COBOL vs R

Overview

COBOL

Stacks130

Followers147

Votes2

R Language

Stacks3.9K

Followers1.9K

Votes418

COBOL vs R: What are the differences?

Introduction

In this comparison, we will highlight key differences between COBOL and R programming languages.

Syntax and Structure: COBOL follows a more verbose syntax and structure compared to R, which is more concise and compact. COBOL code tends to be lengthier and requires more lines of code to achieve a similar outcome in R.
Data Types: COBOL has a limited set of data types compared to R, which offers a wide range of data types including vectors, matrices, lists, and data frames. R is more flexible in handling different types of data and provides more sophisticated data structures.
Application Domain: COBOL is predominantly used in the business and finance sectors for tasks like batch processing, data processing, and report generation. On the other hand, R is primarily used for statistical computing, data analysis, and visualization in various fields such as research, academia, and data science.
Platform Compatibility: COBOL is more platform-dependent and is often used in traditional mainframe systems. In contrast, R is compatible with multiple platforms and operating systems, making it versatile for use on different types of hardware and environments.
Specialized Libraries: R is rich in specialized libraries and packages specifically designed for statistical analysis, machine learning, and data visualization. COBOL, on the other hand, lacks such extensive libraries and is more focused on generic business applications.
Learning Curve: R has a steeper learning curve compared to COBOL, as it requires understanding of statistical concepts, data manipulation techniques, and programming paradigms specific to data analysis. COBOL, being a more traditional language, is relatively easier to grasp for novice programmers.

In Summary, differences between COBOL and R include syntax, data types, application domains, platform compatibility, specialized libraries, and learning curves.

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Advice on COBOL, R Language

Samuel

Oct 11, 2021

Decided

MACHINE LEARNING

Python is the default go-to for machine learning. It has a wide variety of useful packages such as pandas and numpy to aid with ML, as well as deep-learning frameworks. Furthermore, it is more production-friendly compared to other ML languages such as R.

Pytorch is a deep-learning framework that is both flexible and fast compared to Tensorflow + Keras. It is also well documented and has a large community to answer lingering questions.

158k views158k

Comments

Mohiuddin

Mar 7, 2022

Needs advice

Extract the daily COVID-19 confirmed cases for City1, City2, and City3 from all the cities. Normalize the daily COVID-19 confirmed cases for the three cities using their respective populations. The 2019 mid-year estimated population figures for City1, City2, and City3 are 100,000, 200,000, and 300,000 respectively.

df <- read.csv ("coronavirus.csv", header = TRUE ) library(dplyr) df %>% group_by(City.name) %>% summarise(Sum = sum(Daily.cases))

Cant select multiple variables from dplyr::Groupby. Can anyone help me with the right code along with the second part of the question as I am not able to find solution as well.

3.15k views3.15k

Comments

Detailed Comparison

COBOL	R Language
COBOL was one of the first programming languages to be standardised: the first COBOL standard was issued by ANSI in 1968. COBOL is primarily used in business, finance, and administrative systems for companies and governments.	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.
Statistics
Stacks 130	Stacks 3.9K
Followers 147	Followers 1.9K
Votes 2	Votes 418
Pros & Cons
Pros 2 Business Oriented Language Cons 2 Extremely long code for simple functions	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

What are some alternatives to COBOL, R Language?

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 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

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

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 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

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 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# (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 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 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.

Related Comparisons

COBOL vs R: What are the differences?

Introduction

In this comparison, we will highlight key differences between COBOL and R programming languages.

Syntax and Structure: COBOL follows a more verbose syntax and structure compared to R, which is more concise and compact. COBOL code tends to be lengthier and requires more lines of code to achieve a similar outcome in R.
Data Types: COBOL has a limited set of data types compared to R, which offers a wide range of data types including vectors, matrices, lists, and data frames. R is more flexible in handling different types of data and provides more sophisticated data structures.
Application Domain: COBOL is predominantly used in the business and finance sectors for tasks like batch processing, data processing, and report generation. On the other hand, R is primarily used for statistical computing, data analysis, and visualization in various fields such as research, academia, and data science.
Platform Compatibility: COBOL is more platform-dependent and is often used in traditional mainframe systems. In contrast, R is compatible with multiple platforms and operating systems, making it versatile for use on different types of hardware and environments.
Specialized Libraries: R is rich in specialized libraries and packages specifically designed for statistical analysis, machine learning, and data visualization. COBOL, on the other hand, lacks such extensive libraries and is more focused on generic business applications.
Learning Curve: R has a steeper learning curve compared to COBOL, as it requires understanding of statistical concepts, data manipulation techniques, and programming paradigms specific to data analysis. COBOL, being a more traditional language, is relatively easier to grasp for novice programmers.

In Summary, differences between COBOL and R include syntax, data types, application domains, platform compatibility, specialized libraries, and learning curves.

COBOL vs R

Overview

COBOL vs R: What are the differences?