This is not a perfect dataset for demonstrating this, but using ribbon can be useful. In this example we will create a 30-day running average using the filter() function so that our ribbon is not too noisy.
chic$o3run <- as.numeric(stats::filter(chic$o3, rep(1/30, 30), sides = 2))
ggplot(chic, aes(x = date, y = o3run)) +
geom_line(color = "chocolate", lwd = .8) +
labs(x = "Year", y = "Ozone")Warning: Removed 29 rows containing missing values or values outside the scale range
(`geom_line()`).
How does it look if we fill in the area below the curve using the geom_ribbon() function?
ggplot(chic, aes(x = date, y = o3run)) +
geom_ribbon(aes(ymin = 0, ymax = o3run),
fill = "orange", alpha = .4) +
geom_line(color = "chocolate", lwd = .8) +
labs(x = "Year", y = "Ozone")Warning: Removed 29 rows containing missing values or values outside the scale range
(`geom_line()`).
Nice to indicate the area under the curve (AUC) but this is not the conventional way to use geom_ribbon().
geom_area()
Actually a nicer way to achieve the same is geom_area().
ggplot(chic, aes(x = date, y = o3run)) +
geom_area(color = "chocolate", lwd = .8,
fill = "orange", alpha = .4) +
labs(x = "Year", y = "Ozone")Warning: Removed 29 rows containing non-finite outside the scale range
(`stat_align()`).
Instead, we draw a ribbon that gives us one standard deviation above and below our data:
chic$mino3 <- chic$o3run - sd(chic$o3run, na.rm = TRUE)
chic$maxo3 <- chic$o3run + sd(chic$o3run, na.rm = TRUE)
ggplot(chic, aes(x = date, y = o3run)) +
geom_ribbon(aes(ymin = mino3, ymax = maxo3), alpha = .5,
fill = "darkseagreen3", color = "transparent") +
geom_line(color = "aquamarine4", lwd = .7) +
labs(x = "Year", y = "Ozone")Warning: Removed 29 rows containing missing values or values outside the scale range
(`geom_line()`).