R or SAS: which one is the best statistical software used in medical field?

I am on the starting stage to study a software. I know spss only. I want to study a software either R or SAS.Some of them say R is better and others SAS is better. I am totally confused. Which one is best and easily understanding and highly used in medical field?


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  • Jeff Skinner · National Institute of Allergy and Infectious Diseases
    I prefer using R because I find it easier to program in R. But both programs have their own advantages.

    R is definitely more cutting edge than SAS, because the software is open source and thousands of R users can submit new features to R to quickly add new analyses, graphics and functions to the software. If SAS wants to add a new feature, then they must pay statisticians and software developers to create the new features and test them prior to release. This development process will take months or even years. Most like the new features will not be available until SAS releases a new version or a new expensive "add-on" software. That's why professional statisticians, statistics graduate students and bioinformaticians do most of their work in R ... because R makes it easy to share your new methods with the rest of the world.

    The main advantages of SAS are customer support and its handling of large data. SAS is very expensive. It probably costs more than $1000 USD to purchase the base SAS software and each add-on package can costs hundreds or thousands more. A company could easily spend $10,000 or more on SAS licenses for a single researcher. However, all that money buys you world-class customer support. If something doesn't work right in SAS, then paid customer support representatives will help you over the phone or by email to resolve your problem. I've had a number of interactions with SAS customer support and they are mostly pretty good. If something doesn't work in R, then all you can do is post a message on the R mailing list / bbs and hope that someone chooses to help you in their free time.

    SAS is also slightly better at handling large data than R. By default, the R software stores its data in the RAM memory of your computer. For most people, that means you will only be able to store about 2 GB to 16 GB of data in R. The base SAS software stores your data in "virtual memory" on your computer's hard drive. That means you could easily handle 100 GB or more on a relatively cheap and old Windows PC with a fast hard drive. There are options you can use to expand the data handling capabilities of R, but using the default settings ... SAS is better at handling large data. Both SAS and R can manipulate data via SQL queries on large databases, so the differences with respect to large data are not terribly significant ... but I would give SAS a very slight edge.

    I've been using SAS since 2000 and R since 2004. When I started using SAS back in 2000, it seemed like the average SAS user was still some kind of a statistics geek. The annual SAS User's Group International (SUGI) conferences mostly featured statisticians and other researchers focused on quantitative work. However, over the last 10 years it seems like SAS has largely abandoned academic researchers to devote most of its time to big business analytics. Their annual SUGI conference became the "SAS Global Forum" in 2007 and since then many of their keynote speakers have...
  • Abhijit Dasgupta · National Institutes of Health
    Depends on the purpose. If you are interested in doing "standard" analyses, then any software is good and will serve your purpose, given your available budget. However, many studies, and exploration of data, are not standard. Visualization and data mining are very important, as are working with high-dimensional data in this omics age. SAS has improved greatly, but the typical SAS installation (without Enterprise Miner) is a bit hamstrung for modern needs in modern medicine. If you have the money, SAS can do most things, and its graphical capabilities have finally entered the 21st century. One disadvantage I find, is that it is hard to leverage other tools that are out there, perhaps more suited for a task, from within SAS. One exception, interestingly, is R, which can be accessed either using a macro or via PROC IML (which is not a favorite proc for SAS users)

    R has a learning curve, but also a very strong ecosystem and you can customize analyses and simulations to do almost anything you would like. It is not menu-driven, though there are modules to help with that even. Today you can even create dynamic graphs using Javascript directly from R, so mining and slicing and visualizing data is quite powerful.

    My take is that SAS is the old warhorse, with, for all its cost and "enterprise level" promise, is not that much better than R, and in some respects worse than R. Given the flexibility needed in current medical research and practice (think decision support and personalized medicine) as well as the changing demands with data volume and velocity, R is probably a wiser choice going forward.

