Hi, I'm a very fresh newbie to R. My first main question is, what the limitations of R are, what methods can R NOT do, esp. compared to (a) SPSS and (b) SAS? The second question is, how do you handle the data entry, data management and data manipulation in R, to me it seems to be really complicated and confusing?! Are there a kind of "helping tools"? The third question: are there differences in linux and windows versions of R? At the monemt I'm running R on a WinXP System. Is this ok or would a Linux solutuon be the better way (for using R)? I hope my questions are not to lame ... Cheers, Frank --
SPSS and SAS are data analysis packages with some scripting
capabilities. The S language is an object oriented programming language
for statistics. If you want to analyze data using traditional
techniques, use SPSS or SAS or Statistica or Excel or you-name-it. If
you need to invent new statistical techniques tailored to some
particular application, then you need S, and its most popular current
dialect seems to me to be R. There are doubtless things that SPSS, SAS,
etc., can do that cannot be accomplished in R with 2 or 10 fairly
obvious commands. However, I believe that if R had been available 35
years ago, SAS, SPSS, etc., would have been written in R (or in some
other dialect of S not subject to the GNU license, which attorneys
consider controversial and dangerous). Beyond these generalities, I
believe you can find answers to many of your questions with "the posting
guide! http://www.R-project.org/posting-guide.html".
hope this helps. spencer graves
F.Kalder wrote:
>Hi,
>
>I'm a very fresh newbie to R.
>
>My first main question is, what the limitations of R are, what methods can R
>NOT do, esp. compared to (a) SPSS and (b) SAS?
>
>The second question is, how do you handle the data entry, data management
>and data manipulation in R, to me it seems to be really complicated and
>confusing?! Are there a kind of "helping tools"?
>
>The third question: are there differences in linux and windows versions of
>R? At the monemt I'm running R on a WinXP System. Is this ok or would a
>Linux solutuon be the better way (for using R)?
>
>I hope my questions are not to lame ...
>
>
>Cheers, Frank
>
>--
>
>______________________________________________
>R-help at stat.math.ethz.ch 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
>
>
On Fri, 18 Jun 2004, F.Kalder wrote:> I'm a very fresh newbie to R.First piece of advice: read the posting guide before posting, and in particular use a meaningful subject line.> My first main question is, what the limitations of R are, what methods can R > NOT do, esp. compared to (a) SPSS and (b) SAS?R is a full-featured programming language, with no such limitations.> The second question is, how do you handle the data entry, data management > and data manipulation in R, to me it seems to be really complicated and > confusing?! Are there a kind of "helping tools"?There is documentation. For example, chapter 2 of MASS (see the FAQ or the posting guide) is devoted to this, and R ships with a `Data Import/Manual'. We don't know your background or skill level, but the FAQ points you to lots of documentation.> The third question: are there differences in linux and windows versions of > R? At the monemt I'm running R on a WinXP System. Is this ok or would a > Linux solutuon be the better way (for using R)?That's in the README of the Windows version. BTW, perhaps you should ask in a suitable forum what the limitations of Windows are relative to Linux since like any application R is limited by the OS it runs on. -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
Hi, Thank you all who anwered me. I think, I mainly thought to understand the difference between SPSS /SAS and R, but didn't really get the point (what explains the question, wich metods R can't do). Maybe, because I don't have much experience with programming (near to none). My background in stats goes also only back to indroductory classes and an advanced course in multivariate statistics. To this, I'm working with Hair, Anderson, Tatham & Blacks's "Multivariate Data Analysis" (5th Ed.) as my ressource, mainly with questionnaire analysis (Reliability Analysis and Factor Analysis, also MDS, Conjoint etc. plus sometimes standard MANOVA, Multiplke Regression etc.). So, maybe my stats aren't sophisticated enough to use R, I'm just a standard user of applied statistical methods, not an academic researcher or even a statistician. It was mainly a descision by costs, because R is free software. With the concept, I completely mistook the R concept as a programming environment more as a kind of advanced SPSS Syntax (because I also would call it "programming" when using it), which I now know, is completely wrong. So, I again thank for your help. Cheers, Frank. --
Hi Frank. I am (somewhat) new to R as well, but almost a 10 yr SAS veteran. I work for a very large US Bank and have spent a considerable part of my career in Corp Mktg leveraging data for, arguably, data mining, next purchase, attrition, balance diminishment and the like. I am now managing an Operations Research group in their Customer Service and Support (aka Telephone/Call Center Support) within the forecasting and analytics group. What I have found, broadly and personally, regarding R vs. sAS is the following: 1. You simply can't beat the price of R vs. Insightful Corp.'s S-Splus, not to mention SAS. 2. The support folks for R are among the very best, (e.g., most helpful, energetic and enthusiastic to help) 3. R is far, far leaner from what I have seen thus far for modeling, binning/discretizing, graphing, etc. vs. SAS. 4. SAS is, per a previous post, (quite debatably) superior for manipulation and handling of fantastically large datasets. I have found that R's strength is not really in merging datasets and dataset manipulation. Although, major caveat here, it greatly depends on what you need done to the data. For lagging, diffing, binning, R is superior. For match merging, at this stage, I vote for SAS. (Again, I stress I've only 6-8 mos of moderate R experience.) 