filter(temp == 'ub') %>% pull(value))
               ggplot(temp) + geom_density(aes(temp)) + 
                   xlim(0,100) + xlab("Probability in %")
               })

@Tjebo 建议的另一种方法

temp <- data_long %>%
         separate(variable, c("scenarios", "questions", "temp"),sep = "_") %>%
         group_split(id) %>%
         map_df(~{
          data.frame(x = rtriang(n =100000, 
                     min = .x %>% filter(temp == 'lb') %>% pull(value),
                     mode = .x %>% filter(temp == 'ml') %>% pull(value),
                     max = .x %>% filter(temp == 'ub') %>% pull(value)))
               }, .id = "id")


ggplot(temp) + geom_density(aes(x, color = id)) + 
      xlim(0,100) + xlab("Probability in %")