Today I am going to share with you the detailed comparison between R vs SPSS. Majority of statistics students doubt these two programming languages. But this blog will help you to clear all your doubt more effectively than ever before.

Let’s get started with a little comparison between R vs SPSS. Lets have a look on the overview of the R language. R is an open source programming language it is based on the S language.

R was developed in the University of Auckland by Ross Ihaka and Robert Gentleman. It is one of the best programming languages for data analysis and data visualization.

The best part of the R programming language is R offers the best GUI editors than any other language. RGui and R studio are commonly used GUI editors of R language.

On the other hand, SPSS stands for “statistical package for social science. It was launched in the year 1968. Later on it was acquired by IBM in the year 2009.

After that, it is officially known as IBM SPSS. SPSS is the best software for data cleaning and data analysis. Data can come from any source i.e., Google Analytics, CRM, or any other database software.

The best part of SPSS is it can open all the file format that is used for structured data. Some of the most common types are a relational database, SAS, Stata, CSV, and spreadsheet. Let’s start the in-depth comparison between R vs SPSS or SPSS vs R.

**First, let’s check the difference between R vs SPSS in tabular form**

PARAMETERS | R | SPSS |

Platform/Updates | R is composed of Fortran and C. It 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. |

User Interface | It consists of a less interactive analytic device. Still, compilers are accessible for giving GUI help for coding in R. It is the best device for practicing hands-on and learning analytics, as they support the analyst the various analytics commands and steps. | It has a more user-friendly and interactive interface. SPSS illustrates information in a spreadsheet-like manner. |

Visualization | It provides more possibilities to optimize and customize charts because of the broad range of available projects. The broadly utilized module in R is ggplot2. These charts are quickly created, which enables programmers to operate the data. | The SPSS’s graphical abilities are completely practical. However, it is conceivable to perform lesser modifications to the chart, to customize the charts and visualizations of them in SPSS. |

Data Management | A significant disadvantage of R is that various functions need to store into the memory before implementation that can set a particular limit on the handled amounts. | IBM SPSS is the same as that of R in terms of data mangement. It gives functions like sorting, transposition, aggregation, and merging of a table. |

Decision making | R does not provide various algorithms for decision tree. Various packages of R can execute CART (Classification and Regression Tree), and its interface is not considered as user-friendly. | SPSS is much better than R for decision trees because it does not give numerous algorithms. The SPSS interface is considered to be understandable and user-friendly. |

Documentation | R has quickly accessible describe documentation records. R community, however, is considered to be one of the most powerful open-source communities. | SPSS lacks in terms of documentation features due to its restricted usage. |

Cost | It is open-source software that has an active community for software updations by combining new libraries. | SPSS is not available free of cost. If the user wants to study SPSS, then they can use the trial version of it. |

Easy to learn | Being open-source programming, R is quite easy to learn. And one can have a better command of this language. | SPSS is also easy to learn because it offers an interface like MS excel spreadsheets. But the only drawback is, it is not freely available for the users. |

#### Below are the crucial differences between R vs SPSS

### Definition

I’ve already provided you an overview of the R programming language. Let’s learn more about R programming. In the year 2000, the University of Auckland officially launched the first version of R. R is primarily focused on statistical modeling, and it was open-sourced under GNU license. R is an open-source programming language. It is also the most preferred statistics programming language for startups.

Now comes to the question of what is SPSS, SPSS was developed at North Carolina State University. The primary focus to improve SPSS was to able the statisticians to analyze large quantities of agriculture data. As mentioned earlier, SPSS stands for Statistical Package for the Social Sciences.

In the 1980s the demand for these kinds of software was increasing at a rapid pace. That’s why the SPSS comes into existence. In the year 1976.

SPSS was the first-ever statistical programming language for the PC. Statistical Package. It was developed many years ago before it became commercially available for the users.

It was developed in the year 1968 at the University of Stanford. After eight years later the company SPSS Inc. was founded which launched the official version of SPSS. In the year 2009, it was bought by IBM

### Updates

R is an open-source programming language. Open source programming languages usually have a large community of active members. That’s why R offers faster software updates and keep adding new libraries to provide better functionality to the users.

On the other hand, IBM SPSS is not an open source programming language. It is a commercial product of IBM. You can only have the free trial of SPSS for one month. SPSS doesn’t have the community like R and also doesn’t offer the quick updates.

### Language

R is written in the ancient aged language i.e., C and Fortran. But R also offers the object-oriented programming facilities.

On the other hand, SPSS is written in Java language. SPSS provides the best in class GUI, which is written in Java. Statisticians use R for statistical analysis and interactivity.

### Statistical Analysis Decision Trees

When we test R In statistical analysis decision trees. Then R does not offer the many algorithms. Besides, most of the packages of R can only implement Classification and Regression Tree. And the worst part of R packages is their interface is not as user-friendly.