5. The challenge with R is, perhaps, it's very strength--language density. Once I learn how to do something in R vs. SAS, R's code is fractionally as large as SAS. Literally, it may take 10 lines of code in SAS vs. a one liner in R. That's powerful. However, due to my SAS experience, I've banged out the SAS code and am still looking/hunting for the R equivalent. However, once doing so, it's, borrowing from a popular vegetable drink slogan, "Wow, I could have done that in R." 6. And, lastly, while R is well documented, I seem to find one of the areas of documentation somewhat lacking is a great big R "recipe" book. (Suggestions, BTW, are welcome here.) Documentation of the R language is in place with more being published, (alongside S-Plus), annually. However, there does not appear to be, for example, an "R Transition Recipes for Experienced SAS Users" book. That, ultimately, is what would help me, (I think.) Again, the issue really is simply learning and using the language. Experienced R users, I'm convinced, could do everything R I'm doing in SAS, (with money left over for a few coffees at Starbuck's). In conclusion, I still think that, given one's budget and projects, there's a place for SAS and R to co-exist. But, that paradigm diminishes as (1) the size of the datasets become smaller and, (2) your problems are more academic/researchy/specific in nature. For graphing, esp. w/the Lattice package, R is simply superior (IMHO), period, to SAS. (For some reason, SAS has just not felt the need to improve their graphics, at least the SAS/Graph part of their offering.) And, for the SAS lovers out there, this opinion is mine only as I continue to be primarily a SAS client attempting to transition to R. Frank, while I've probably been too wordy, I've attempted to provide another perspective for you. Good luck. Thanks, Charles ------------------------------ Message: 7 Date: Sat, 19 Jun 2004 18:15:19 +0200 (MEST) From: "F.Kalder" <Kalderf at gmx.de> Subject: Re: [R] Another NEWBIE To: r-help at stat.math.ethz.ch Message-ID: <6411.1087661719 at www45.gmx.net> Content-Type: text/plain; charset="us-ascii" Hi, Thank you all who anwered me. I think, I mainly thought to understand the difference between SPSS /SAS and R, but didn't really get the point (what explains the question, wich metods R can't do). Maybe, because I don't have much experience with programming (near to none). My background in stats goes also only back to indroductory classes and an advanced course in multivariate statistics. To this, I'm working with Hair, Anderson, Tatham & Blacks's "Multivariate Data Analysis" (5th Ed.) as my ressource, mainly with questionnaire analysis (Reliability Analysis and Factor Analysis, also MDS, Conjoint etc. plus sometimes standard MANOVA, Multiplke Regression etc.). So, maybe my stats aren't sophisticated enough to use R, I'm just a standard user of applied statistical methods, not an academic researcher or even a statistician. It was mainly a descision by costs, because R is free software. With the concept, I completely mistook the R concept as a programming environment more as a kind of advanced SPSS Syntax (because I also would call it "programming" when using it), which I now know, is completely wrong. So, I again thank for your help. Cheers, Frank. --
Hello, And thanks again for your answers, perspectives and more... So, as I understood, R can (nearly) do anything. So, also because it's free, it is worth a try ;-). I then next will start with reading some introductory texts. And, wow, I'm quite 'overloaded', because there is so much stuff available, I don?t know where to start and get a foot in the door. I think I take one of the advices and will begin with the "Notes on the use of R for psychology experiments and questionnaires" text. The hint to the Rcmdr package was nice :-). That was nearly like SPSS base system. When at last to know, which package to use and for what kind of problem is another thing of course ... Still I have had problems with importing SPSS files. But I will read on it too. The factor analysis tool of Rcmdr I didn?t fully understand. So, there also will be much work to do. I also noticed that my stats abilities aren?t very profound. I?m so 'drilled' in doing 'standard stuff' and using more the SPSS output than knowing exactly what I have to do, that I will have to have a much closer look into a good book on stats ... well, the Hair et al. is a good book of course, but any recommendations are welcome :-). You all wrote about the graphics in R and those in Rcmdr I saw are to me mostly the same as in SPSS. Nevertheless, I even don?t know when and for what to use that whole bunch of graphics anyway ... still looking sometimes a bit envy on huge 3D-graphics of multivariate bell curves and stuff on book covers but can?t do anything with it. The data entries by ASCII files are strange to me, because I?m so used to work with a (the SPSS) spread sheet (mostly the good old typing in from paper & pencil questionnaires), that I don?t know how to handle that yet. Maybe using a SPSS- or at least Excel-like tool would be helpful for that. So, my next move will be reading the mentioned text on questionnaires and then some basic introduction on R, knowing then, how it principally works, what?s with the packages, how to manage the data and so on ... I also think, I have to do some homework on stats either ... Thanks again ... Frank -- +++ Jetzt WLAN-Router f侟r alle DSL-Einsteiger und Wechsler +++ GMX DSL-Powertarife zudem 3 Monate gratis* http://www.gmx.net/dsl