In this post, we will learn about four geoms in ggplot2 that are useful for revealing uncertainty in numerical variables with multiple categories. The four geoms, geom_errorbar(), geom_linerange(), geom_crossbar(), and geom_pointrange() are useful when we have categeorical x values and we are interested in the “distribution of y conditional on x and use the aesthetics ymin and ymax to determine the range of the y values”. The third edition of ggplot2 book has a great chapter describing these geoms.
Let us get started by loading tidyverse and palmer penguin pacakges.
library(tidyverse) library(palmerpenguins) theme_set(theme_bw(16))
df <- penguins %>%
drop_na() %>%
group_by(species) %>%
summarize(n= n(),
mean_body_mass= mean(body_mass_g),
sd = sd(body_mass_g))
df # A tibble: 3 × 4 species n mean_body_mass sd <fct> <int> <dbl> <dbl> 1 Adelie 146 3706. 459. 2 Chinstrap 68 3733. 384. 3 Gentoo 119 5092. 501.
Visualizing Uncertainty with geom_crossbar()
df %>%
ggplot(aes(species, mean_body_mass,
ymin = mean_body_mass - sd,
ymax = mean_body_mass + sd)) +
geom_crossbar()
ggsave("visualizing_uncertainty_with_geom_crossbar.png")
Visualizing Uncertainty with geom_errorbar()
df %>%
ggplot(aes(species, mean_body_mass,
ymin = mean_body_mass - sd,
ymax = mean_body_mass + sd)) +
geom_errorbar(linewidth = 1)
ggsave("visualizing_uncertainty_with_geom_errorbar.png")
Visualizing Uncertainty with geom_pointrange()
df %>%
ggplot(aes(species, mean_body_mass,
ymin = mean_body_mass - sd,
ymax = mean_body_mass + sd)) +
geom_pointrange()
ggsave("visualizing_uncertainty_with_geom_pointrange.png")
Visualizing Uncertainty with geom_linerange()
df %>%
ggplot(aes(species, mean_body_mass,
ymin = mean_body_mass - sd,
ymax = mean_body_mass + sd)) +
geom_linerange(color="blue", linewidth=2)
ggsave("visualizing_uncertainty_with_geom_linerange.png")
