r - 为什么R中的分位数函数给出的每组值的计数不相等

Question

我正在尝试将连续值数据分组为三分位数。我正在使用函数 quantile 来执行此操作。以下是我的代码

dd$wbc_tert = with(dd, 
               cut(wbc, 
                   vTert, 
                   include.lowest = T, 
                   labels = c("Low", "Medium", "High")))

不是应该在每个组中给出相同数量的值吗？我在各组中得到的计数不同。

> dd %>% filter(wbc_tert == 'High') %>% select('wbc')  %>% nrow() 
[1] 143
> dd %>% filter(wbc_tert == 'Low') %>% select('wbc')  %>% nrow()
[1] 148
> dd %>% filter(wbc_tert == 'Medium') %>% select('wbc')  %>% nrow()
[1] 139

这是值的输出

c(10.9, 5.4, 9.1, 7.4, 6.6, 5.5, 4.4, 6.7, 7.8, 6.7, 6.6, 8.6, 
8.4, 4.8, 7, 5.2, 7, 6.7, 10.4, 7.5, 8.5, 6.8, 8.5, 9.4, 4.6, 
6.8, 10.2, 6.7, 4.6, 4.9, 6.7, 8.9, 5.9, 5.9, 9.9, 4.1, 8.4, 
9, 7.7, 8.2, 5.7, 8.4, 7.7, 4.6, 6.5, 7.3, 4.9, 3.8, 6.2, 7.9, 
5.3, 8.9, 6, 4.8, 5.9, 5.4, 8.6, 6.1, 9.5, 5.8, 6.2, 5.8, 7.9, 
9.6, 6.6, 9.6, 7, 10.1, 9, 6.9, 9.1, 6.8, 8.4, 9.6, 4.4, 10.5, 
7.9, 5.6, 5.1, 6.6, 6.5, 12.7, 5.3, 7.7, 4.8, 4.7, 6.1, 4.3, 
6.1, 11.6, 5.9, 7.4, 5.7, 4.7, 4.8, 8.5, 5.6, 7.9, 9.1, 7.8, 
5.3, 5, 8.1, 8.3, 4.7, 5.4, 7.6, 7.2, 5.7, 7.9, 7.9, 6.4, 3.8, 
4.7, 6.2, 5, 7.6, 5.8, 5.4, 4.3, 6, 4.7, 6, 6.1, 5.8, 5.6, 4.7, 
5, 11.5, 6.3, 4.4, 6.8, 6.6, 6.8, 6.1, 4.8, 5.4, 5.8, 5.2, 7.1, 
5.4, 9.1, 6.9, 5.4, 8.5, 5.3, 7.3, 6.9, 9, 6.3, 8.4, 7.8, 5.7, 
6.4, 5.3, 9.6, 6.4, 9.9, 8.9, 7.7, 6.2, 7.2, 4.6, 5.4, 4.6, 11.2, 
3.1, 12.3, 5.9, 11.1, 6.2, 6.6, 4.1, 7.4, 9.4, 4.1, 6.7, 6.7, 
6.1, 6.3, 5.6, NA, 3.7, 6.8, 6.7, 6.4, 7.3, 5.7, 6.7, 6.9, 5.7, 
5.3, 4, 5.6, 4.8, 5.5, 6, 6.6, 3.6, 5.6, 8.9, 6.3, 5.8, 8.2, 
8.6, 8.5, 5.7, 8.6, 6, 5.1, 5.7, 8.2, 5.4, 6.9, 6.9, 8.3, 9.5, 
5.4, 10.2, 8.8, 7.2, 4.8, 9.8, 4.6, 6.3, 5.8, 4.9, 12.7, 7.5, 
10.6, 9.3, 5.5, 10.7, 6.2, 9.3, 8.3, 7.8, 8.05, 9.57, 6.62, 6.21, 
5.34, 6.11, 10.37, 4.45, 5.55, 8.05, 8.31, 5.06, 6.05, 4.76, 
9.09, 9.11, 9.04, 6.99, 6.33, 9.47, 6.48, 4.46, 9.44, 6.88, 7.09, 
5.75, 10.89, 6.68, 3.64, 6.55, 8.69, 5.89, 9.05, 6.38, 11.62, 
9.11, 9.22, 7.97, 9.64, 12.76, 8.39, 6.57, 8.1, 7.3, 10.1, 4.7, 
6.4, 7.2, 5.5, 3.7, 5.1, 9.8, 7.6, 7.7, 6, 3.9, 6.8, 5.4, 5.4, 
9.7, 9, 6, 7.3, 6.3, 5.8, 8.3, 7, 4.1, 11.2, 5, 7.6, 6.5, 4.8, 
8, 10.1, 7.1, 7.4, 4.3, 4, 10.12, 4.3, 7.26, 8.84, 8.44, 8.44, 
8.12, 6.5, 8.58, 8.55, 8.82, 4.53, 9.51, 4.93, 4.42, 4.69, 8.69, 
5.77, 3.37, 6.58, 3.72, 3.09, 7.13, 8.11, 7.2, 12.18, 6.52, 7.91, 
5.69, 8.24, 7.67, 5.69, 4.85, 7.03, 4.16, 3.57, 8.1, 4.61, 5.98, 
5.13, 7.68, 5.47, 5.54, 4.59, 6, 11.62, 7.38, 7.06, 8.74, 8.02, 
6.73, 7.19, 6.36, 4.86, 6.55, 8.4, 7.76, 4.73, 4.8, 5.73, 8.53, 
4.6, 7.96, 9.48, 6.59, 5.75, 6.61, 6.49, 7.91, 6.92, 7.14, 6.24, 
12.53, 7.03, 4.73, 8.05, 7.26, 4.07, 6.7, 5.7, 7.39, 5.2, 6.61, 
6.8, 6.77, 5.65, 6.08, 7.24, 6.13, 7.92, 7.37, 7.99, 3.31, 9.72, 
8.71, 8.35, 5.05, 8.15, 5.1, 5.4, 8.8, 4.9, 5, 7.43, 10.3, 6.3, 
9.5, 6.9, 6.7, 5.4, 7.7, 8, 6.5, 5.6, 9.7)

有人可以帮忙看看这可能是什么原因吗

Answer 1

下面的示例显示分位数不一定在值的数量上对称。

# Define some data
x <- 1:10
y <-  rep(1:2, 10)
# Look at the quantiles
quantile(x)
#>    0%   25%   50%   75%  100% 
#>  1.00  3.25  5.50  7.75 10.00
# Due to the added y we now have asymmetry in sizes
quantile(c(y,x))
#>    0%   25%   50%   75%  100% 
#>  1.00  1.00  2.00  2.75 10.00
# Notice how the number of values below 50 % and 75 % changes.
## Without y we get roughly the same bin size
sum(x<quantile(x, .5))
#> [1] 5
sum(x<quantile(x, .75))
#> [1] 7
## But when we add y, there is a doubling of values despite we only increase
## the percentile with 25 %
sum(c(y,x)<quantile(c(y,x), .5))
#> [1] 11
sum(c(y,x)<quantile(c(y,x), .75))
#> [1] 22

^{由reprex package于2022年8月18日创建(v2.0.1)}