    Just one comment to another of the commenters: SAS output is parsimonious?!! It will give you every possible test under the sun. I've had new users (non-statisticians) asking which is the right one to use; there is no guidance from the manual or the software. Not giving you everything is sometimes a good thing.
  • Martin Schädler · Helmholtz-Zentrum für Umweltforschung
    Our working group often works with very large data sets and complex designs (multiple split-plots, nested and repeated with different random factors - and everything in the same analyses). We have also many very experienced R-Users which were not able to develop running models for these data sets in R. Today, there are very powerful Procs in SAS (Glimmix, HPMixed) which have been developed to analyse very complex designs with complicated data structure.
  • Pushpakanthie Wijekoon · Faculty of Science, University of Peradeniya
    According to my experience R can handle very large data sets, and can do complex analysis.
  • Jeff Jarrett · University of Rhode Island
    SAS is the standard but SPSS, Minitab and other are very good as well.
  • Charles White · Charles E. White's Biostatistical Consulting, LLC
    To me, the primary reason for picking R or SAS is whether you want to work in a bleeding edge environment (R) or a stable development environment (SAS).

    There are core (relatively stable and well documented) R packages provided by the R-Project and many more user provided packages which vary in quality up to the level of extending the influence of new research in statistics. Fundamental changes in the software occur roughly every six months and upgrading may break your old programs.

    SAS adds new scripting languages and procedures but the old stuff works much like it always did and a ten year old program may well run and provide the same results it did ten years earlier. SAS can do just about anything... as long as the SAS programmer is willing to spend the time making it do anything. If you're a pharmaceutical company taking decades to develop new drugs for submission to FDA, this kind of stability may be helpful. It may not be as helpful if you're just publishing on the cutting edge of bioinformatics.

    Secondary considerations are the size of your datasets and price.

    R can only work with datasets that can be loaded into the random access memory of your computer. On the other hand, it doesn't cost you any money.

    Some (but not all) SAS procedures will develop summary statistics one record at a time so that you can work with any dataset that you can store on a device. The IRS finds this to be helpful. For a single user of SAS, the cost ranges from thousands to tens of thousands of dollars. For institutional users of SAS, the cost per user goes way down as the number of institutional users increases.

    Ease of use is not a consideration. Both R and SAS are openly user hostile. 8-)

    Depending on the work at hand, I have used both R and SAS extensively. I prefer R.
  • Jean-daniel Zucker · Institute of Research for Development
    I have been using both SAS and R for quite a while and without any hesitation I would chose R. It is open, free and flexible and you can very easily share your development with the research community. With an environment such as Eclipse or RStudio (both free) you can do pretty productive software development. As a researcher I would go for R.
  • Elmabrok Masaoud · University of Zawia
    I have been using both R and SAS for years, you probably need to use both of them at some point in time. SAS is commercial software and has been out there for years and years and many jobs (especially, pharmaceutical companies) require that you have a decent knowledge of SAS. However, there is an advantage of using R for graphical manipulation over SAS. R is free and easy to use. SAS is very well developed and documented, tons of procedures. So depending on the work at hand, for simple work, they agree totally and there will be no difference (Go for R). For advance programing, it depends on the type of research question at hand.
  • Patrice Rasmussen · University of South Florida
    R is a trend. It has a good name and therefore seems the precise choice, however not a good choice. Let me help you. SAS actually cares about statisticians. They provide me in their Academic Research program which is global free SAS courses equivalent to up to $11,000. They also offer every professor free teaching materials and use of their SAS on demand. I have met the leaders when I delivered my SAS paper at the SAS Global forum in San Francisco. They gave me books as well as free software. They write to me to ensure I am doing well and offer me yearly extensions on my programming courses if I am too busy with Ph.D. research. Now, you tell me which company is better to use. I can guarantee you that Dr. Goodnight delivers. His entire team at SAS are personally involved and they know me and care about me. They even offered to write a success story about me when I spoke at San Francisco and I wanted to wait until later when I achieved more. They even cared about my baby Gabriel and did an interview with me and discussed how Gabriel is just 2 and sent me the entire K-12 curriculum free. I can personally guarantee you if you get involved with SAS you will never look back. They are my personal trainers. The people involved I know by name and when I met them they knew my name and all my details. You just do not get this type of attention, support and help. They are worth what they charge and if only investigated you also can become a free consumer of all their training. SAS is amazing and I love their company. What they have done for me personally, given me in training and also personal support at the conference and online. I can get in touch with Julie Petlick and she will meet all my needs with SAS and the training. If you do not get invested in SAS you are missing a major educational opportunity. Statistics is a life long study and they are they with you on your journey to being an effective statistician or researcher.
  • Sandro Sperandei · Fundação Oswaldo Cruz
    Patrice, I have to disagree... It seems that SAS is very interested and cares a lot about statisticians. But we are not choosing a retirement plan. There is no doubt that SAS is an excelent software. But R is free, always up to date and can be used by the biggest and smallest companies. So, it is powerful, updated and democratic. You can choose any field of statistical analysis and you will find a package in R, no matter how new it is. And if you don't like the way the package perform, you simply change it. So, R is flexible too!
  • J. Patrick Kelley · University of British Columbia - Vancouver
    Of the many discussions of more stable programs (SPSS, SAS, etc) and open-source programs like R, Charles White's is the best I've seen. Though I'm a hardcore R-user, he makes a great argument for adopting the historical approaches on one's own field.

