Need advice about which tool to choose?Ask the StackShare community!


+ 1
R Language

+ 1
Add tool

Julia vs R: What are the differences?

Developers describe Julia as "A high-level, high-performance dynamic programming language for technical computing". Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. On the other hand, R is detailed as "A language and environment for statistical computing and graphics". R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.

Julia and R can be primarily classified as "Languages" tools.

"Lisp-like Macros" is the top reason why over 7 developers like Julia, while over 58 developers mention "Data analysis " as the leading cause for choosing R.

Julia is an open source tool with 22.7K GitHub stars and 3.43K GitHub forks. Here's a link to Julia's open source repository on GitHub.

Instacart, Key Location, and Custora are some of the popular companies that use R, whereas Julia is used by inFeedo, Platform Project, and N26. R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to Julia, which is listed in 5 company stacks and 5 developer stacks.

Decisions about Julia and R Language
Alexander Nozik
Senior researcher at MIPT · | 3 upvotes · 144.2K 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.

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Julia
Pros of R Language
  • 22
    Fast Performance and Easy Experimentation
  • 21
    Designed for parallelism and distributed computation
  • 17
    Free and Open Source
  • 16
    Dynamic Type System
  • 16
    Lisp-like Macros
  • 16
    Calling C functions directly
  • 15
    Multiple Dispatch
  • 9
    Powerful Shell-like Capabilities
  • 8
    Jupyter notebook integration
  • 7
  • 4
    String handling
  • 4
    Emojis as variable names
  • 3
  • 83
    Data analysis
  • 62
    Graphics and data visualization
  • 53
  • 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
    Preferred Medium
  • 6
    Shiny interactive plots
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 3
    Machine Learning
  • 2
    Graphical visualization
  • 1
    Flexible Syntax

Sign up to add or upvote prosMake informed product decisions

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

Sign up to add or upvote consMake informed product decisions