R and SPSS difference

Question

I will be analysing vast amount of network traffic related data shortly, and will pre-process the data in order to analyse it. I have found that R and SPSS are among the most po

Answer 1

SPSS provides a GUI to easily integrate existing R programs or develop new ones. For more info, see the SPSS Community on IBM Developer Works.

Answer 2

There are some great responses above, but I will try to provide my 2 cents. My department completely relies on SPSS for our work, but in recent months, I have been making a conscious effort to learn R; in part, for some of the reasons itemized above (speed, vast data structures, available packages, etc.)

That said, here are a few things I have picked up along the way:

1. Unless you have some experience programming, I think creating summary tables in CTABLES destroys any available option in R. To date, I am unaware package that can replicate what can be created using Custom Tables.

2. SPSS does appear to be slower when scripting, and yes, SPSS syntax is terrible. That said, I have found that scipts in SPSS can always be improved but using the EXECUTE command sparingly.

3. SPSS and R can interface with each other, although it appears that it's one way (only when using R inside of SPSS, not the other way around). That said, I have found this to be of little use other than if I want to use ggplot2 or for some other advanced data management techniques. (I despise SPSS macros).

4. I have long felt that "reporting" work created in SPSS is far inferior to other solutions. As mentioned above, if you can leverage LaTex and Sweave, you will be very happy with your efficient workflows.

5. I have been able to do some advanced analysis by leveraging OMS in SPSS. Almost everything can be routed to a new dataset, but I have found that most SPSS users don't use this functionality. Also, when looking at examples in R, it just feels "easier" than using OMS.

In short, I find myself using SPSS when I can't figure it out quickly in R, but I sincerely have every intention of getting away from SPSS and using R entirely at some point in the near future.

Answer 3

The truth is: both packages are useful if you do data analysis professionally. Sure, R / RStudio has more statistical methods implemented than SPSS. But SPSS is much easier to use and gives more information per each button click. And, therefore, it is faster to exploit whenever a particular analysis is implemented in both R and SPSS.

In the modern age, neither CPU nor memory is the most valuable resource. Researcher's time is the most valuable resource. Also, tables in SPSS are more visually pleasing, in my opinion.

In summary, R and SPSS complement each other well.

Answer 4

Check out this video why is good to combine SPSS and R...

Link

http://bluemixanalytics.wordpress.com/2014/08/29/7-good-reasons-to-combine-ibm-spss-analytics-and-r/

If you have a compatible copy of R installed, you can connect to it from IBM SPSS Modeler and carry out model building and model scoring using custom R algorithms that can be deployed in IBM SPSS Modeler. You must also have a copy of IBM SPSS Modeler - Essentials for R installed. IBM SPSS Modeler - Essentials for R provides you with tools you need to start developing custom R applications for use with IBM SPSS Modeler.

Answer 5

I work at a company that uses SPSS for the majority of our data analysis, and for a variety of reasons - I have started trying to use R for more and more of my own analysis. Some of the biggest differences I have run into include:

1. Output of tables - SPSS has basic tables, general tables, custom tables, etc that are all output to that nifty data viewer or whatever they call it. These can relatively easily be transported to Word Documents or Excel sheets for further analysis / presentation. The equivalent function in R involves learning LaTex or using a odfWeave or Lyx or something of that nature.
2. Labeling of data --> SPSS does a pretty good job with the variable labels and value labels. I haven't found a robust solution for R to accomplish this same task.
3. You mention that you are going to be scripting most of your work, and personally I find SPSS's scripting syntax absolutely horrendous, to the point that I've stopped working with SPSS whenever possible. R syntax seems much more logical and follows programming standards more closely AND there is a very active community to rely on should you run into trouble (SO for instance). I haven't found a good SPSS community to ask questions of when I run into problems.

Others have pointed out some of the big differences in terms of cost and functionality of the programs. If you have to collaborate with others, their comfort level with SPSS or R should play a factor as you don't want to be the only one in your group that can work on or edit a script that you wrote in the future.

If you are going to be learning R, this post on the stats exchange website has a bunch of great resources for learning R: https://stats.stackexchange.com/questions/138/resources-for-learning-r

Answer 6

I have not data for it, but from my experience I can tell you one thing:

SPSS is a lot slower than R. (And with a lot, I really mean a lot)

The magnitude of the difference is probably as big as the one between C++ and R.

For example, I never have to wait longer than a couple of seconds in R. Using SPSS and similar data, I had calculations that took longer than 10 minutes.

As an unrelated side note: In my eyes, in the recent discussion on the speed of R, this point was somehow overlooked (i.e., the comparison with SPSS). Furthermore, I am astonished how this discussion popped up for a while and silently disappeared again.