我正在使用R编程语言。
我有以下数据集：

set.seed(123)
library(dplyr)

var1 = rnorm(10000, 100,100)
var2 = rnorm(10000, 100,100)
var3 = rnorm(10000, 100,100)
var4 = rnorm(10000, 100,100)
var5 <- factor(sample(c("Yes", "No"), 1000, replace=TRUE, prob=c(0.4, 0.6)))
var6 <- factor(sample(c("Yes", "No"), 1000, replace=TRUE, prob=c(0.4, 0.6)))

my_data = data.frame( var1, var2, var3, var4, var5, var6)

我想根据分类变量计算此数据集中不同列的“分组百分位数”（例如，在任意水平）。

最初，我尝试使用函数来实现这一点-但这一直给我带来了很多困难（例如R: Difficulty Calculating Percentiles?）。

**因此，我尝试同时“手动”执行此操作。**例如，假设：

• 基于var 5和var 6分组
• 我想创建一个变量“class 3”，将var 3分成10个百分点的组
• 我想创建一个变量“class 4”，将var 4分成20个百分点的组

举个例子，下面是我尝试的两种不同方法：

**方法1：**产生一些NA？

library(dplyr)
final = my_data %>% group_by(var5, var6) %>%
  mutate(class3 = case_when(ntile(var3, 10) == 1 ~ paste0(round(min(var3), 2), " to ", round(quantile(var3, 0.1), 2), " decile 1"),
                            ntile(var3, 10) == 2 ~ paste0(round(quantile(var3, 0.1), 2), " to ", round(quantile(var3, 0.2), 2), " decile 2"),
                            ntile(var3, 10) == 3 ~ paste0(round(quantile(var3, 0.2), 2), " to ", round(quantile(var3, 0.3), 2), " decile 3"),
                            ntile(var3, 10) == 4 ~ paste0(round(quantile(var3, 0.3), 2), " to ", round(quantile(var3, 0.4), 2), " decile 4"),
                            ntile(var3, 10) == 5 ~ paste0(round(quantile(var3, 0.4), 2), " to ", round(quantile(var3, 0.5), 2), " decile 5"),
                            ntile(var3, 10) == 6 ~ paste0(round(quantile(var3, 0.5), 2), " to ", round(quantile(var3, 0.6), 2), " decile 6"),
                            ntile(var3, 10) == 7 ~ paste0(round(quantile(var3, 0.6), 2), " to ", round(quantile(var3, 0.7), 2), " decile 7"),
                            ntile(var3, 10) == 8 ~ paste0(round(quantile(var3, 0.7), 2), " to ", round(quantile(var3, 0.8), 2), " decile 8"),
                            ntile(var3, 10) == 9 ~ paste0(round(quantile(var3, 0.8), 2), " to ", round(quantile(var3, 0.9), 2), " decile 9"),
                            ntile(var3, 10) == 10 ~ paste0(round(quantile(var3, 0.9), 2), " to ", round(max(var3), 2), " decile 10"))) %>%

  mutate(class4 = case_when(ntile(var4, 20) == 1 ~ paste0(round(min(var4), 2), " to ", round(quantile(var4, 0.1), 2), " pcile 1"),
                            ntile(var4, 20) == 2 ~ paste0(round(quantile(var4, 0.1), 2), " to ", round(quantile(var4, 0.2), 2), " pcile 2"),
                            ntile(var4, 20) == 3 ~ paste0(round(quantile(var4, 0.2), 2), " to ", round(quantile(var4, 0.3), 2), " pcile 3"),
                            ntile(var4, 20) == 4 ~ paste0(round(quantile(var4, 0.3), 2), " to ", round(quantile(var4, 0.4), 2), " pcile 4"),
                            ntile(var4, 20) == 5 ~ paste0(round(quantile(var4, 0.4), 2), " to ", round(quantile(var4, 0.5), 2), " pcile 5")))

**方法2：**减少NA？

final = my_data %>% group_by(var5, var6) %>%  mutate(class3 = paste0(cut(var3, breaks = c(-Inf, quantile(var3, c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)), Inf), 
                                                                 labels = c("ptile 1", "ptile 2", "ptile 3", "ptile 4", "ptile 5", "ptile 6", "ptile 7", "ptile 8", "ptile 9", "ptile 10")),
                                                             " (", round(min(var3), 2), " to ", round(max(var3), 2), ")")) %>% 
    mutate(class4 = paste0(cut(var4, breaks = c(-Inf, quantile(var4, c(0.2, 0.4, 0.6, 0.8)), Inf), 
                               labels = c("ptile 1", "ptile 2", "ptile 3", "ptile 4", "ptile 5")),
                           " (", round(min(var4), 2), " to ", round(max(var4), 2), ")"))

我认为方法2可能更正确，因为生成的NA值更少-但同时，有人能帮我验证一下（方法2中的）方法是否正确......如果不正确，我该如何更正？
谢谢！