On the other hand, the SPSS interface is more likely to excel spreadsheet. SPSS offers a more user-friendly GUI based user interface. If you’re familiar with excel. Then you can find it easier to use than R.

### Interface

R is considered as a less interactive analytical tool than SPSS. But it has a variety of editors that are providing GUI support for programming in R. If you want to learn and practice the analytics then R is much better to learn the analytics steps and commands.

On the other hand, the SPSS interface is more likely to excel spreadsheet. SPSS offers the more user friendly GUI based user interface. If you’re familiar with excel. Then you can find it more easy to use than R.

### Visualizations

R has an extensive set of packages to R modify and optimize graphs. ggplot2 and R shiny are the most widely used packages in R. It is quite easy to design and graph in R language, which allows the users to play with data.

On the other hand, SPSS doesn’t offer interactive graphs like R. In SPSS, and you can create only basic and straightforward graphs or charts.

### Data Management

Both R and SPSS offer almost the same data management. But in the case of R, the most functions of R loads the data into the memory before the execution of the program. It makes R relatively slower than the other programming language. Because there is a limited volumes of data can be handle.

On the other hand, SPSS provides faster data management functions i.e. sorting, aggregation, transposition, and for merging of the table.

### Decision Making

R is not the best programming language for decision making. The reason is R does not offer many algorithms. And most of its packages can only implement CART (Classification and Regression Tree).

And the worst part is their interface is not as user-friendly. That’s why it is overwhelming for the users to use R packages for decision-making purposes.

On the other hand, SPSS is one of the best statistical programming languages for decision trees. The reason is SPSS offers the best among the best user-friendly and understandable user interface.

It is quite easy to use for the users and also helpful in quick decision making.

### Documentation

R offers the best documentation because it has a large community where you can find the well explain documentation files. You can also solve all your queries and problems with the help of most robust open source communities of R.

On the other hand, SPSS is a commercial product; thus, it doesn’t offer vast documentation. But when you purchase the SPSS from IBM, then you get some documentation along with the purchase.

### Cost

R is an open-source programming language. It means that you need not pay a single penny to anyone if you want to use R. You can also collaborate in the development phase of the R language to make it better for you and other users.

Besides other programmers keep doing a great job to keep adding new libraries and updates in R without charging anything. On the other hand, SPSS is not a free product.

You need to pay some subscription charges to use it. You can also use the trial version of SPSS before purchasing the licensed version.

### Easy of learning

It is pretty evident that open source programming is easy to learn and implement. In the case of R, it is also quite easy for the student to have a better command over this language.

There are lots of sources available online to learn R. You can also take the help of R community to clear all your doubts while learning R.

On the other hand, SPSS is also easy to learn because it offers the interface like MS excel spreadsheets. But the only drawback is, it is not freely available for the users. You need to purchase the licensed version of SPSS to learn it more effectively.

### Used by Companies

#### The following companies use r

- Microsoft
- Uber
- Airbnb
- IBM
- ANZ
- HP
- Ford

#### Companies using SPSS

- eBay
- KPMG
- Cognizant Technology Solutions
- Capillary Technologies
- IBM
- Accenture
- Genpact and Symphony Marketing solutions
- Infosys
- Wipro
- Capgemini

### Conclusion R vs SPSS

This log has provided information about what is R and what is SPSS along with their differences. In the end, I would like to say that both R and SPSS are analytics amazing analytics tools and also offers excellent career options. R is an open-source programming language. Thus it is easy to learn and implement.

On the other hand, SPSS is a paid product, and you need to buy it for permanent use. If you are a statistics students and not much aware of data analytics, then you should opt for SPSS.

The reason is SPSS offers the best user interface to perform statistical analysis with ease. But if you are like to do more data visualization work, then you should opt for R.

Because R has a wide range of packages for data visualizations. Moreover, R is also the best option for (EDA) exploratory data analysis. In the end, I would like to suggest you that you should opt SPSS if you’re new in statistics.

On the other hand, if you’ve enough time to learn R, then you should choose R. Now you may be well confident to choose between R vs SPSS or SPSS vs R.

If you are a statistics student and looking to get the best SPSS assignment help and SPSS homework help, then we are here to help you. We are also offering the best R programming assignment help at nominal charges.

**What are the advantages of R?**R has several kinds of functions, like statistical modeling, data manipulation, and graphics. One major advantage of this programming language is its extensibility. Software developers can write software easily and share as add-on packages to others.

The Development Core Team of R faces various difficulties making R compatible with different software and hardware types. It implies that it is feasible for Unix systems (like Linux), Windows, and Mac. This makes it user friendly, and because of its community library, it is easy to understand the programming language.

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