Compute weighted mean with stat_weighted_mean()

# Compute weighted mean with `stat_weighted_mean()`

    library(ggstats)
    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")