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  1. Stackups
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  5. GraphQL vs R

GraphQL vs R

OverviewDecisionsComparisonAlternatives

Overview

R Language
R Language
Stacks3.9K
Followers1.9K
Votes418
GraphQL
GraphQL
Stacks34.9K
Followers28.1K
Votes309

GraphQL vs R: What are the differences?

# **Key Differences Between GraphQL and R**

GraphQL and R are both powerful tools used in different domains. Below are the key differences between the two technologies:

1. **Query Language vs Statistical Computing Language**: At its core, GraphQL is a query language for APIs that enables clients to request only the data they need. On the other hand, R is primarily a statistical computing language used for data analysis, visualization, and machine learning tasks.
   
2. **Server-side vs Client-side**: GraphQL is typically implemented on the server-side where it acts as a middle layer between clients and databases, providing a unified interface to access various data sources. In contrast, R is predominantly used on the client-side, allowing data scientists and analysts to work directly with data on their local machines.

3. **Data Retrieval and Processing**: While GraphQL excels in providing a flexible and efficient way to fetch data from multiple sources in a single request, R shines in its extensive libraries and built-in functions for data manipulation, modeling, and visualization.

4. **Real-time Data Handling**: GraphQL is well-suited for real-time applications where data is constantly changing as it allows for subscriptions that can update clients in real-time. In contrast, R is more focused on batch processing and analysis of static datasets, rather than handling dynamic real-time data.

5. **Community and Ecosystem**: The GraphQL ecosystem is rapidly growing, with various tools and libraries to support development, documentation, and testing of GraphQL APIs. On the other hand, R has a well-established community with a wide range of packages and resources dedicated to statistical analysis and data science.

6. **Use Cases and Domains**: GraphQL is commonly used in the context of APIs, web development, and data fetching scenarios where flexibility and efficient data retrieval are crucial. In contrast, R is widely used in academic research, business analytics, and data-driven decision-making where statistical analysis and modeling play a significant role.

In Summary, GraphQL and R exhibit distinct characteristics in terms of their functions, usage, and ecosystems, catering to different needs in the fields of API development and data analysis, respectively.

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Advice on R Language, GraphQL

Raj
Raj

Oct 10, 2020

Review

It purely depends on your app needs. Does it need to be scalable, do you have lots of features, OR it is a simple project with very simple needs - many of those parameters clarify which technologies will fit.

If you are looking for a quick solution, that reduces lot of development time, take a look at postgraphile (https://www.graphile.org/postgraphile/). You have to just define the schema and you get the entire graph-ql apis built for you and you can just focus on your frontend.

On frontend, React is good, but also need to remember that it is popular because it introduced one way data writes and in-built virtual dom + diffing to determine which dom to modify. Though personally I liked it, am recently more inclined to Svelte because its lightweightedness and absence of virtual dom and its simplicity compared to the huge ecosystem that React has surrounded itself with.

In all situations, frameworks keep changing over time. What is best today is not considered even good few years from now. What is important is to have the logic in a separate, clean manner void of too many framework related dependencies - that way you can switch one framework with another very easily.

3.77k views3.77k
Comments

Detailed Comparison

R Language
R Language
GraphQL
GraphQL

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.

GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012.

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Hierarchical;Product-centric;Client-specified queries;Backwards Compatible;Structured, Arbitrary Code;Application-Layer Protocol;Strongly-typed;Introspective
Statistics
Stacks
3.9K
Stacks
34.9K
Followers
1.9K
Followers
28.1K
Votes
418
Votes
309
Pros & Cons
Pros
  • 86
    Data analysis
  • 64
    Graphics and data visualization
  • 55
    Free
  • 45
    Great community
  • 38
    Flexible statistical analysis toolkit
Cons
  • 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
Pros
  • 75
    Schemas defined by the requests made by the user
  • 63
    Will replace RESTful interfaces
  • 62
    The future of API's
  • 49
    The future of databases
  • 12
    Get many resources in a single request
Cons
  • 4
    Hard to migrate from GraphQL to another technology
  • 4
    More code to type.
  • 2
    Takes longer to build compared to schemaless.
  • 1
    No support for caching
  • 1
    No built in security

What are some alternatives to R Language, GraphQL?

JavaScript

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

Python

Python

Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

PHP

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

Golang

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

HTML5

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

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