咨询：在R中实现PDF输出时仅栅格化数据点的解决方案

Hey fellow R user! I totally feel your pain—there's nothing worse than waiting ages for a massive PDF to load when it's packed with plots full of thousands of data points. The good news is we can replicate that clever Matplotlib trick (rasterizing only the data points while keeping labels/axes as crisp vectors) right in R. Here's how to do it with the two most common plotting workflows:

Using ggplot2 (Recommended for Most Users) #

The ggfx package makes it super easy to target specific layers for rasterization, leaving your text and axes in vector format. Here's a complete example:

# Install required packages if needed
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("ggfx")) install.packages("ggfx")

library(ggplot2)
library(ggfx)

# Generate a large dataset for testing
set.seed(123)
big_dataset <- data.frame(
  x = rnorm(15000),
  y = rnorm(15000)
)

# Build your plot: rasterize only the scatter points
slim_plot <- ggplot(big_dataset, aes(x, y)) +
  # Wrap the data layer with with_raster() to rasterize it
  with_raster(geom_point(size = 0.4, alpha = 0.6), dpi = 300) +
  # All text/axes stay vectorized
  labs(
    title = "This Title Remains Vectorized",
    x = "X Axis (Crisp Vector Text)",
    y = "Y Axis (Crisp Vector Text)"
  ) +
  theme_minimal()

# Create a multipage PDF
pdf("slim_multipage_plots.pdf", onefile = TRUE, width = 8, height = 6)
# Loop to generate 5 pages of plots
for (page_num in 1:5) {
  print(slim_plot + labs(title = paste("Plot", page_num, "of 5")))
}
dev.off()

Key Notes for ggplot2: #

• Adjust the dpi argument to balance file size and quality: 300dpi is great for print, 150dpi works well for screen viewing
• You can rasterize any layer (not just points)—use with_raster() on geom_line(), geom_histogram(), etc., if needed

Using Base R Plots #

If you prefer base graphics, you can manually separate vector elements from rasterized data by first rendering the data to a temporary PNG, then overlaying it on a vector-based plot frame:

# Install required package if needed
if (!require("png")) install.packages("png")

library(png)

# Generate large dataset
set.seed(123)
x_vals <- rnorm(15000)
y_vals <- rnorm(15000)

# Create a temporary PNG to store rasterized data points
png("temp_raster_points.png", width = 8, height = 6, units = "in", res = 300)
# Plot only the data (no axes/labels)
plot(x_vals, y_vals, axes = FALSE, xlab = "", ylab = "", pch = 16, cex = 0.4)
dev.off()

# Read the rasterized data
raster_data <- readPNG("temp_raster_points.png")

# Generate multipage PDF
pdf("base_r_slim_plots.pdf", onefile = TRUE, width = 8, height = 6)
for (page_num in 1:5) {
  # First draw the vector-based plot frame (axes, labels, title)
  plot(
    1, type = "n",
    xlim = range(x_vals), ylim = range(y_vals),
    main = paste("Base R Plot", page_num),
    xlab = "X Axis (Vector)", ylab = "Y Axis (Vector)"
  )
  # Overlay the rasterized data points
  rasterImage(
    raster_data,
    xleft = par("usr")[1], ybottom = par("usr")[3],
    xright = par("usr")[2], ytop = par("usr")[4]
  )
}
dev.off()

# Clean up the temporary PNG file
file.remove("temp_raster_points.png")

Key Notes for Base R: #

• The temporary PNG acts as a raster "stamp" for your data—make sure the dimensions match your PDF page size
• This method gives you full control over which parts are rasterized, but requires a bit more manual setup

Both of these approaches will drastically reduce your PDF file size while keeping all text elements sharp and editable—perfect for sharing or printing!

内容的提问来源于stack exchange，提问作者NicolasBourbaki