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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.
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.
Pros of COBOL
- Business Oriented Language2
Pros of R Language
- Data analysis84
- Graphics and data visualization63
- Free54
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Shiny interactive plots6
- Preferred Medium6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
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Cons of COBOL
- Extremely long code for simple functions2
Cons of R Language
- Very messy syntax6
- Tables must fit in RAM4
- Arrays indices start with 13
- Messy syntax for string concatenation2
- No push command for vectors/lists2
- Messy character encoding1
- Poor syntax for classes0
- Messy syntax for array/vector combination0
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What is COBOL?
What is R Language?
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What companies use COBOL?
What companies use R Language?
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What tools integrate with COBOL?
What tools integrate with R Language?
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