varied significantly by industry, ranging from a high of 93% (Internet & Mobile Apps) to a relatively low of 59% (Virtual Reality, Gaming, Entertainment, Media).

What are the pros and cons of the R programming language?

I am glad to say that, through this answer, I will resolve all your queries related to R programming.

Additionally, I will also tell you the reasons why it is one of the best languages to learn.

R has become the expression of data science and statistics. It is the most popular analytical tool. Every tool and programming language has some positive as well as negative aspects. Here are some pros and cons of R programming languages that provide you deep knowledge about R.

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THE FACTORS DRIVING R PROGRAMMING

  • Open-source
    R is an open-source programming language. R enjoys community support of avid developers who work on releasing new packages, updating R, customizing its packages, developing or resolving issues and making it a steadfast programming language for Data Science without any licensing or free.
  • Data Wrangling
    R provides exemplary support for data wrangling. It is capable of transforming the messy unstructured data into a structured form. It’s dplyr, and readr are the packages that help in this transformation.

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  • Choice of Language for Data Scientists
    R programming language provides an intensive environment to analyze, process, transfer, and visualize data. R contains a set of packages that appeal to all the forms of discipline like astronomy, biology, etc. It is considered as one of the most preferred language amongst Data Scientists.
  • Wide Array of Packages
    R is widely used due to its vast availability of libraries. It has a CRAN repository holding more than 10,000 packages. These packages appeal to every functionality and different field that deals with data. The packages involve different features vary accordingly on the user’s requirements and preferences.
  • Quality Plotting and Graphing
    R promotes quality plotting and graphing. It is having a massive collection of libraries like ggplot2, plotly, etc that are capable of making aesthetic and quality visualizations. All these set R apart from other programming languages.
  • Highly Compatible
    R is highly compatible. It can be paired with many other programming languages like Python, C, C++, and Java. It is also compatible with technology like Hadoop as well as other database management systems.

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  • Platform Independent
    R is known as a platform-independent or cross-platform programming language. It can run easily on any OS in any software environment like Windows, Linux, and Mac.
  • Eye-catching Reports
    With packages, like Shiny and Markdown, reporting the results of an analysis is extremely easy with R. You can make reports with the data, plots, and R scripts embedded in them.
  • Machine Learning Operations
    R provides various facilities for carrying out machine learning operations like classification, regression and also provides features for developing artificial neural networks.
  • Continuously Growing
    R is continuously evolving programming language. It is changing or developing over time like our taste and preferences. Likewise, R is a state of the art technology that provides updates whenever any new feature is added.

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FACTORS HINDERING R

  • Weak Origin
    R programming language based packages do not have support for dynamic and 3D graphics. As it shares its origin with the much older programming language “S”. With the help of common packages of R like ggplot2, plotly it is possible to create dynamic and 3D graphics.
  • Data Handling
    R utilizes more memory as compared with python. R requires all the data at a single place in memory. Therefore, it is not an appropriate option while handling big data.
  • Basic Security
    R programming language lacks basic security. However, there are some restrictions in R as it cannot be inserted into the web application.
  • Complicated Language
    R is complicated to learn as it has a steep learning curve. Users who do not have prior experience find it difficult to learn.
  • Lesser Speed
    R programming language is having lesser speed than other programming languages like MATLAB and Python. It is also affected by its origin.
  • Spread across various Packages
    The algorithm of R is spread over different packages. Sometimes programmers find it difficult to implement the algorithm.

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To conclude, keeping aside its few disadvantages, that every other language also has, R Programming Language is surely a top language to learn.

Hope this helps.

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