如何用R基于种族比例频率表绘制年份维度的折线图？

Hey there! Great job getting your proportion table set up with prop.table()—you're already halfway there. To make that line plot with years on the x-axis and each race/ethnicity as a separate line, we just need to get your data into a plot-friendly format and then use either ggplot2 (for polished, customizable plots) or base R (no extra packages needed). Let's break both methods down:

Method 1: Using ggplot2 (Recommended for Nice Visuals) #

First, we'll convert your matrix perrace into a long-format data frame (ggplot2 works best with this structure). We'll use the tidyr package for reshaping, and scales to format the y-axis as percentages.

Step 1: Prepare the Data #

# Install packages first if you haven't: install.packages(c("ggplot2", "tidyr", "scales"))
library(ggplot2)
library(tidyr)
library(scales)

# Convert the proportion matrix to a data frame and keep race names as a column
perrace_df <- as.data.frame(perrace)
perrace_df$race <- rownames(perrace_df)  # Add race names as a dedicated column
rownames(perrace_df) <- NULL  # Remove redundant row names

# Reshape from wide to long format (one row per race-year combination)
perrace_long <- pivot_longer(perrace_df,
                             cols = -race,  # Keep the race column, reshape all year columns
                             names_to = "year",
                             values_to = "proportion")

# Convert year from character to numeric (so ggplot treats it as a continuous variable)
perrace_long$year <- as.numeric(perrace_long$year)

Step 2: Create the Line Plot #

ggplot(perrace_long, aes(x = year, y = proportion, color = race, group = race)) +
  geom_line(linewidth = 1.2) +  # Thicken lines for better readability
  geom_point(size = 2.5) +      # Add points to highlight exact yearly values
  scale_y_continuous(labels = percent_format(accuracy = 1)) +  # Show y-axis as percentages
  labs(
    title = "Race/Ethnicity Proportions Over Time",
    x = "Year",
    y = "Proportion of Population",
    color = "Race/Ethnicity"
  ) +
  theme_minimal()  # Clean, modern theme (swap with theme_bw() for a classic look)

This gives you a polished plot with automatic legends, easy-to-read axes, and tons of room to tweak colors or themes later.

Method 2: Using Base R (No Extra Packages) #

If you prefer not to load external packages, base R can handle this too. We'll plot each race's line manually and add a legend to clarify which line corresponds to which group.

# Convert year column names to numeric values
years <- as.numeric(colnames(perrace))

# Initialize the plot with the first race's data
plot(years, perrace["Other", ],
     type = "l",  # Start with a line
     col = "#E63946",  # Red for "Other"
     xlab = "Year",
     ylab = "Proportion",
     ylim = c(0, 0.5),  # Set y-axis range to fit all values
     main = "Race/Ethnicity Proportions Over Time",
     lwd = 2)  # Line width

# Add lines for the remaining races
lines(years, perrace["Latino", ], col = "#457B9D", lwd = 2)
lines(years, perrace["non-hispanic black", ], col = "#1D3557", lwd = 2)
lines(years, perrace["non-hispanic white", ], col = "#A8DADC", lwd = 2)

# Add points to each line for clarity
points(years, perrace["Other", ], col = "#E63946", pch = 16)
points(years, perrace["Latino", ], col = "#457B9D", pch = 16)
points(years, perrace["non-hispanic black", ], col = "#1D3557", pch = 16)
points(years, perrace["non-hispanic white", ], col = "#A8DADC", pch = 16)

# Add a legend to identify each line
legend("topleft",
       legend = rownames(perrace),
       col = c("#E63946", "#457B9D", "#1D3557", "#A8DADC"),
       lty = 1,  # Line type
       pch = 16,  # Point type
       lwd = 2)

Either method will produce the line plot you're looking for. The ggplot2 approach is more flexible if you want to adjust colors, labels, or themes down the line!

内容的提问来源于stack exchange，提问作者Kelly