graphique tornade

Code R de l'exemple ggplot2

library(ggplot2)
library(dplyr)
library(palmerpenguins)

## Linear model of body mass. Starting from a reference penguin (a female
## Adelie with median measures), each predictor varies alone: from the 10th
## to the 90th percentile for a measure, across its values for species and sex.
birds <- filter(penguins, !is.na(sex))
model <- lm(body_mass_g ~ bill_length_mm + bill_depth_mm + flipper_length_mm + species + sex, data = birds)
reference <- data.frame(bill_length_mm = median(birds$bill_length_mm),
                        bill_depth_mm = median(birds$bill_depth_mm),
                        flipper_length_mm = median(birds$flipper_length_mm),
                        species = factor("Adelie", levels = levels(birds$species)),
                        sex = factor("female", levels = levels(birds$sex)))
change <- function(variable, values) {
  others <- reference[rep(1, length(values)), ]
  others[[variable]] <- values
  range(predict(model, others) - predict(model, reference))
}
swings <- bind_rows(
  tibble(predictor = "Bill length", range = list(change("bill_length_mm", quantile(birds$bill_length_mm, c(0.1, 0.9))))),
  tibble(predictor = "Bill depth", range = list(change("bill_depth_mm", quantile(birds$bill_depth_mm, c(0.1, 0.9))))),
  tibble(predictor = "Flipper length", range = list(change("flipper_length_mm", quantile(birds$flipper_length_mm, c(0.1, 0.9))))),
  tibble(predictor = "Species", range = list(change("species", levels(birds$species)))),
  tibble(predictor = "Sex", range = list(change("sex", levels(birds$sex))))
) |>
  mutate(low = sapply(range, min), high = sapply(range, max),
         predictor = reorder(predictor, high - low))

p <- ggplot(swings, aes(y = predictor)) +
  geom_rect(aes(xmin = low, xmax = 0, ymin = as.integer(predictor) - 0.35, ymax = as.integer(predictor) + 0.35),
            fill = "#2e7891") +
  geom_rect(aes(xmin = 0, xmax = high, ymin = as.integer(predictor) - 0.35, ymax = as.integer(predictor) + 0.35),
            fill = "#9b2367") +
  geom_vline(xintercept = 0, colour = "#535353", linewidth = 0.4) +
  annotate("text", x = c(-150, 600), y = 5.65, label = c("Lighter", "Heavier"),
           colour = c("#2e7891", "#9b2367"), size = 3) +
  scale_x_continuous(labels = lex_number) +
  scale_y_discrete(expand = expansion(add = c(0.6, 0.9))) +
  labs(title = "Species moves the predicted mass most, flipper length next",
       subtitle = "Change from a reference penguin as each predictor varies alone",
       x = "Change in predicted body mass (g)", y = NULL,
       caption = "Data: palmerpenguins (Horst, Hill & Gorman 2020)") +
  theme_lex_viz(grid = "x", arrows = "")