    For interested users, this particular discussion has surfaced before on ResearchGate:

    Once again (and like others), I caution Patrice's approach to this question. As Charles White has addressed in a couple of ways, SAS no doubt has its place in business and a number of other disciplines that (1) demand a stable working environment, and (2) are more conservative in the speed at which they adopt new analytical tools. This is not to say that these are fields that stagnate; rather, these may be fields--like medicine--in which rapid adoption of a new analytical tools may be difficult to deploy or may be altogether detrimental to users. From my own experience, I would guess that most current scientists are willing to give new analytical tools a try, after reviewing their advantages and disadvantages. After all, most of us are constantly searching for modeling frameworks that best match our data (i.e. matching model assumptions to our data). So, once again, we're faced with rapidly-developing R and relatively stable (aka static) programs like SAS and SPSS when considering overall functionality. For this, here's a resource showing that R now has >31,000 functions compared to SAS's 1,100. And, they're being developed more rapidly for R due to its open-source structure. Sure, arguments could be made for SAS having additional functions for various outputs, etc, but that's still quite a beating that R is giving SAS. Here's the article with the amateur analysis:

    Re: "R is a trend" (Patrice Rasmussen above). From a review of Google Scholar citation,

    R does show a upward trend, whereas SAS shows a steep decline (also a trend). This can be explained in a number of ways, and I realize the downfalls of such a sampling methodology. And, of course, the absolute number is still awe-inspiring (though I only know two people in my field who actually use SAS). Still, given the lack of additional information on R versus SAS usage among various fields (and my current laziness to procure any), these data suggest that R is on the steady rise and SAS is starting to wane.

    Patrice, I also must ask again, just to prove my and others point: What did you personally pay for your copy of SAS? As a scientist who interacts and mentors several with several Latin American students with little access to such expensive resources, I think it's a requirement to consider return on initial investment (especially when one is using, say, taxpayer's dollars to build educational infrastructure).
  • Azubuike Chukwuka · University of Ibadan
    ..the cost of acquiring a SAS license is the only obvious issue limiting the wide spread use of SAS. Outside this issue of cost, SAS is undeniably one of the best options around.
  • J. Patrick Kelley · University of British Columbia - Vancouver
    And, to belabor the discussion, a nicely summarized comparison of SAS and R:

    To this, I add: R has more concise code (aka fewer lines), which is better for those of us wanting to distribute reproducible code. (I limit myself to base R to avoid some of confusion of function names, etc., but the code is often still smaller than SAS--in terms of numbers of lines).

    I've already recognized SAS as a powerful player in many fields. My only criticism is of the hard-liners who are intent are naming it the only/best option in town.
  • Charles White · Charles E. White's Biostatistical Consulting, LLC
    J. Patrick Kelley,

    Thank you for the kind words you used to describe my earlier post and your analyses. The link you have provided is interesting and relevant but obvious marketing of software services implemented in R. So, I'd like to make a few comments with regard to what the link says about documentation, what the link says about cost, and expand a little on my agreement with your discussion of "the best option in town." I tend to write strongly but I recognize the following are only my opinions.

