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Julia vs R: What are the differences?


Julia and R are programming languages commonly used in data analysis and scientific computing. While both languages have their distinct features and strengths, there are several key differences between them. This Markdown code provides a concise comparison of these differences.

  1. Performance: Julia is known for its high performance and speed, making it ideal for computationally intensive tasks. It uses a just-in-time (JIT) compilation approach that allows it to dynamically compile code while executing it, leading to faster execution times. On the other hand, R has traditionally been slower due to its interpreted nature and lack of native support for parallel computing. However, recent advancements in R packages, such as 'dplyr' and 'data.table', have improved its performance.

  2. Language Design: The design philosophy of Julia revolves around providing a unified language that is both user-friendly and efficient. It aims to bridge the gap between high-level dynamic languages like Python and low-level compiled languages like C/C++. Julia achieves this through multiple dispatch, which allows functions to behave differently based on the types of their arguments. In contrast, R is a domain-specific language primarily focused on statistical analysis and modeling. It provides a wide range of built-in statistical functions and packages, making it convenient for data analysis tasks.

  3. Syntax: Julia has a relatively modern and mathematically inclined syntax. It uses mathematical notations, such as Unicode operators, that make code more concise and readable. Julia also supports multiple programming paradigms, including functional, procedural, and object-oriented programming. On the other hand, R has a syntax that is more similar to traditional programming languages like C. It uses a combination of functions and operators for computations, making it convenient for statisticians and data analysts familiar with traditional statistical software.

  4. Community and Package Ecosystem: R has a vibrant and extensive community, providing a vast collection of packages for statistical analysis, data visualization, and machine learning. The Comprehensive R Archive Network (CRAN) hosts thousands of packages, making it easy to find and install packages in R. Julia, although relatively new, has been rapidly growing its package ecosystem. The Julia package manager, known as 'Pkg', provides a convenient way to install and manage packages. The Julia community also focuses on developing high-quality packages, making it comparable to the R ecosystem.

  5. Interoperability: R has a good level of interoperability with other programming languages. It supports interfaces with languages like C, C++, Python, and Java, allowing users to leverage existing code and libraries. Julia, on the other hand, has built-in support for calling C and Fortran code directly, making it easier to integrate with existing libraries. Julia also provides a Python-like syntax for calling Python functions, enabling interoperability with Python code.

  6. Learning Curve: While both languages have their learning curves, R is often considered easier to learn for users with a background in statistics and data analysis. Its syntax and vast range of statistical functions make it intuitive for statisticians. Julia, on the other hand, requires a deeper understanding of programming concepts and may pose a steeper learning curve for novice programmers. However, Julia's simplicity and consistency in syntax make it easier to write and read complex algorithms and code.

In summary, Julia offers high performance with its JIT compilation and unified language design, while R provides a comprehensive package ecosystem and ease of use for statisticians. Understanding the key differences between these languages can help determine the most suitable option for specific data analysis and scientific computing tasks.

Decisions about Julia and R Language
Alexander Nozik
Senior researcher at MIPT · | 3 upvotes · 172.2K views

After writing a project in Julia we decided to stick with Kotlin. Julia is a nice language and has superb REPL support, but poor tooling and the lack of reproducibility of the program runs makes it too expensive to work with. Kotlin on the other hand now has nice Jupyter support, which mostly covers REPL requirements.

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Pros of Julia
Pros of R Language
  • 24
    Fast Performance and Easy Experimentation
  • 21
    Designed for parallelism and distributed computation
  • 18
    Free and Open Source
  • 17
    Dynamic Type System
  • 16
    Multiple Dispatch
  • 16
    Calling C functions directly
  • 16
    Lisp-like Macros
  • 10
    Powerful Shell-like Capabilities
  • 9
    Jupyter notebook integration
  • 8
  • 4
    String handling
  • 4
    Emojis as variable names
  • 3
  • 84
    Data analysis
  • 63
    Graphics and data visualization
  • 54
  • 45
    Great community
  • 38
    Flexible statistical analysis toolkit
  • 27
    Easy packages setup
  • 27
    Access to powerful, cutting-edge analytics
  • 18
  • 13
    R Studio IDE
  • 9
  • 7
    Shiny apps
  • 6
    Shiny interactive plots
  • 6
    Preferred Medium
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 3
    Machine Learning
  • 2
    Graphical visualization
  • 1
    Flexible Syntax

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Cons of Julia
Cons of R Language
  • 5
    Immature library management system
  • 4
    Slow program start
  • 3
    JIT compiler is very slow
  • 3
    Poor backwards compatibility
  • 2
    Bad tooling
  • 2
    No static compilation
  • 6
    Very messy syntax
  • 4
    Tables must fit in RAM
  • 3
    Arrays indices start with 1
  • 2
    Messy syntax for string concatenation
  • 2
    No push command for vectors/lists
  • 1
    Messy character encoding
  • 0
    Poor syntax for classes
  • 0
    Messy syntax for array/vector combination

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