R：如何可视化连续数据和分类数据之间的关系

Question

我有以下 data.frame，其中包含 3 个分类变量（不同类型的血管病理学）和 1 个连续变量（输出）。我有兴趣了解输出与不同类型的血管病理之间的关系，即 higher/lower 输出是否与 mild/severe 病理相关？

> dput(df)
structure(list(Vascular_Pathology_M = structure(c(1L, 2L, 3L, 
1L, 1L, 2L, 4L, 3L, 1L, 2L), .Label = c("Absent", "Mild", "Mild/Moderate", 
"Moderate/Severe", "Severe"), class = "factor"), Vascular_Pathology_F = structure(c(4L, 
2L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 1L), .Label = c("Absent", "Mild", 
"Mild/Moderate", "Moderate/Severe", "Severe"), class = "factor"), 
    Vascular_Pathology_O = structure(c(1L, 3L, 4L, 3L, 1L, 2L, 
    1L, 1L, 1L, 2L), .Label = c("Absent", "Mild", "Mild/Moderate", 
    "Moderate/Severe"), class = "factor"), Output = c(1.01789418758932, 
    1.05627630598801, 1.49233946102323, 1.38192374975672, 1.13097652937671, 
    0.861306979571144, 0.707820561413699, 1.16628243128399, 0.983163398006992, 
    1.23972603843843)), .Names = c("Vascular_Pathology_M", "Vascular_Pathology_F", 
"Vascular_Pathology_O", "Output"), row.names = c(1L, 3L, 4L, 
5L, 6L, 7L, 8L, 10L, 11L, 12L), class = "data.frame")
> df
   Vascular_Pathology_M Vascular_Pathology_F Vascular_Pathology_O    Output
1                Absent      Moderate/Severe               Absent 1.0178942
3                  Mild                 Mild        Mild/Moderate 1.0562763
4         Mild/Moderate               Absent      Moderate/Severe 1.4923395
5                Absent               Absent        Mild/Moderate 1.3819237
6                Absent               Absent               Absent 1.1309765
7                  Mild               Absent                 Mild 0.8613070
8       Moderate/Severe                 Mild               Absent 0.7078206
10        Mild/Moderate      Moderate/Severe               Absent 1.1662824
11               Absent               Absent               Absent 0.9831634
12                 Mild               Absent                 Mild 1.2397260

Answer 1

您可以简单地根据分类变量绘制输出图

plot(df[, 1], df[, 4])
plot(df[, 2], df[, 4])
plot(df[, 3], df[, 4])

Answer 2

您有一个 4 维数据集。一种选择是在一个小的多序列（还有一个维度）中绘制散点图（x/y = 二维），并将输出变量映射到类似大小的视觉对象（还有第四个维度）。

例如，将数据放入名为 my_dat 的 data.frame 中（因为 df 已分配给 R 中的函数）。点抖动以显示每个点的多个观察值，并按 Y 位置着色以帮助清楚哪个点属于哪个类别。

library(ggplot2)

my_dat$O_with_labels <- 
  factor(my_dat[, 3], labels=paste('Vasc Path O:', levels(my_dat[, 3])))

ggplot(my_dat, 
       aes(x=Vascular_Pathology_M, y=Vascular_Pathology_F)) + 
  geom_jitter(aes(size=Output, color=Vascular_Pathology_F)) + 
  facet_wrap(~O_with_labels) +
  theme_bw() + 
  theme(axis.text.x = element_text(angle=45, hjust=1))

Answer 3

您可以查看各种病症的相互作用。例如，使用条形图

## Make the interaction variable
df$interact <- interaction(df[, 1:3], sep="_")

## Look at means of groups
library(dplyr)
df %>% group_by(interact) %>%
  dplyr::summarise(Output = mean(Output)) -> means

ggplot(means, aes(interact, Output))+
  geom_bar(stat="identity") +
  theme(axis.text=element_text(angle=90)) +
  xlab("Interaction")

或积分

ggplot(df, aes(interact, Output))+
  geom_point() +
  theme(axis.text=element_text(angle=45, hjust=1)) +
  xlab("Interaction") +
  geom_point(data=means, col="red") +
  ylim(0, 1.6)

R：如何可视化连续数据和分类数据之间的关系

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