    Documentation. Free SAS documentation appears to be of significantly higher quality than free R documentation. Both sets of free documentation are available on the web for your own evaluation. However, SAS and R both have high quality statistical authors who sell books related to the software.

    Cost: To me, the single biggest cost to using any software is training the user. Software, classes, and books are obvious but seriously look at the user’s hourly wage, factor in overhead costs, and factor in the amount of time spent maintaining the software. Time spent actually using the software is time spent learning how to use it better. If you’re going to encourage someone to change software, please recognize that you’re asking them to give up valuable training and the new software needs to have a reasonable potential to provide that user/organization with more value.

    Best Option in Town: If you start with no history of software that does what you need done, I recommend making a list of what it is you want done with new software, finding every software package that does what you want done, and asking around about the cost, available service, user community, and available training. Of course, most people reading this message will have a history of software that does what they need done.

    As I have said before, I use SAS or R depending on job requirements but I prefer R.

  • Azubuike Chukwuka · University of Ibadan
    ..a very important point highlighted by Chuck."training the user is the biggest cost'! I agree!
  • J. Patrick Kelley · University of British Columbia - Vancouver
    @ Chuck. Very good and informative message. I enjoyed reading that. I kept expecting to read some very strong verbiage (as you mentioned), but I saw nothing!

    I agree with all of your points. I do think you're right on about the cost of training the user. The original poster's question--like many other questions---here on ResearchGate are inquiring about program recommendations as if the user has no previous experience with a program in a particular field. The cost of training certainly is relevant to those already in a field that may already have a history with SAS or another program. I have no argument with that. But, for a new user, the cost of procurement trumps the cost of training.
  • Murali Dhar · Manipal University
    Depends on what is the objective of learning additional package. If it is for using for your data analysis, you may for R because it is available free. If you want to enrich your CV for better job prospects especially in corporate sector, go for SAS because a good proportion of corporate sector possesses and uses SAS and therefore require SAS personnel. According your investment on learning the package will less for R than the same for SAS
  • J. Patrick Kelley · University of British Columbia - Vancouver
    @Murali. You raise a good point. Also consider that there are many companies that use R, including Merck, Google, and Facebook. In those cases, any programming knowledge will likely help your resume.
  • Sam Alessi · Almanac Systems
    also, SAS can run R code and we are working on ways to generalize using Phoenix Integration's ModelCenter software
  • Shakila Zaman · Lahore Medical and Dental College, Univesity of Health Sciences, Lahore, Pakistan
    Try SAS and you will never be disappointed!!!
  • Udari Wanigasekara · University of Manitoba
    I think R is good and it is free.
  • Milan Seth · University of Michigan
    I've worked with both R and SAS and generally find R easier. That said, when I was working in industry and had access to dedicated SAS customer support services, I found the quality of SAS people and their commitment to customer support just phenomonal - several times I had gone to them with modeling questions and it was akin to hiring an experienced consultant. I can really appreciate the value that SAS as an entity adds to the field, but at the same time it is almost always easier for me to explore problems and formulate/code up solutions in R.
    As for data management, I've found now that R connectivity to Oracle, MySQL, Postgres databases is really quite comparable to the SAS ACCESS add on, using ROracle and related packages, though this was not always the case.
    Memory limitations can be an issue though.
    Anyway I'm probably being too long winded here - my vote is for R.
  • Nagarajan Govindhan · Sri Ramachandra University
    In simple r is not tailor made but where as sas is tailor made , working with r takes time but gives lots idea for your area of studies .sas frame work pattern and you can able to think out of the box -researcher should always go for r
  • Juan Steibel · Michigan State University
    I could not imagine working without one of those two. Both are good and any applied biostatistician should have access to both. That is my experience and opinion.
  • Dennis Clason · New Mexico State University
    If you know SPSS (and I mean really know SPSS, as in you write your own "syntax"), there is no reason to learn SAS. Anything SAS can do, SPSS can do. In fact, over the years, SPSS has adopted a lot of SAS jargon (Type I and III SS and LSMEANS are just two examples).

