R is a open source programming language with loads of packages to perform statistical analyses or create graphs and diagrams. The base of R is comprehensive, but the real power is in the packages people create for it. IBM controls the development of SPSS, but they will not have as many programmers as contributors to R. R graphics are more advanced then SPSS. R has at least 3 different graphics programs.

The consequence is that R can handle very complex statistical analytics. The advantage of SPSS is that it can perform parallel computing, sometimes using IO to harddisk. I have a 650 Gb dataset that was created with SPSS as R could not create it. I cannot load this dataset in R. On my pc R can not handle data and codes over 2.8Gb. So I would have to split the dataset in very small sizes to work with.

To overcome some of these problems, I have a installation of SPSS that connects to R. When it is necessary, I can combine the power of both.

The output of SPSS and R are different when you use the programs, but with R Studio and R markdown, you can create nicely formatted exports from R. In SPSS you can do this using the output management system.

SPSS is menu driven, but R is command driven. Both can end up having 'syntaxes (SPSS)' or 'script (R)'. Whatever you do, always create a syntax/script to redo you analyses and make them reproducible.

If you need a 'translation manual' turn to Muenchen, R for SAS and SPSS Users, second edition. Springer Verlag.

R is a open source programming language with loads of packages to perform statistical analyses or create graphs and diagrams. The base of R is comprehensive, but the real power is in the packages people create for it. IBM controls the development of SPSS, but they will not have as many programmers as contributors to R. R graphics are more advanced then SPSS. R has at least 3 different graphics programs.

The consequence is that R can handle very complex statistical analytics. The advantage of SPSS is that it can perform parallel computing, sometimes using IO to harddisk. I have a 650 Gb dataset that was created with SPSS as R could not create it. I cannot load this dataset in R. On my pc R can not handle data and codes over 2.8Gb. So I would have to split the dataset in very small sizes to work with.

To overcome some of these problems, I have a installation of SPSS that connects to R. When it is necessary, I can combine the power of both.

The output of SPSS and R are different when you use the programs, but with R Studio and R markdown, you can create nicely formatted exports from R. In SPSS you can do this using the output management system.

SPSS is menu driven, but R is command driven. Both can end up having 'syntaxes (SPSS)' or 'script (R)'. Whatever you do, always create a syntax/script to redo you analyses and make them reproducible.

If you need a 'translation manual' turn to Muenchen, R for SAS and SPSS Users, second edition. Springer Verlag.

I think the most obvious difference between R and SPSS is that the R program is based on coding.

R has more powerful object-oriented coding abilities than other statistical computing languages. SPSS GUI (graphical user interface) is composed of Java, which mainly utilizes statistical Analysis and interactive.

The following link may help you to discover more differences:

https://www.educba.com/r-vs-spss/

SPSS- Follow what is already there and get the results.

R - Develop things in your own way using commands to get the results. Free, open-source

If you are a social science researcher then SPSS is a better choice but for high-quality research In mathematical modelling in physics, mathematics, statistics and engineering etc., R is highly recommended.

Maybe some people do not know but SPSS has its own programming language: SAX Basics. It has been framed on Visual Basic and can be used in conjunction with WORD and EXCEL. You could start SPSS from Word and have commands executed, store output in a WORD file or create an EXCEL file. But working with R Markdown might be easier.

To see some possibilities, go to https://www.spsstools.net/en/

SPSS is option-based (more user friendly), but R is based on programming. You write codes of the programs you need by means of the prepared packages. R is extendable, much more well-equipped, updated, and specific(professional).

SPSS looks like a typical spreadsheet application but can perform more advanced statistical analyses but limited to the given user interfaces. It is easy to use for any person without any coding knowledge as it focuses on social science researches or studies. Recently it facilitates extensions, Python and R programming language code to integrate with open-source software.

R is a programming language which you need at least the basic coding knowledge. It comes up with lots of packages to perform many statistical analyses and also user can create his own functions.

Showkat Ahmad Lone , even if you are a social science researcher, you need R. SPSS has not developed its scale module as it should. You can do some scale models, but none of the more advanced IRT models (Rasch, PCM, GPM or Mokken). If you integrate SPSS with R, you have more options, but no way near the possibilities R will give you.

I think R is one of the vital playing role in programming languages for data analysis and data visualization.

Where as SPSS is applied for data cleaning and data analysis .

R is one of the best statistical software packages out there because people create packages for it. You can customize pretty much all of your output in R whereas SPSS does not, even though it has gotten better in later editions.

Personally, I tend to use SPSS to clean my data and transform it quickly instead of writing a script to do so. I like this because I can see the raw data much easier in a table format then on R and I often have different spreadsheets with different problems (thus I cant automate it). However, as soon as my data is clean, I export it to R to do all of my calculations. I do not trust SPSS to do even simple regressions as I have found differences between it and R or SAS. I do a lot of time series analysis and SPSS cannot handle this.

