Saving High-Resolution ggplots: How to Preserve Semi-Transparency

August 4, 2017
By

[This article was first published on Easy Guides, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

This article describes solutions for preserving semi-transparency when saving a ggplot2-based graphs into a high quality postscript (.eps) file format.

Saving High-Reslution ggplots

Contents:

Create a ggplot with semi-transparent color

To illustrate this, we start by creating ggplot2-based survival curves using the function ggsurvplot() in the survminer package. The ggsurvplot() function creates survival curves with the 95% confidence bands in a semi-transparent color.

First install (if needed) survminer as follow:

install.packages("survminer")

Then type, this:

# Fit survival curves
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)

# Visualize
library("survminer")
p <- ggsurvplot(fit, data = lung,
         surv.median.line = "hv", # Add medians survival
         pval = TRUE,             # Add p-value and tervals
        
         conf.int = TRUE,        # Add the 95% confidence band
         risk.table = TRUE,      # Add risk table
         tables.height = 0.2,
         tables.theme = theme_cleantable(),
         palette = "jco",
         ggtheme = theme_bw()
    )
print(p)

In the plot above, the confidence band is semi-transparent. It can be saved to a PDF file without loosing the semi-transparent color.

If you try to export the picture as vector file (EPS or SVG, …), the 95% confidence interval will disappear and the saved plot looks as follow:

ggsurvplot in .eps format: confidence bands disapear

The problem is that EPS in R does not support transparency.

In the following sections, we’ll describe convenient solutions to save high-quality ggplots by preserving semi-transparency.

Save ggplots with semi-transparent colors

Use cairo-based postscript graphics devices

You can use the ggsave() function in [ggplot2] as follow:

ggsave(filename = "survival-curves.eps",
       plot = print(p),
       device = cairo_eps)

Or use this:

cairo_ps(filename = "survival-curves.eps",
         width = 7, height = 7, pointsize = 12,
         fallback_resolution = 300)

print(p)

dev.off()

Note that, the argument fallback_resolution is used to control the resolution in dpi at which semi-transparent areas are rasterized (the rest stays as vector format).

Export to powerpoint

You can export the plot to Powerpoint using the ReporteRs package. ReporteRs will give you a fully editable vector format with full support for transparency as well.

We previously described how to Create and format PowerPoint documents from R software using the ReporteRs package. We also described how to export an editable ggplot from R software to powerpoint.

Briefly, to export our survival curves from R to powerpoint, the script looks like this

library('ReporteRs')

# Create a new powerpoint document
doc <- pptx()

# Add a new slide into the ppt document 
doc <- addSlide(doc, slide.layout = "Two Content"  )

# Add a slide title
doc <- addTitle(doc, "Survival Curves: Editable Vector Graphics" )

# Print the survival curves in the powerpoint
doc <- addPlot(doc, function() print(p, newpage = FALSE), 
               vector.graphic = TRUE  # Make it editable
               )

# write the document to a file
writeDoc(doc, file = "editable-survival-curves.pptx")

The output looks like this:

Editable survival curves

Edit the plot in powerpoint. See the video below: Editing ggplots Exported with ReporteRs into PWPT

Infos


To leave a comment for the author, please follow the link and comment on their blog: Easy Guides.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)