    If you're going to learn a new package to add skills and analyses not easily available to you in SPSS, you should learn R. R is an order or magnitude (or two) faster than SAS PROC IML in my benchmarking. R is a much better platform for bootstrapping and Markov-Chain Monte Carlo methods. R's graphics are still much superior to SAS, especially for publication purposes.

    I know (as in, I can and do program in) SAS, SPSS and R. SAS simply doesn't bring enough new tools to the table to be viable (vis a vis SPSS). R, by contrast, does bring a fair amount of new techniques (not least being a good implementation of Lee Wilkinson's Grammar of Graphics ideas) to the party.

    Unless you have strong reasons for learning SAS, pick something that gives you options you didn't have before. When should you choose SAS? Well, suppose you've taken a job in the Pharm industry and part of your job is doing analyses for regulatory submission. R isn't (last I heard) on the regulator's approved list. Given its rate of change, I don't expect to ever be there. In that case, you need SAS, because SAS is what everyone uses.
  • Charles White · Charles E. White's Biostatistical Consulting, LLC
    Dennis Cason, there isn't any regulator approved list of analysis programs for submitting to FDA. There are standards for software validation and change management, which are applicable to software from any source. Some FDA regulators are also R users. The advantage of SAS over R for FDA regulated studies is that SAS makes a point of being stable over decades while R (my software of choice) will gleefully break old code if members of the R-Project core group believe it will improve the software over the long term. Regulated projects can run from 5 to 20 years. R releases updates roughly once every six months. For more (cryptic and convoluted discussion) on software validation for FDA Regulated studies see: General Principles of Software Validation; Final Guidance for Industry and FDA Staff
  • Dennis Clason · New Mexico State University
    Point taken, Charles. Wasn't there an effort to bring R (or perhaps some fork from R) into compliance with the FDA Guidance? If that effort has borne any fruit, I'm unaware of it.

    But I believe my point remains valid: the rate of change (and the change policies) of the R code base are such that R in its present form isn't a viable tool for regulated studies.

    Anyone who doubts that should look at the help wanted advertisements in AmStat News. Regulated shops seek SAS proficient programmers.
  • Charles White · Charles E. White's Biostatistical Consulting, LLC
    Regulated shops absolutely seek SAS proficient programmers.. Guidance for using R in FDA regulated environments can be found at:

    I'm a professional and I will use whatever tools (software) my clients will make profitable for me to use. The general purpose statistical software I have experience using includes SAS, R, and Minitab. OK..., I've used SPSS but that was almost 30 years ago.... ;-)
  • Kaustav Aditya · Indian Agricultural Statistics Research Institute
    Use any of them both are alike
  • Issam Ashqer · An-Najah National University
    I really recommend SAS to be used in Medical field.
  • Richard Gill · Leiden University
    R is for professional and creative statisticians. People who understand what they are doing and need to create novel analyses because they face new problems or are unsatisfied with standard solutions. SAS is for people who need to press the buttons in order to perform standard analyses, without knowing what they are doing.
  • Jeff Jarrett · University of Rhode Island
    I quote " SAS is for people who need to press the buttons in order to perform standard analyses, without knowing what they are doing." This statement is all wrong.
    Richard will you ever learn?
  • Emiliano Valdez · Michigan State University
    While I started many years ago as a SAS user, I have been more comfortable with R recently. Two factors are driving this. First unlike before data size is no longer much of a constraint in R. Second R graphics is far superior and more flexible than SAS. Somebody in SAS has to be reminded how important graphs are for statistical analyses. They sure can do a better job.
  • Richard Gill · Leiden University
    I exaggerate. Over the years, SAS has grown to allow other paradigms of statistical analysis from those embodied in its original design. Similarly, R has evolved and moreover could be used as the engine inside a SAS-like system. However I beiieve that the original design philosophies still dominate the character of the present organisms

    Moreover, R remains an open system which grows as new needs arise while SAS is commercial, closed.
  • Dennis Clason · New Mexico State University
    One for two isn't bad. SAS is a closed commercial system. But it is not entirely closed: DATA paragraphs are entirely procedural in nature, and the language is syntactically complete. More to the point, IML is a fully functional matrix language. The system is user extensible.

