What is the most efficient way to cast a list as a data frame?

Very often I want to convert a list wherein each index has identical element types to a data frame. For example, I may have a list: ``` > my.list [[1]] [[1]]$global_stdev_ppb [1] 24267673 [[1]]$range [1] 0.03114799 [[1]]$tok [1] "hello" [[1]]$global_freq_ppb [1] 211592.6 [[2]] [[2]]$global_stdev_ppb [1] 11561448 [[2]]$range [1] 0.08870838 [[2]]$tok [1] "world" [[2]]$global_freq_ppb [1] 1002043 ``` I want to convert this list to a data frame where each index element is a column. The natural (to me) thing to go is to is use `do.call`: ``` > my.matrix<-do.call("rbind", my.list) > my.matrix global_stdev_ppb range tok global_freq_ppb [1,] 24267673 0.03114799 "hello" 211592.6 [2,] 11561448 0.08870838 "world" 1002043 ``` Straightforward enough, but when I attempt to cast this matrix as a data frame, the columns remain list elements, rather than vectors: ``` > my.df<-as.data.frame(my.matrix, stringsAsFactors=FALSE) > my.df[,1] [[1]] [1] 24267673 [[2]] [1] 11561448 ``` Currently, to get the data frame cast properly I am iterating over each column using `unlist` and `as.vector`, then recasting the data frame as such: ``` new.list<-lapply(1:ncol(my.matrix), function(x) as.vector(unlist(my.matrix[,x]))) my.df<-as.data.frame(do.call(cbind, new.list), stringsAsFactors=FALSE) ``` This, however, seem very inefficient. Is there are better way to do this?