When it comes to statistical modeling and analysis, R is a preferred language. However, being in its evolving phase, it also has some cons associated with it in the current updates. Some important pros and cons of R are discussed below.
ADVANTAGES OF R:
|Open Source Language
- R programming language is open-source.
- R can be used without any license or a fee.
- R also facilitates the contributions of users in its development, such as
- Optimizing the packages
- Developing new packages
- Resolving any prevailing issues
|Platform Independent Language
- R programming language is platform-independent.
- R programming language is cross-platform.
- The codes written in R can run on all operating systems, Windows, Linux, and Mac.
- Software can be developed for several competing platforms.
- The codes need to be written only once for all the platforms.
|Machine Learning Operations
- Several machine learning operations can be performed in R.
- It includes classification and regression.
- Several packages and features in R facilitate the development of the artificial neural network.
- Data scientists prefer R all over the globe.
|Performing data wrangling
- Data wrangling is supported in R.
- Messy data can be transformed into a structured form in R.
- Packages such as dplyr, and readr facilitates such transformations.
|Quality plotting and graphing
- Simplified quality plotting and graphing in R.
- R facilitates visually appealing and aesthetic graphs with libraries such as ggplot2 and plotly.
|A rich set of packages
- Several sets of packages are available in R.
- In the CRAN repository, there are over 10,000 packages.
- The number of packages is constantly increasing.
- Different packages for data science and machine learning operations are available with R.
|Language of Statistics
- R programming language is the language of statistics.
- It is a preferred language when it comes to the development of statistical tools.
|Continuously Evolving and Improving
- R programming language is constantly developing, evolving and improving with the addition of new features on a frequent basis.
DISADVANTAGES OF R:
|Data Handling in R
- Storage of objects in physical memory.
- R is different from Python, in this respect.
- More memory is utilised in R, than in Python.
- The entire data needs to be stored in the memory.
- For Big Data, this is not an ideal condition.
|Basic Security in R
- Basic security is missing in R.
- Many restrictions are applicable with R.
- R codes can’t be embedded in a web application.
|Complicated Programming Language
- The steep learning curve for R makes it a complicated language.
- A prior knowledge of programming is a prerequisite to learning R.
|Weak Origin of R
- R has no support for dynamic or 3D graphics.
- This is because R shares its origin with the S Programming language.
- S is a very old programming language.
|Slower Speed in R
- R language and its packages are slower than MATLAB and Python.
- Also, algorithms in R are distributed across diverse packages.
- With no prior programming experience, implementation of these algorithms is difficult.
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