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General Properties of R Programming Language:

1. R makes it extremely easy to code complex mathematical or statistical procedures, though the programs may not run all that quickly. You can interface R with other languages (C, C++, Fortran) to provide fast implementations of subroutines, but writing this code (and making it portable) will typically take longer. Where the advantage falls in this trade-off will depend upon what you’re doing; for most things you will encounter during your degree, R is sufficiently fast.
2. R is open source and widely adopted by statisticians, biostatisticians, and geneticists. There is a huge wealth of existing libraries so you can often save time by using these, though it is sometimes easier to start from scratch than to adopt someone else’s function to meet your needs. Contributing new packages to the central repository (CRAN) is easy: even your lecturer has managed it. As a result, R packages are not build to very high standards (but see Bioconductor).
3. R is portable and works equally well on Windows, OS X and Linux.

 

SELF-DEFINED AS

R is an open source language that is specifically designed for conducting statistical analysis. As such, it is highly popular within fields such as data science, engineering, and other cognitive disciplines. The R Project for Statistical Computing describes the R language as an environment specifically designed for “statistical computing and graphics.”

STRENGTHS

R is far more efficient at conducting statistical analysis; provided packages automate much of this process. Strictly speaking, R is a statistical environment rather than a programming language, thus it is more adept at doing intensive statistical analysis. However, many data scientists prefer Python’s scikit-learn library when it comes to implementing machine learning algorithms.

HURDLES

R is significantly slower than Python and is less adept at integrating with other programming languages. Whereas R has the upper hand when it comes to statistical analysis, it is significantly slower than Python, and the latter language is often more flexible when it comes to algorithmic development or for final program release.

LICENSE

GNU General Public License (Both Python and R have “permissive” licenses, allowing redistribution of modified language implementations and tools without source code)

POPULARITY AND USAGE

#8 on TIOBE index

#9 on IEEE Spectrum ranking

#9 on GitHub (by opened pull request)

#8 Packt’s 2017 Developer Skills and Salary Report Ranking

#3 R is the third most popular language among data scientists according to O’Reilly’s 2017 US Data Science Survey, with slightly more than 50% of respondents using this language.

BACKWARD COMPATIBILITY

Package dependent. R expert Hadley Wickham describes backward compability as somewhat of an “academic” issue among the R community, given that packages are updated to the latest version using the update.packages() command, which updates to the latest package version even if there has been a change in the major version.

COMMUNITY

R-bloggers. The community comprises more than 750 contributors that regularly contribute useful information on the R language.

MARQUEE USERS

Engineers, researchers, statisticians. R has the upper hand when it comes to statistics-oriented tasks such as regression analysis or time series modeling.

MAIN USE CASES

Statistics, regression analysis, time series analysis, visualisation. R has been known to have a slower run time than Python, and less flexibility in interacting with other languages. With that being said, R is significantly more intuitive when it comes to implementing statistics or machine learning functions and therefore is ideal for designing a program/testing before implementing in another language.

EASE OF LEARNING

Better suited to users who have been exposed to other statistical environments or languages such as MATLAB, SAS, and SPSS. R is technically a statistical environment rather than a programming language, per se. Users who are already adept at using other statistical environments will likely find the transition to R to be more seamless than that of Python.

SALARY EXPECTATIONS

R-only salary: $50,000–125,000 according to the 2017 O’Reilly US Data Science Survey. In general, those users who had a command of both Python and R are seen as more marketable from a career standpoint.

INTEGRATION

The microstrategyR package is of particular interest to business analysts, allowing for connection of R to business intelligence platforms, whereas sparklyrallows for analysis of large datasets through connection to Apache Spark.

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