ggplot技术问询：为已分箱变量设置与未分箱图一致的连续标签

To make your second plot's x-axis match the continuous style of the first plot (without modifying your original cut()/Freq() workflow), we can use a custom axis transformation to map the discrete bin factor levels to their numeric midpoints. This will make the x-axis appear continuous, with the same range and break points as your first plot.

Step-by-Step Implementation #

First, let's assume your original second plot is stored as p2:

library(DescTools)
library(ggplot2)

# Original second plot code
p2 <- iris$Petal.Length %>% 
  cut(30) %>% 
  Freq() %>% 
  ggplot(aes(x = level, y = freq, group = 1)) + 
  geom_line()

Now, append the following code to convert the x-axis to a continuous style:

library(scales)

# Calculate midpoints of the 30 equal-width bins
min_pl <- min(iris$Petal.Length)
max_pl <- max(iris$Petal.Length)
bin_width <- (max_pl - min_pl) / 30
midpoints <- seq(min_pl + bin_width/2, max_pl - bin_width/2, by = bin_width)

# Create a custom transformation to map factor codes to bin midpoints
bin_trans <- trans_new(
  name = "bin_midpoint",
  transform = function(x) midpoints[x],  # Map factor index to midpoint
  inverse = function(x) findInterval(x, midpoints - bin_width/2) + 1  # Reverse for interaction
)

# Apply the transformation to your plot
p2 +
  scale_x_continuous(
    trans = bin_trans,
    limits = c(min_pl, max_pl),  # Match range to first plot
    breaks = seq(floor(min_pl), ceiling(max_pl), by = 1)  # Match break intervals to first plot
  ) +
  labs(x = "Petal.Length")  # Ensure x-axis label matches first plot

How This Works #

1. Calculate Bin Midpoints: We first compute the midpoint of each of the 30 equal-width bins using the min/max of Petal.Length.
2. Custom Transformation: The trans_new function creates a mapping between the discrete factor indices (1 to 30) and their corresponding bin midpoints. This tells ggplot to treat the discrete x-values as continuous midpoints.
3. Align Axis with First Plot: We set the x-axis limits to match the full range of Petal.Length and use regular integer breaks, just like your first plot's continuous axis.

Alternative (No Additional Package) #

If you prefer not to use the scales package, you can manually set discrete labels to midpoints and style the axis to look continuous:

# Calculate midpoints from the plot's data
p2_data <- p2$data
p2_data$midpoint <- sapply(strsplit(gsub("[\\(\\)\\[\\]]", "", as.character(p2_data$level)), ","), 
                          function(x) mean(as.numeric(x)))

# Update the plot
p2 +
  scale_x_discrete(
    breaks = p2_data$level,
    labels = round(p2_data$midpoint, 1)
  ) +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) +
  labs(x = "Petal.Length")

This method makes the axis appear continuous by using numeric labels, but note that it's still a discrete scale under the hood. The first method with the custom transformation creates a true continuous axis, which is a closer match to your first plot.

内容的提问来源于stack exchange，提问作者J. Doe