While trying to log in to SAS university edition (which has been unsuccessful so far), a question came to my mind: "Who in the world would like to use SAS when there is "R", which is way better than SAS? Just imagine, you have to purchase SAS. If you don't have money then you might use University Edition, which you have to go terrible procedures in order to download it and be able to use. But with R, it is free and very convenient.
R is a statistical package, SAS is a data warehousing framework that covers many more topics than R.
The fact that YOU think that R is better does not mean that the world outside thinks the same.
Start to look beyond your horizon, it improves life.
BTW, SAS already provides a rather easy-to-use interface to R, so you can use the power of both worlds.
Because the same code I use for my 20 million rows dataset I can run on my 200 million rows dataset without worrying about not even being able to load the data.
Because there are standard and tested Procs that are well documented and I'm not searching between 14 packages that do the same thing. 6 aren't supported in my R version, 3 don't have the options I need, and 5 give me different results. True story.
Because it's commonly used in a variety of big industries such as health, banking, finance and gov so if I want a job in those fields I'm likely to find one.
And last, but not least, I like open source software, but people who don't want to pay for software often don't see the value in paying for programs, training and/or programmers. Think of all that work that went into R for free, personally, I want to be paid for my work.
PS I programs in R, Python & SAS. It isn't one or the other. And installing SAS UE is relatively easy compared to trying to find a package for R. Most people seem to skip reading the instructions.
Forget one, support.
If you're having issues installing SAS email email@example.com and they'll help. They'll even screen share and help debug if necessary. Even for setting up SAS UE.
Most Qs are answered in the Analytics U forum anyways.
1. SAS is way more than statistical analysis.
2. In many high stakes fields (health care analytics, aviation, etc.), SAS is much more preferred due to its high level quality control, whereas R provides absolutely no warranty.
3. SAS is much easier to use than R
and many more
R and SAS can somewhat coexist.
The key is to know what tool to use for what job.
1. Maturity of the Language
R is still in many ways a developing language. Example: An R user might want to create a table that shows totals of X by Y. This can be done by coding it. But can also be done by creating a 'function'. This "R" user has now with her/his function the ability to share that function with others, presumably in his/her department. But that new function they created can also be created by another department. While you may think this is a great feature - please note that NO one function is being certified as accurate or error free in the "R" realm. This is both a great feature and problem. What if Department "A" calls the function "total by department" - but department "B" calls it "deparment total". While both languages (R/SAS) can suffer from disperate naming conventions. SAS as the mature language - has standard libraries of universal functions which SAS Institute warranties for users. And not only for users of paid licenses - but also of any of its SAS products.
That same "wonderful" feature in R of 'freedom' to create new functions is also a problem. - Where is the documentation? And without documentation - how do you know it works? This presumably is why there are so many so called "job openings" in "R". Its likely because no one knows what the heck functions someone else created does. Ok - So in SAS the functions available (many many functions) are well documented. Not to mention with often plenty of user papers and user documentation and examples.
A novice - will claim that SAS is expensive and R is free. Well i can assure you that when your usage of R goes into a corporate side or enterprise level need and usage - R is NOT for free. Furthermore - SAS is free or rather affordable. But people just dont know it. SAS Institute has worked hard over the last few years to get SAS software integrated into Universities and higher learning. For free. What I see being propogated is a bias about the price of SAS. See IMHO - i see belly achers whine about the prices of SAS - but only because they wish they can use it as consultants - and they ooops must now learn R. And while R is 'free' - its not competitive as an entire -package- (end to end solutions) as SAS is. I dont 'often' see students complaining about prices when they are enrolled in a school that has SAS Education working with faculty. But alas - the rumors persist. Oh well.
4. I dont care about R - well not exactly.
I have learned enough about R to see how it fits in to my work stream and work environment. Its a great tool for those colleagues who love to code in MS Excel. Those who wish to be "developers" but in reality - are excel jock's. Leave them be. In fact... I wish more people go to R. It leaves less of us with a deeper, richer understanding of Data, SAS and software development. Let them "r" developers flood the market and bring the rates down for them. In fact while i state this quite often - i dont usually type it up. This is the first time i really go out there with this. I personally think there is plenty of space for R, SAS, Python. But I am very ok with less people learning SAS if thats the case.
Meh - i think thats about as subjective as any of my opinions. You can go from location/company to company with R standards all over. I still cringe when i hear that another department is about to create yet another set of R functions... when its just finally getting its hands around functions from its parent department or sibling department. R can be very distracting - and for no reason. I'm sorry but if you really want a consistent learning environment - there is NOTHING like SAS which runs exactly the same code on linux, windows, mac etc.
Hooray to diversity! I think its great. I think SAS corporate should get off its but and find ways to differentiate more and also put long/old rumors to rest. I think R users will always have a place, Python too. In fact IMHO - python and sas as skills are more compelling than R/SAS. But thats just me. If you like "R" and never want to touch SAS - fine. Then please be the best at it and help your fellow users out. I see them floundering about and wish they would just wake up. If you are like me and hear all sorts of things about R, Python... dont sweat it. I've heard for years (yikes - decades) that SAS is done. Well thats fine (if true) but it will be decades before that manifests itself. And I dont think SAS corporate will let that happen.
I really love the attention, discussion, debates people can have and bring more attention to "data" in general. That to me is the winning point of all this.
Tell me about...
1. how you solved something in: R, Python, SAS
2. how well your code and "box" handles not just 1m rows but 1m times 1m rows by 100 fields etc. Scalability
3. how well does your 'server/box' handle not just item 2 above - but by "x" number of users. Its great that you are such a SAS-basher now that you are creating functions - but what about your other users who will soon learn (or try) and (try to run) R.
4. how well does your "team a" code integrate with "team b" code - no matter which language. This is a constant challenge in a team environment.
It looks like you let your frustration get the better of your judgement and posted a silly slap against an amazinly successful software suite on a bulletin board used by thousands to discuss how to use it to solve programming issues and best practices.
I hope that you have overcome your initial issues with installing SAS University Edition and are now enjoying exploring the power and functionality of SAS. I also hope that you have taken the comments and advice of the learned responders to your query to heart!
Opinions on the R/SAS squable.
1) SAS can not and does not have the intellectual firepower that R does. R has thousands of Statistical/machine learning professors/lecturers/Post docs developers around the world. This keeps ideas and techniques flowing. Whether there all correct or useful is another matter. No company can keep up with those resources.
2) Obviously SAS has some excellent statisticians/machine learning developing SAS code. But by not having the sheer weight of numbers of quality people examining and assessing the ideas, the techniques can become outmoded. Rejected techniques like CHAID are still maintained. Peer review is importand and R has more people at the highest level who can peer review openly and sometimes are quite nasty.
3) Every vendor has a way of intgegrating different platforms and data streams together. R can access the power of a teradata warehouse. R can be implemented on a mainframe thru code generation of a R model into a DB2/COBOL program. SAS does something similar with its model. Integration of R and data across an organisation business model is pretty routine.
4) I think SAS future is as a statistical service company. They are not a leader in statisitical /machine learning research(more of a follower), ETL can be done by so many different platforms, deployment of R models across platforms is becoming easier. But I think they do statistical services well.
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