    The original design concept of SAS was that each procedure would do one thing, and do it very well. A SAS program would be like a strong of beads, linked together by Data sets. Early versions of GLM had no facilities for plotting (that was the purpose of PLOT), nor for multiple comparisons (which was the purpose of another procedure whose name I have forgotten now.) SAS has evolved over the decades, but most of that evolution has been directed by the users. Users didn't like having to change procedures to get a new component in the analysis. They asked for and received expansions to GLM (and REG and other core statistical procedures). Now, MIXED is a one-stop shop or nearly so, for linear statistical analyses.
  • David Abbott · U.S. Department of Veterans Affairs
    Deepak mentions 3 criteria: best, easiest to understand, most highly used. As Dennis said, it is easy to confirm that SAS is required more often than R in medical field job postings, so I give it "most highly used" in this domain. Easiest to understand? Well, that all depends on prior experience, cognitive style, and task focus. Best? For the comp sci oriented, I think it is pretty clear that the R language is a more regular and less quirky language than SAS. And it certainly is more modern overall. But does that make it better in the way Deepak intends, again this is hard to say. You can pretty much do whatever statistics you want with either one.
  • Charles White · Charles E. White's Biostatistical Consulting, LLC
    SAS will be the most highly used statistical software in FDA regulated clinical trials for drugs and devices. If your area of research is pre-clinical, epidemiology, or something else, SAS won't have as much market penetration.
  • William Fisher · University of Massachusetts Lowell
    R is wonderful mainly because it's free. I have both R and Stata (and SPSS, which still can do a lot of things well, although it seems to get no respect these days). I found that the learning curve for Stata was less steep than for SAS and certainly "flatter" than for R. I don't do really exotic things, but I have yet to find anything Stata didn't do. It's a little pricey, but I think in the long run a good deal. There is also a community of Stata users who share their programs. That said, some versions of Stata cannot handle really large data sets (i.e., > ~13,000 records). SAS rules for big data sets.
  • Charles White · Charles E. White's Biostatistical Consulting, LLC
    Hello William Fisher,

    I have two minor quibbles.

    1. Large dataset issues between R and SAS are now more related to hardware than software. SOME SAS procedures process one record at a time so that the size of the dataset is limited by the size of your hard drives. R can use all of your random access memory (RAM) to store datasets. That used to be a big limitation. You know all those big NSA datasets with the privacy issues? They're in RAM.

    2. I certainly enjoy the fact that R doesn't cost anything but I also find R wonderful for being a much more logically consistent programming language than SAS. Your mileage may vary. 8-)

