Introduction to Formal Arguments in R
Formal arguments in R are essentially variables you define within a function’s code block. These arguments act as placeholders for the data that are provided when a user uses the function. Formal are the formal arguments of function returned as an object of class pairlist which can be thought of as something similar to a list with an important difference:
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is.null(pairlist()) is.null(list())
That is a pairlist of length zero is NULL while a list is not.
Specifying Formal Arguments Positions
Formal arguments in R can be specified by position or by name and we can mix positional matching with matching by name. The following are equivalent.
mean(x = 1:5, trim = 0.1) mean(1:5, trim = 0.1) mean(x = 1:5, 0.1) mean(1:5, 0.1) mean(trim = 0.1, x = 1:5)
Functions Formals Default Values
Functions formals may also have the construct symbol=default, which unless differently specified, forces any argument to be used with its default value. Specifically, functions mean() also have a third argument na.rm that defaults to FALSE and as a result, passing vectors with NA values to mean() returns NA.
mean(c(1, 2, NA))
while by specifying na.rm=TRUE we get the mean of all non-missing elements of vector x.
mean(c(1, 2, NA), na.rm = TRUE)
we can redefine mean() function that defaults na.rm to TRUE by simply
mean(c(1, 2, NA))
Now we have a copy of mean.default() in our globalenv:
exists("mean.default", envir = globaenv())also, notice
environment(mean.default)
The … Argument in a Function
The … argument of a function is special and can contain any number of symbol=value arguments. The … argument is transformed by R into a list that is simply added to the formal list:
h<-function(x, …){
           0
}
formals(h)The … argument can be used if the number of arguments is unknown. Suppose one wants to define a function that counts the number of rows of any given number of data frames. One can write:
count_rows<-function(…){
list<-list(…)
lapply(list, nrow)
}
count_rows(airquality, cars)By effectively using formal arguments in R Language, one can create reusable and adaptable functions that make the R code more concise and efficient.
