> From: Barry Rowlingson > > A colleague is receiving some data from another person. That person > reads the data in SAS and it takes 30s and uses 64k RAM. That person > then tries to read the data in R and it takes 10 minutes and uses a > gigabyte of RAM. Person then goes on to say: > > It's not that I think SAS is such great software, > it's not. But I really hate badly designed > software. R is designed by committee. Worse, > it's designed by a committee of statisticians. > They tend to confuse numerical analysis with > computer science and don't have any idea about > software development at all. The result is R. > > I do hope [your colleague] won't have to waste time doing > [this analysis] in an outdated and poorly designed piece > of software like R. > > Would any of the "committee" like to respond to this? Or > shall we just > slap our collective forehead and wonder how someone could get > such a view? > > Barry
My $0.02: R, being a flexible programming language, has an amazing ability to cope with people's laziness/ignorance/inelegance, but it comes at a (sometimes hefty) price. While there is no specifics on the situation leading to the person's comments, here's one (not as extreme) example that I happen to come across today: > system.time(spam <- read.table("data_dmc2003_train.txt", + header=T, + colClasses=c(rep("numeric", 833), + "character"))) [1] 15.92 0.09 16.80 NA NA > system.time(spam <- read.table("data_dmc2003_train.txt", header=T)) [1] 187.29 0.60 200.19 NA NA My SAS ability is rather serverely limited, but AFAIK, one needs to specify _all_ variables to be read into a dataset in order to read in the data in SAS. If one has that information, R can be very efficient as well. Without that information, one gets nothing in SAS, or just let R does the hard work. Best, Andy ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html