如何在 R 中优化可视化分类回归结果

Question

在我的数据中，所有变量（依赖和独立）都是分类的。 它可以是 或 1 或 2（分类值）。

lp1=structure(list(a = c(2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L), b = c(1L, 1L, 1L, 2L, 2L, 2L, 
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L), c = c(1L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L
), d = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 
2L, 2L, 2L, 2L, 2L), e = c(1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 
1L, NA, 2L, 2L, 2L, 2L, 1L, 2L, 1L), f = c(2L, 2L, 2L, 2L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L), g = c(NA, 
1L, 2L, NA, 2L, 2L, 1L, 2L, 1L, NA, NA, NA, NA, NA, 1L, NA, NA, 
NA), h = c(2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L), i = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L), j = c(2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), k = c(2L, 
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, NA, 1L, 2L, 1L, 1L, 1L, 1L, 
1L), l = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, NA, 2L, NA, 
1L, 1L, 1L, 1L, 2L), m = c(1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, NA, 2L, 2L, 2L, 1L), n = c(1L, 2L, 2L, 2L, 1L, 
2L, 2L, 2L, 2L, NA, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L), xxx = c(2L, 
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L)), class = "data.frame", row.names = c(NA, -18L))

我尝试获取两个变量之间的图（xxx 是依赖的，a 是独立的）

library(ggplot2)

ggplot(lp1, aes(xxx, a)) +
  geom_point() +
  theme_minimal()

该图的信息量不是很大，因为只有 2 个值或 1 或 2。

有没有办法以最佳方式绘制两个分类变量之间的关系？ 也许有些东西可以标准化数据？

如果我在错误的论坛中提出这个问题，我深表歉意。 谢谢你。

Answer 1

您可以使用table()函数来更好地了解正在发生的事情。 这不是图形表示，但在我看来，它的简单性并不是失败。

library(tidyverse)
lp1 = structure(
  list(
    a = c(2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L),
    b = c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L),
    c = c(1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L),
    d = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
    e = c(1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, NA, 2L, 2L, 2L, 2L, 1L, 2L, 1L),
    f = c(2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L),
    g = c(NA, 1L, 2L, NA, 2L, 2L, 1L, 2L, 1L, NA, NA, NA, NA, NA, 1L, NA, NA, NA),
    h = c(2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
    i = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L),
    j = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
    k = c(2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, NA, 1L, 2L, 1L, 1L, 1L, 1L, 1L),
    l = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, NA, 2L, NA, 1L, 1L, 1L, 1L, 2L),
    m = c(1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, NA, 2L, 2L, 2L, 1L),
    n = c(1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, NA, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L),
    xxx = c(2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L)
  ),
  class = "data.frame", row.names = c(NA, -18L)
)

lp1 %>% 
  select(a, xxx) %>% 
  table()
#>    xxx
#> a   1 2
#>   1 4 2
#>   2 8 4