JMP vs R

JMP vs R | Popularity, Salary, Pricing, Features, and Applications

JMP and R are both powerful tools for data analysis, statistics, machine learning, and data visualization but they have some key differences that make them better suited for different types of tasks.

If you want a free, highly customizable tool to use for data analysis, statistical computing, data mining, bioinformatics, etc. you should use R.

On the other hand, if you want a powerful, highly customizable, easy-to-use software for statistical analysis, machine learning, data analysis, interactive visualization, and more, you should use JMP.

JMP is a statistical software for insight-driven improvements, it is used in applications such as quality control and six sigma.

JMP is also popular for the design of experiments, engineering, pharmaceuticals, and research in science and social sciences.

R on the other hand is an open-source programming language and environment for statistical computing and graphics.

R is used among data miners, bioinformaticians, and statisticians for data analysis and developing statistical software.

R Stats

There are many factors to consider when learning a new technology or programming language.

Popularity, opportunities, types of projects, salaries, resources, learning curve, etc. are some of the factors that many people consider when choosing to learn a programming language or software package.


If you want to learn a programming language or a statistical software solely for its popularity among users, you should learn R over JMP.

As of April 2023, R ranked 16th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020.

JMP is not a programming language and is not included in many rankings that include R. It is worth noting that JMP has a scripting programming language called JSP but it is used with the JMP package.

R still remains generally more popular than JMP in many areas where the usage overlap.


Some software packages can be learned more easily than others.

If you want a package or language that you can easily pick up to work on data analysis and statistical projects, you should use JMP over R.

JMP is generally easier to use than R, but R is more versatile and customizable.

JMP is very easy to use, it is straightforward and you can quickly get the results that you want. It has a point and click interface that makes it easy even for complete beginners.

Unlike JMP, R does not have a user-friendly interface, and you must have some programming skills in order to use it effectively.

However, the R community is large and active, and there are many resources available to help users learn R, such as tutorials, forums, and documentation.


JMP comes with a scripting language called JSL, an interface for R programming, and an add-in for Excel.

JSL makes automation and customization in JMP easy and straightforward. JSL also helps to extend the functionality of JMP.

You can use JSL scripts to perform analyses and visualizations not available in the point-and-click interface

Power users can develop scripts to extend JMP’s functionality and automate a regularly scheduled analysis in production settings.

And if for some reason you do not want to learn JSL, JMP can easily write the scripts for you.


JMP has an interface for R programming language that help R developers easily use the JMP platform.

JMP allows code written in Python, R, Matlab, and SAS to be executed in JSL, this integration makes JMP more powerful, robust, and efficient.

JMP is used by many companies such as Nike, Micron, Seagate, Medtronic, Intel, Symrise, NXP, Mulata Finland, Novozymes, DuPont, Dairygold, IQE, NVIDIA, Roche, Siemens Healthineers, Imperial College London, Lufthansa, McDonald’s, and many others.

On the other hand, R has a wide range of built-in functions and packages for data analysis, and users can create custom functions and scripts to perform specific tasks.

R is also powerful for data visualization, thanks to the ggplot2 library and the lattice library, which provides a high-level interface to powerful data visualization.

JMP is a good choice for users who want a user-friendly interface and a wide range of built-in statistical tools, and for those who do not have programming skills.

R, on the other hand, is a good choice for users who have programming skills or are willing to learn, and for those who want to create custom functions or scripts to perform specific tasks.

R is also more popular among researchers and the academic community.

R is used by companies such as Amazon, Flipkart, Microsoft, Oracle, IBM, Twitter, HP, Uber, Airbnb, American Express, and many others

Also read Best way to learn R


The official R software environment is an open-source free software environment within the GNU package. R and many R libraries are free to use for personal and commercial purposes.

On the other hand, JMP is not a free software, it is a proprietary software package that requires a purchase.

JMP is available in many versions such as JMP, JMP Pro, JMP Live, and JMP Clinical.

Each of these products has its own features and pricing. For example, JMP starts at $1,200 per year.

Because JMP is not free, many users prefer free-to-use, open-source alternatives such as R and Python with many libraries related to data analysis like Pandas, NumPy, ggplot2, and many others.


Both JMP and R have their own strengths and weaknesses, and the choice between them will depend on the specific needs and goals that you have.

If you are looking for a tool that is easy to use and has a wide range of built-in statistical tools, you should use JMP.

If you have programming skills and want a tool that is highly customizable for data analytics, data visualization, machine learning, Statistics, data mining, and more, you should use R.