R forcats fct_reorder 通過沿另一個變量排序來重新排序因子水平

fct_reorder(
  .f,
  .x,
  .fun = median,
  ...,
  .na_rm = NULL,
  .default = Inf,
  .desc = FALSE
)

fct_reorder2(
  .f,
  .x,
  .y,
  .fun = last2,
  ...,
  .na_rm = NULL,
  .default = -Inf,
  .desc = TRUE
)

last2(.x, .y)

first2(.x, .y)

# fct_reorder() -------------------------------------------------------------
# Useful when a categorical variable is mapped to position
boxplot(Sepal.Width ~ Species, data = iris)

boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width), data = iris)


# or with
library(ggplot2)
ggplot(iris, aes(fct_reorder(Species, Sepal.Width), Sepal.Width)) +
  geom_boxplot()


# fct_reorder2() -------------------------------------------------------------
# Useful when a categorical variable is mapped to color, size, shape etc

chks <- subset(ChickWeight, as.integer(Chick) < 10)
chks <- transform(chks, Chick = fct_shuffle(Chick))

# Without reordering it's hard to match line to legend
ggplot(chks, aes(Time, weight, colour = Chick)) +
  geom_point() +
  geom_line()


# With reordering it's much easier
ggplot(chks, aes(Time, weight, colour = fct_reorder2(Chick, Time, weight))) +
  geom_point() +
  geom_line() +
  labs(colour = "Chick")