R Advantages and Disadvantages

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.


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.


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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