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The educabr2 visualization system

Source: vignettes/visualization.Rmd
visualization.Rmd

educabr2 ships a small, opinionated plotting toolkit so that a query becomes a publication-ready figure in a few lines. The design follows the principles of Kieran Healy’s Data Visualization: A Practical Introduction: build on a clean minimal theme, use a colorblind-safe palette, label honestly, and let the data — not the chrome — carry the story.

The three components

  1. theme_educabr() — a serif theme built on ggplot2::theme_minimal(), with quiet grid lines, a bottom legend and tuned text sizes. If a local TinyTeX installation is found (and the showtext/sysfonts packages are installed), the LaTeX font Latin Modern Roman is registered automatically, so your plots match LaTeX documents typographically; otherwise the theme falls back silently to the system serif font.
  2. scale_colour_educabr() / scale_fill_educabr() — discrete scales mapping to the Okabe-Ito palette, safe for the three common forms of color-vision deficiency.
  3. scale_x_year_educabr(years) — a year axis designed for the century-long series that are the norm in this package. Default ggplot2 breaks assume short spans and become unreadable over a century; this scale picks the break spacing from the span of the data you actually plot (every 20 years beyond six decades, every 10 years for spans of 25–60 years, pretty() breaks below that), always labels the first and last year present in the series, and drops any grid break that would collide with those extreme labels.

A first figure 

df <- get_schooling(geo_level = "BR", dimension = "sex")

ggplot(df, aes(year, value, colour = dim_sex)) +
  geom_line(linewidth = 0.9) +
  scale_colour_educabr(name = NULL, labels = tools::toTitleCase) +
  scale_x_year_educabr(df$year) +
  theme_educabr() +
  labs(
    x = NULL, y = "Mean years of schooling",
    title    = "The gender gap in schooling reversed over the 20th century",
    subtitle = "Brazil, population aged 15-64, 1925-2015 (Walter & Kang 2024)"
  )

Titles inside or outside the figure: plot_titles

For slides, blog posts and dashboards you usually want the title inside the image (the default above). For LaTeX/Quarto manuscripts the caption and source note live in the document, and a title baked into the image would duplicate them. theme_educabr(plot_titles = FALSE) strips in-canvas titles, subtitles and captions:

ggplot(df, aes(year, value, colour = dim_sex)) +
  geom_line(linewidth = 0.9) +
  scale_colour_educabr(name = NULL, labels = tools::toTitleCase) +
  scale_x_year_educabr(df$year) +
  theme_educabr(plot_titles = FALSE) +
  labs(x = NULL, y = "Mean years of schooling")

This is exactly how the figures of the educabr2 paper/appendix are produced: clean canvases, with captions and notes written in the manuscript.

A century-long, multi-series example 

ratio <- get_expenditure(indicator = "double_ratio_es_ef1")

ggplot(ratio, aes(year, value)) +
  geom_line(colour = "#0072B2", linewidth = 0.9) +
  scale_x_year_educabr(ratio$year) +
  theme_educabr() +
  labs(
    x = NULL, y = "ES / EF1 per-student spending (ratio)",
    title    = "Brazil spent 66x more per university student than per pupil in 1933",
    subtitle = "By 2010 the double ratio had fallen below 9 (Kang & Menetrier 2024)"
  )

Note the axis: on a 1933–2010 span, scale_x_year_educabr() places decadal-style breaks and labels both endpoints — no crowding, no unlabeled extremes.

Palette reference

The Okabe-Ito palette used by scale_colour_educabr() / scale_fill_educabr(), in order:

# Hex Name
1 #E69F00 orange
2 #56B4E9 sky blue
3 #009E73 bluish green
4 #0072B2 blue
5 #D55E00 vermillion
6 #CC79A7 reddish purple
7 #F0E442 yellow
8 #000000 black

Beyond 8 categories the scales will error — by design. A line chart with more than eight simultaneously coloured series is rarely readable; prefer faceting, or highlight a few series against a grey background.