{ggblanket}: simplify beautiful ggplot2 visualisation

David Hodge

1 year ago

A few days ago, I released {ggblanket} onto CRAN.

This package took over my brain for 1.5 months, and I worked obsessively on it. I hope people find it useful.

The objective of {ggblanket} is to make beautiful {ggplot2} visualisation simpler.

With this objective in mind, the {ggblanket} package:

uses quick functions that wrap around a single geom
merges col and fill aesthetics into a single col aesthetic
provides colour customisation via a pal argument
treats faceting as an aesthetic
pushes x and y limits to the max of the x and y breaks by default
arranges horizontal geom y and col labels etc to be in correct order
converts titles and labels to sentence case and comma format by default
provides arguments for scale adjustment and legend placement
allows users to access all other non-aesthetic geom functionality
changes default colours, alphas, widths and themes

library(dplyr)
library(ggplot2)
library(ggblanket)

penguins2 <- palmerpenguins::penguins %>% 
  tidyr::drop_na() %>% 
  mutate(body_mass_kg = body_mass_g / 1000) 
penguins2 %>% 
  ggplot() +
  geom_histogram(aes(x = body_mass_kg))

penguins2 %>% 
  gg_histogram(x = body_mass_kg)

penguins2 %>%
  group_by(species, sex, island) %>%
  summarise(body_mass_kg = mean(body_mass_kg)) %>%
  ggplot() +
  geom_col(
    aes(x = body_mass_kg, y = species, fill = sex), 
    position = "dodge"
    ) +
  facet_wrap( ~ island) +
  theme(legend.position = "bottom")

penguins2 %>%
  group_by(species, sex, island) %>%
  summarise(body_mass_kg = mean(body_mass_kg)) %>%
  gg_col(
    x = body_mass_kg,
    y = species,
    col = sex,
    facet = island,
    position = "dodge",
    col_legend_place = "b"
  )

Other examples

storms %>% 
  group_by(year) %>% 
  summarise(wind = mean(wind, na.rm = TRUE)) %>% 
  gg_line(x = year, 
          y = wind, 
          y_zero = TRUE,
          x_labels = ~.x, 
          title = "Storm wind speed",
          subtitle = "USA average storm wind speed, 1975\u20132020", 
          y_title = "Wind speed (knots)", 
          caption = "Source: NOAA",
          theme = gg_theme(y_grid = TRUE)) +
  geom_point()

penguins2 %>% 
  gg_density(
    x = body_mass_kg, 
    col = species, 
    facet = sex, 
    col_legend_place = "b")

penguins2 %>%
  gg_jitter(
    x = species,
    y = body_mass_g,
    col = flipper_length_mm,
    col_intervals = ~santoku::chop_quantiles(.x, probs = seq(0, 1, 0.25)),
    position = position_jitter(width = 0.2, height = 0, seed = 123), 
    y_zero = TRUE)

penguins2 %>% 
  gg_smooth(
    x = bill_length_mm,
    y = flipper_length_mm,
    col = species,
    )

penguins2 %>%
  gg_histogram(
    x = body_mass_kg,
    col = species, 
    facet = sex, 
    col_legend_place = "b", 
    pal = pals::brewer.dark2(3))

df <- data.frame(
  trt = factor(c(1, 1, 2, 2)),
  resp = c(1, 5, 3, 4),
  group = factor(c(1, 2, 1, 2)),
  upper = c(1.1, 5.3, 3.3, 4.2),
  lower = c(0.8, 4.6, 2.4, 3.6)
)

dodger <- position_dodge(width = 0.75)

gg_blank(df, x = resp, xmin = lower, xmax = upper, y = trt, col = group) +
  geom_col(position = dodger, width = 0.75, alpha = 0.9) +
  geom_errorbar(position = dodger, width = 0.2, col = "#232323")

For further information, see the ggblanket website.

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