This blog will highlight the following key areas:
- R vs Python
- Comparison between R and Python on the basis of following:
- Ease of Installation
- Robustness and Flexibility
- Ease of Learning
- Speed of Processing
- Statistical and Analytics Ability
- Suitable Area
- Reasons to Learn R and Python
- Which is Better for Data Analysis and Data Science?
- Is Python better than R?
- Real-life Use Cases of R and Python
- Career Opportunities in R and Python
- Which Language to Go First?
What are R and Python?
R is an open-source programming language developed for statistical analysis and computations.
Like R, Python is also an open-source programming language that was initially developed as a general-purpose programming language, and later branched out to be a language for Statistical Analysis and Machine Learning Modeling.
Let’s understand the difference between these two highly popular Data Science languages:
Ease of Installation
Well to start with, R packages are solely managed by CRAN (The Comprehensive R Archive Network) repository that manages the updated versions, their installations, and related documentation of R Packages. All the packages you install in R are stored in CRAN. Also, any new package to be added in R should be submitted to CRAN. Currently CRAN has over 16000 additional statistical packages. This is why it is easier to install R.
On the other hand, Python has two package management platforms, Conda and PyPI (Python Package Index) that include over 100k Python packages. There have been inconsistencies found in Packages, Libraries, and Versions while installing Python due to two repositories. Due to this reason, it is a little tedious to install Python.
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