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publication-figures-spatial-R.md

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Publication quality figures
R

Have students install devtools and patchwork (using devtools)

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(raster)

dtm_harv <- raster("data/neon-airborne/harv_dtmcrop.tif")
dtm_harv_cropped <- crop(dtm_harv, extent(dtm_harv, 731500, 732000, 4713200, 4713500))
dtm_harv_df = as.data.frame(dtm_harv_cropped, xy = TRUE)

ggplot(dtm_harv_df, aes(x = HARV_dtmCrop)) +
  geom_histogram()

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

Color palettes

  • When choosing colors to use in images we need to think about more than what looks good to you
  • How will your plots look to other people
    • 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() +
  geom_raster(data = dtm_harv_df, mapping = aes(x = x, y = y, fill = HARV_dtmCrop)) +
  scale_fill_viridis_c()
ggplot() +
  geom_raster(data = dtm_harv_df, mapping = aes(x = x, y = y, fill = HARV_dtmCrop)) +
  scale_fill_viridis_c(options = "magma")

Saving and exporting multiple plots

Themes

  • These can be used to make coordinated changes to groups of options
p1 + theme_classic()

Combining multiple plots

  • Bivariate plot + histogram example
  • Map + picture + plot example
elevation_map = ggplot() +
  geom_raster(data = dsm_harv_df, 
              aes(x = x, y = y, fill = HARV_dsmCrop))
elevation_histogram = ggplot() +
  geom_histogram(data = dsm_harv_df, 
                 aes(x = HARV_dsmCrop))
  • Mention cowplot