Data Carpentry for Biologists

Publication quality figures

Notes

  • Have students install devtools and patchwork (using devtools)
  • Open https://ggplot2.tidyverse.org/reference/ggtheme.html in browser

File formats

  • Raster vs vector (just like in spatial data)
  • Raster
    • Right choice for photos (or rasters)
    • Made of pixels, so gets grainy
    • JPEG, GIF, PNG, TIFF
    • PNG is a good compromise format
  • Vector
    • Right choice for plots, line drawings
    • Provides infinite scaling
      • EPS, AI, PDF, SVG
  • Save in different file formats using different extensions
library(ggplot2)
library(readr)

trees <- read_tsv("https://ndownloader.figshare.com/files/5629536")

ggplot(trees, aes(x = HEIGHT, y = CIRC)) +
  geom_point()

ggsave("acacia_size_scaling.png")
ggsave("acacia_size_scaling.svg")
ggsave("acacia_size_scaling.pdf")
  • Show differences in zoom
  • Some journals will require you to submit vector plots in a raster format
  • You now know enough to cry a little inside when they do

Resolution, DPI, and Image Dimensions

  • Images have dimensions of height and width
ggsave("acacia_size_scaling.png", height = 7, width = 10)
  • Resolution determines how many pixels occur per unit area within those dimensions
  • DPI -> Dots per inch
  • Journals typically request at least 300 DPI
ggsave("acacia_size_scaling.png", dpi = 300)
ggsave("acacia_size_scaling.png", dpi = 30)

Color palettes

  • Choose colors that work well for everyone
    • People with different kinds of color blindness
    • People who printed your paper out without a color printer
  • Need to be correctly interpreted
  • Viridis is a new color scale that is designed to provide a good set of default colors for addressing all of these concerns
ggplot(trees, aes(x = HEIGHT, y = CIRC, color = SPECIES)) +
  geom_point() +
  scale_color_viridis_d()

ggplot(trees, aes(x = HEIGHT, y = CIRC, color = SPECIES)) +
  geom_point() +
  scale_color_viridis_d(option = "magma")

ggplot(trees, aes(x = HEIGHT, y = CIRC, color = HEIGHT)) +
  geom_point() +
  scale_color_viridis_c(option = "magma")

Themes

  • Can customize every aspect of plots in ggplot
  • Themes are an easy way to change the overall look of figures
  • These can be used to make coordinated changes to groups of options
ggplot(trees, aes(x = HEIGHT, y = CIRC, color = SPECIES)) +
  geom_point() +
  scale_color_viridis_c() +
  theme_classic()
  • Show theme examples at https://ggplot2.tidyverse.org/reference/ggtheme.html

Saving and exporting multiple plots

  • To save plots to work with later assign them to a variable
species_scaling <- ggplot(trees, aes(x = HEIGHT, y = CIRC, color = SPECIES)) +
  geom_point() +
  scale_color_viridis_d()

species_scaling
ggsave("species_scaling.jpg", species_scaling)

Combining multiple plots

  • Often want to combine multiple distinct plots into a single figure

  • Two popular packages for this, patchwork and cowplot

  • patchwork is not on CRAN so install using devtools
  • devtools lets us install packages from a variety of sources, including GitHub, one of the major hubs of software development
install.packages('devtools')
library(devtools)
install_github('thomasp85/patchwork')
  • patchwork works by “adding” plots to one another
library(patchwork)

height_dist <- ggplot(trees, aes(x = HEIGHT, fill = SPECIES)) +
  geom_histogram() +
  scale_fill_viridis_d()

species_scaling + height_dist
  • Can control the positioning of subplots
species_scaling + height_dist +
  plot_layout(ncol = 1)
  • The relative sizes of subplots
species_scaling + height_dist +
  plot_layout(ncol = 1, heights = c(3, 1))
  • And labeling
species_scaling + height_dist +
  plot_layout(ncol = 1, heights = c(3, 1)) +
  plot_annotation(tag_levels = "A")
  • Combined this can result in fairly complex figures for publication with a small amount of code
height_dist <- height_dist +
  theme_void() +
  theme(legend.position='none')

height_dist + species_scaling + plot_layout(ncol = 1, heights = c(1, 5))