SPSS may be able to handle data files larger than 2.8gb but it is very rare that you will get such a file to work with. Most files are much much smaller. Therefore, R can basically do everything you need it to.

• R can do anything and everything remotely related to statistics - classical tests, graphing, resampling, etc.
• R is exhaustively documented. It is also a complete programming language that can be used to do almost anything and everything you would use an interpreted language for - automation scripts, I/O functions for file management, basic networking, etc.
• R has several GUI graphical interface suites that are probably just as good as SPSS.
• R is free and open source - use it at home, at work. If you want, you can take apart the underlying C code-base.
• R is fairly well-suited for teaching students to think about what they are doing when running statistical analyses. The cookbook SPSS approach turns the art of statistics into a black-box of check-boxes. Of course R can be used like this, as well. My opinion is that the best teaching approach is for students to code even basic statistical procedures from first principles, checking for accuracy with R's built-ins, afterwards.
• SPSS can do much of the above, but not as well. More importantly, it is neither free nor open source. It is an absurdly expensive lumbering dinosaur that requires institutions to pay exorbitant licensing fees for dedicated 'SPSS lab' computers. You cannot use it at home without a license, so students are tethered to a few computers and to OS's that are supported.
• SPSS has all sorts of draconian and invasive activation/genuine-license checks that call home prior to the software even starting... or not. I believe physical dongles are even required at the home institution! Someone can confirm.
• SPSS's licensing restrictions make it such that scientists without a license cannot check others' methods or reproduce a statistical work-flow in a paper, even with all the raw data available. Note that such verification is essential to science!
• So take your pick! What does everyone think?

Miky Timothy , you picture a very bleak picture for SPSS, but it is not very fair. In the old days, all statistical packages had to be paid for (SAS, SPSS, SYSTAT, GLIM4, BMDP). BMDP and GLIM no longer exist. but SAS is also not free of charge.

SPSS can however handle very large data sets. I have a 650Gb on my home pc which I can easily analyze with SPSS, but neither R nor Python could work on it easily as it does not fit into memory. All records of the data set are unique, so aggregation is impossible, even zipping does not work.

With SPSS, i can aggregate data more quickly then with R as SPSS allow different functions to be used in one syntax. if you only use dropdown menu's in SPSS, you are limited, but you can use SPSS own programming language or connect to R. That way you have the best of both worlds.

And SPSS used to free (long ago) and S-Plus (another implementation of S, earlier than R) can be bought. I think one difference that can be important is the object oriented-ness of R, discussed by Wickham, Chambers, and others. Another difference, as least at the moment, is embedding R code and output using packages like knitr. knitr would with other packages like SAS and Python (https://yihui.org/knitr/demo/engines/), so might work for SPSS. Also worth noting that there is an add on for SPSS to run R code. As noted, there are specific things that are easier in R than SPSS. Also stackexchange and other sources like that are good for R questions (if you don't mind getting harsh replies if you ask a bad question).

I agree with Daniel Wright , R being objected oriented is an advantage, but can also be a when object used inside packages change, your script might no longer work.

I think in a few years time, SPSS may no longer exist. IBM is investing in Watson (AT, STUDIO). With Watson you can do many things SPSS can and more. It also will not be free. You can have a 30-day trial account.

When talking about "R", it is worth also mentioning Microsoft's version, https://mran.microsoft.com/open, which could go in many directions. And also TIBCO, who own S-Plus and have an R version: https://community.tibco.com/modules/terr. And while we are at it PSPP, the free alt to SPSS, https://www.gnu.org/software/pspp/.

For the sake of completeness we should also mention Jamovi which looks and feels like an even user friendlier version of SPSS that has not yet been programmed by IBM. The important feat about Jamovi is that it runs on R code which can be displayed and modified and thus provides a good starting point for learning R , but it can be also seen as another open source, free alternative to SPSS.

R and SPSS are both industry leading technologies for statistical data analysis. R is an open-source programming language. It is supported by many developers who develops and adds new modules or libraries to the software. It is widely used as the preferred option for analytics in many instances on regular basis.

However, SPSS (Statistical Package for the Social Science) is not a free software it is owned and supported by the IBM. Therefore, it is limited in its capabilities and applications.

Kindly check these links:

https://www.inwt-statistics.com/read-blog/comparison-of-r-python-sas-spss-and-stata.html

http://stanfordphd.com/Statistical_Software.html

Vincent F Adegoke , you might want to look up the acronym for SPSS. For several years, it already is 'Statistical Product and Service Solutions'. But it used to be called ' Statistical Package for the Social Science'.

Sven Greving , we should also not forget JASP. It also is an friendly user interface build upon R. I especially like it's factor analysis procedure, because it includes parallel analysis. It is still under development. It also is free.