如何在R语言中使用ggplot2将多个绘图合并为单个图形？

Hey there! Looks like you want to combine those separate ggplot smooth plots into a single figure for easier comparison—great call, that’s way better than flipping between plots. Here are two solid approaches to do this, depending on your workflow:

This is the cleanest approach because it leverages ggplot’s built-in grouping and legend handling. The idea is to add an identifier column to each dataset, then merge them into one data frame.

First, add a column to label each merchandise group:

# Add a merch identifier to each dataset
merch42$merch_label <- "Merch 42"
merch323$merch_label <- "Merch 323"
merch24$merch_label <- "Merch 24" # Assuming this follows the same structure as the others

Next, combine all datasets into one using dplyr::bind_rows() (install the dplyr package if you don’t have it already):

library(dplyr)
combined_data <- bind_rows(merch42, merch323, merch24)

Now you can plot all trends in one go, with colors mapped to the merch label (and a proper legend automatically generated):

library(ggplot2)

ggplot(combined_data, aes(x = day_code, y = avg_logistic_review_score, color = merch_label)) +
  geom_smooth(method = "loess", span = 1/25) +
  # Customize colors to match your original plots (optional but consistent)
  scale_color_manual(values = c("Merch 42" = "red", "Merch 323" = "blue", "Merch 24" = "darkgreen")) +
  # Add clear labels for readability
  labs(
    title = "Average Logistic Review Score Trend by Merchandise",
    x = "Day Code",
    y = "Average Logistic Review Score",
    color = "Merchandise"
  ) +
  theme_minimal() # Or your preferred theme

If you don’t want to merge your datasets for some reason, you can add each geom_smooth() layer to the same base ggplot object. Just make sure to specify the data argument for each layer:

library(ggplot2)

# Start with an empty base plot (or use one dataset as the base—doesn't matter)
combined_plot <- ggplot() +
  # Add Merch 42 trend
  geom_smooth(
    data = merch42,
    aes(x = day_code, y = avg_logistic_review_score),
    method = "loess", span = 1/25,
    color = "red"
  ) +
  # Add Merch 323 trend
  geom_smooth(
    data = merch323,
    aes(x = day_code, y = avg_logistic_review_score),
    method = "loess", span = 1/25,
    color = "blue"
  ) +
  # Add Merch 24 trend
  geom_smooth(
    data = merch24,
    aes(x = day_code, y = avg_logistic_review_score),
    method = "loess", span = 1/25,
    color = "darkgreen"
  ) +
  # Add labels and a manual legend (since we're hardcoding colors)
  labs(
    title = "Average Logistic Review Score Trend by Merchandise",
    x = "Day Code",
    y = "Average Logistic Review Score"
  ) +
  scale_color_identity(
    name = "Merchandise",
    breaks = c("red", "blue", "darkgreen"),
    labels = c("Merch 42", "Merch 323", "Merch 24"),
    guide = "legend"
  ) +
  theme_minimal()

A quick note: Method 1 is almost always better long-term—it’s easier to update, add more merch groups later, and ggplot handles legends/consistency for you. Just make sure all your datasets have matching column names (day_code and avg_logistic_review_score) for either method to work smoothly.

内容的提问来源于stack exchange，提问作者Phil R