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Nick Cox
Nick Cox
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R and SAS have each theretheir pros and cons. I think more statisticians need to embrace the fact that lots of great statistical software is available, rather than endlessly bicker about which is superior.

R is free. SAS is very expensive. R gives you the ability to do just about anything. SAS may or may not. R and has amazing graphical abilities. Seeing SAS graphics makes it feel like 1985 all over again. SAS has great customer support. R support = hours of searching mailing list archives. Also with a name like "R", search engine results are often poor. R is extremely slow and does not deal well with large data sets. SAS does fine with large data sets. SAS tends to be more robust. In my experience, when it comes to mixed effects modeling or anything involving design of experiments (such as analyzing crossover designs), SAS is superior.

For large scale, brute force simulations, I use Fortran. I used to use C, but have found Fortran is much easier to use. I've never used MATLAB. If I need statistical power of R but the speed of Fortran, I will write the time-intensive operations (i.e. loops) in Fortran and call the subroutine from R.

R and SAS have each there pros and cons. I think more statisticians need to embrace the fact that lots of great statistical software is available, rather than endlessly bicker about which is superior.

R is free. SAS is very expensive. R gives you the ability to do just about anything. SAS may or may not. R and has amazing graphical abilities. Seeing SAS graphics makes it feel like 1985 all over again. SAS has great customer support. R support = hours of searching mailing list archives. Also with a name like "R", search engine results are often poor. R is extremely slow and does not deal well with large data sets. SAS does fine with large data sets. SAS tends to be more robust. In my experience, when it comes to mixed effects modeling or anything involving design of experiments (such as analyzing crossover designs), SAS is superior.

For large scale, brute force simulations, I use Fortran. I used to use C, but have found Fortran is much easier to use. I've never used MATLAB. If I need statistical power of R but the speed of Fortran, I will write the time-intensive operations (i.e. loops) in Fortran and call the subroutine from R.

R and SAS have each their pros and cons. I think more statisticians need to embrace the fact that lots of great statistical software is available, rather than endlessly bicker about which is superior.

R is free. SAS is very expensive. R gives you the ability to do just about anything. SAS may or may not. R has amazing graphical abilities. Seeing SAS graphics makes it feel like 1985 all over again. SAS has great customer support. R support = hours of searching mailing list archives. Also with a name like "R", search engine results are often poor. R is extremely slow and does not deal well with large data sets. SAS does fine with large data sets. SAS tends to be more robust. In my experience, when it comes to mixed effects modeling or anything involving design of experiments (such as analyzing crossover designs), SAS is superior.

For large scale, brute force simulations, I use Fortran. I used to use C, but have found Fortran is much easier to use. I've never used MATLAB. If I need statistical power of R but the speed of Fortran, I will write the time-intensive operations (i.e. loops) in Fortran and call the subroutine from R.

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MichaelSnot
MichaelSnot

R and SAS have each there pros and cons. I think more statisticians need to embrace the fact that lots of great statistical software is available, rather than endlessly bicker about which is superior.

R is free. SAS is very expensive. R gives you the ability to do just about anything. SAS may or may not. R and has amazing graphical abilities. Seeing SAS graphics makes it feel like 1985 all over again. SAS has great customer support. R support = hours of searching mailing list archives. Also with a name like "R", search engine results are often poor. R is extremely slow and does not deal well with large data sets. SAS does fine with large data sets. SAS tends to be more robust. In my experience, when it comes to mixed effects modeling or anything involving design of experiments (such as analyzing crossover designs), SAS is superior.

For large scale, brute force simulations, I use Fortran. I used to use C, but have found Fortran is much easier to use. I've never used MATLAB. If I need statistical power of R but the speed of Fortran, I will write the time-intensive operations (i.e. loops) in Fortran and call the subroutine from R.

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