Add ribbon showing mean and interquartile range to ggplot2

Question

I use the following example data and code

# Example
x1<- as.Date("2013-12-31")
adddate1 <- 1:60
dts <- x1 + adddate1

df <- data.frame(a=runif(100),b=runif(100),c=runif(100) ,d=rnorm(2700), dates=dts)
df$Metric <- ifelse(df$a > 0.5,"a", "b")

df$Methodology <- ifelse(df$a > 0.5,"One", "Two")
df$Methodology <- factor(df$Methodology)

pl<-df %>%
  group_by(Methodology) %>%
  do(
    plots = ggplot(data=., aes(x = dates, y = b)) +
              geom_point() +
              stat_smooth(method="auto",size=1.5) +
              stat_summary(fun.data=median_hilow, fun.args=(conf.int=1)) + # Show IQR
              scale_x_date(date_breaks = "1 week", date_labels =  "%d-%b-%y") +
              facet_wrap(~Metric, scales="free") +
              ggtitle(unique(.$Methodology)) 
    )
pl[[1,2]]

The output I see is:

However, I would like to see IQR, as calculated by stat_summary or some such routine, shown as a ribbon plot, as well as a line showing the median value.

I suspect I will have to write a user defined function and play with that.

Appreciate any hints or tips.

Answer 1

You can use stat_summary with geom_smooth:

library(ggplot2)
set.seed(47)

df <- data.frame(a = runif(100), 
                 b = runif(100), 
                 c = runif(100), 
                 d = rnorm(2700), 
                 dates = as.Date("2013-12-31") + 1:60)
df$Metric <- ifelse(df$a > 0.5, "a", "b")
df$Methodology <- factor(ifelse(df$a > 0.5, "One", "Two"))


ggplot(df, aes(x = dates, y = b)) +
    geom_point() +
    stat_smooth(size = 1.5) +
    geom_smooth(stat = 'summary', alpha = 0.2, fill = 'red', color = 'red',
                fun.data = median_hilow, fun.args = list(conf.int = 1)) + 
    scale_x_date(date_breaks = "1 week", date_labels =  "%d-%b-%y") +
    facet_wrap(~ Methodology + Metric, ncol = 1)
#> `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Since conf.int = 1, this plots a ribbon between the minimum and maximum at each x value, with the median as the line. If you actually want to plot 25th and 75th percentiles, set conf.int = 0.5. On this data, there aren't enough observations at each x value for that to look very different, though, so on some new sample data,

library(ggplot2)
set.seed(47)

ggplot(tibble::tibble(x = rep(seq(0, 4*pi, length.out = 50), 50), 
                      y = rnorm(2500) * sin(x) + sin(x)), 
       aes(x, y)) + 
    geom_point(alpha = 0.1) + 
    geom_smooth(fill = 'darkblue') + 
    geom_smooth(stat = 'summary', color = 'red', fill = 'red', alpha = 0.2, 
                fun.data = median_hilow, fun.args = list(conf.int = 0.5))
#> `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

median_hilow (really Hmisc::smedian.hilow) doesn't allow you to set the type of quantile, though, so for more precise control, rewrite the function (returning a similarly structured data frame) or pass separate functions for each statistic to the fun.y, fun.ymin and fun.ymax parameters.

Answer 2

Something's changed in either R or ggplot 2, but stat_summary() no longer works with the geom = 'smooth' option. It needs to be geom = 'ribbon'. This works as advertised in R 3.6.0 and ggplot 3.1.1

library(ggplot2)
set.seed(47)

ggplot(tibble::data_frame(x = rep(seq(0, 4*pi, length.out = 50), 50), 
                          y = rnorm(2500) * sin(x) + sin(x)), 
       aes(x, y)) + 
    geom_point(alpha = 0.1) + 
    geom_smooth(fill = 'darkblue') + 
    stat_summary(fun.data = median_hilow, fun.args = list(conf.int = 0.5), 
                 geom = 'ribbon', color = 'red', fill = 'red', alpha = 0.2)