• 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))