基于给定DataFrame数据集，在同一绘图框架绘制双函数的技术咨询

No problem at all! Let's break down two straightforward methods to visualize your S and D columns against the Date values in the same plot. I'll cover both base R (great for quick, no-frills plots) and ggplot2 (ideal for customizable, polished graphs).

First, let's make sure your data is properly formatted—your Date column is numeric (like 199011 for November 1990), so we'll convert that to a proper date type first. I've completed the truncated row in your sample data for consistency:

# Load the full sample data
data2 <- read.table(text = "Date S D
199011 1.023247 1.009845
199012 1.050828 1.015818
199101 1.066754 1.023077
199102 1.147112 1.033462
199103 1.160859 1.042610
199104 1.164412 1.049691
199105 1.204586 1.058778
199106 1.173015 1.063795
199107 1.220449 1.074115
199108 1.210946 1.075537
199109 1.219717 1.076117
199110 1.256516 1.080941
199111 1.220505 1.087333
199112 1.288720 1.100406
199201 1.306862 1.106454
199202 1.304459 1.108409
199203 1.250000 1.110000", header = TRUE)

# Convert numeric Date to actual date format (using first day of each month)
data2$Date <- as.Date(paste0(data2$Date, "01"), "%Y%m%d")

Method 1: Base R Plotting #

This is perfect for quick checks when you don't need fancy customization:

# First plot the S series as a blue line
plot(data2$Date, data2$S, type = "l", col = "blue", 
     xlab = "Date", ylab = "Value", main = "S vs D Over Time")

# Add the D series as a red line on top
lines(data2$Date, data2$D, type = "l", col = "red")

# Add a legend to clarify which line is which
legend("topleft", legend = c("S", "D"), col = c("blue", "red"), lty = 1)

• type = "l" tells R to draw a line instead of individual points
• lines() adds the second series without overwriting the initial plot
• The legend ensures readers can distinguish between the two lines easily

Method 2: ggplot2 (Recommended for Customization) #

ggplot2 is part of the tidyverse and excels at creating flexible, publication-ready visuals. We'll first reshape our data to "long" format (tidy data) which plays better with ggplot's syntax:

# Load required packages
library(ggplot2)
library(tidyr)

# Reshape data from wide to long format
data_long <- pivot_longer(data2, cols = c(S, D), names_to = "Series", values_to = "Value")

# Create the plot
ggplot(data_long, aes(x = Date, y = Value, color = Series)) +
  geom_line(linewidth = 1) +
  labs(title = "S vs D Over Time", x = "Date", y = "Value") +
  scale_color_manual(values = c("S" = "blue", "D" = "red")) +
  theme_minimal()

• pivot_longer() stacks the S and D columns into a single Value column, with a Series column to track which value belongs to which group
• aes(color = Series) automatically assigns distinct colors to each series
• theme_minimal() gives a clean, modern look—swap it for theme_bw() or theme_classic() if you prefer a different style

Both methods will give you a single figure with both lines plotted against the date axis. Base R is faster for quick checks, while ggplot2 lets you tweak every detail (like fonts, line thickness, or legend placement) to match your needs.

内容的提问来源于stack exchange，提问作者Mads