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R language is a machine learning language used for data analysis, visualization and sampling. The R language is a free and open source program that support cross-platforms which runs on different operating systems. It is great for statistical computations and creating mathematical functions. Let’s look at the pros and cons of using this language in data analysis.
1. Easy to code: The R language is an open source statistical language which is considered as one of the easy to code language. It is also very easy to install and configure.
2. Integration with other languages: It allows you to integrate with other programming languages like C, C++, Java, and Python and use different data sources.
3. Effective statistical tool: R language is the most comprehensive and effective tool for statistical analysis and statistical computing as new technology and ideas always appear in R.
4. Open source program: R language is an open source software which can be downloaded for free and runs in any workstation at any time.
5. Powerful: This machine learning language offers a breadth of techniques which are used for data analysis, sampling, and visualization. It has more advanced tools for analysis of statistical data.
6. State-of-the-art: R language uses various techniques to develop state-of-the-art algorithms and release them as R packages. You can easily access these algorithms in R before any other platform.
7. Cross-platform: It is a cross-platform language which can run in any operating system; GNU/Linux or in Windows. It is platform independent.
8. Open for modifications: Anyone can contribute to R language by developing code to fix a bug, develop new package or enhancement of the code.
9. Multiple libraries: There are a lot of libraries and good online support you can rely on when developing different algorithms and modules to solve problems. The language is also suitable for working with large datasets.
10. Easy to update programs: R language can explicitly document each step of data analysis to make it easy for you to reproduce and update the analysis. This makes it possible for you to try different ideas to solve a problem.
1. Little consistency in the algorithms: All the algorithms developed differ from one package to the other making it difficult to adopt an algorithm to your problem. This inconsistency is also extended to the documentation of the software.
2. Time-consuming: If you want to use different algorithms from different packages, you have to spend more time making predictions and determine how each method fits.
3. Decentralized packages: Algorithms are implemented on different packages and this makes it difficult to know which package has the algorithm you need to use.
4. Incomplete documentation: Algorithm documentations are always partially incomplete. They do not give an example demonstrating how to solve a canonical problem or how to use implementation.
5. The complexity of algorithms: Algorithms vary in complexity based on their implementation and description. This can affect you as you jump from one package to the other.
6. Memory intensive language: R languages are memory dependent and can consume all the available memory. The R commands can affect memory management process.
7. Native R is slower than its main competitor: Packages written in R language tend to be slower than those written in Python or Matlab.
8. Flexible programming language: Since the language is extremely flexible, you need a lot of discipline to maintain proper coding standard otherwise if the code is complex, it will be difficult to maintain it.
9. Security issues: Security capability is not built in the R language and it’s not embedded in a web browser making it difficult to use it for web or internet-like apps.