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Julia vs R
Julia and R are popular options for Data Science, Statistics, Machine Learning, and Data Analytics.
Julia is a high-level, dynamic programming language that was designed for high performance. It has many great features that make it well-suited for numerical analysis and computational science.
If you are interested in working on Data Visualization & Plotting, Data Science, Machine Learning, Parallel & Heterogeneous Computing, and Scientific Computing, you should learn Julia over R.
On the other hand, R is used by scientists, economists, programmers, and mathematicians for machine learning, data mining, data analysis, bioinformatics, and statistics.
R can also be used to develop and build statistical software and web applications.
If you are interested in statistical computing, data mining, data analysis, bioinformatics, data science, and machine learning, you should learn R.
Comparing programming languages and choosing which one to learn can be tricky, there are many factors to consider in order to choose the right programming language for the job you want to do.
Popularity, opportunities, types of projects, salaries, resources, learning curve, etc. are some of the factors that many people consider when comparing programming languages and choosing which one to learn.
Here are some of the comparisons and considerations you should make when choosing to learn a new programming language.
JULIA VS R POPULARITY
Comparing the popularity of programming languages is not an easy task because each programming language is different and they all seek to solve different problems.
Plus, other programming languages have been around for a longer time than others, giving them more time to be tried and tested, so, bear that in mind.
If you want to learn a programming language or framework solely for its popularity among developers, you should learn R over Julia.
Generally, R is more popular than Julia. The TIOBE index 2023 ranks R as the 16th most popular programming language while Julia is ranked as the 33rd most popular programming language.
According to a Stack Overflow survey of 2022, R is the 22nd most commonly used programming language, it is used by 3.56% of professional developers.
On the other hand, Julia is the 35th most commonly used programming language, it is used by 1.04% of professional developers according to the same survey.
It is worth noting that Julia is a newer language compared to R, it is actually rapidly growing in popularity. There are more developers who love Julia than those who love R.
As of 2022, Julia is loved by 72.51% of developers versus 27.49% of developers who dreaded it. Compare that to 41.60% of developers who love R versus 58.40% of developers who dreaded it.
Ultimately, if your choice of which programming language to learn depends on popularity, you should learn R.
Plus, there are more opportunities for R Developers than for Julia developers because R has been around for a very long time and has been used on many programs than Julia.
Also read Best Way to Learn Julia
JULIA VS R SALARY
Another popular criterion that many people use to compare programming languages and as an incentive to learn a new programming language is salary.
Salaries for developers differ from one company to the other and from one country to the other.
Experience is another factor that comes into play as far as salaries are concerned. The more experience you have with a certain technology or programming language, the more likely you are of getting a higher salary.
Generally, Julia developers get higher salaries than R developers. According to a Stack Overflow survey of top-paying programming languages, Julia developers get an average salary of $77,966 per year.
On the other hand, R developers get an average salary of $67,734 per year, about $10,000 less than Julia developers.
According to Ziprecruiter, Julia developers in the United States get an average salary of $107,448 per year, while R developers get an average of $96,155.
So, if the salary is your major incentive for learning a language, you should learn Julia over R, because you are more likely to get a higher salary as a Julia developer than as an R developer.
Also read Best Way to Learn R
IS JULIA EASIER THAN R
Julia is generally easier than R, Julia being a newer language than R has modern features that make it easy to work with.
Although Julia has some advanced and new programming concepts like macros, which can be a little bit difficult, especially for beginners, it is generally easy to work with.
R has a steep learning curve compared to Julia, but if you are coming from a numerical and statistical background, you may find R easy and attractive.
The good news is that there are plenty of helpful resources for both R and Julia to help you learn the languages.
The R and Julia communities are very active and helpful, in case you get stuck with something.
JULIA VS R PERFORMANCE
When it comes to performance comparison, Julia is a go-to language over R, if you plan on working on applications where top-notch performance is critical, you should learn Julia.
Julia is a very fast and high-performance language, it is generally faster than R. Julia is one of the few high-level programming languages in which petaFLOPS computations have been achieved, others being C, C++, and Fortran.
WHY IS JULIA FASTER THAN R
Julia was designed from the beginning for high performance. Julia programs compile to efficient native code for multiple platforms via LLVM, this makes Julia programs very fast.
Julia is a compiled language while R is an interpreted language. Compiled languages are converted directly into machine code that the processor can execute.
For this reason, compiled languages tend to be faster and more efficient to execute than interpreted languages.
FEATURES AND APPLICATIONS
Julia has many advanced features and libraries that make it a popular choice for Data Science, Machine Learning, Scientific Computing, Parallel Computing, and Data Visualization & Plotting.
Julia provides asynchronous I/O, metaprogramming, debugging, logging, profiling, a package manager, and more. This makes it possible to build entire applications and Microservices in Julia.
It has a built-in package manager called Pkg that handles operations such as installing, updating, and removing packages.
Julia also uses multiple dispatch as a paradigm, making it easy to express many object-oriented and functional programming patterns.
The beauty of Julia is that you call C programming language functions directly without wrappers or special APIs.
It also has foreign function interfaces that make it work well with other programming languages such as Python, R, C++, Java, and many others.
Python and R packages such as PyJulia and JuliaCall can be used to call Julia packages in a Python or R codebase.
Julia is used by many companies such as Aviva, NASA, Brazilian INPE, Moderna, BlackRock, Climate Modelling Alliance, Google, Microsoft, Federal Reserve Bank of New York, and many others.
On the other hand, R is heavily used in statistics and building machine learning models because of its flexibility.
Another strength of R is static graphics; it can produce publication-quality graphs that include mathematical symbols.
R and its libraries are used to implement various techniques such as linear, generalized linear, and nonlinear modeling, classical statistical tests, plotting, data processing, spatial and time-series analysis, classification, clustering, etc.
R is used by companies such as Amazon, Google, Flipkart, Firefox, LinkedIn, ANZ, Accenture, Infosys, BOA, HCL Technologies, Cognizant, American Express, Barclays Bank, and many others.
SHOULD I LEARN JULIA OR R
Once you have compared the languages and evaluated all the factors, you can choose which programming language to learn depending on the factors that are on your side and what you want to build.
If you want a fast and high-performance language that you can use for Data Visualization & Plotting, Data Science, Machine Learning, Parallel & Heterogeneous Computing, and Scientific Computing, you should learn Julia over R.
If you want a popular language that you can use for statistical computing, data mining, data analysis, bioinformatics, data science, and machine learning, you should learn R.