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Publication figures and tables

Source: vignettes/articles/publication.Rmd
publication.Rmd

Every autoplot() method returns an ordinary ggplot, so anything can be changed with +. The common changes for a journal figure are built in.

library(tidydiversity)
library(dplyr)
library(ggplot2)
set.seed(1)
wet <- filter(png_ants, season == "wet")
curves <- div_rarefy(wet, nboot = 100)

Plot options

Okabe-Ito colours, solid rarefaction and dashed extrapolation, 95% bootstrap ribbons and the publication theme.

autoplot(curves)

Default rarefaction plot

autoplot(filter(curves, q == 0),
  xlab = "Number of ant workers",
  ylab = "Species richness", ncol = 2, scales = "fixed"
)

Rarefaction plot with custom labels and two columns

palette = "grey" is for print journals; line type still separates rarefaction and extrapolation.

autoplot(curves, palette = "grey")

Greyscale rarefaction plot

Pass a vector of colours, or theme = NULL to start from the ggplot2 default and add your own theme.

autoplot(curves,
  palette = hcl.colors(8, "Dark 3"), ci = FALSE,
  theme = NULL
) +
  theme_classic(base_size = 10) +
  theme(legend.position = "right")

Rarefaction plot with custom colours and theme

autoplot(div_rarefy(filter(wet, site == "MW0200"), nboot = 100),
  facet = FALSE
)

All orders of q on one panel

The publication theme

theme_tidydiversity() has a white background, light horizontal grid, bold strip labels and the legend at the bottom. It takes base_size, base_family, legend_position and grid ("y", "xy" or "none"). The palettes are also available as ggplot2 scales.

div_hill(png_ants, .by = c(season, elevation)) |>
  filter(q == 1) |>
  ggplot(aes(elevation, diversity, colour = season)) +
  geom_line() +
  geom_point() +
  scale_colour_tidydiversity() +
  labs(x = "Elevation (m)", y = "Shannon diversity") +
  theme_tidydiversity(base_size = 12, grid = "xy")

Custom ggplot using the package theme and palette

Tables

div_table() formats any result with the estimate and an APA-style interval, for example 12.3 [9.8, 15.1], and renders it as a tibble, knitr kable, gt table or flextable (for Word).

est <- div_estimate(wet, nboot = 100)
div_table(est,
  digits = 1, output = "kable",
  caption = "Asymptotic diversity, wet season"
)
Asymptotic diversity, wet season
site Order q Observed Estimate [95% CI]
MW0200 0 21.0 32.9 [23.0, 90.9]
MW0200 1 10.9 13.8 [10.9, 17.9]
MW0200 2 7.0 7.6 [7.0, 9.7]
MW0700 0 28.0 75.3 [40.2, 211.3]
MW0700 1 21.6 38.8 [22.2, 55.4]
MW0700 2 17.2 24.0 [17.2, 33.7]
MW1200 0 20.0 33.3 [22.4, 94.4]
MW1200 1 13.9 18.8 [13.9, 25.3]
MW1200 2 10.1 12.0 [10.1, 17.0]
MW1700 0 10.0 18.7 [11.4, 63.3]
MW1700 1 5.3 7.0 [5.3, 11.2]
MW1700 2 3.6 3.9 [3.6, 5.9]
MW2200 0 5.0 6.9 [5.3, 18.9]
MW2200 1 3.6 4.5 [3.6, 7.3]
MW2200 2 2.8 3.3 [2.8, 5.8] 
div_table(est, digits = 1, wide = TRUE, output = "kable")
site observed (q = 0) estimate (q = 0) observed (q = 1) estimate (q = 1) observed (q = 2) estimate (q = 2)
MW0200 21.0 32.9 [23.0, 90.9] 10.9 13.8 [10.9, 17.9] 7.0 7.6 [7.0, 9.7]
MW0700 28.0 75.3 [40.2, 211.3] 21.6 38.8 [22.2, 55.4] 17.2 24.0 [17.2, 33.7]
MW1200 20.0 33.3 [22.4, 94.4] 13.9 18.8 [13.9, 25.3] 10.1 12.0 [10.1, 17.0]
MW1700 10.0 18.7 [11.4, 63.3] 5.3 7.0 [5.3, 11.2] 3.6 3.9 [3.6, 5.9]
MW2200 5.0 6.9 [5.3, 18.9] 3.6 4.5 [3.6, 7.3] 2.8 3.3 [2.8, 5.8] 
div_table(est,
  digits = 1, wide = TRUE, output = "gt",
  caption = "Asymptotic diversity"
)
Asymptotic diversity
site observed (q = 0) estimate (q = 0) observed (q = 1) estimate (q = 1) observed (q = 2) estimate (q = 2)
MW0200 21.0 32.9 [23.0, 90.9] 10.9 13.8 [10.9, 17.9] 7.0 7.6 [7.0, 9.7]
MW0700 28.0 75.3 [40.2, 211.3] 21.6 38.8 [22.2, 55.4] 17.2 24.0 [17.2, 33.7]
MW1200 20.0 33.3 [22.4, 94.4] 13.9 18.8 [13.9, 25.3] 10.1 12.0 [10.1, 17.0]
MW1700 10.0 18.7 [11.4, 63.3] 5.3 7.0 [5.3, 11.2] 3.6 3.9 [3.6, 5.9]
MW2200 5.0 6.9 [5.3, 18.9] 3.6 4.5 [3.6, 7.3] 2.8 3.3 [2.8, 5.8] 
ft <- div_table(est,
  digits = 1, ci_style = "parentheses",
  output = "flextable"
)
ft

site

Order q

Observed

Estimate (95% CI)

MW0200

0

21.0

32.9 (23.0-90.9)

MW0200

1

10.9

13.8 (10.9-17.9)

MW0200

2

7.0

7.6 (7.0-9.7)

MW0700

0

28.0

75.3 (40.2-211.3)

MW0700

1

21.6

38.8 (22.2-55.4)

MW0700

2

17.2

24.0 (17.2-33.7)

MW1200

0

20.0

33.3 (22.4-94.4)

MW1200

1

13.9

18.8 (13.9-25.3)

MW1200

2

10.1

12.0 (10.1-17.0)

MW1700

0

10.0

18.7 (11.4-63.3)

MW1700

1

5.3

7.0 (5.3-11.2)

MW1700

2

3.6

3.9 (3.6-5.9)

MW2200

0

5.0

6.9 (5.3-18.9)

MW2200

1

3.6

4.5 (3.6-7.3)

MW2200

2

2.8

3.3 (2.8-5.8)

Save it to Word with flextable::save_as_docx(ft, path = "table1.docx").

Saving figures

Journals usually ask for a set width (single column about 85 mm, double column about 175 mm) and 300 dpi or more for raster images, or a vector format such as PDF or EPS.

p <- autoplot(curves, theme = theme_tidydiversity(base_size = 8))

# Single column, 300 dpi TIFF with LZW compression
ggsave("figure1.tiff", p,
  width = 85, height = 70, units = "mm",
  dpi = 300, compression = "lzw"
)

# Double column, vector PDF (embed fonts with cairo)
ggsave("figure1.pdf", p,
  width = 175, height = 90, units = "mm",
  device = cairo_pdf
)

# High-resolution PNG for slides or preprints
ggsave("figure1.png", p,
  width = 175, height = 90, units = "mm",
  dpi = 600, bg = "white"
)

Set base_size to the font size the journal asks for (often 8 to 10 pt) because ggsave does not rescale text when the figure is made smaller.