Compute weighted mean with `stat_weighted

library(ggstats)
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) : 
#>   object 'type_sum.accel' not found
library(ggplot2)

stat_weighted_mean() computes mean value of y (taking into account any weight aesthetic if provided) for each value of x. More precisely, it will return a new data frame with one line per unique value of x with the following new variables:

y: mean value of the original y (i.e. numerator/denominator)
numerator
denominator

Let’s take an example. The following plot shows all tips received according to the day of the week.

data(tips, package = "reshape")
ggplot(tips) +
  aes(x = day, y = tip) +
  geom_point()

To plot their mean value per day, simply use stat_weighted_mean().

ggplot(tips) +
  aes(x = day, y = tip) +
  stat_weighted_mean()

We can specify the geometry we want using geom argument. Note that for lines, we need to specify the group aesthetic as well.

ggplot(tips) +
  aes(x = day, y = tip, group = 1) +
  stat_weighted_mean(geom = "line")

An alternative is to specify the statistic in ggplot2::geom_line().

ggplot(tips) +
  aes(x = day, y = tip, group = 1) +
  geom_line(stat = "weighted_mean")

Of course, it could be use with other geometries. Here a bar plot.

p <- ggplot(tips) +
  aes(x = day, y = tip, fill = sex) +
  stat_weighted_mean(geom = "bar", position = "dodge") +
  ylab("mean tip")
p

It is very easy to add facets. In that case, computation will be done separately for each facet.

p + facet_grid(rows = vars(smoker))

stat_weighted_mean() could be also used for computing proportions as a proportion is technically a mean of binary values (0 or 1).

ggplot(tips) +
  aes(x = day, y = as.integer(smoker == "Yes"), fill = sex) +
  stat_weighted_mean(geom = "bar", position = "dodge") +
  scale_y_continuous(labels = scales::percent) +
  ylab("proportion of smoker")

Finally, you can use the weight aesthetic to indicate weights to take into account for computing means / proportions.

d <- as.data.frame(Titanic)
ggplot(d) +
  aes(x = Class, y = as.integer(Survived == "Yes"), weight = Freq, fill = Sex) +
  geom_bar(stat = "weighted_mean", position = "dodge") +
  scale_y_continuous(labels = scales::percent) +
  labs(y = "Proportion who survived")

Compute weighted mean with `stat_weighted_mean()`