The logic of plyr is very similar to aggregate() and apply(). In fact, the title of the package is Tools for splitting, applying, and combining data.

Its main functions can be easily understood since they are all named for its input and output objects, based upon the following references:

So, for instance, laply receives a list and returns an array, ddply receives a data frame and returns a data frame, and so on.

Although the package has a wide variety of functions available, all the ones that have a data frame as input are the most important ones (also, the ones starting with d). The following are a few of its usage:

ddply(iris,.(Species), summarize, indicator1=quantile(Sepal.Length,0.75), indicator2=sum(Sepal.Width)/sum(Petal.Length))
##      Species indicator1 indicator2
## 1     setosa   ...