Differences Between R and SPSS
R and SPSS are the two industry-leading technologies for statistical data analysis. R is an open-source programming language that is widely used as the preferred option for analytics. Whereas SPSS is known as Statistical Package for the social science owned by IBM. R is the scripting language and supports limited Graphical User Interface features as compared to IBM SPSS that has built-in features for data quality processing and analysis. R has several package support from the community user. Whereas SPSS is fully managed by IBM for support and features enhancement. R is known for its customization visualization support whereas SPSS is limited with the visualization features.
Head to Head Comparison Between R and SPSS (Infographics)
Below is the top 7 comparison between R vs SPSS
Key Differences Between R and SPSS
Below are the most important key differences between R vs SPSS
- R is open source free software, where R community is very fast for software update adding new libraries on a regular basis new version of stable R is 3.5. IBM SPSS is not free if someone wants to use SPSS software then it has to download the trial version first due to the cost-effectiveness of SPSS, most of the start-ups opt R software.
- R is written in C and Fortran. R has stronger object-oriented programming facilities than SPSS whereas SPSS graphical user interface is written using Java language. It is mainly used for interactively and statistical analysis.
- In statistical analysis decision trees, R does not provide many algorithms and most of the packages of R can only implement Classification and Regression Tree and their interface is not as user-friendly. On the other hand, Decision trees in IBM SPSS are better than R because R does not offer many tree algorithms. For decision trees, SPSS interface is very user-friendly, understandable and easy to use.
- R has a less interactive analytical tool than SPSS but its editors are available for providing GUI support for programming in R. for learning and practicing hands-on analytics R us best tool as it really helps the analyst to master the various analytics steps and commands. Moreover, SPSS interface is more or less similar to excel spreadsheet.
- R offer much more opportunities to modify and optimize graphs due to a wide range of packages that are available. The most widely used package in R is ggplot2 and R shiny. Graphs in R are also easily made interactive, which allow users to play with data. In SPSS graphs are not that interactive as in R where you can create only basic and simple graphs or charts. Data management in both R and SPSS is almost same. A major drawback of R is that most of its functions have to load all the data into memory before execution whereas in SPSS provides data management functions such as sorting, aggregation, transposition and for merging of the table.
R vs SPSS Comparison Table
Below is the comparison table
| Basis for Comparison | R | SPSS |
| User Interface | R has the less interactive analytical tool but editors are available for providing GUI support for programming in R. for learning and practicing hands-on analytics R us best tool as it really helps the analyst to master the various analytics steps and commands. |
SPSS has more interactive and user-friendly interface. SPSS displays data in a spreadsheet-like fashion |
| Decision Making | For decision trees, R does not offer many algorithms and most of the packages of R can only implement CART (Classification and Regression Tree) and their interface is not as user-friendly. | For Decision trees, IBM SPSS is better than R because R does not offer many tree algorithms. For decision trees, SPSS interface is very user-friendly and understandable. |
| Data Management | A major drawback of R is that most of its functions have to load all the data into memory before execution, which set a limit on the volumes that can be handled. | In terms of data management, IBM SPSS is more or less similar to R. it provides data management functions such as sorting, aggregation, transposition and for merging of the table. |
| Documentation | In terms of documentation R has easily available explain documentation files. R community, however, is one of the strongest open source communities. | While SPSS is lag behind in this feature. SPSS lack this feature due to its limited use. |
| Platform | R is written in C and Fortran. R has stronger object-oriented programming facilities than most statistical computing languages. | SPSS graphical user interface (GUI) is written in Java. It uses for interactive and statistical Analysis mainly. |
| Cost | R is open source free software, where R community is very fast for software update adding new libraries. | IBM SPSS is not free if someone wants to learn SPSS then it has to use trial version first. |
| Visualizations | R offer much more opportunities to customize and optimize graphs due to a wide range of modules that are available. The most widely used module in R is ggplot2. These graphs are also easily made interactive, which allow users to play with data. | The graphical capabilities of SPSS are purely functional although it is possible to make minor changes to the graph, to fully customize your graph and visualizations in SPSS can be very cumbersome. |
Conclusion
R and SPSS both are analytics tools and have great career potential. Since R is open source, one could easily learn and implement. SPSS is licensed and you need to buy it for permanent use but you can learn SPSS through IBM SPSS trial version. If someone is new to data analytics then SPSS is a better choice because of its user-friendly interface to perform statistical analysis with ease from SPSS you can create basic visualization this problem can be overcome by R, R has a wide range of visualizations. In R you can use ggplot2 and R shiny to perform visualizations. R is best for (EDA) exploratory data analysis. R and SPSS both are slow when it comes to handling large data to solve this problem you have to go for another tool.
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