R语言ggplot2箱线图分组标注问题：自定义X轴标签需求

Got it, let's fix that messy X-axis label issue for your boxplot. I'm assuming you're using ggplot2 (the standard for R data visualization) since that's where this kind of combined-factor label problem usually pops up. Here's a step-by-step solution:

First, Let's Simulate Your Data (for testing) #

I'll create a sample dataset matching your variable types and values so you can replicate the fix:

set.seed(123) # For reproducible results
df <- data.frame(
  livingsetting = factor(rep(c("1", "7", "13"), each = 30)),
  factor = factor(rep(c("CKD", "HD", "Transplant"), 30)),
  outcome = rnorm(90, mean = c(5, 6, 7, 8, 9, 10, 11, 12, 13), sd = 1)
)

Why You're Seeing Redundant Labels #

Chances are, you mapped the interaction of livingsetting and factor to the X-axis (like x = interaction(livingsetting, factor)), which combines both factors into one variable—hence the 1.CKD, 13.CKD style labels.

Solution 1: Group by factor, Distinguish livingsetting with Color/Fill #

If you want to keep the livingsetting subgroups visible but have clean X-axis labels (only CKD, HD, Transplant), map factor to the X-axis and use livingsetting for fill/color:

library(ggplot2)

ggplot(df, aes(x = factor, y = outcome, fill = livingsetting)) +
  geom_boxplot(position = position_dodge(width = 0.8)) # Keeps boxes side-by-side
  labs(
    x = "Patient Group",
    y = "Outcome Value",
    fill = "Living Setting"
  ) +
  theme_minimal()

This will give you three main X-axis categories, each with three boxplots (one for each living setting) colored differently.

Solution 2: Combine livingsetting Data Within Each factor Group #

If you don't need to distinguish livingsetting and just want one boxplot per factor group, simply map only factor to the X-axis:

ggplot(df, aes(x = factor, y = outcome)) +
  geom_boxplot() +
  labs(
    x = "Patient Group",
    y = "Outcome Value"
  ) +
  theme_minimal()

This collapses all livingsetting data into a single boxplot for each of the three main groups, with clean X-axis labels.

Either approach will eliminate those redundant combined labels and give you the clean, grouped visualization you want.

内容的提问来源于stack exchange，提问作者Luiance