How did you experience the transition from SPSS to R? [closed]

Question

The discussion in this question is the direct cause for me asking this question. The more general reason is the fact that I often have to explain R use to people that are only familiar with SPSS. I know most of the basics of SPSS, as we still use it in the base course statistics. But as I'm more of an R guy, it's difficult to know how SPSS users experience the first meeting with R.

I know there is the book R for SAS and SPSS users and that contains already some information. Yet, I would like to know what the more difficult parts are when you switch from SPSS to R.

Or in other words : if you would have to explain R in one day to SPSS users, which topics would you focus on? This is not a hypothetical question by the way (yeah, I know, it's not because one get paid for it that it always makes sense...).

Answer 1

Firstly, data manipulation has been the most challenging thing to learn coming from SPSS/SAS to R. I've found, personally, that getting the data in the right shape for an analysis is usually much more difficult than the analysis itself. Secondly, a true understanding of how to deal with categorical values through the use of factors. Lastly, summary statistics and descriptives can sometimes be challenging to get in a format that is transmutable to PPT or Excel which are what (my) clients generally expect/demand for reporting.

I would focus on:

1 Data manipulation

Understanding data structures. Import/Export. Then in-depth training on the use of packages like plyer, reshape with a particular focus on how to effectively use cast with formulas and melt with ids. How to apply numerical functions within a data.frame using ddply.

2 Factoring Data

In general, an explanation of dealing with recoding with, epicalc or a user-defined function. Also an explanation of the significance of factors, levels, and labels

3 Descriptives

Take a few minutes to introduce xtabs(), table(), prop.table() using cast() from reshape to create columnar tables of data that are more reasonably exported to Excel.

Graphics are optional, if you've done a good job of the above they should be able to get the data they need to create graphs in whatever software they are most comfortable with.

4 Graphics

If you've done a good job teaching the data manipulation, getting data into the shape needed for graphing should be pretty straightforward (or at least reproducible) at this point. ggplot2 is complicated and requires a day just by itself to be played with. But it is possible to give a quick overview of it. Alternatively, base graphics are simple to understand and the help is much more clear on what things do and how the syntax works.

Note: I left out statistical analysis. However, an overview of lm() and perhaps anova(), or cor() would be helpful as a start point. But this should be explained at the same time as data.manipulation.

Answer 2

Although I "wrote the book" on R to SPSS migration, that was aimed at programmers and most SPSS users that I know prefer to "point-and-click" instead. A graphical user interface like Deducer (or R Commander) can help them feel at home while teaching them how R programming code works if they want to see it. Deducer's Plot Builder also does a nice job letting you create complex plots easily, and if you want to learn to ggplot2 code, it will show you that as well. Ian did a great job with it!

However, while the SPSS graphical user interface covers 98% of what SPSS can do, Deducer covers perhaps 1% of what R can do. That's probably still 75% of what your average researcher needs, but R is so broad that to get the most out of it people will need to learn to program. The free version of my book, "R for SAS and SPSS Users" is only 80 pages & covers the areas of programming that I think are most likely to confuse beginners. It's at http://r4stats.com.

Answer 3

Just recently I've had a student who was somewhat versed in statistics and did some analysis beforehand in SPSS. I then showed him how to do the exact same thing in R. We went through the code and plotting, explaining and debating each line. He realized how easy and convenient it is to do it in R. Thus, R community grew by 1. :)

Answer 4

The biggest issue that the researchers I've dealt with have is the lack of point-and-click GUI. While there are a number of efforts out there in the R community, none of them have reached the ease-of-use/power level that SPSS has.

Since coding is second nature to R users, sometimes we forget that the majority of users of statistical software can't program (and would avoid it like the plague), even though they may have a strong practical understanding of statistics.

If I had one day to bring an SPSS user into R, I'd start them on Deducer. Deducer is an R GUI project (Self promotion note: I'm the author) that should feel very familiar to a user coming from SPSS. As they find themselves needing more advanced functions, they will naturally move to the command line to fulfill their needs.