**R – Statistical Odds & Ends**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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When you are plotting many points on one plot, changing the transparency, or ** alpha**, of the points is often a good idea. For example, the plot below plots price vs. carat for each of 53,940 diamonds:

library(ggplot2) ggplot(diamonds, aes(x = carat, y = price)) + geom_point()

We get a mess/mass of points! We can see the general trend between price and carat, but we don’t know how many diamonds there really are in each part of the plot. By reducing the alpha of the points, we get a cleaner, more informative plot:

library(ggplot2) ggplot(diamonds, aes(x = carat, y = price)) + geom_point(alpha = 0.05)

We immediately learn 2 things that we couldn’t tell from the previous plot: (i) there are a lot more small diamonds (<1 carat) than big ones; and (ii) the carat sizes of diamonds seem to clump just above “nice” values like 1, 1.5 and 2.

Changing the alpha of points is really easy in ggplot2. It turns out that it’s not too difficult in base R plots as well: we just need the scales package. When drawing the points, define the “col” option as a function call to the alpha function in scales. The code snippet below gives an example of how to do this:

library(scales) with(diamonds, plot(x = carat, y = price, pch = 19, col = alpha("black", 0.05)))

The code above produces the plot below:

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**R – Statistical Odds & Ends**.

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