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.