R and SPSS are both the closest competitors to each other. Both of them are widely used for statistical data analysis. There are a lot of differences between R vs SPSS. The major difference between R vs SPSS is that R is a programming language whereas SPSS is a software created by IBM. Let’s start the comparison between them with a quick introduction to both of these statistical tools or languages.
R is a multipurpose open source programming language. It is considered one of the best programming languages for data analytics. R is a command-line based programming language. There is limited support for the graphical user interface. But you can get the GUI in R programming with the help of R studio. You can extend the features of R with the help of the packages. R is the best programming language for data visualization.
SPSS stands for statistical package for social science. It is the product of IBM. SPSS is a GUI based software. Therefore it is quite easy to perform a variety of operations in SPSS with just simple clicks. SPSS doesn’t support the packages, but it gets regular updates from IBM. SPSS has limited support for data visualization.
R vs SPSS
R is getting the updates in a shorter period as compared with SPSS. It is an open-source programming language; thus, millions of R programmers are contributing to updating the R programming. That is why it is getting faster updates. There are hundreds of libraries that keep adding in R to make it more powerful and useful for everyone.
On the other hand, IBM SPSS also gets regular updates. But remember that you need to pay for the updates. You can’t get SPSS for free. But yes, you can try the trial version of SPSS.
The core code of R programming is written in C and Forton. It is the complete object-oriented programming language. But it just lacks a graphical user interface.
On the other hand, the core code of SPSS is written in Java. It offers the best in the class graphical user interface that is used for interactive and statistical analysis.
R doesn’t support the decision tree for statistics analysis. R only supports the classification and the regression tree as most packages and libraries are not designed for the decision tree. The trees created by R programming do not provide a user-friendly interface.
Whereas IBM SPSS has full support for decision trees. It is quite handy to work with the decision tree in IBM SPSS because of its simple and clean interface.
When it comes to interactivity, then R is not as good as SPSS. R is offering the command line interface, which is not that easy for the users to do interactive analytical operations. On the other hand, SPSS is quite convenient to use interactive analytical tools. It has a built-in editor that makes it easy to use for the users. But when it comes to using the full potential of interactive analytics, R is best because there are numerous commands for interactive analytics.
Charts and Graphs
It is quite easy to modify and optimize R’s graphs because of its wide range of packages and modules. R is also one of the best programming languages for data visualization. Data scientists use the ggplot2 and R shiny packages in R for data visualization. It is quite easy to do data visualization in R because you can use any graphs to showcase your data. In comparison, SPSS has limited support for data visualization. You can only create the basic as well as simple charts and graphs in SPSS.
R is an open-source programming language; therefore, it provides a command-line interface like most open-source programming languages. But you can have the graphical user interface in R programming using R studio. But R studio is not free. You need to pay for it. I want to suggest you code in R using the command-line interface. You may struggle initially, but you will find it quite easy to use after getting a good command over it.
On the other hand, SPSS is offering the best in the class graphical user interface. It is offering a user-friendly UI that is easy to use. It is similar to spreadsheet softwares.
R is not that easy to manage the data in R programming. However, R processes a huge amount of data. But the problem is when you need to perform any function on the data. Then the data needs to be loaded into the memory before the execution. In this way, there is a limited number of data that can be handled simultaneously.
On the other hand, IBM SPSS has great data management functionality. It offers the sorting, aggregation, transposition, and merging of the table.
R has one of the best documentation files available over the internet for free. R has the largest community in the world. Millions of R programmers offer the best documentation to beginners. SPSS documentation is not that great because it has limited use, and it is only accessible to the users who have its licensed version.
R is an open-source programming language. You need not pay a single penny to anyone for an R programming challenge. It is also getting updates at regular intervals and also keeps updating the new libraries.
On the other hand, IBM SPSS is a paid software. You need to pay some amount to use SPSS. But if you want to learn SPSS, then you can have the trial version of SPSS.
Let’s end this battle between R vs SPSS. R is a great programming language for statistics. It is quite easy to implement lots of statistics functions using R programming. R requires some training to master the concepts. And you should also have the basic programming knowledge before getting started with R programming. Therefore R is not a good option for absolute beginners in data analytics.
On the other hand, data analytics can be easily done in SPSS. You can also perform a variety of statistical analysis operations with SPSS with ease. But when it comes to data visualization, then SPSS has limited options. On the other hand, R has a variety of operations for data visualization. It also has the best support for exploratory data analysis (EDA). In the end, I would like to suggest that you should go with R programming even if you want to work beyond your limits. It will take time to master the concepts, but once you are done with those concepts, you will do lots of R programming tasks with ease. But if you want to do data analysis within limited functionality, then you should use SPSS.