  • William Fisher · University of Massachusetts Lowell
    Hi Chuck

    You're right. I still associete SAS with being the "big data set" package, which for years it was. Actually, as I may have mentioned, I have downloaded R and plan to spend part of the summer famliarizing myself with it. I do a lot of survival analysis stuffand I'm told that R's capabilities in that area are quite good. So far, though, I'm really liking Stata a lot.
  • A. Saeidifar · Islamic Azad University, Arak, Iran
    I think that any software
    (SAS or SPSS or S-Plus or Minitab or Stata or R and etc.)
    have an especial properties that other software does not have. Therefore this is depend to your research that which software can be comfortable for your computations.
  • Sartaj Alam · Texas Children's Hospital
    These software are overlapping more than 95% and minuscule part is what is mutually exclusive. No matter what you want to accomplish, you will find all the software equally helpful with some exceptions in terms of your exposure, graphic user interface (GUI), echo system or pure financial constraints. I had been using SAS for last ten years, transitioned to SPSS and then now Stata as well as MatLab and R. Productivity is positively correlated with exposure; overall, Stata being the most efficient of all these packages.
  • Hans Sieburg · Sanford-Burnham Medical Research Institute
    From long experience in the medical application field, I can only recommend using R. I have used, taught and fretted over all the other main tools over the years and found R to be "most fitting" to solve problems in the medical field quickly, cleanly and understandably. Moreover, as medicine (and bio-medicine) are changing towards increasing use of molecular biology, R and R's BioConductor packages do what none of the other platforms can. Namely, to seamlessly combine analyses ranging from the molecular to the physiological scale.
  • Oliver Fisher · St. Vincent's Centre for Applied Medical Research
    If you're going to learn a "new language" anyways, go for R. With all of the packages that are online and all the free resources, forums, help pages etc. I think it's hard for any program to come anywhere close to it.
    The learning curve is really tough, especially if you have no computer programming language training (like me), but once you master it, the logic behind the program starts to unfold and you really, really appreciate it. Trust me, there'll probably be a package for EVERYTHING you want to do.
    When it comes down to graphing however, you'd sometimes wish to have a bit more of GUI, but when you learn how to use the different packages properly, it also just goes beyond the other programs capabilities.
    I also really, really like hans Sieburg's comment - BioConductor is truely a package oriented towards the future of a lot of research, which deals with genomic and meta-genomic data.
    Finally, R's for free, which to me (once you discover only some of its capabilities) puts in the same league as google's search engine in my books.
    Take the time and learn R. You'll never regret it and won't have to worry about any bills.
  • William Fisher · University of Massachusetts Lowell
    I have to agree with my long lost cousin Oliver :) Since my last posting I've begun dabbling in R and find it a less onerous task than I had imagined. I do plan to get up to speed this summer.

    One point: Each package has its own strengths .For example, I still maintain that tasks such as recoding etc. are ones in which SPSS still excells.And for better or worse, SPSS remains the package to which most undregrad social science majors are exposed, and it actually does most basic statistical tasks well. In more advanced areas it falls short, however (for example, no zero-inflaed models for count data regression.) If one plans on teaching undergrad social science statitistics, SPSS will porbablystill have the largest institutional presence. But we are having our doctoral students download R, though, and they like it. I guess my point is that a familiarity with ALL of the major packages would be a plus for anyone who plans to be a "quant."
  • Rafael Roman · University of Zulia
    There are several reasons to believe that the SAS is the best package for data analysis :
    1. SAS has a friendly, practical interface.
    2. Allows you to manipulate the data, working in the DATA STEPS.
    3. Most possible statistical analysis required by a researcher are included in SAS.
    4. SAS is supported by the best researchers in statistics.
    5. There are books on each SAS procedures, supported by recognized statisticians.
    6. SAS manuals are available online covering all aspects of DATA and PROC STEPS.
    7. SAS is always being updated and improved.

    R is a very good application and with R manuals and with good books in statistics, you can do any analysis that you can imagine in SAS, but you would have to work a lot more to get to the solution of a problem even with the possibibility of an error. The issue is time and this time the researchers would like to use for their areas of interest, leaving the SAS Institute, the task of improving the statistical software.
  • Miguel Pereira · Massachusetts General Hospital
    I do not have great experience with SAS but I can tell you that both SAS and R are great and will do pretty much anything you want/need.
    R has the great advantage of being free but it has the downside of working almost solely via command-lines (R studio changed that a bit, though).
    SAS is paid and has nice drop-down menus that can be useful when you don't know the commands.

    Given that R is free you can just download it and try it. If it works for you just keep using it!
  • Charles White · Charles E. White's Biostatistical Consulting, LLC

    RStudio looks like a nice programming environment. I've been using JGR and I'll have to evaluate whether to move. With regard to having a really useful point and click interface, I recommend RCmdr (R Commander). You have to deal with the command line long enough to install the RCmdr package but when it's running it's a reasonably self contained point and click environment.

  • Veeramanikandan R · Christian Medical College Vellore
    This thread is really interesting to read. From my part I would like to vote for R, as I used it for more than a year. I am really comfortable and happy with it. I have used SPSS too, but I really love the way things happen in R particularly for the graphics part. Though it takes a bit effort to understand R, once you are in you will love it. I don't have any idea about SAS and STATA, hence i cannot make a statement regarding those.

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