R Language Exporting And Saving Plots Complete Guide

Exporting and Saving Plots in R

Introduction

Exporting and saving plots in R enables you to share your graphical results with others. Whether you are preparing a report or a presentation, being able to save your plots in various formats is essential. The most common formats include PNG, JPEG, PDF, SVG, and TIFF.

Basic Plotting in R

Before diving into exporting and saving plots, let's understand how to create a basic plot in R:

# Load necessary libraries (if any)
library(ggplot2)

# Sample data
data(mtcars)

# Create a simple plot using ggplot2
plot <- ggplot(mtcars, aes(x = wt, y = mpg)) +
          geom_point() +
          labs(title = "Car Weight vs Miles Per Gallon",
               x = "Weight (1000 lbs)",
               y = "Miles per Gallon")

# Display the plot
print(plot)

Saving Graphics Devices in R

R uses different graphical devices to handle output formats. These devices can be divided into raster and vector types.

• Raster Formats (PNG, JPEG, BMP, TIFF):

  • Pixel-based images.
  • Best for web use, emails, etc., because they can be compressed without losing quality.
  • Not suitable for resizing as quality can degrade.

• Vector Formats (PDF, EPS, PS, SVG):

  • Object-based images where each element (line, curve, shape) is stored mathematically.
  • Best for print output and presentations, as they can be resized without loss of quality.

Functions for Exporting Plots

PNG

Use png() function to create a PNG file. Specify the filename, width, height, and the resolution (res in dpi).

# Save the plot in PNG format
png("car_weight_vs_mpg.png", width = 6, height = 4, res = 300, units = "in")
print(plot)
dev.off()  # Close the device

JPEG

Use jpeg() function for JPEG files.

# Save the plot in JPEG format
jpeg("car_weight_vs_mpg.jpg", width = 600, height = 400)
print(plot)
dev.off()

PDF

Use pdf() function for saving in PDF format, which is ideal for vector graphics.

# Save the plot in PDF format
pdf("car_weight_vs_mpg.pdf", width = 6, height = 4, onefile = FALSE)
print(plot)
dev.off()

• onefile = FALSE saves separate pages into individual PDF files if plotting multiple graphs.

SVG

Use svg() function for SVG graphics. Note that base R does not have an svg() function, so you might need to use external packages such as RSvgDevice.

# Install and load RSvgDevice package if not already installed
if (!requireNamespace("RSvgDevice", quietly = TRUE))
    install.packages("RSvgDevice")
library(RSvgDevice)

# Save the plot in SVG format
svg("car_weight_vs_mpg.svg", width = 6, height = 4)
print(plot)
dev.off()

TIFF

Use tiff() function for TIFF files.

# Save the plot in TIFF format
tiff("car_weight_vs_mpg.tif", width = 600, height = 400, res = 300)
print(plot)
dev.off()

Important Information

1. File Size and Resolution:

   • For raster formats (PNG, JPEG), setting higher resolutions (higher dpi or ppi) and dimensions will increase the file size.
   • In vector formats (PDF, SVG), the file size is generally unaffected by changing the plot dimensions.

2. Choosing the Right Format:

   • Web: Use PNG or JPEG since they are lightweight and widely supported.
   • Print: Use PDF or SVG as they maintain clarity at high resolution and can be resized without losing image quality.
   • Editable: Use EPS or SVG if you need to edit the plot after saving.

3. Color Management:

   • Ensure that colors are consistent across devices. Sometimes, colors might look different when exported to certain formats due to color profiles.
   • It’s advisable to use color palettes specifically designed for R like viridis or ggthemes.

4. Annotation and Labels:

   • Use appropriate font sizes, labels, and legends that will remain legible after exporting.
   • Utilize functions like labs(), theme(), and annotate() from ggplot2 to customize these elements.

5. Multiple Plots:

   • If saving multiple plots in the same PDF file, set onefile = TRUE:

     pdf("multipage_plots.pdf", width = 7, height = 5, onefile = TRUE)
     print(plot)
     print(plot + theme(plot.background = element_rect(fill = 'gray')))
     dev.off()
     

6. Handling Complex Layouts:

   • For complex layouts involving multiple panels or subplots, use grid.arrange() from the gridExtra package.

     library(gridExtra)
     
     p1 <- plot + geom_smooth(method = "lm")
     p2 <- plot + geom_boxplot()
     
     png("complex_layout.png", width = 10, height = 6)
     grid.arrange(p1, p2, ncol = 2)
     dev.off()
     

7. Saving Interactive Plots:

   • Packages like plotly produce interactive plots. To save them, use htmlwidgets::saveWidget().

     library(plotly)
     p_interactive <- ggplotly(plot)
     
     htmlwidgets::saveWidget(p_interactive, "interactive_plot.html")
     

8. Customizing Plot Appearance:

   • Tailor the appearance of plots before saving to ensure they are publication-ready. This includes adjusting themes, modifying axis scales, and ensuring consistency in annotations.

9. Batch Processing:

   • When dealing with a large number of plots, consider writing scripts that loop over your data and export each plot automatically. This can save time and reduce errors.

Example: Combining All into a Script

Here’s a script that combines the basics of creating and saving plots in different formats.