Since you are dynamically building a variable name as a character value, it makes more sense to do assignment using standard data.frame indexing which allows for character values for column names. For example:
multipetal <- function(df, n) {
varname <- paste("petal", n , sep=".")
df[[varname]] <- with(df, Petal.Width * n)
df
}
The mutate function makes it very easy to name new columns via named parameters. But that assumes you know the name when you type the command. If you want to dynamically specify the column name, then you need to also build the named argument.
With the latest dplyr version you can use the syntax from the glue package when naming parameters when using :=. So here the {} in the name grab the value by evaluating the expression inside.
multipetal <- function(df, n) {
mutate(df, "petal.{n}" := Petal.Width * n)
}
dplyr starting with version 0.7 allows you to use := to dynamically assign parameter names. You can write your function as:
# --- dplyr version 0.7+---
multipetal <- function(df, n) {
varname <- paste("petal", n , sep=".")
mutate(df, !!varname := Petal.Width * n)
}
For more information, see the documentation available form vignette("programming", "dplyr").
Slightly earlier version of dplyr (>=0.3 <0.7), encouraged the use of "standard evaluation" alternatives to many of the functions. See the Non-standard evaluation vignette for more information (vignette("nse")).
So here, the answer is to use mutate_() rather than mutate() and do:
# --- dplyr version 0.3-0.5---
multipetal <- function(df, n) {
varname <- paste("petal", n , sep=".")
varval <- lazyeval::interp(~Petal.Width * n, n=n)
mutate_(df, .dots= setNames(list(varval), varname))
}
Note this is also possible in older versions of dplyr that existed when the question was originally posed. It requires careful use of quote and setName:
# --- dplyr versions < 0.3 ---
multipetal <- function(df, n) {
varname <- paste("petal", n , sep=".")
pp <- c(quote(df), setNames(list(quote(Petal.Width * n)), varname))
do.call("mutate", pp)
}