The SAS vs. R Debate
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The SAS vs. R Debate
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Re: The SAS vs. R Debate
#2Is this even a discussion? Anyone serious about analyzing data will use either R, Python (with Pandas/SciPy, etc), or Julia. For truly immense data sets that require pipelines, you'll use tools like spark, hadoop, etc - but SAS is basically a slightly improved excel.
Re: The SAS vs. R Debate
#3Is this even a discussion? Anyone serious about analyzing data will use either R, Python (with Pandas/SciPy, etc), or Julia. For truly immense data sets that require pipelines, you'll use tools like spark, hadoop, etc - but SAS is basically a slightly improved excel.
Being a slightly improved Excel has significant advantages as well:
It's more accessible to people who are not coders but need to do statistics on real data, i.e. almost all researchers.
Although personally, I would use R/Python (the UI is more suited to me, and it's free, and I trust the results a little more, and I can read the code if I need to)... do you actually need anything more than SAS/SPSS provides? I doubt it.
Re: The SAS vs. R Debate
#4Is this even a discussion? Anyone serious about analyzing data will use either R, Python (with Pandas/SciPy, etc), or Julia. For truly immense data sets that require pipelines, you'll use tools like spark, hadoop, etc - but SAS is basically a slightly improved excel.
> SAS is basically a slightly improved excel.
SAS is a pretty big system, and I have worked with SAS for about 10 years, and I can't see any similarity with Excel at all. Which part of the SAS system do you think resembles Excel ? Here is a list of their products (http://support.sas.com/documentation/productaz/index.html)
Btw. I do think I am doing serious data analysis in a bank :-)
Re: The SAS vs. R Debate
#5Is this even a discussion? Anyone serious about analyzing data will use either R, Python (with Pandas/SciPy, etc), or Julia. For truly immense data sets that require pipelines, you'll use tools like spark, hadoop, etc - but SAS is basically a slightly improved excel.
I'm an R person, but I used SAS in a past life, and I have to say, this is very wrong. SAS is nothing at all like an improved Excel in look, operation, or user-base. It can do much of what R can do (build forecast models, ML models, even OR models), but it just looks very different.
And the licensing model will make you pull your hair out. I ran into issues both with geography (not being allowed to use my license on a project in another country) and functionality (hey, that's a cool PROC...wait, I don't have the license to call it)
I think R and python will win the day, but it's not because SAS is anything like Excel. And there are a shit ton of "serious" data analysis people using SAS. They're just all in the enterprise. Every Fortune 500 company I've worked with used SAS except for one (who used R).
Re: The SAS vs. R Debate
#6Is this even a discussion? Anyone serious about analyzing data will use either R, Python (with Pandas/SciPy, etc), or Julia. For truly immense data sets that require pipelines, you'll use tools like spark, hadoop, etc - but SAS is basically a slightly improved excel.
Being a slightly improved Excel has significant advantages as well: It's more accessible to people who are not coders but need to do statistics on real data, i.e. almost all researchers. Although personally, I would use R/Python (the UI is more suited to me, and it's free, and I trust the results a little more, and I can read the code if I need to)... do you actually need anything more than SAS/SPSS provides? I doubt…
The way in which SAS is like Excel is that everything is a table.
SAS is not very accessible, especially to non-coders.
Re: The SAS vs. R Debate
#7Is this even a discussion? Anyone serious about analyzing data will use either R, Python (with Pandas/SciPy, etc), or Julia. For truly immense data sets that require pipelines, you'll use tools like spark, hadoop, etc - but SAS is basically a slightly improved excel.
> SAS is basically a slightly improved excel. SAS is a pretty big system, and I have worked with SAS for about 10 years, and I can't see any similarity with Excel at all. Which part of the SAS system do you think resembles Excel ? Here is a list of their products ( http://support.sas.com/documentation/productaz/index.html ) Btw. I do think I am doing serious data analysis in a bank :-)
The only similarity I can think of is that everything is a table :).
Re: The SAS vs. R Debate
#8I use of both Python (pandas) and base SAS at work for UK government.
I have lots of experience in SAS, and enjoy using it. The macro language allows for very succinct solutions to difficult data manipulation problems.
However, given SAS's huge expense it's difficult for me to identify any 'killer' areas where it's significantly better than open source tools. Indeed, I find pandas faster and easier to use for many problems.
I find it hugely frustrating that the government pays so much money for SAS licences and training when most people use it for simple use cases, where they would be better picking up transferable skills (e.g. Python, SQL, R).
My understanding is that that SAS supposed to be good at processing very large datasets because it uses RAM efficiently (only the PDV is stored in RAM). But in reality, a small minority of users are processing datasets that are too big for RAM (e.g. 16gb+) and there are probably better tools for the job in this use case.
One user here comments that SAS is like an 'improved Excel'. In fact, I find pandas much closer to Excel than SAS because (in ipython notebook at least), you get nice visual representations of your tables, and it usually isn't difficult to translate an Excel operation into a pandas one. I especially like the multi-index and pivot table based capabilities. With a background in VBA for Excel, it's also relatively easy to pick up Python.
None of this is quite so obvious in SAS, which has quite an unusual data step and macro programming language. It's very powerful, but is quite unintuitive to begin with due to a complete reliance on the program data vector.
Re: The SAS vs. R Debate
#9The article says this particular instance of the debate started in 2011. Things have shifted a little since then, and I think Python has won more mindshare with Pandas, SciPy, NumPy, and all the rest. I've used both Python and R, and think the next debate will be between those two, as people find that R is not a very good programming language and lacks decent libraries for things like web scraping.
Python can be a single tool that integrates with every part of your workflow. R right now still wins in the number of algorithms implemented in it (there are statistical methods not available in R but not Python), and R has more terse syntax which some people like for interactive use. But for really Big Data, terse syntax and an endless variety of esoteric algorithms are not as important as, say, robust error handling and debugging (a weak area in R, but a strong one in Python).
Re: The SAS vs. R Debate
#10Earlier quoted context omitted.
Being a slightly improved Excel has significant advantages as well: It's more accessible to people who are not coders but need to do statistics on real data, i.e. almost all researchers. Although personally, I would use R/Python (the UI is more suited to me, and it's free, and I trust the results a little more, and I can read the code if I need to)... do you actually need anything more than SAS/SPSS provides? I doubt…
The way in which SAS is like Excel is that everything is a table. SAS is not very accessible, especially to non-coders.
Apologies, I haven't used SAS in so long that I had it confused slightly with SPSS.